Meta-analysis has increasingly been used to synthesize the ecosystem services literature, with some testing of the use of such analyses to transfer benefits. These are typically based on local primary studies. However, meta-analyses associated with ecosystem services are a potentially powerful tool for transferring benefits, especially for environmental assets for which no primary studies are available. In this study we use the Ecosystem Service Valuation Database (ESVD), which brings together 1350 value estimates from more than 320 studies around the world, to estimate meta-regression functions for provisioning, regulating & maintenance and cultural ecosystem services across 12 biomes. We tested the reliability of these meta-regression functions and found that even using variables with high explanatory power, transfer errors could still be large. We show that meta-analytic transfer performs better than simple value transfer and, in addition, that local meta-analytical transfer (i.e. based on local explanatory variable values) provides more reliable estimates than global meta-analytical transfer (i.e. based on mean global explanatory variable values). Thus, we conclude that when taking into account the characteristics of the study area under analysis, including explanatory variables such as income, population density and protection status, we can determine the value of ecosystem services with greater accuracy.

Hyaluronic acid, in the form of a gel or viscoelastic colloidal solution, is currently used for viscosupplementation of joints affected by osteoarthritis, but its effectiveness is under debate in relation to newer alternatives. Based on meta-analytical arguments, the present article reinforces the opinion that there are still no decisive arguments for its complete replacement, but for its use adapted to the peculiarities of the disease manifestation and of the patients. A “broad” comparison is first made with almost all alternatives studied in the last decade, and then a meta-regression study is performed to compare and predict the effect size induced by viscosupplementation and platelet-rich plasma therapies. If they are computerized, the developed models can represent tools for clinicians in determining the appropriateness of the option or not for viscosupplementation, in a manner adapted to the pain felt by the patients, to their age, or to other clinical circumstances. The models were generated using the latest algorithms for meta-analysis and meta-regression implemented in the R language and assembled in different R packages. All primary data and necessary R scripts are provided in accordance with the philosophy of reproducible research. Finally, HA-based products, currently under circumspection, are still clinically useful.

Currently, the NRC amino acids (AA) requirements for pigs published in 2012 are used as a reference in variable swine industries. However, recent results in several articles suggest that the standardized ileal digestible (SID) AA: Lysine (Lys) ratio significantly evolved over the last two decades, while some publications report inconsistent outcomes. Therefore, the present study used a meta-regression analysis to assess the relative ratio to lysine to maximize the feed efficiency of four essential amino acids (tryptophan, valine, isoleucine, and methionine) in pig diets. According to the PRISMA guidelines, articles examining the target AA requirement using a basal diet supplemented with varying levels of crystalline AA (tryptophan, valine, isoleucine, or methionine) were identified across Scopus, PubMed, and Science Direct. As a result, 23, 22, 16, and 9 articles using tryptophan, valine, isoleucine, and methionine were selected and categorized into experiments for inclusion in our meta-analysis. The results suggested that the requirements of tryptophan, valine, isoleucine, and methionine in our meta-regression analysis were superior to NRC recommendations regardless of the regression models and the growth phases with significant RSQ values (RSQ ≈ 1). Also, the QUAD and CLP regression models emphasized higher requirements than the LP model for the great majority of amino acids and growth phases. The results of the QUAD and CLP models were selected as estimations of amino acid requirements for pigs under challenged conditions whereas the LP model was chosen to estimate the amino acid requirements of genetically improved pigs under a modern housing system. The results of this meta-regression analysis might be used to refresh the information on the NRC amino acids (AA) requirements for swine.

Water management can improve the quality of valuable ecosystem services but can be costly to implement and the management costs are covered by national taxes collected by water users. Based on 30 valuation studies of water quality improvement from the Environmental Valuation Information System (EVIS) database provided by Korea Environment Institute (KEI), a meta-regression analysis was employed to measure the benefits that major river basins provided to the society. We compare these benefits to the costs, namely River Management Funds (RMFs) which are financial resources to support a variety of projects for managing and improving upstream water quality. Based on benefit-cost comparison, this study evaluates the efficiency of water resource management in South Korea. This study also provides policy options that are helpful to maintain the sustainability of water resource by improving the planning and performance of water management in the long run.

For consciousness to exist, an entity must have prerequisite characteristics and attributes to give rise to it. We explore these “building blocks” of consciousness in detail in this paper, which range from perceptive to computational to higher-order characteristics of an entity’s cognitive architecture. We show how each cognitive attribute is strictly necessary for the emergence of consciousness, and how the building blocks are collectively sufficient for any entity to be classified as being conscious. The list of building blocks is not limited to human or organic consciousness and may be used to classify artificial and organisational conscious entities. We further explore a list of attributes that seem intuitively necessary for consciousness, but on further investigation, are neither required nor sufficient. The building blocks do not represent a theory of consciousness but rather a meta-theory on the emergence and classification of consciousness.

Background and Objectives: Daratumumab, a monoclonal antibody against CD38, has shown promising results in multiple myeloma (MM). We aimed to analyze its efficacy in newly diagnosed patients with MM. Materials and Methods: A search was conducted in PubMed, Cochrane, and Clinicaltrials.gov using the MeSH terms “daratumumab” or “Darzalex” and “multiple myeloma” covering the period until 2024. The Mantel-Haenzel formula was used to calculate the pooled risk ratio (RR), and the Generic Inverse Variance method was used to determine the pooled hazard ratio for disease progression, using RevMan version 5.4 at 95% confidence interval (CI). A P value of less than 0.05 was significant. Results: A total of 6 phase-III RCTs were analyzed. The use of daratumumab was associated with 39% fewer events of progression of disease or deaths in the TE group (RR= 0.61; 95% CI= 0.39 to 0.93; p=0.02) and 29% lower in the TIE group (RR= 0.71; 95% CI= 0.53 to 0.94; p=0.02). The hazard ratio for progression or death with daratumumab group versus control in both transplant eligible (TE) (HR= 0.44; 95% CI= 0.35 to 0.57; p<0.00001) or transplant ineligible (TIE) (HR= 0.47; 95% CI= 0.38 to 0.59; p<0.00001) group was significantly lower. Daratumumab significantly increased overall response rate (ORR) (RR= 1.20; 95% CI= 1.11 to 1.30 ; p<0.00001) and complete response (CR) (RR= 1.34; 95% CI= 1.02 to 1.76; p=0.04) in the TIE group only. Conclusions: Daratumumab showed benefits in both TE and TIE patients with newly diagnosed multiple myeloma. It significantly reduced the risk of disease progression or death and improved clinical outcomes regarding ORR and CR, suggesting an effective treatment option in these patient groups.

Systematic reviews and meta-analyses have become an important way to summarize data. The hierarchy of evidence-based medicine confers a high platform to meta-analyses. Hence, it is imperative that the analytical and reporting strategy associated with a meta-analysis be standardized. The modern tradition of reporting and publishing a meta-analysis is riddled with several methodological flaws. The most common of these flaws involve the choice of the model used to conduct the meta-analysis and the way that the heterogeneity of studies is assessed. The use of the I² statistic as a means of detecting within-study heterogeneity is a common methodological flaw encountered in reporting summary statistics.One of the ways to rectify the above flaws is to arm reviewers with an objective way to scrutinize the manuscript. This narrative review enlists six checklists for reviewers to improve the reporting of meta-analyses. The first two checklists address reporting of the risk of bias and assessment of publication bias with the help of a funnel plot. Checklists 3, 4, and 5 address the choice of model selected for the meta-analysis, the method to assess the confidence interval, and the measures of heterogeneity. The final checklist addresses the question asked in the meta-analysis, binary versus an effect size estimate. Although there are many more aspects which can help improve the assessment of a manuscript, these basic checklists can be employed by reviewers without the need for an external statistical analysis to enhance and improve meta-analyses.

Corn silage (Zea mays L.) is the most widely used energy resource in the diets of dairy cattle around the world; it stands out for its higher biomass yields, good palatability, homogeneous quality at harvest and ease of silage due to its higher soluble sugar content. It was carried out a search of studies related to dry matter yield (ton ha-1), population density (plant density ha-1), dry matter (DM), crude protein (CP), neutral detergent fiber (NDF), non-fibrous carbohydrates (NFC), organic matter (OM), DM digestibility (DMD) and neutral detergent fiber digestibility (NDFD) and milk production per hectare (kg of milk ha-1) that was determined using MILK2006®. It was carried out a cluster analysis (CL, PROC CLUSTER) obtaining six groups of corn silage: CL1: "Starch", which included DMD, DFDN, TDN1xDM, Mega calories per kg of DM and kg of milk/ton/DM; CL2: "Fats" which included TDN1xMS, Mcal/kg/DM and kg of milk/ton/DM; CL3: "Dry Matter", conformed only by DFDN; CL4: "Plant Density" including Yield of DM ha-1 and Milk Production ha-1; CL5: "Neutral Detergent Fiber" and CL6: "Raw Protein". It is concluded that CL1 was characterized by a higher DMD, DFND, CNF and starch that allow a higher TNDx1DM and an energy concentration (Mcal/kg/DM) that shows a higher milk production (kg of milk/ton/DM ha-1). The characteristics of CL2 with higher EE, allow a higher TNDx1DM and an energy concentration (Mcal/kg/DM) with a higher milk production (kg of milk/ton/DM ha-1).

A recent systematic review for 19 selected articles after searching through to 30 September 2017 showed vitamin D deficiency was associated with ischemic stroke (IS), not hemorrhagic stroke (HS). But a heterogeneity would be introduced with comparing the lowest and highest category of vitamin D. The aim of this article was to conduct an updated meta-analysis (UMA) with searching through to 31 March 2019. An interval collapsing method as information extraction was applied in order to decrease a heterogeneity among studies. Additional articles were selected from cited lists from 19 selected articles using citation discovery tools. Random effect model was applied if I-squared value was over 50%. A funnel plot and Egger’s test were used to detect a publication bias. After 5 new studies were added, the summary RRs [and their 95% confidence intervals] (I-squared value) were 1.52 [1.33–1.74] (0.0%) in IS, and 2.44 [1.34–4.46] (69.7%) in HS. This UMA supported the hypothesis that serum vitamin D deficiency was associated with an increased risk of HS as well as IS. Diverse public health programs against vitamin D deficiency status would be needed for higher risk group, especially elderly people.

This study aims to spatially analyze the distribution characteristics of vacant homes in Asan-si, Chungcheongnam-do, and understand the differences in factors causing vacant homes by type. We use a variety of statistical methods to examine these factors and identify key influencing factors such as the number of vacant stores, changes in overall population, land use complexity, and physical characteristics of land and buildings. The findings reveal that the factors contributing to the occurrence of vacant homes vary significantly by region and property type, highlighting the need for region-specific policies. For instance, in old town areas, small lot ratios and land use complexity are significant, while in rural village, the average number of floors and land use complexity play a major role. This study underscores the diversity of factors influencing vacant homes, suggesting that policies should be tailored to the unique characteristics of different regions to effectively address this issue.

Magnesium matrix composites have attracted significant attention due to their lightweight nature and impressive mechanical properties. However, the fabrication process for these alloy compo-sites is often time-consuming, expensive, and labor-intensive. To overcome these challenges, this study employed machine learning (ML) techniques to predict the mechanical properties of magnesium matrix composites. Regression models were utilized to forecast the yield strength of magnesium alloy composites reinforced with various materials. The study incorporated previous research on matrix type, reinforcement type, heat treatment, and mechanical working. The re-gression models employed in this study included decision tree regression, random forest re-gression, extra tree regression, and XGBoost regression. Model performance was assessed using metrics such as RMSE and R2. The XGBoost Regression model out-performed others, exhibiting an R2 value of 0.94 and the lowest error rate. Feature importance analysis indicated that the rein-forcement particle form had the greatest influence on the mechanical properties. The study iden-tified the optimized parameters for achieving the highest yield strength, which was 186.99 MPa. Overall, this study successfully demonstrates the effectiveness of ML as a valuable tool for opti-mizing the production parameters of magnesium matrix composites.

Correlated binary data in a 2x2 table has been analyzed from both the frequentist and Bayesian perspectives, but a fully Bayesian hierarchical model has not been proposed. This is a commonly used model for correlated proportions when considering, for example, a diagnostic test performance where negative subjects are tested a second time. We consider a new hierarchical Bayesian model for the parameters resulting from a 2x2 table with a structural zero. We investigate the performance of the hierarchical model via simulation. We then illustrate the usefulness of the model by showing how a set of historical studies can be used to build a predictive distribution for a new study that can be used as a prior distribution for both the rate ratio and marginal probability of a positive test. We then show how the prior based on historical 2x2 tables can be used to power a future study that accounts for pre-experimental uncertainty. High quality prior information can lead to better decision making by improving precision in estimation and by providing realistic numbers to power studies.

Majority of the studied MADS box members are linked with flowering and fruit traits. However, higher volumes of studies on the type II of their two types so far suggest that florigenic effect of the gene members could just be tip of the iceberg. In current study we used a systematic approach to have general overview on the MADS box members their cross-trait and multifactor associations as well as their pleiotropic potentials based on local reference database curated for MADS box members. While doing so, we screened for the co-occurrence of the terms of interest within the title or abstract of each reference with threshold of 3 hits. Analysis results showed that our approach can retrieve multi-faceted information on the subject of study (MADS box gene members in current case) which could otherwise have been skewed depending on the authors’ expertise and/or volume of literature reference base. Overall, our study discusses on the roles of MADS box members in association with plant organs and traits-linked factors among plant species. Our assessment showed that plants with majority of the MADS box member studies include tomato, apple, rice etc., after Arabidopsis. Furthermore, based on the degree of their multi-trait associations, FLC, SVP, SOC1, etc. are suggested to have relatively higher pleiotropic potential among others in plant growth, development and flowering process. The approach devised in this study is expected applicable for having basic understanding on any study subject of interest regardless of depth prior knowledge.

Triticale is known for being an alternative energy source to usual cereals such as maize and wheat in poultry nutrition. Several studies suggested opposite results regarding its incorporation as a feed ingredient on laying hens’ production parameters. This article used a meta-analysis to assess the factors influencing the effects of triticale on layers’ performance. In July 2022, papers studying the effect of triticale on layers’ egg production (EP), egg weight (EW), egg yolk color (EYC), feed intake (FI), and feed conversion ratio (FCR) were identified on Google Scholar, PubMed, and Science Direct. According to the eligibility criteria of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, 6 articles were registered and segmented into 16 experiments for our meta-analysis. Overall, the trim and fill method revealed that triticale had similar impacts to conventional cereals on laying hens' performance. However, the meta-ANOVA and the meta-regression emphasized that triticale percentage, triticale strains and laying hens strains could significantly influence the effect of triticale on layers' performance. Indeed, the meta-Anova highlighted that the Hy-Line Brown strain and Joesong and Juanilo triticale strains in-duced the best laying hens’ performance. Moreover, the meta-regression emphasized a positive correlation between the triticale inclusion percentage and the EW in Juanilo triticale diets and a negative correlation between the triticale inclusion percentages and the EYC in the triticale and laying hens strains studied. Even though the funnel plot and the Egger linear test suggested sig-nificant publication bias, the present meta-analysis remains consistent research to understand how certain factors could influence the impact of triticale on layers' performance.

This investigation aimed to explore the impact of acute and regular physical exercise on Brain-Derived Neurotrophic Factor (BDNF) levels in individuals with obesity. Using the Population, Intervention, Comparator, Outcome, and Study design (PICOS) approach, the inclusion criteria were defined as follows: i) population - individuals with overweight or obesity of any age or gender; ii) intervention - acute or chronic interventions involving aerobic or anaerobic methodologies; iii) comparator - for acute effect studies, pre- and post-evaluations served as comparators, while interventions included at least one experimental group or the inclusion of a control group; iv) outcome - BDNF variability concerning the applied methodology; v) study design - restricted to experimental studies exclusively. Conducted in August 2022, the searches were carried out on PubMed, Academic Search Complete, and Web of Science. The risk of bias was assessed using the Risk of Bias in Non-randomized Studies (RoBANS) tool. Out of 98 potentially relevant articles, 16 met the eligibility criteria, encompassing 23 trials. Substantial heterogeneity was observed for both acute (I2 = 80.4%) and long-term effects (I2 = 88.7%). Additionally, RoBANS analysis indicated a low risk of bias for the included studies. The study's findings revealed a significant increase in the concentration of circulating BDNF in individuals with obesity compared to the control group following acute exercise. However, such an effect was not evident in the context of long-term physical exercise, with a small effect size (ES = 0.49) recorded in the latter scenario. The observed significant increase in BDNF levels post-acute exercise suggests a potential acute response mechanism contributing to the intricate interplay between exercise and neurotrophic factors in this population. Nevertheless, the absence of a similar effect in the context of long-term physical exercise implies a potential adaptation or ceiling effect. These findings underscore the importance of considering both the duration and nature of exercise interventions when exploring the neurobiological responses associated with obesity and exercise. The study provides valuable insights for future research and clinical implications in understanding the nuanced relationship between exercise, neurotrophic factors, and obesity.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with high heterogeneity and has a prevalence of 0.7% to 3.2% in children. Gut microbiota are a collection of microorganisms that inhabit in human guts, which can produce various metabolites that affect the homeostasis and functions of nervous and endocrine systems. There are many studies on the relationship between various gut microbiota and ASD, but the alteration pattern of microbial profiles in ASD children is not conclusive. In order to more robustly describe the deregulations of gut microbiota compositions in ASD, a meta-analysis was performed. The related investigations from PubMed, Embase and Web of Science were collected and manually reviewed. By procedure, 26 eligible studies until 2023, with a total of 1021 ASD and 951 typically developed children and adolescents, were included for the meta-analysis. RevMan5.4 was used to assess the overall effect of 8 microbes at the phylum level and 19 microbes at the genus level. Results demonstrated relatively up-regulated abundance of Bacteroidetes, Verrucomicrobia, Bacteroides, Clostridium, Dorea and Sutterella, and down-regulated abundance of Proteobacteria, Bifidobacterium, Coprococcus, and Akkermansia in ASD children, indicating partly agreement in the ASD-associated microbes, albeit the heterogeneity of ASD.

Background: Pharmacologic and non-pharmacologic treatments for stroke are essential but could be costly or harmful whereas probiotic has been a promising alternative. This scoping review aimed to synthesize the in vitro and in vivo evidence of probiotics on stroke-related neurological, biochemical, and histochemical outcomes. Method: We searched in PubMed, Embase, and Cochrane Central Register of Clinical up to May 7, 2021, and screened by two independent reviewers. We included the use of probiotics, prebiotics, and symbiotics both in vitro and in vivo for the prevention or treatment of the stroke-related model. Result: Of 6,293 articles, 4,990 passed the initial screen, of which 36 theme-related full-texts were assessed and 13 were included in this review. Probiotics could ameliorate the neurological deficit and show their property as an anti-inflammation and anti-oxidative stress. Histopathologically decreased loss of cerebral volume and inhibition of neuronal apoptosis were found. Conclusion: There are potential cognitive benefits of probiotic supplementation, especially among animal models, on decreasing cerebral volume, increasing neurological score, and decreasing the inflammatory response. However, further investigation is needed to validate these conclusions in various populations.

The pervasiveness of cardiovascular disease and physician misdiagnosis creates the urgent need for artificial intelligence models to improve diagnosis accuracy. The first objective of this study was to train machine learning models on publicly available data sets containing simple medical information of patients to diagnose cardiovascular disease. The Multilayer Perceptron (MLP) assembled for this task performed optimally with an F1 score of 0.8968. This prompted the creation of an open-source, automated cardiovascular disease diagnosis tool, powered by the MLP. The second objective of this study was to employ a meta-learning methodology called Local Interpretable Model-Agnostic Explanations (LIME) to understand the impact of different features on the model's diagnosis in the form of marginal probabilities. K-Means Clustering was employed to segment the data into ten clusters, after which each data example was passed through LIME. The resulting histograms depict the complex relationship between feature, cluster, and impact on diagnosis. A series of P-values with contrasting orders of magnitude shows the nuances in the MLP's understanding of patients from different clusters. The results of meta-learning analysis reveal that the most important features for cardiovascular disease diagnosis are fasting blood sugar, type of chest pain, and slope of the ST segment on an electrocardiogram. Future experiments should replicate the novel methodology introduced in this study on data sets containing more specialized medical features in order to gain practical medical insights about different types of cardiovascular disease represented by each cluster. Finally, feature engineering pathways should be explored with consideration of these results to create versatile diagnosis models not only for cardiovascular disease, but adaptable to other diseases as well.

SacreBLEU, by incorporating a text normalizing step in the pipeline, has been well-received as an automatic evaluation metric in recent years. With agglutinative languages such as Korean, however, the metric cannot provide a conceivable result without the help of customized pre-tokenization. In this regard, this paper endeavors to examine the influence of diversified pre-tokenization schemes –word, morpheme, character, and subword– on the aforementioned metric by performing a meta-evaluation with manually-constructed into-Korean human evaluation data. Our empirical study demonstrates that the correlation of SacreBLEU (to human judgment) fluctuates consistently by the token type. The reliability of the metric even deteriorates due to some tokenization, and MeCab is not an exception. Guiding through the proper usage of tokenizer for each metric, we stress the significance of a character level and the insignificance of a Jamo level in MT evaluation.

(1) Purpose: This work aims at developing a generalizable MRI reconstruction model in the meta-learning framework. The standard benchmarks in meta-learning are challenged by learning on diverse task distributions. The proposed network learns the regularization function in a variational model and reconstructs MR images with various under-sampling ratios or patterns that may or may not be seen in the training data by leveraging a heterogeneous dataset. (2) Methods: We propose an unrolling network induced by learnable optimization algorithms (LOA) for solving our nonconvex nonsmooth variational model for MRI reconstruction. In this model, the learnable regularization function contains a task-invariant common feature encoder and task-specific learner represented by a shallow network. To train the network we split the training data into two parts: training and validation, and introduce a bilevel optimization algorithm. The lower-level optimization trains task-invariant parameters for the feature encoder with fixed parameters of the task-specific learner on the training dataset, and the upper-level optimizes the parameters of the task-specific learner on the validation dataset. (3) Results: The PSNR increases 1.5 dB on average compared to the network trained through conventional supervised learning on the seen CS ratios. We test the result of quick adaption on the unseen tasks after meta-training, the average PSNR arises 1.22 dB compared to the conventional learning procedure that is directly trained on the unseen CS ratios in the meanwhile saving half of the training time. The average PSNR arises 1.87 dB for unseen sampling patterns comparing to conventional learning; (4) Conclusion: We proposed a meta-learning framework consisting of the base network architecture, design of regularization, and bi-level optimization-based training. The network inherits the convergence property of the LOA and interpretation of the variational model. The generalization ability is improved by the designated regularization and bilevel optimization-based training algorithm.

To evaluate the efficacy of fish oil for protection against coronary heart disease (CHD), we conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) evaluating the use of fish oil for protection against CHD. We retrieved relevant articles published from January 1966 to January 2020 by searching the PubMed, EMBASE, Cochrane CENTRAL, and Web of Science databases. RCTs of fish oil in preventing CHD were selected. The study quality was evaluated using the Cochrane Risk of Bias tool with RevMan 5.3 software. The first selection involved 350 citations. After screening and evaluation of suitability, 19 RCTs adjusted for clustering were included in the meta-analysis. All selected manuscripts considered that fish oil was effective in preventing CHD, secondary outcome measures included angina, sepsis and death. Compared with the control group, fish oil may confer significant protection against CHD (odds ratio = 0.84; 95% confidence interval: 0.72–0.98). There was no significant difference in the incidence of secondary outcomes between the observation group and the control group (P > 0.05). The above results show that fish oil plays an important role in reducing CHD and cardiovascular events. However, because of the suboptimal quality of the studies included into the meta-analysis, these results do not justify adding fish oils systematically to the heavy pharmaceutical assortment already recommended in CHD patients.

The effects of buckwheat intake on cardiovascular diseases (CVD) have not been systematically investigated. The aim of the present study was to comprehensively summarise studies in humans and animals evaluating the impact of buckwheat consumption on CVD risk markers and to conduct a meta-analysis of relevant data. Thirteen randomised, controlled human studies, two cross-sectional human studies and twenty-one animal studies were identified. Using random effects models, the weighted mean difference of post-intervention concentrations of blood glucose, total cholesterol and triglycerides were significantly decreased following buckwheat intervention compared with controls [differences in blood glucose: -0.85 mmol/L (95% CI: -1.31, -0.39), total cholesterol: 0.50 mmol/L (95% CI: -0.80, -0.20) and triglycerides: 0.25 mmol/L (95% CI: -0.49, -0.02)]. Responses of a similar magnitude were seen in two cross-sectional studies. For animal studies, nineteen of twenty-one studies showed a significant reduction in total cholesterol of between 12 and 54%, and fourteen of twenty studies showed a significant reduction in triglycerides of between 2 and 74%. All exhibited high unexplained heterogeneity. There was inconsistency in HDL cholesterol outcomes in both human and animal studies. It remains unclear whether increased buckwheat intake significantly benefits other markers of CVD risk, such as weight, blood pressure, insulin, and LDL-cholesterol, and underlying mechanisms responsible for any effects are unclear.

MicroRNAs (miRNA) exert regulatory actions via base-pairing with their binding sites on target mRNAs. Cooperative-binding, i.e., synergism, among binding sites on a mRNA is biochemically well-characterized. We studied whether this synergism is reflected in global relationship between miRNA-mediated regulatory activity and miRNA-binding-site count on the target mRNAs, i.e., leading to a non-linear relationship between the two. Recently, using our own and public datasets, we have enquired into miRNA regulatory actions: first, we analyzed the power-law distribution pattern of miRNA binding sites; second, strikingly, mRNAs for core miRNA regulatory apparatus proteins have extra-ordinarily high binding site counts, forming self-feedback-control loops; third, we revealed that tumor suppressor mRNAs generally have more sites than oncogene mRNAs; and fourth, we characterized enrichment of miRNA target mRNAs in translationally less-active polysomes relative to more-active polysomes. In these four studies, obvious correlation was observed qualitatively between the extent to which a mRNA is miRNA-regulated and its binding sites count. This paper summarizes the used datasets. We also re-analyzed the correlation by comparative linear and non-linear regression analyses. The results support a non-linear, instead of linear, relationship between the two parameters, conceivably a transcriptome-level reflection of cooperative-binding/synergism among miRNA binding sites on a target mRNA.

In a limestone quarry mine, fragmentation is a crucial outcome of blasting operations. The optimization of blasting operations greatly benefits from the prediction of rock fragmentation. The main factors that affect fragmentation are rock mass characteristics, blast geometry, and explosive properties. This paper is a step towards the implementation of machine learning and deep learning algorithms for predicting the extent of fragmentation (in percentage) in opencast mining. Various parameters can affect fragmentation. But, in this paper initially, ten parameters (spacing, drill hole diameter, burden, average bench height, powder factor, number of holes, charge per delay, uniaxial compressive strength, specific drilling, and stemming) are collected to train the model. However, due to a weak correlation with rock fragmentation, drill diameter, Average bench height, compressive strength, stemming, and charge per delay are eliminated to reduce model complexity. A total of 219 data sets having five input features i.e., the number of holes, spacing, burden, specific drilling, and powder factor are used to develop the models. To predict rock fragmentation due to blasting in limestone quarry mines, both machine learning models (Random Forest Regression (Bagging), Support Vector Regression, and XG Boost Regression (Boosting)), as well as a deep learning model (Neural Network Regression), are applied to develop a model that can optimize the prediction of fragmentation. The Artificial neural network model optimization showed that the model with architecture 64-32-16-1 can perform well giving MSE (mean squared error) values of 41.32 and 28.59 on training and test data respectively. The R2 value for both training and test is 0.83. Random Forest regression is also performing well compared to SVR and XG boost with the MSE value 12.37 and 9.89 on training and testing data respectively. Here, the R2 value for both sets are 94%. Based on the permutation importance and Shapely plot values, the powder factor has the highest impact, and the burden has the lowest impact on fragmentation.

Notion of median in one dimension is a foundational element in nonparametric statistics. It has been extended to multi-dimensional cases both in location and in regression via notions of data depth. Regression depth (RD) and projection regression depth (PRD) represent the two most promising notions in regression. Carrizosa depth DC is another depth notion in regression. Depth induced regression medians (maximum depth estimators) serve as robust alternatives to the classical least squares estimator. The uniqueness of regression medians is indispensable in the discussion of their properties and the asymptotics (consistency and limiting distribution) of sample regression medians. Are the regression medians induced from RD, PRD, and DC unique? Answering this question is the main goal of this article. It is found that only the regression median induced from PRD possesses the desired uniqueness property. The conventional remedy measure for non-uniqueness, taking average of all medians, might yield an estimator that no longer possesses the maximum depth in both RD and DC cases. These and other findings indicate that the PRD and its induced median are highly favorable among their leading competitors.

Data regarding the pandemic's impact on tilapia culture remain limited, but it is known that there was a significant decline in production and marketing since 2020. The post-pandemic challenges confronting tilapia farming necessitate prompt solutions, encompassing the management of bacterial infections and the adoption of more advanced technologies by small-scale producers in developing nations. Probiotics, acknowledged as a viable alternative, are presently extensively employed in tilapia aquaculture. Multiple studies have suggested that the application of diverse probiotics in tilapia culture has yielded favorable outcomes. Nonetheless, only a limited number of studies have employed statistical methods to evaluate such findings. To address this gap, a regression analysis was carried out to investigate the existence of a linear relationship between the probiotic dosage added to the feed and two key dependent variables: the specific growth rate (SGR) and the feed conversion ratio (FCR). Additionally, a hierarchical regression analysis was undertaken to ascertain the extent to which the variance observed in these responses could be explained by the variable "probiotic dosage in feed," after accounting for covariates such as initial weight, test duration, water temperature, and number of replicate tanks. Finally, two Pearson correlation matrices were constructed since different studies were included for the SGR and FCR analyses.

The study also found that older age was a protective factor in prospective studies. The consistency of results across different study designs and sensitivity analyses further supported the robustness of the findings. It is important to note that the existing literature on revictimization in women facing intimate partner violence is limited and exhibits significant heterogeneity in terms of methodology and conceptual frameworks.

Objective The aim of this study was to explore the prognosis and influencing factors of recurrent ependymoma in children. Methods PubMed, Embase, Cochrane Library, and Web of Science were searched to collect studies on survival outcomes and influencing factors of recurrent ependymoma in children. The search time frame was from the establishment of the database to September 2023. Two evaluators independently screened the literature, extracted data, and evaluated the quality of the included studies. Meta-analysis was performed using RevMan 5.4 software. Results A total of 11 studies involving 1120 patients were included. The integrated results of mOS from the 11 studies showed significant heterogeneity (I2 = 92%). Therefore, a random-effects model was used for merging, and the results showed statistically significant differences (P < 0.00001). In all the studies, the pooled estimate of median OS from the time of recurrence was 15.54 months (95% CI 8.80-27.45; P < 0.00001), and the combined median progression-free survival (PFS) from the time of first recurrence was 6.7 months (95% CI 5.59-7.64; P < 0.0001). The median OS for patients who underwent surgery at the time of recurrence was 20.7 months (95% CI 12.40–34.72; P < 0.00001), while the mOS for patients who received radiation therapy was 29.5 months (95% CI 18.97–46.00; P < 0.0001), and for patients who received chemotherapy mOS was 18.0 months (95% CI 8.62–37.75; P < 0.00001). The mOS for patients under 3 years old at the time of recurrence was 20.1 months (95% CI 1.98-204.50; P < 0.00001), while for patients over 3 years old at the time of recurrence mOS was 16.6 months (95% CI 9.40-29.35; P < 0.00001). Conclusion The results of the study show poor prognosis for children with recurrent ependymoma, and there are significant differences in these results. The influencing factors include patient age, tumor recurrence location, and treatment methods. These findings can further guide clinical research on new treatment methods and strategies to improve the prognosis of this population.

Background: Gestational diabetes is a common complication during pregnancy that can lead to numerous adverse outcomes. Some studies suggest that probiotics may be used to treat gestational diabetes, however, the results remain controversial. We conducted a systematic review and meta-analysis to evaluate the effect of probiotics on blood glucose and pregnancy outcomes in women with gestational diabetes. Methods: A systematic search of PubMed, Embase, and Cochrane databases was performed (start date to August 22, 2023). Primary outcomes included fasting blood sugar (FBS), fasting serum insulin (FSI), homeostasis model assessment of insulin resistance (HOMA-IR), and quantitative insulin sensitivity check index (QUICKI). Secondary outcomes included pregnancy and neonatal outcomes. Results: 15 articles (n = 1006 women) met the inclusion criteria and were included in the analysis. Compared to a placebo, probiotics can decrease FBS (MD -2.58, 95% CI -4.38 to -0.79, p &lt; 0.01), FSI (MD -2.29, 95% CI -3.40 to -1.18, p &lt; 0.01), HOMA-IR (MD -0.56, 95% CI -0.81 to -0.32, p &lt; 0.01), birthweight (MD -101.20, 95% CI -184.62 to -17.77, p = 0.02), neonatal intensive care unit (NICU) (RR 0.60, 95% CI 0.40 to 0.89, p = 0.01), and hyperbilirubinemia (RR 0.31, 95% CI 0.16 to 0.61, p &lt; 0.01), alongside higher QUICKI (MD 0.01, 95% CI 0.00 to 0.01, p &lt; 0.01). However, no other significant results were obtained. Conclusion: Probiotics may improve blood glucose indicators and reduce neonatal hyperbilirubinemia, NICU admissions, and birth weight in women with GDM.

It is a well-established fact that inadequate Vitamin D (Vit-D) levels have negative effects on the development and progression of malignant diseases, mainly cancer. The purpose of this paper was to elucidate the effects of Vit-D intake and serum 25-hydroxyvitamin-D (25(OH)D) levels on cancer incidence and mortality, the current evidence in this field, and the biases of this evidence using the meta-meta-analysis method. Meta-analyses focusing on Vit-D intake, serum 25(OH)D levels, and cancer risk/mortality were identified. A structured computer literature search was performed in PubMed/Medline, Web of Science (WoS), and Scopus electronic databases using predetermined keyword combinations. Primary and secondary meta-meta-analyses were carried out, combining odds ratios (ORs), risk ratios (RRs), and hazard ratios (HRs) for outcomes reported in selected meta-analyses. A total of 35 eligible meta-analyses assessing the association between Vit-D and cancer incidence and/or mortality were included in this study. In the pooled analysis, higher Vit-D intake and serum 25(OH)D levels were associated with lower cancer risk (OR = 0.93, 95% CI: 0.90-0.96, p < 0.001; OR = 0.80, 95% CI: 0.72-0.89, p < 0.001, respectively) and cancer-related mortality (RR = 0.89, 95% CI: 0.86-0.93, p < 0.001; RR = 0.67, 95% CI: 0.58-0.78, p < 0.001, respectively). When meta-analyses whose primary reports included only randomized controlled trials were pooled, there was no significant association between Vit-D intake and cancer risk (OR = 0.99, 95% CI: 0.97-1.01, p = 0.320). In subgroup analysis, Vit-D consumption was associated with a significant decrease in colorectal and lung cancer incidence (OR = 0.89, 95% CI: 0.83-0.96, p = 0.002; OR = 0.88, 95% CI: 0.83-0.94, p < 0.001, respectively). Taken together, both Vit-D intake and higher 25(OH)D levels may provide remarkable benefits in terms of cancer incidence and mortality, however, careful evaluation according to cancer types is critically important and recommended.

Objective: To carry out a bibliometric analysis study of systematic reviews and meta-analyses in dentistry, divided into different thematic areas. Material and method: A search was conducted in the Science Citation Index-Expanded on the core collection of Web of Science, they were selected from the area of Dentistry and Oral Surgery and Medicine categories, the data was downloaded on April 20, 2022 and The 100 most cited articles from each of the thematic areas were selected. Results: An increase in this type of articles was observed in the last decade in the areas of pediatric and medical dentistry and oral pathology. The thematic area that received the most citations was periodontics. The two authors with the most citations are Zwahlen, Marcel and Pjetursson, Bjarni Eluar. The countries that receive the most citations are European, along with the USA and China. The topics that are most published in this type of article deal with Cancer with 50 publications, caries treatment with 25 and fluoridation with 1. The entities that finance this type of articles the most are private companies (26.76%). Conclusions: Together with an exponential increase in the number of publications in dentistry, there has been an increase in the number of publications in systematic reviews. The areas publishing the most articles and having the most citations are Periodontics and Implantology, despite the fact that the most studied topic is cancer.

This mini-review discusses the importance of microorganisms in various biological processes and the paradigm shift in researchers' perception of microorganisms' biology and genetics. Microorganisms are now understood to interact with their associated microorganisms, and advanced sequencing technology is used to study these interactions. The article highlights the importance of careful study design to efficiently generate and integrate data derived from meta-omics approaches such as (meta)genomics, (meta)transcriptomics, (meta)proteomics, and (meta)metabolomics to fully explore the extent of interactions between host organisms and their associated microbiota. The article also discusses the usefulness of metabarcoding and metagenomics in studying coral holobiont diversity and composition. While metabarcoding can comprehensively explore the genetic diversity of various biological taxa, it has limitations in classifying prokaryotes based on 16S rRNA amplicons. On the other hand, metagenomics generates vast amounts of short reads sequencing data that can be used to examine microbial diversity with greater precision and predict the possible functions of the gene set that is presented in the sample. However, the ability to link species to their functional capabilities is challenging, especially for microbes with uncertain evolutionary relationships, hidden microbes, and microeukaryotes. Finally, the article highlights the potential usefulness of these approaches in conservation biology and molecular and environmental science.

Purpose: Pitch plays an important role in auditory perception of music and language. This study provides a systematic review with meta-analysis to investigate whether individuals with autism spectrum disorder (ASD) have enhanced pitch processing ability and identify the potential factors associated with processing differences between ASD and neurotypicals. Method: We conducted a systematic search through six major electronic databases focusing on the studies that used nonspeech stimuli to provide a qualitative and quantitative assessment across existing studies on pitch perception in autism. We identified potential participant- and methodology-related moderators and conducted meta-regression analyses using mixed-effects models. Results: On the basis of 22 studies with a total of 464 participants with ASD, we obtained a small-to-medium positive effect size (0.26) in support of enhanced pitch perception in ASD.Moreover, the mean age and non-verbal IQ of participants were found to significantly moderate the between-studies heterogeneity. Conclusion: Our study provides the first meta-analysis on auditory pitch perception in ASD and demonstrates the existence of different developmental trajectories between individuals with ASD and neurotypicals. Non-verbal ability can be a significant contributor to the lower-level/ local processing bias in ASD. We highlight the need for further investigation of pitch perception in ASD under challenging listening conditions. Future neurophysiological and brain imaging research studies with a longitudinal design are also needed to better understand the nature of the atypical processing in ASD to obtain new insights into the underlying neural mechanisms and to help guide auditory-based interventions for improving language and social functioning.

The oral route remains one of the most popular and important routes of administration that warrants the development of advanced drug delivery systems such as the polymeric nanoparticles capable of enhancing the absorption and bioavailability of the used drugs. In this work, systematic reviewing through several databases followed by a meta-analysis study were utilized in order to navigate the published studies and reach literature-based evidence about the capability of polymeric nanoparticulate systems of augmenting the absorption and the bioavailability of the orally administered drugs. The pharmacokinetic parameter; area under the curve (AUC) was utilized as the “effect” of the meta-analysis study. The meta-analysis study demonstrated the significant increase AUC as compared to the conventional formulations. Furthermore, comparing the synthetic polymeric nanoparticles versus the naturally-based counterparts, as subgroups of the meta-analysis, revealed no significant differences.

The paper aims to illustrate the different roles that social sciences can play in the study of the energy transition. To this end, a scheme is developed that frames the relative position of the social sciences with respect to other disciplines (metaframe). Secondly, socialization is identified as a charismatic category capable of providing an original, typically sociological contribution to the complex and hesitant energy transition (masterframe)

In this paper, we implement a new method binary Particle Swarm Optimization (PSO) for solving the kidney exchange problem, which will improve the future decisions of kidney exchange programs. Because using a kidney exchange, we can help incompatible patient-donor couples to swap donors to receive a compatible kidney. Kidney paired donation programs provide an innovative approach for increasing the number of available kidneys. Further, we implementing binary particle swarm optimization in parallel with MATLAB with one, two, three and four threads and from the computations point of view, the authors compare the performance to reduce the running time for kidney exchange to match patients as fast as possible to help clinicians. Moreover, implementing binary particle swarm optimization in solving the kidney exchange problem is an effective method. The obtained results indicate that binary PSO outperforms other stochastic-based methods such as genetic algorithm, ant lion optimization, and efficient the number of resulting exchanges.

Background: Overweight and obese adults seek effective exercise interventions to reduce accumulated fat, but the effectiveness of these interventions vary across studies. The purpose of this meta-analysis was to investigate the effectiveness of exercise interventions in overweight and obese adults based on measurement of accumulated fat distributions. (2) Methods: Databases were used to select eligible studies for this meta-analysis. Randomized controlled trials with a control and experimental group were included. Degrees of effectiveness of exercise interventions were computed to assess the benefits on reducing weight and subcutaneous, visceral, and ectopic fat accumulation. (3) Results: A total of twenty-one studies were included in this meta-analysis. Participation in exercise interventions showed beneficial effects in reducing weight and subcutaneous and visceral fat. The effectiveness of exercise interventions on ectopic fat accumulation could not be assessed due to the limited number of studies measuring ectopic fat. Additionally, effectiveness of exercise interventions that depended on measurements of accumulated fat varied. The average exercise intervention for overweight and obese individuals was moderate to vigorous intensity, 4 times per week, 50 minutes per session, and 22 weeks duration. (4) Conclusions: Participating in exercise interventions has favorable effects on reducing weight and accumulation of subcutaneous and visceral fat.

Despite the considerable focus on job characteristics and individual differences in job crafting research, the influence of social factors on job crafting has not been well-acknowledged. This meta-analysis estimates the associations between social factors and job crafting, and how social factors contribute to employee outcomes through their job crafting. Based on a sample of 51 empirical studies that included 54 independent effect sizes (N = 17,863), we found that social factors of positive leadership styles (e.g., empowering and transformational) and coworker support were positively related to employee job crafting. Further, our study showed that employee job crafting positively mediate the relationships between social factors and work outcomes (e.g., job performance and well-being). Our study contributes to job crafting literature by integrating social factors into the job crafting model and demonstrating that the social context of work (in particular organizational-insiders) play a crucial role in shaping employees’ job crafting behavior. We also emphasize the critical role that job crafting plays in transmitting valuable social resources into improved work outcomes. Building on our results, we provide future direction for job crafting research and discuss how our results can imply practice in terms of job crafting training.

Stroke is one of the most prevalent cardiovascular diseases worldwide, both in high-income countries and in medium and low-medium income countries. The WHO report on non-communicable diseases (NCDs) indicates that the highest behavioral risk in NCDs is attributable to incorrect nutrition. The objective of our work is to present an overview of meta-analyses that have investigated the impact of different foods and / or drinks in relationship with the risk of stroke events (ischemic/ hemorrhagic). The papers to be included in the overview were sought in the MEDLINE, EMBASE, Scopus, Clinicaltrials.gov, Web of Science, and Cochrane Library and were selected according to PRIMA flow chart. Quality assessment were made according to AMSTAR scale. This overview shows that all primary studies came from countries with high income level. This evidence shows that many countries are not represented. Therefore, different lifestyles, ethnic groups, potentially harmful or virtuous eating habits are not reported. It is important to underline how the choose of foods may help reduce the risk of cardiovascular diseases and stroke in particular.

Background. Upper respiratory infections (URIs) remains as significant cause of morbidity in children. Evidence on efficacy of probiotics to prevent URIs in children is increasing. This systematic review was assembled to analyze evidence about the efficacy of probiotics to reduce duration of upper respiratory infections in ambulatory children. Methods. Randomized controlled trials (RCTs) comparing probiotics vs. placebo to prevent URIs, published between 2001 and 2016 were considered. Quality evaluation was evaluated using CONSORT. Standard mean difference (SMD) or risk ratio (RR) was calculated. Network Meta-Analysis (NMA), using a random effect model was assembled. Results. 31 RCTs were evaluated and 20 studies were included with 3,635 children randomized to probiotics and 3,433 to placebo. Lactobacillus reuteri [SMD -0.56 CI95% (-0.72 to -0.41), p 0.0001] and Lactobacillus acidophillus [SMD -0.33 CI95% (-0.60 to -0.06), p 0.01] were superior to placebo to reduce duration of URIs. L. rhamnosus GG showed tendency [SMD -0.14 CI95% (-0.28 to 0.0), p 0.048]. On the network forest plot L. reuteri showed preventive equivalence when was compared to L. rhamnosus GG, L. casei and BB12. Conclusions. Lactobacillus reuteri, Lactobacillus rhamnosus GG and Bifidobacterium BB12 are evidence-based alternatives to be considered to prevent URIs in children.

Theoretical studies propose an association between family violence and low self-control in adolescence, yet empirical findings of this association are inconclusive. The aim of the present research was to systematically summarize available findings on the relation between family violence and self-control across adolescence. We included 27 studies with 143 effect sizes, representing more than 25,000 participants of eight countries from early to late adolescence. Applying a multi-level meta-analyses, taking dependency between effect sizes into account while retaining statistical power, we examined the magnitude and direction of the overall effect size. Additionally, we investigated whether theoretical moderators (e.g., age, gender, country), and methodological moderators (cross-sectional/longitudinal, informant) influenced the magnitude of the association between family violence and self-control. Our results revealed that family violence and self-control have a small to moderate significant negative association (r = -.191). This association did not vary across gender, country, and informants. The strength of the association, however, decreased with age and in longitudinal studies. This finding provides evidence that researchers and clinicians may expect low self-control in the wake of family violence, especially in early adolescence. Recommendations for future research in the area are discussed.

French Guiana, located in the Guiana Shield, is a natural reservoir for many zoonotic pathogens that are of considerable medical or veterinary importance. Until now, there are few data available on the description of parasites circulating in this area, especially on protozoan belonging to the phy-lum Apicomplexa; conversely, the neighbouring countries describe a high parasitic prevalence in animals and humans. Epidemiological surveillance is necessary, as new potentially virulent strains may emerge from these forest ecosystems, such as Amazonian toxoplasmosis. However, there is no standard tool for detecting protozoa in wildlife. In this study, we developed Meat-Borne-Parasite, a high-throughput meta-barcoding workflow for detecting Apicomplexa based on Oxford Nanopore Technologies sequencing platform using the 18S gene of fourteen Apicomplexa-positive samples collected in French Guiana. Sequencing reads were then analysed with MetONTIIME pipeline. Thanks to a scoring rule, we were able to classify as Apicom-plexa-positive 10 samples out of 14 and reveal the presence of co-carriages. The same samples were also sequenced with Illumina platform, for validation purposes. On samples identified as Apicomplexa-positive by both platforms, a strong positive correlation at up to genus level was reported. Overall, the presented workflow represents a reliable method for Apicomplexa detec-tion, which may pave the way for more comprehensive biomonitoring of zoonotic pathogens.

A rapid synthesis of chiral sulfoxide-functionalized meta-terphenyl derivatives by a 2,5-[C4+C2] ring transformation reaction of pyrylium salts with in-situ generated enantio-merically pure .alpha.-sulfinylacetaldehydes is described. This synthetic method demonstrates for the first time the use of .alpha.-sulfinylacetaldehydes in a reaction sequence initiated by nu-cleophilic attack of pyrylium salts by .alpha.-sulfinylcarbanions to generate chiral aromatic systems. The method presented shows broad applicability starting with various methyl sulfoxides and a number of functionalized pyrylium salts, furnishing meta-terphenyls with complex substitution patterns from readily accessible starting compounds.

Dengue virus infection is caused by a positive stranded RNA virus called Dengue virus, which is spread by arthropods. It is a fast growing acute febrile disease with potentially lethal consequences that is a global public health problem, mostly in tropical and subtropical countries. In Ethiopia dengue fever is understudied despite the fact that the virus is still being transmitted and viral infection rates are rising. This systematic review and meta-analysis was aimed to estimate the pooled prevalence of DENV infection in Ethiopia. A literature search was done on PubMed, Hinari and Google Scholar databases to identify studies published before July, 2023. Random effects and fixed effects models were used to estimate pooled prevalence of all the three markers. The Inconsistency Index was used to assess the level of heterogeneity. A total of 11 articles were included in this review. Majority of the studies had moderate risk of bias and no study had a high risk of bias. A meta-analysis estimated pooled IgG prevalence of 21% (95% CI: 19-23), a pooled prevalence of IgM 9% (95%CI: 4-13) and a pooled DENV-RNA prevalence of 48% (95% CI: 33-62). There is evidence of possible publication bias in IgG but not found in the rest of markers. The prevalence of DENV infection is high in Ethiopia. Healthcare providers, researchers and policymakers should give more attention to dengue fever.

The dynamics of hydrological lateral nutrient fluxes contribute to our understanding of ecological functions related to energy, materials, and organism flows across various spatiotemporal scales. To explore the connectivity between multiple spatial flow processes, we conducted a one-year field measurement to assess lateral hydrologic carbon (C) and nitrogen (N) fluxes over the continental shelf in the Yangtze estuary. We observed a significant correlation between the differences in remote sensing-based estimates of gross primary production (GPP) (∆GPPMODIS) and the differences in eddy covariance (EC) tower-based GPP (∆GPPEC) at both high-elevation and low-elevation sites. Over the course of a year, our predicted daily maximum tidal elevation (TE) closely matched the observed values in the creek, which facilitated the development of theoretical models to simulate biogeochemical cycling processes and aquatic ecosystem functions. Our findings indicate that the studied saltmarsh acts as a net exporter of dissolved total C (DTC) while serving as a net sink for dissolved total N (DTN). Furthermore, there is a significant correlation in the total dissolved stoichiometry of the C/N ratio between imports and exports. These findings highlight the importance of integrating ecological stoichiometric principles to gain a deeper understanding of the complex relationships between physical, chemical, and biological processes, particularly within the context of the meta-ecosystem framework. Additionally, when considering reciprocal hydrological lateral C and N flows, single ecosystem can function both as sources and sinks within the meta-ecosystem framework.

Aims: The aims of this meta-analysis were to assess: the prevalence of Post-Acute COVID-19 sequalae in HIV positive patients; average time of diagnosis; and meta-regress for possible moderators of PACS.Methods: A standard search strategy was used in PubMed, and then later modified according to each specific database to get the best relevant results. These included Medline indexed journals; PubMed Central; NCBI Bookshelf and publishers' Web sites in line with the Preferred Reporting Items for Systematic Review and Meta-Analysis statement. Search terms included “long COVID-19 or post-acute COVID-19 syndrome/sequalae”, “persons living with HIV or HIV. The criteria for inclusion were published clinical articles reporting HIV in association with long COVID-19, further, the average time to an event of post-acute COVID-19 sequelae among primary infected patients with COVID-19. Random-effects model was used. Rank Correlation and Egger's tests were used to ascertain publication bias. Sub-group, sensitivity and meta-regression analysis were conducted. A 95% confidence intervals were presented and a p-value < 0.05 was considered statistically significant. Review Manager 5.4 and comprehensive meta-analysis version 4 (CMA V4) were used for the analysis. The review/trial was PROSPERO registered (CRD42022328509).Results: A total of 43 studies reported post-acute COVID-19 syndrome. Of those, five reported post-acute COVID-19 sequalae in PLHIV. Prevalence of post-acute COVID-19 sequalae was 43.1% (95% CI 20.5% to 68.9%) in persons living with HIV (PLWH). The average time to PACS diagnosis was 4 months at 64% [0.64 (95% CI 0.230, 0.913) (P < 0.0000), I²= 93%] and at one year to PACS diagnosis was at 70 %, however with non-significant correlation (P > 0.05). On comorbidities, asthenia was associated with PACS at 17.6 % [0.176 (95% CI 0.067, 0.385) (P = 0.008), I²= 86%] while fatigue at 82%, however not related with PACS event incidence (P < 0.05). Americas, Asian and European regions showed PACS events rates of 82%, 43% and 19 % respectively (P<0.05) relative to HIV infection. Conclusion: PACS prevalence in PLWH was 43% occurring at an average time of 4 months at 64% and 70 % at 12 months however non-significant with PACS. Asthenia was significantly associated with PACS at 17.6 % while fatigue at 82%, however not related with PACS event incidence. Americas recorded the highest PACS event rates in PLWH.

Background and Objectives: Angioembolization has emerged as an effective therapeutic approach for pelvic hemorrhages; however, its exact effect size remains uncertain. Therefore, we conducted this systematic review and meta-analysis to investigate the effect size of embolization-related pelvic complications after nonselective angioembolization compared to that after selective angioembolization in patients with pelvic injury accompanying hemorrhage. Materials and Methods Relevant articles were collected by searching the PubMed, EMBASE, and Cochrane databases until June 24, 2023. Meta-analyses were conducted using odds ratios (ORs) for binary outcomes. Quality assessment was conducted using the risk of bias tool in non-randomized studies of interventions. Results: Five studies examining 357 patients were included in the meta-analysis. Embolization-related pelvic complications did not significantly differ between patients with nonselective and selective angioembolization (OR 1.581, 95% confidence interval [CI] 0.592 to 4.225, I2 = 0%). However, in-hospital mortality was more likely to be higher in the nonselective group (OR 2.232, 95% CI 1.014 to 4.913, I2 = 0%) than in the selective group. In the quality assessment, two studies were found to have a moderate risk of bias, whereas two studies exhibited a serious risk of bias. Conclusions: Despite the favorable outcomes observed with nonselective angioembolization concerning embolization-related pelvic complications, determining the exact effect sizes was limited owing to the significant risk of bias and heterogeneity. Nonetheless, the low incidence of ischemic pelvic complications appears to be a promising result.

This study aimed to evaluate the diagnostic and prognostic roles of GATA3 immunohistochemistry in urothelial carcinoma (UC) through a meta-analysis. We investigated GATA3 immunohistochemical expression rates and performed a subgroup analysis based on tumor site, study location, and histological subtypes. The overall survival rates of patients with GATA3-positive and negative UC were compared. The estimated GATA3 expression rate was 0.748 (95% confidence interval [CI]: 0.704–0.787). GATA3 expression rates in the urinary bladder and urinary tract were 0.775 (95% CI: 0.727–0.818) and 0.614 (95% CI: 0.426–0.774), respectively. The GATA expression rates of noninvasive and invasive UCs were 0.965 (95% CI: 0.938–0.980) and 0.644 (95% CI: 0.581–0.702), respectively. In invasive UCs, there was a significant difference in GATA3 expression between non-muscular invasion and muscular invasion subgroups (0.937, 95% CI: 0.883–0.967 vs. 0.753, 95% CI: 0.645–0.836). GATA3 expression was the highest in the microcytic subtype among the histologic subtypes (0.952, 95% CI: 0.724–0.993). There was a significant correlation between GATA3 expression and better prognosis (hazard ratio: 0.402, 95% CI: 0.311–0.521). Taken together, GATA3 expression significantly correlated with low-stage and better prognosis in UC. GATA3 expression is highly variable across histological subtypes and it is careful interpreting GATA3 expression.

Recently, the move from cost-tied to open-access data has led to the mushrooming of research in pursuit of algorithms for estimating aboveground grass biomass (AGGB). Nevertheless, a comprehensive synthesis or direction on the milestones archived or an overview of how these models perform is lacking. This study synthesises the research work from decades of experiments in order to point researchers in the direction of what was done, the challenges faced, as well as how the models perform. A pool of findings from 108 remote sensing-based AGGB studies published from 1972 to 2020 show that about 62% of the remote sensing-based algorithms were tested in the Steppe grasslands, mostly in the temperate climate zone. An uneven annual publication yield was observed with approximately 36% of the research output from Asia whereas countries in the global south yielded few publications (&lt;10%). Optical sensors, particularly MODIS, remain a major source of satellite data for AGGB studies, whilst studies in the global south rarely use active sensors such as Sentinel-1. Optical data tend to produce poor regression accuracies that are highly inconsistent across the studies compared to Radar. Vegetation indices, particularly the Normalised Difference Vegetation Index (NDVI), remain a major predictor variable. Predictor variables such as Sward height, Red edge position and Backscatter coefficients produced slightly consistent accuracies. Deciding on the optimal algorithm for estimating AGGB is daunting due to the lack of overlap in the grassland type, location, sensor types, and predictor variables, signalling the need for further studies around the transferability of remote sensing-based AGGB models.

Objective: The objective of this review was to establish a modified meta-ethnography framework by integrating the traditional phases with methods from social constructivist grounded theory. The modified version was required to identify methods which can ensure analytic generalisability and honour critical inquiry. Method: A narrative synthesis review was undertaken using 4 electronic databases. Articles were located that described the methods and methodologies of meta-ethnography and social constructivist grounded theory. A synthesis was undertaken which integrated the methods of social constructivist grounded theory. Results: The meta-ethnography was undertaken in 5 phases: (1) The positionality of the researcher and the area of interest identified. (2) Identifying what is relevant. (3) Reading the study and determining how studies are related. (4) The iterative process of idea generation required for theory generation. And (5) Expression of the theory, model, or process. Conclusion: The current study provides a useful tool to support the development of a substantive theory (a theory in one area) which can be used then to consider its application in other areas. This approach will help create new lines of research and aid the explanation of problems in other areas.

Abstract: Background: Pain management is one of the main parts of treatments for Temporomandibular Disorders (TMDs). However, there is still a lack of high-quality evidence that compare the overall effects of these non-surgical treatments. The objective of this systematic review is to identify the most potential treatment protocol in dealing with pain caused by TMDs through a mixed comparison of interventions based on network meta-analysis. Methods: A systematic review and network meta-analysis of studies identified by searching PubMed, Embase, Medline, Ovid, and CINAHL. All the included studies should have characteristics that: (1) participants with TMDs of any age; (2) non-surgical treatments; (3) score of Visual Analog Scale (VAS) as the outcome measure; (4) randomized controlled trials. The Cochrane Bias Assessment Toll was used to assess the bias, the CINeMA website was applied to rate the confidence of evidence, and ADDIS software was used to conduct the network meta-analysis. Results: 46 studies were included in this review. The agreement between authors reached a kappa value of 0.78. The results of the network meta-analysis showed that wearable therapy devices are more likely to be the best choice for reducing the pain of patients with myogenic TMDs, whereas a combination of platelet-rich plasma injection and wearable therapy devices is more likely to be the best choice for reducing the pain in a long term after treatment for patients with mix-type TMDs. Moreover, the application of therapy equipment has the most potential in reducing pain in a long term after treatment for patients with articular TMDs. Discussion: Wearable devices have a great potential for pain syndrome caused by TMDs, the mechanism might come from a biomechanical perspective. However, the overall confidence rating of evidence is low. Studies with high quality are still needed in the future. Other: The PROSPERO Registration Number of this systematic review is CRD42021253442.

Overweight/obesity is one of the most important health problem worldwide. Birth by cesarean section has been shown to influence long-term health outcome including obesity. The aim of this systematic review-meta-analysis is to examine whether cesarean section increases the risk of offspring’ s overweight/obesity. The study follows the PRISMA and MOOSE guidelines. A systematic literature search was con-ducted on Scopus, PubMed, and WoS until December 2020. For inclusion, studies must have re-ported either (I) both Birth by cesarean section and adult (≥ 18 years) offspring BMI, (II) cohort or case–control study design and (III) a risk estimate. Heterogeneity testing was performed using Cochran's Q and I2 statistics. Publication bias was assessed by Egger’s test and Begg’s test. Me-ta-analysis was performed through a random effect model. Eleven studies with a combined population of 180.408 subjects were included in the meta-analysis. The overall analysis (n = 18) yielded a combined risk estimate for overweight/obesity of 1.19 (95% CI 1.08-1.31) and the test of heterogeneity resulted Q=53,37 (I2 = 70,37 %, P≤0•0001). The risk of offspring obesity is 1.23 (95% CI 1.09-1.39) and the test of heterogeneity resulted Q=39.50 (I2= 72,15%, P≤0•0001). Children born by cesarean section have an increased risk of developing obesity in adulthood

Background: Acute ischemic stroke is among the main causes of mortality worldwide; a rapid and opportune diagnosis is crucial to improve a patient's outcome. MicroRNAs are quite useful for a rapid and accurate diagnosis.Methods: We perform both structural networks approach and a meta-analysis (using a random-effect model to evaluate the heterogeneity and risk bias, according to the PRISMA statement) to analyze the feasibility to develop a microRNA-based biomarker panel for an opportune AIS diagnosis. Results: Structural networks identify a set of eight miRNAs (miR-16, miR-124-3p, miR-484, miR-15a, miR-4454, miR-107, miR-125b-5p and miR-320b) as preliminary microRNA-based biomarker panel, from these only three microRNAs are significantly associated with the main risk factors of AIS, (miR-107: hypertension, 95% CI 9.74-53.24 p<0.0001, type 2 Diabetes mellitus, 95% CI 2.18-19.26); p=0.0008; miR-16 hypertension, 95% CI 1.26-3.56 p=0.0046, smoking, 95% CI 1.07-3.54 p=0.0277; and miR-15a hypertension, 95% CI 1.26-3.56 p=0.0046; smoking, 95% CI 1.07-3.54 p=0.0277). However, the meta-analysis reveals that data is highly heterogeneous and biased; and only microRNAs isolated from plasma samples and further processed in microarrays are the most reliable to distinguish AIS patients.Conclusions: Together our results show that although there are some miRNAs that seem to be associated with AIS, we are still far to develop a miRNA-based biomarker for AIS diagnosis and it is necessary to harmonize the protocols, results and include more populations for further studies otherwise we will remain throwing punches in the dark.

This study presents a meta-analysis of studies that investigate the effectiveness of chitosan administration on lifestyle-related disease in murine models. A total of 34 published studies were used to evaluate the effect of chitosan supplementation. The effect sizes for various items after chitosan administration were evaluated using the standardized mean difference. Using Cochran’s Q test, the heterogeneity of effect sizes was assessed, after which a meta-ANOVA and –regression test was conducted to explain the heterogeneity of effect sizes using the mixed-effect model. Publication bias was performed using Egger’s linear regression test. Among the items evaluated, blood triglyceride and HDL-cholesterol showed the highest Q statistics and I2 values, respectively. Other than blood HDL-cholesterol, total cholesterol, and triglyceride in feces, most items evaluated showed a negative effect size with high significance in the fixed- and random-effect model (p<0.0001). In the meta-ANOVA and -regression test, administering chitosan and resistant starch was revealed to be most effective in lowering body weight. In addition, chitosan supplementation proved to be an effective solution for TNF-α inhibition. In conclusion, chitosan has been shown to be somewhat useful in improving symptoms of lifestyle-related disease. Although there are some limitations in the results of this meta-analysis due to the limited number of animal experiments conducted, chitosan administration nevertheless shows promise in enhancing the quality of human life.

Aim of study:Aim of this meta-analysis is to evaluate post-operative procalcitonin as a marker to predict post- operative infectious complications after pancreatic surgeries.Material and Methods:Systemic literature search was performed using MEDLINE, EMBASE and to identify studies evaluating the diagnostic accuracy of Procalcitonin (PCT) as a predictor for detecting infectious complications on postoperative days (POD) 3 and 5 following pancreatic surgery. A meta-analysis was performed using random effect model and pooled predictive parameters for POD 3 and 5 were derived. Geometric means were calculated for PCT cut offs. Results:6 studies included day 3 PCT analysis, 2 studies included both day 3 and day 5 analysis. Total data of 471 patients were derived. 161 patients developed infectious complications. Pooled sensitivity, specificity, pooled area under curve, diagnostic odds ratio (DOR), positive and negative like hood ratio of day 3 PCT were 74%,79%,0.8453, 11.03,3.17 and 0.31 respectively. Pooled sensitivity, specificity, diagnostic odds ratio (DOR), positive and negative like hood ratio of day 5 PCT were 83%,70%,12.91,2.91 and 0.25 respectively. Geometric means for PCT cut off for day 3 and 5 were 0.80 and 0.43. Conclusion:Postoperative procalcitonin particularly day 3 procalcitonin levels predict post-operative infectious complications following pancreatic surgeries.

Antibiotics (ABs) are common medications used for treating infections. In cancer patients treated with immune checkpoint inhibitors (ICIs), concomitant exposure to ABs may impair the efficacy of ICIs and lead to a poorer outcome compared to AB non-users. We report here the results of a meta-analysis evaluating the effects of ABs on the outcome of patients with solid tumors treated with ICIs. PubMed, the Cochrane Library, and Embase were searched from inception until September 2019 for observational or prospective studies reporting prognosis of adult patients with cancer treated with ICIs and with or without ABs. Overall survival (OS) was the primary endpoint, and progression-free survival (PFS) was the secondary endpoint. The effect size was reported as hazard ratios (HRs) with a 95% confidence interval (CI), and an HR > 1 associated with a worse outcome in ABs users compared to no-ABs users. Fifteen publications were retrieved for a total of 2363 patients. In the main analysis (n = 15 studies reporting data), OS was reduced in patients exposed to ABs before or during treatment with ICIs (HR = 2.07, 95%CI 1.51–2.84; P<.01). Similarly, PFS was inferior in ABs users in n = 13 studies with data available (HR = 1.53, 95%CI 1.22–1.93; p<.01). In cancer patients treated with ICIs, AB use significantly reduces OS and PFS. Short duration/course of ABs may be considered in clinical situations in which they are strictly needed.

Meta-aminobenzoic acid, an important model system in the study of polymorphism and crystallization of active pharmaceutical ingredients, exist in water in both the nonionic (mABA) and zwitterionic (mABA^±) forms. However, the constituent molecules of the polymorph that crystallizes from aqueous solutions are zwitterionic. This study reports atomistic simulations of the events surrounding the early stage of crystal nucleation of meta-aminobenzoic acid from aqueous solutions. Ab initio molecular dynamics was used to simulate the hydration of mABA± and mABA, and to quantify the interaction of these molecules with the surrounding water molecules. Density functional theory calculations were conducted to determine the low-lying energy conformers of meta-aminobenzoic acid dimers and compute the Gibbs free energies in water of nonionic, (mABA)₂, zwitterionic, (mABA^±)₂ and nonionic-zwitterionic, (mABA)(mABA^±), species. Classical molecular dynamics simulations of mixed mABA–mABA^± aqueous solutions were carried out to examine the aggregation of meta-aminobenzoic acid. According to these simulations the selective crystallization of the polymorph which constituent molecules are zwitterionic is driven by the formation of zwitterionic dimers in solution, which are thermodynamically more stable than (mABA)₂ and (mABA)(mABA^±) pairs. This work represents a paradigm of the role of molecular processes during the early stages of crystal nucleation in affecting polymorph selection during crystallization from solution.

Introduction Recent evidence from relatively small randomized controlled trials would seem to support a useful role of ranolazine for the prevention and treatment of atrial fibrillation (AF). The present study is aimed at providing information about the possible beneficial anti-arrhythmic properties of ranolazine. In particular, the meta-analysis carried out in this study focuses on the application of ranolazine to prophylaxis and treatment of atrial fibrillation.Methods Both randomized controlled trials (RCTs) and non randomized observational studies concerning the effects of ranolazine on AF were included in the meta-analysis. In each of the considered studies, a comparison was made between a group of patients taking ranolazine and a second group treated instead with another antiarrhythmic therapy , or assigned to placebo. Efficacy outcomes were the risk of new- onset AF, the probability of conversion to sinus rhythm of patients with recent occurrence(≤ 48 h)of AF and the time to conversion to sinus rhythm. Safety endpoints were death, adverse events, QTc prolongation and hypotension.Results Ten studies ( 8 RCTs and 2 nonrandomized observational studies) were gathered on the whole. Ranolazine was effective in preventing the occurrence of AF when compared to controls (RR= 0.60; 95% CI: 0.43–0.83; p = 0.002). Subgroup analysis showed a more pronounced preventive effect of ranolazine against AF in the postoperative setting of coronary artery bypass grafting(CABG) surgery (RR= 0.39; 95% CI: 0.18-0.83; p=0.02) when compared to non- postoperative AF (RR= 0.76; 95% CI: 0.63-0.92; p=0.04). Ranolazine enhanced the chances of successful cardioversion when added to intravenous amiodarone compared to amiodarone alone (RR 1.18; 95% CI: 1.05–1.33; p = 0.004) and significantly decreased the time to cardioversion(SMD= −10.35 h; 95% CI: −18.13 hours to − 2.57 hours; p < 0.001). Overall risks of death, adverse events, and QTc prolongation were shown to be similar in the comparison between patients treated with ranolazine and controls. Conclusions Ranolazine given orally at appropriate doses showed the property to significantly quicken the conversion of AF to sinus rhythm when combined with the iv amiodarone, compared to iv amiodarone alone . Furthermore, in patients in sinus rhythm, ranolazine proved to reduce the frequency of new onset AF as well as of its recurrences, especially in patients undergone CABG surgery, known to be at high risk of developing postoperative AF. In addition, ranolazine use seems to be safe and associated with relatively few adverse events.

Background: Diabetic retinopathy (DR) is one of the most prominent pathological microvascular complications in diabetes. A series of studies reported that vitamin D deficiency was associated with increased prevalence of retinopathy in diabetic patients but the results were inconsistent. In this study we focused on evaluating the relationship between vitamin D deficiency and DR by conducting a meta-analysis of observational studies. Methods: Systematic computerized searches were performed in PubMed, MEDLINE, and the Cochrane Library for relevant original articles till November 20, 2016. The pooled odds ratios (ORs) with corresponding confidence intervals (CIs) were calculated to assess the associated value of vitamin D deficiency to the risk of DR. 9 studies including 6332 participants were subjected to final analysis. Results: The results indicated that vitamin D deficiency increases the risk of DR (OR = 1.57, 95% CI 1.32-1.87) with a little heterogeneity (I2 = 23%). In addition, the subgroup analysis demonstrated that there were obvious heterogeneities in T2DM (I2 = 47.8%). Sensitivity analysis showed that the results were relatively stable and reliable. Conclusion: our meta-analysis demonstrated that vitamin D deficiency could increase the risk of DR.

Dietary patterns were recently applied to examine the relationship between eating habits and prostate cancer (PC) risk. While the associations between PC risk with glycemic index and Mediterranean score have been reviewed, no meta-analysis is currently available on dietary patterns defined by “a posteriori” methods. Search was carried out (PubMed, Web of Science) to identify studies reporting the relationship between dietary patterns and PC risk. Relevant dietary patterns were selected and the risk estimated were calculated by a random-effect model. Multivariable-adjusted odds ratios (ORs) for a 1st-percentile increase in dietary pattern score were combined by a dose response meta-analysis. 12 observational studies were included in the meta-analysis which identified “Healthy pattern” and “Western pattern”. The Healthy pattern was not related to PC risk (OR=0.96; 95% CI: 0.88-1.04) while the Western pattern significantly increased it (OR=1.34; 95% CI: 1.08-1.65). In addition, a “Carbohydrate pattern”, identified in four articles, was positively associated with a higher PC risk (OR=1.64; 95% CI: 1.35-2.00). A significant linear trend between the Western (p=0.011) and the Carbohydrate (p=0.005) pattern and the increment of PC risk was observed. The small numbers of studies included suggest that further investigations are necessary to support these findings.

Dipteryx spp. is an important species in reforestation in the Amazon. The objective of this study is to characterize and compare the relationships between dendrometric variables in Dipteryx spp. stands in the Western Amazon by fitting linear regression equations for total height and crown diameter. Six forest stands were evaluated in three municipalities. Dendrometric variables collected included diameter at 1.3 m height (dbh), total height (ht) and crown diameter (dc). Simple and multiple linear regression equations were fitted to characterize the relationships between ht and dc. The total aboveground biomass of Dipteryx spp. trees and the carbon stock of the stands were estimated. The general equations showed higher R² values, exceeding 0.7. The general equations for estimating ht and dc were significant for all coefficients. The trees averaged 22 t/ha of aboveground biomass in the stands. There was a variation in carbon sequestration potential among stands, ranging from 5.12 to 88.91 t CO2.ha-1. Single-input equations using dbh as an independent variable are recommended for estimating dc and ht for individual Dipteryx spp. stands. Stands in the Western Amazon play a significant role in carbon sequestration and accumulation. Trees can sequester an average of 4.8 tons of CO2 per year.

Accurate transfer learning of clinical outcomes, e.g., of the effects and side effects of drugs or other interventions, from one cellular context to another (in-vitro versus ex-vivo versus in-vivo, or across tissues), between cell-types, developmental stages, omics modalities or species, is considered tremendously useful. Ultimately, it may avoid most drug development failing in translation, despite large investments in the preclinical stages, which includes animal experiments requiring careful justification. Thus, when transferring a prediction task from a source (model) domain to a target domain, what counts is the high quality of the predictions in the target domain, requiring molecular states or processes common to both source and target that can be learned by the predictor, reflected by latent variables. These latent variables may form a compendium of knowledge that is learned in the source, to enable predictions in the target; usually, there are few, if any, labeled target training samples to learn from. Transductive learning then refers to the learning of the predictor in the source domain, transferring its outcome label calculations to the target domain, considering the same task. Inductive learning considers cases where the target predictor is performing a different yet related task as compared to the source predictor, making some labeled target data necessary. Often, there is also a need to first map the variables in the input/feature spaces (e.g. of gene names to orthologs) and/or the variables in the output/outcome spaces (e.g. by matching of labels). Transfer across omics modalities also requires that the molecular information flow connecting these modalities is sufficiently conserved. Only one of the methods for transfer learning we reviewed offers an assessment of input data, suggesting that transfer learning is unreliable in certain cases. Moreover, source domains feature their very own particularities, and transfer learning should consider these, e.g., as differences in pharmacokinetics, drug clearance or the microenvironment. In light of these general considerations, we here discuss and juxtapose various recent transfer learning approaches, specifically designed (or at least adaptable) to predict clinical (human in-vivo) outcomes based on molecular data, towards finding the right tool for a given task, and paving the way for a comprehensive and systematic comparison of the suitability and accuracy of transfer learning of clinical outcomes.

The main objective of this article is to explore the causes of household electricity poverty in Spain from an innovative perspective. Based on evidence of energy inequality across households with different income levels, a quantile regression approach was used to better capture the heterogeneity of determinants of energy poverty across different levels of electricity expenditure. The results illustrate some interesting and counter-intuitive findings about the relationship between household income and electricity poverty, and the technical efficiency of quantile regression compared to the imprecise results of a standard single coefficient/OLS approach.

The study compares three machine learning and deep learning models—Support Vector Regression (SVR), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM)—for predicting market prices using the NGX All-Share Index dataset. The models were evaluated using multiple error metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), and R-squared. RNN and LSTM were tested with both 30 and 60-day windows, with performance compared to SVR. LSTM delivered better R-squared values, with a 60-day LSTM achieving the best accuracy (R-squared = 0.993) when using a combination of endogenous market data and technical indicators. SVR showed reliable results in certain scenarios but struggled in fold 2 with sudden spike that shows high probability of not capturing the entire underlying NGX pattern in the dataset correctly as witnessed with the high validation loss during the period. Additionally, RNN faced the vanishing gradient problem that limits its long-term performance. Despite challenges, LSTM's ability to handle temporal dependencies, especially with the inclusion of On-Balance Volume, led to significant improvements in prediction accuracy. The use of the Optuna optimization framework further enhanced model training and hyperparameter tuning, contributing to the performance of the LSTM model.

Hypertension stands as the foremost modifiable risk factor for cardiovascular disorders in South Asia. This research demonstrates the superiority of statistical models over traditional approaches in identifying clinical risks related to hypertension. Leveraging individual-level data from the Bangladesh Demographic and Health Survey (2017-18), a unified dataset was created. Adhering to JNC7 and ACC/AHA guidelines for hypertension classification, two prominent regression models were employed to estimate the prevalence of undiagnosed hypertension. A substantial 16% of hypertension cases in Bangladesh remain undetected. The study identifies undiagnosed hypertension as a heightened risk, particularly among the youth and residents of Sylhet and Rangpur divisions. Statistical models exhibit efficacy in discerning undiagnosed hypertension and associated factors in South Asia. Subsequent investigations should incorporate biological markers to enhance statistical models and ascertain their practicality.

Training supervised machine learning models requires labeled examples. A judicious choice of examples is helpful when there is a significant cost associated with assigning labels. This article improves upon a promising extant method – Batch-mode Expected Model Change Maximization (B-EMCM) method – for selecting examples to be labeled for regression problems. Specifically, it develops and evaluates alternate strategies for adaptively selecting batch size in B-EMCM. By determining the cumulative error that occurs from the estimation of the stochastic gradient descent, a stop criteria for each iteration of the batch can be specified to ensure that selected candidates are the most beneficial to model learning. This new methodology is compared to B-EMCM via mean absolute error and root mean square error over ten iterations benchmarked against machine learning data sets. Using multiple data sets and metrics across all methods, one variation of AB-EMCM, the max bound of the accumulated error (AB-EMCM Max), showed the best results for an adaptive batch approach. It achieved better root mean squared error (RMSE) and mean absolute error (MAE) than the other adaptive and non-adaptive batch methods while reaching the result in nearly the same number of iterations as the non-adaptive batch methods.

Effective communication in automated chat systems hinges on the ability to understand and respond to context. Traditional models often struggle with determining when additional context is necessary for generating appropriate responses. This paper introduces Context-Aware BERT (CA-BERT), a transformer-based model specifically fine-tuned to address this challenge. CA-BERT innovatively applies deep learning techniques to discern context necessity in multi-turn chat interactions, enhancing both the relevance and accuracy of responses. We describe the development of CA-BERT, which adapts the robust architecture of BERT with a novel training regimen focused on a specialized dataset of chat dialogues. The model is evaluated on its ability to classify context necessity, demonstrating superior performance over baseline BERT models in terms of accuracy and efficiency. Furthermore, CA-BERT's implementation showcases significant reductions in training time and resource usage, making it feasible for real-time applications. The results indicate that CA-BERT can effectively enhance the functionality of chatbots by providing a nuanced understanding of context, thereby improving user experience and interaction quality in automated systems. This study not only advances the field of NLP in chat applications but also provides a framework for future research into context-sensitive AI developments.

Objective: The main purpose of this meta-analysis was to assess the efficacy and safety of Oncolytic Viruses (OVs) in glioma therapy. Methods: We searched the literature in PubMed, EMBASE, Web of Science, and the Cochrane Library. The primary outcomes assessed were overall survival (OS), progression-free survival (PFS), objective response rate (ORR), and adverse events (AEs). The risk of bias was evaluated by sensitivity analysis and publication bias. Results: We identified 21 studies with 440 patients in this meta-analysis. In the single-arm analysis, the results showed that the 1-year OS rate was 47% (95% CI: 34%-61%, I2 = 75%) and the 2-year OS rate was 14% (95% CI: 10%-20%, I2 = 0%). The median OS was 12.48 months (95% CI: 10.71-14.25, I2 = 96%). The 1-year PFS rate was 13% (95% CI: 5%-24%, I2 = 0%), with the median PFS being 4.01 months (95% CI: 2.99-45.03, I2 = 96%). The pooled estimate of ORR was 7% (95% CI: 3%-12%, I2 = 23%). Funnel plots for median PFS were asymmetric with Egger’s test P < 0.01 indicating publication exists. The incidence of OVs-related AEs was 49% (95% CI: 20%-79%, I2 = 95%), and AEs > grade 3 was 8% (95% CI: 3%-16%, I2 = 62%). Conclusion: This meta-analysis indicated that OVs therapy was relatively safe but did not significantly extend survival in patients with gliomas.

Background: Diagnostic accuracy of Parkinson’s disease (PD), a synucleinopathy, based on diagnostic criteria is suboptimal. A biomarker for synucleinopathies is pivotal both from a clinical and from a research aspect. CSF a-synuclein has been extensively studied over the past two decades as a candidate biomarker of synucleinopathies. Herein we present data on studies focusing on total, phosphorylated and oligomeric CSF a-synuclein in PD. Methods: Pubmed, Scopus and Web of Science were searched for studies with &gt;10 PD patients and control subjects, with data (mean, SD) on total, phosphorylated or oligomeric a-synuclein. Cohen’s d, as a measure of effect size, was calculated for all a-synuclein forms. Subgroup analysis and meta-regression were performed in an effort to explain between-study heterogeneity. Results: Thirty studies on total, six studies on oligomeric and one study on phosphorylated a-synuclein were included. Total a-synuclein was decreased and oligomeric a-synuclein increased in PD patients vs. controls. Effect size was medium for total and high for oligomeric a-synuclein. A-syn forms provided suboptimal combined sensitivity/specificity for the differentiation of PD from controls. There was significant between-study heterogeneity. PD cohort characteristics (sex, age, disease duration, UPDRS, H&amp;Y) and study characteristics (study design, healthy vs. neurological controls, control for CSF blood contamination, method of a-syn measurement) could not account for between-study heterogeneity. Publication bias was limited. Conclusions: CSF a-synuclein levels lack sufficient accuracy to be used as biomarkers for PD. Standardization of (pre)analytical variables may improve the discriminatory power of a-synuclein forms in the future.

More than half of the states in the U.S. report that over 30% of adults are obese. Obesity increases the risk of many chronic diseases, including type 2 diabetes, hypertension, cardiovascular disease, and can even reduce one’s lifespan. Similarly, the prevalence of type 2 diabetes follows a comparable trend. As a result, researchers are striving to find solutions to reduce obesity rates, with a particular focus on gut health, which has been previously linked to both obesity and type 2 diabetes. Recent studies suggest that Akkermansia muciniphila(AKK) may have a positive probiotic effect on preventing the onset of type 2 diabetes and obesity. We conducted a quantitative meta-analysis of 15 qualified animal studies investigating the effects of AKK administration as a probiotic. The statistical analyses showed that AKK administration significantly reduced body weight gain by 10.4% and fasting blood glucose by 21.2%, while also significantly improving glucose tolerance by 22.1% and increasing blood insulin levels by 26.9%. However, our analysis revealed substantial heterogeneity between the control and experimental groups across all subgroups. Overall, AKK appears to be effective at reducing the onset of type 2 diabetes and diet-induced obesity. Long-term studies with larger sample sizes are needed to confirm these beneficial effects, as the current animal studies were of short duration (less than 20 weeks).

Research has shown that unsociability, reflecting as a personal choice, is not necessarily associated with socio-emotional problems in Western countries, and the associations between unsociability and peer problems are consistently evident in Chinese culture. Yet the strength and direction of the associations were mixed. The present study aimed to examine whether unsociability is associated with peer problems and explored the potential moderators among the associations. A meta-analysis was conducted using publications that measured unsociability and peer problems. A total of 21 articles involving 43 effect sizes from 12,696 Chinese children and adolescents were included. The results revealed that (1) unsociability was positively associated with peer problems (r = .32, p &lt; .001) among children and adolescents. (2) informants (i.e., self-reports, peer nominations, teacher ratings, parent ratings) and living areas (i.e., urban, suburban, and rural areas) significantly moderated the associations between unsociability and peer problems. Specifically, the associations were stronger for peer-nominated unsociability, self-reported peer problems, and samples in suburban areas. These findings shed light on unsociability linked to higher levels of peer problems among Chinese children and adolescents. Still, the influences are unique to peer problems and moderated by both data sources and environmental factors.

Colorectal cancer (CRC) with KRAS mutation is prone to develop metachronous metastasis. However, effective measures for prediction and prevention of such clinical issue are currently unavailable. In this study, we confirmed that KRAS mutation is a risk factor for CRC metachronous metastasis through meta-analysis and public database analysis. Based on the ICGC-AGRO database, we have constructed a risk scoring model for predicting metachronous metastasis in KRAS-mutant CRC using machine learning, which was validated in public datasets. Furthermore, we discovered that KRAS inhibitors can effectively suppress the migration and invasion capabilities of high risk CRC cells by wound healing assay and transwell experiment, which was validated ex vivo and in vivo using organoids and splenic liver metastatic mouse model. Finally, RNA sequencing, RT-qPCR, and western blots have proven that KRAS inhibitors can suppress the epithelial-mesenchymal transition (EMT) and transforming growth factor beta (TGF-beta) signaling pathway in CRC. Collectively, our study suggests that KRAS inhibitors may inhibit the metastatic ability of CRC by suppressing EMT and the TGF-beta pathway, suggesting their potential for the prevention of metachronous metastasis in CRC with KRAS mutation.

Today, LLMs are not good at personalization providing recommendation. They advise physicians and financial advisors to ask professionals in respective fields for help, even having user information available. Answering questions of software professionals, LLM needs to deliver in-depth answers with codes or algorithms, whereas for professionals in other fields would need definitions and main concepts. The intent of this chapter is to make LLM answer tailored to the needs of users, taking into account available information about them. To do that, we need to generalize available information about a person like her health record, maintaining the privacy of this person. We rely on meta-learning techniques to design a LLM prompt to produce a personalization prompt to obtain a suitable relevant information. Such “meta-prompt” is produced by generalization operation applied to available documents for the user. These documents need to be de-identified so that they are sufficient for personalization on one hand and will maintain user privacy on the other hand. The second neuro-symbolic technique to support personalization is abductive reasoning, acting in parallel to LLM fine-tuning. Traditional recommendation and personalization techniques as well as modern, deep learning – based are presented, and the comparison is drawn to the proposed approach. We also share the evaluation and comparative analyses of these approaches. We consider an example for how to build personalization LLM systems coming from Langchain platform. We will explore how to construct chains to form a personalization profile for a user and apply it to user search and recommendation requests.

Purpose: Understanding how older adults perceive and interpret emotional cues in speech prosody contributes to our knowledge of cognitive aging. This study provides a systematic review with meta-analysis to investigate the extent of the decline in affective prosody recognition (APR) among older adults in terms of overall and emotion-specific performance and explore potential moderators that may cause between-study heterogeneity. Method: Incorporated into the literature search were six electronic databases, with a specific emphasis on studies comparing the APR performance of older adults with that of younger adults. This comparison was focused on basic emotions. Meta-regression analyses were executed to pinpoint potential moderators related to demographic and methodological characteristics. Results: A total of 19 studies were included in the meta-analysis, involving 560 older adults with a mean age of 69.15 years. The findings indicated a substantial negative effect size (g = －1.21). Furthermore, the magnitude of aggregated effect sizes showed a distinct valence-related recognition pattern with positive prosody exhibiting smaller effect sizes. Years of education were found to moderate the overall performance effect estimate while age and gender significantly influenced the effect estimate of happiness. Conclusions: The results confirmed a significant decline in APR ability among older adults, but this decline was unbalanced across basic emotions. Educational level emerged as a significant factor influencing APR ability. Moreover, participants with a higher mean age exhibited notably poorer performance in recognizing happy prosody. These findings underscore the need to further investigate the neurobiological mechanisms for APR decline associated with aging.

Wireless sensor networks (WSN) are mostly utilized in many applications. Indeed, WSN is composed of sensors to evaluate some physical phenomena. However, the information brought by the sensor still missed if we don't know the exact location of the event. Several localization algorithms have been proposed in order to locate the nodes in WSN. The localization algorithms are categorized into range-based and range-free techniques. The range-based techniques use either distance or time to calculate the coordinates of unknown nodes. Nevertheless, those kinds of techniques need some additional material in computation purpose. Therefore, range-based techniques present an expensive solution for positioning in WSN. Alternatively, range-free techniques may do the same task without involving additional material. But they don’t offer a high precision of localization in comparison with range-based techniques. DVHOP is the most popular range-free technique that uses the hop count method in the localization process. In this work, we propose an improvement of DVHOP localization algorithm to create three improved versions of this algorithm and we have achieved that by adopting two meta-heuristic (simulated annealing, particle swarm optimization) and FMINCON solver dedicated to the optimization in the field of WSN nodes localization. The experimental results obtained in this work show clearly the gain and the good impact of our proposition.

The high costs of cancer treatment and lack of investment in health care are significant obstacles to public health on the African continent. The objective of this study was to estimate the financial cost of treating children suffering from cancer in Africa. We conducted a systematic review and meta-analysis of expert opinions between March 2000 and March 2020. The key search terms included ‘cost’, ‘cancer’ and ‘child’; we selected articles that specifically addressed the financial costs of childhood cancer in African countries. Of the 103 articles found, 18 met the inclusion criteria. Cancer care was a heavy financial burden in most of the countries studied, although costs varied from country to country; the average expenditure on healthcare was US$1017.39 ± US$319.1 per year. In countries without a health insurance system, the highest proportion of cancer care costs, 46.6%, was indirect, whereas in countries with a cancer financing system, the direct cost of treatment was low, 53.4%. The cost of treating childhood cancer is high in Africa in relation to the standard of living of individuals residing in this region.

The capacitated team orienteering problem with time windows (CTOPTW) is a NP-hard combinatorial optimization problem. In the CTOPTW, a set of customers is given each with a profit, a demand, a service time and a time window. A homogeneous fleet of vehicles is available for serving customers and collecting their associated profits. Each vehicle is constrained by a maximum tour duration and a limited capacity. The CTOPTW is concerned with the determination of a preset number of vehicle tours that begin and end at a depot, visit each customer no more than once while satisfying the time duration, time window and vehicle capacity constraints on each tour. The objective is to maximize the total profit collected. In this study we propose an iterated local search (ILS) algorithm to deal with the CTOPTW. ILS is a single solution based meta-heuristic that successively invokes a local search procedure to explore the solution space. A perturbation operator is used to modify the current local optimum solution in order to provide a starting solution for the local search procedure. As different problems and instances have different characteristics, the success of the ILS is highly dependent on the local search procedure, the perturbation operator(s) and the perturbation strength. The basic ILS uses a single perturbation operator and the perturbation strength remains the same during the optimization process. To address these issues, we use three different perturbation operators and a varying perturbation strength which changes as the algorithm progresses. The idea is to assign a larger perturbation strength in the early stages of the search in order to focus on exploring the search space. The perturbation strength is gradually decreased so that we focus more on exploitation. The computational results show that the proposed ILS algorithm is able to generate high quality solutions on the CTOPTW benchmark instances taken from the scientific literature, demonstrating its efficiency in terms of both the solution quality and computational time. Moreover, the proposed ILS produces 21 best known results and 5 new best solutions.

Background: Conclusions of meta-analyses of clinical studies may substantially influence opinions of perspective patients and stakeholders in health care. Nineteen meta-analyses of clinical studies on the management of primary knee osteoarthritis (pkOA) with stem cells, published between January 2020 and July 2021, came to inconsistent conclusions regarding the efficacy of this treatment modality. It is possible that a separate meta-analysis based on an independent, systematic assessment of clinical studies on the management of pkOA with stem cells may reach a different conclusion. Methods: PubMed, Web of Science and the Cochrane library were systematically searched for clinical studies and meta-analyses of clinical studies on the management of pkOA with stem cells. All clinical studies and meta-analyses identified were evaluated in detail, as were all sub-analyses included in the meta-analyses. Results: The inconsistent conclusions regarding the efficacy of treating pkOA with stem cells in the 19 assessed meta-analyses were most probably based on substantial differences in literature search strategies among different authors, misconceptions about meta-analyses themselves, and misconceptions about the biology of stem cells. An independent, systematic review of the literature yielded a total of 183 studies, of which 33 were randomized clinical trials, including a total of 6860 patients with pkOA. However, it was not possible to perform a scientifically sound meta-analysis. Conclusion: Clinicians should interpret the results of the 19 assessed meta-analyses of clinical studies on the management of pkOA with stem cells with caution, and should be cautious of the conclusions drawn therein. Clinicians and researchers should strive to participate in FDA and/or EMA reviewed and approved clinical trials to provide clinically and statistically valid efficacy.

Background: At present, the treatments for patients with advanced lung cancer focus on chemotherapy, targeted therapy, immunotherapy, or a combination of multiple treatments. Purpose: The main purpose of this study is to compare the various chemotherapy-based combination therapies and find the best one for patients with advanced lung cancer. Methods: Based on database (PubMed, EMBASE and Medline) for randomized controlled trials of advanced lung cancer with combination therapy from 2008 to 2020, we searched literatures with overall survival (OS), progression-free survival (PFS), objective response rate (ORR) and adverse as outcome indicators and established a Bayesian mesh meta-analysis for multiple treatment strategies. Then, we combined the results of four outcome indicators to find out the best chemotherapy-based combination therapy strategy for patients with advanced lung cancer, further, we tried to screen out the best drugs of which were commonly used now. Results: It contained a total of 51 studies, including five combination therapies: Chemotherapy/Chemotherapy plus placebo (CT), chemotherapy plus one targeted therapy drug (CT+T), chemotherapy plus two targeted therapy drugs (CT+T+T), chemotherapy combined with immunotherapy (CT+I) or chemotherapy combined with biotherapy (CT+B). In terms of four outcome indicators, CT+I showed the best therapeutic benefits. In the comparison of immunotherapy drugs, pembrolizumab showed the best effect. Conclusion: Our results showed that, among the multiple chemotherapy-based combination therapy strategies, chemotherapy combined with immunotherapy is the best choice for patients with advanced lung cancer, and pembrolizumab combined with chemotherapy has the best effect.

Hearing impairment (HI) is a common sensory disorder that is defined as the partial or complete inability to detect sound in one or both ears. This diverse pathology is associated with a myriad of phenotypic expressions and/or syndromes. HI can be caused by various intrinsic, environmental and/or unknown factors. Some ontologies capture some relevant HI forms, phenotypes and syndromes, but there is no comprehensive knowledge portal which includes aspects specific to the HI disease state. This hampers inter-study comparability, integration and interoperability within and across disciplines. This work describes the HI Ontology (HIO) that was developed based on the Sickle Cell Disease Ontology (SCDO) model. This is a collaboratively developed resource built around the 'Hearing Impairment' concept by a group of experts in different aspects of HI and ontologies. HIO is the first comprehensive, standardized, hierarchical and logical representation of existing HI knowledge. HIO allows researchers and clinicians alike to readily access standardized HI-related knowledge in a single location and promote collaborations and HI information sharing, including epidemiological, socio-environmental, biomedical, genetic and phenotypic information. Furthermore, this ontology illustrates the adaptability of the SCDO framework for use in developing a disease-specific ontology.

Introduction: Cholelithiasis and consumption of alcohol are the most frequent causes of acute pancreatitis (AP), accounting for about 30 to 40% of the cases, respectively. The frequency of acute biliary pancreatitis is high in a certain population in Brazil. Objective: To estimate the global frequencies of acute biliary pancreatitis (ABP), acute alcoholic pancreatitis (AAP) and the cases considered as acute idiopathic pancreatitis (AIP) in studies published from October 2006 to December 31, 2018. Methods: A systematic review of observational studies was performed from October 2006 to December 31, 2018. A meta-analysis by the random effects model was used to calculate the frequencies of global ABP, AIP and AAP and subgroups. Results: Forty-six studies representing 2,341,007 AP cases were included in 36 countries. The overall estimate for acute biliary pancreatitis (ABP) was 41.6% (95% CI 39.2-44.1), followed by acute alcoholic pancreatitis (AAP) with 20.5% (95% CI) 16.6- 24.6) and acute idiopathic pancreatitis (AIP) in 18.3% (95% CI 15.1-27.7). Conclusion: ABP is the most prevalent etiology of AP, being two times more frequent than second-placed pancreatitis. Latin America has a frequency for ABP much higher than the rest of the world. The importance of the etiologic diagnosis is the treatment of the cause for prevention of recurrence.

Abdominal aortic aneurysm (AAA) represents an important public health problem with a prevalence between 1.3% and 12.5%. Several population-based randomized trials have evaluated ultrasound screening for AAA providing evidence of a reduction in aneurysm-related mortality in the screened population. The aim of our study was to perform a systematic review and meta-analysis of the risk factors for AAA. We conducted a systematic review of observational studies and we performed a meta-analysis that evaluated the following risk factors: gender, smoking habits, hypertension, diabetes mellitus, Coronary Artery Disease and family history of AAA. Abdominal aortic aneurysm prevalence is higher in smokers and in males. It is important to underline that all countries, where AAA screening was set up, had high income level and the majority belong to Western Europe (United Kingdom, Sweden, Italy, Poland, Spain and Belgium). Abdominal aortic aneurysm screening is fundamental for public health. It would avoid deaths, ruptures, and emergency surgical interventions if abdominal aortic aneurysm was diagnosed early in the population target for screening.

This study assessed vaccination coverage and associated factors among children aged 12-23 months in the pastoralist Ethiopia. It was conducted in three woredas of the Afar region using a community-based cross-sectional mixed methods design with quantitative and qualitative methods. A total of 413 mothers with children aged 12-23 months participated in the quantitative study via a simple random sampling technique. Logistic regression was used to identify factors associated with vaccination, and thematic analysis techniques were used for qualitative data. The percentage of patients who received full vaccination was 25%. Based on vaccination card observations, the dropout rate from Pentavalent-1 to Pentavalent-3 was found to be 2.9%. Logistic regression analysis revealed significant associations between mothers and caretakers with formal education, those who owned mobile phones, had antenatal care (ANC) visits and birthed at a health faciltiy with full vacination. The overall proportion of full immunization is lower than the target set by the World Health Organization (WHO). The findings suggest that programs and policy makers should prioritize improving access and enrolment of women and caretakers, promoting mobile phone ownership, and encouraging ANC visits, and promotion of health facility deliveries, as these are are associated with higher rates of immunization

Regression models are highly popular in empirical research and come in many different forms to fit virtually any distribution, variable, or research question. Usually, these models also compute how much variation in the outcome variable can be explained by all predictors, which is relevant to understanding whether the predictors are, in sum, able to explain the outcome or whether other potentially unobserved factors are more relevant. This aspect is crucial for interventions and policy as even statistically significant regression coefficients can be meaningless if they have little influence on the outcome overall. Besides having a measurement to judge the overall goodness of fit, ranking predictors by their relative importance is also relevant. For such analyses, it is necessary to decompose the total explanatory power of a model and partition it so that each explanatory variable is assigned a share. This enables the computation of a predictor variable's absolute and relative influence. Dominance analysis is a statistical approach to achieve this goal.

Aim. To identify factors associated with mortality with tuberculosis diagnosis in the indigenous population in Peru 2015-2019. Methods. Case-control study nested in a retrospective cohort, using the registry of persons belonging to indigenous peoples of the National Tuberculosis Prevention and Control Strategy of the Ministry of Health of Peru. A descriptive analysis was applied, and then bivariate and multiple logistic regression was used to evaluate associations between the variables and the outcome (live-deceased), the results were presented as OR with their respective 95% confidence intervals. Results. The mortality rate of the total indigenous population of Peru was 1.75 deaths per 100,000 indigenous people diagnosed with TB. The community of Kukama kukamiria - Yagua reported 505 (28.48%) individuals. The final logistic model showed that indigenous men (OR=1.93; 95% CI: 1.001-3.7), with a history of HIV prior to TB (OR=16.7; 95% CI: 4.7-58.7) and indigenous people in old age (OR=2.95; 95% CI: 1.5-5.7), are factors associated with a greater chance of dying from TB. Conclusions. It is important to reorient health services among indigenous populations, especially those related to improving the timely diagnosis and early treatment of TB-HIV co-infection, to ensure comprehensive care for this population, considering that they are vulnerable groups.

In this paper, we present a relapse based demonstrating way to deal with investigate various arrangement MTC information. A commonplace use of this displaying approach incorporates three stages: first, define a model that approximates the connection between quality articulation and trial factors, with boundaries consolidated to address the exploration premium; second, utilize least-squares and assessing condition methods to gauge boundaries and their relating standard blunders; third, register test insights, P-qualities and NFD as proportions of factual criticalness. The benefits of this methodology are as per the following. To begin with, it tends to the exploration interest in a particular, precise way, and maximally uses all the information and other important data. Second, it represents both orderly and irregular varieties related with the information, and the consequences of such examination give not just quality explicit data applicable to the exploration objective, yet additionally its dependability, in this way helping agents to settle on better choices for subsequent investigations. Third, this methodology is truly adaptable, and can undoubtedly be stretched out to different sorts of MTC considers or other microarray explores by detailing various models dependent on the test plan of the investigations.

Count data analysis presents unique challenges due to its discrete nature, often exhibiting excessive zeros and overdispersion. To address these complexities, count models, such as Poisson regression and Negative Binomial regression, have been developed, enabling modeling and prediction of count-based phenomena. Additionally, it’s important to notice that zero inflation, a phenomenon commonly observed in count data, requires specialized techniques for robust analysis. This article provides an overview of count data and count models, explores zero inflation, introduces Likelihood Ratio Tests, and explains how the Vuong Test can be used as a model selection criteria. Furthermore, we created a Vuong Test implementation from scratch using the Python programming language. This implementation enhances the accessibility and applicability of the Vuong Test in real-world scenarios, providing a valuable contribution to the academic community, since Python didn’t have an implementation of this statistical test.

Financial markets require a great deal of decision making from the investors and market makers. One metric that can help ease the process of decision making is investment risk which can be measured in two parts; systematic risk and idiosyncratic risk. Clear understanding of the volatilities in each risk component can be a powerful signal in recognizing the right assets to maximize the investment returns. In this paper, we focus on the idiosyncratic volatility values and pre-calculate the idiosyncratic volatility values for 31,198 members of NYSE, Amex and Nasdaq markets for the trades occurring between January 1963 and December 2019. Utilizing a subset of dataset, limited to Nasdaq100 index, we consider the application of machine learning techniques in predicting the idiosyncratic volatility values using the raw trade data to explore a data extension option for the future market trade records that have not yet occurred. We offer a deep learning based regression model and compare it with traditional tree-based methods on a small subset of our per-calculated idiosyncratic volatility dataset. Our analytical results show that the performance of the deep learning techniques is much more robust in comparison to that of the traditional tree-based baselines.

Introduction: The care of dependent people is eminently family-oriented, and often, there is a high level of dedication to this family care. Constant and continuous care leads to a series of negative psychological consequences. Social support has been related to improved mental health in family caregivers. We found heterogeneous results regarding the relationship between the types of social support received and the perceived level of support. In addition, to our knowledge, no reviews analyze this relationship among family caregivers. Therefore, we aimed to systematically syn-thesize the relationships between perceived and received social support in informal caregivers. Methodology: We have carried out a quantitative systematic review with meta-analysis, regis-tered in PROSPERO (id: CRD42023470047); the systematic search was carried out in the following databases: PubMed, CINAHL, PsycINFO, and Scopus, until November 2023. After the selection and review of the results, 12 studies were obtained, two of which were eliminated due to a high risk of classification bias. Regarding the results, a medium size positive statistical association was found (r = 0.43). The results were consistent, accurate, and robust. The Trim and Fill test showed a vari-ation of 7%. Subgroup analysis indicated no differences in the age group of the people cared for (adults or children), selection bias, and confounding bias. In conclusion, perceived social support is related to more social support received by family caregivers.

Background/Objectives: The disparities in internalizing and externalizing symptoms between individuals with versus without neurofibromatosis type 1 (NF1) are poorly understood, based on mixed evidence in the literature. The current meta-analysis aimed to synthesize existing findings on the topic and explore moderators of the group disparities. Method: Electronic databases were searched from inception to March 26th 2024, which identified 59 eligible studies (N of NF1 = 3,182, mean ages 2.38 to 46.4 years). The primary outcome was differences in internalizing and externalizing symptoms between individuals with and without NF1. Data were analyzed using robust variance estimation and random-effects models. Results: Compared with unaffected controls, individuals with NF1 showed more severe depressive (k = 21; g = 0.43; 95% CI [0.21, 0.65]), anxiety (k = 24; g = 0.27; 95% CI [0.01, 0.54]), somatic (k = 27; g = 0.56; 95% CI [0.30, 0.83]), total internalizing (k = 75; g = 0.50; 95% CI [0.33, 0.67]), aggression (k = 33; g = 0.33; 95% CI [0.08, 0.58]), delinquency, (k = 37; g = 0.43; 95% CI [0.26, 0.60]), and total externalizing symptoms (k = 47; g = 0.24; 95% CI [0.13, 0.35]). Studies that included more participants with NF1 who had ADHD or a lower verbal IQ reported greater group disparities in total internalizing symptoms or aggression. Conclusions: Findings highlight the importance of including psychosocial needs in monitoring and treating NF1. Future research should identify predictors of internalizing and externalizing symptoms within the NF1 population to inform our knowledge and intervention development.

Background: Multisensory integration (MSI) avoids cognitive overload, and the traditional all-or-nothing perspective for dysfunctional MSI in schizophrenia has been increasingly challenged. This study aimed to systematically review and meta-analyze extant evidence on audiovisual MSI in schizophrenia. Methods: We searched Web of Science, PubMed, PsycINFO and PsycARTICLES databases from inception until April 16^th, 2024. Studies using appropriate audiovisual integration tasks to compare either behavioral or neural responses between schizophrenia individuals and healthy controls were included. Primary outcome measures included between-group differences (Hedge’s g) and symptomatic correlations (Fisher’s z) in three components of audiovisual integration tasks, including audiovisual processing, adaptation and integration. The multilevel random-effects models were used to address statistical dependency. Results: In total, 209 between-group and 116 correlational effect sizes were synthesized from 46 studies, representing 1,108 non-overlapped schizophrenia participants and 1,084 healthy controls. The overall dysfunctional audiovisual MSI in schizophrenia (g = -0.50, [95% CI, -0.64 to -0.37]) was primarily ascribed to the impaired audiovisual processing (g = -0.53, [95% CI, -0.71 to -0.35]) and adaptation (g = -0.71, [95% CI, -0.99 to -0.42]), rather than integration itself (g = -0.23, [95% CI, -0.56 to 0.11]). Pooled correlational effect size was extremely small (z = 0.16, [95% CI, 0.03 to 0.30]). Conclusions: Individuals with schizophrenia show dysfunctional multisensory processing and adaptation, but preserve a less impaired multisensory integration ability. Our findings provide implications for clinical interventions, as the relatively preserved audiovisual integration ability may serve as a possible avenue for cognitive training programs.

Introduction: Biliary Atresia (BA) is a common cause of neonatal cholestatic jaundice which, if recognized and treated early, can be life-saving for the infant. If the liver biopsy suggests the presence of BA, exploratory laparotomy is then carried out to confirm the diagnosis, along with the Kasai procedure to provide drainage to the bile secreted by the liver. The purpose of this study is to get a deep dive into all the various imaging modalities that can diagnose BA more effectively and with absolute certitude, thus helping prevent several children from a grave prognosis.Methodology: For the collection of the data, a search was done by two individuals using PubMed, Google Scholar, and Cochrane Library databases for all relevant literature. Each qualifying paper was evaluated independently by two reviewers. Each article was analyzed for the number of patients, their age, procedure modality, complexity of ethology, and incidence of the pre-decided complications. The statistical software packages RevMan (Review Manager, version 5.3), SPSS (Statistical Package for the Social Sciences, version 20), Google Sheets, and Excel in Stata 14 were used to perform the statistical analyses. The data was obtained and entered into analytic software.Results: A total of 39 studies with 3,679 subjects were selected for the study, out of which 7 studies showed sensitivity above or equal to 95%, and an astounding 14 studies showed specificity above 95%. The value of True Positive (TP) was 1280, that of True Negative (TN) was 1817, that of False Positive (FP) was 239, and that of False Negative (FN) was 343. With a confidence interval of 95%, sensitivity, specificity, and positive predictive values were calculated. The sensitivity of Ultrasonography for Biliary Atresia is 0.79, with a CI of 95% in a range of 0.77 to 0.81. The specificity of Ultrasonography for Biliary Atresia is 0.88, with a CI of 95% in a range of 0.87 to 0.90. Conclusion: Even though several radiological techniques at present help to detect BA, USG of the abdomen proves to be an effective diagnostic and a modest screening test for the detection of BA.

Multimodal remote sensing data classification can enhance the model’s ability to distinguish land features through multimodal data fusion. In this context, how to help models understand the relationship between multimodal data and target tasks is the focus of researchers. Inspired by human feedback learning mechanisms, causal reasoning mechanisms, and knowledge induction mechanisms, this paper integrates causal learning, reinforcement learning, and meta learning into a unified remote sensing data classification framework and proposed the causal meta-reinforcement learning (CMRL). First, based on feedback learning mechanisms, we have overcome the limitations of traditional implicit optimization of fusion features and customized a reinforcement learning environment for multimodal remote sensing data classification tasks. Through feedback interactive learning between agents and the environment, we help them understand the complex relationships between multimodal data and labels, thereby achieving full mining of multimodal complementary information. Second, based on the causal inference mechanism we designed causal distribution prediction action, classification reward, and causal intervention reward, capturing pure causal factors in multimodal data and cutting off false statistical associations between non-causal factors and class labels. Finally, based on the knowledge induction mechanism, we designed a bi-layer optimization mechanism based on meta-learning. By constructing a meta training task and meta validation task simulation model in the generalization scenario of unseen data, we helped the model induce cross-task shared knowledge, thereby improving its generalization ability for unseen multimodal data. The experimental results on multiple sets of multimodal datasets show that the proposed method achieved state-of-the-art performance in multimodal remote sensing data classification tasks.

Objectives: Accumulating evidence suggests that physical activity (PA) is an efficient intervention to maintain functional capabilities and mitigate physiological changes in the older population. However, an attempt has yet to be made to comprehensively investigate the published landscape on the subject. Methods: This study had two aims. The first aim was to perform a bibliometric analysis for two keywords, “aging” and “PA”, to analyze the research trend. Since ‘frailty’ is the most noticeable co-occurring keyword with the two keywords, the second aim was to investigate the effects of PA, particularly resistance training (RT), on frailty via meta-analysis to provide a summary of the current evidence base. Results: Bibliometric analysis revealed that the number of publications on this research topic has gradually increased, highlighting the importance of understanding the role of PA in aging. A meta-analysis found that RT had significant beneficial effects on physical frailty factors, including handgrip strength, lower limb strength, balance, gait speed, and stair climbing ability. Conclusion: These findings demonstrate that RT is an effective intervention for improving physical function in frail populations; thus it has important implications for the development of PA programs for older adults with frailty. Future research is warranted to explore the optimal dose, frequency, and duration of RT programs for older adults, as well as the potential benefits of combining RT with other forms of PA, such as aerobic or balance exercises.

To determine the impacts of metabolizable energy levels on the performance indices of geese and to forecast the ideal range of dietary metabolizable energy levels, this study was carried out. Literature on the effects of dietary metabolized energy level on the performance of geese was searched from CNKI, Wanfang, VIP, PubMed, Web of Science, and ScienceDirect databases from January 1, 2000, to December 31, 2022. A dose-response meta-analysis was performed by STATA (14.0), data were calculated by the robust error meta-regression model (REMR), and nonlinear dose-effect curves were plotted by the restricted cubic spline method (RCS). 29 papers were chosen from the 1,475 literatures that fit the criteria and were included in the dose-response meta-analysis. There were 6,079 geese included in the sample. The following production performance indices included average daily gain (ADG), average daily feed intake (ADFI), feed-to-gain ratio (F/G), dressed percentage, percentage of half-eviscerated yield, percentage of eviscerated yield, percentage of breast muscle, percentage of leg muscle and percentage of abdominal fat. The Egger test assessed publication bias, while sensitivity analysis was used to examine the heterogeneity. The verified results are consistent and reliable. The results indicated that for each group, the optimal dietary metabolizable energy level was about 13 to 13.5MJ/kg for medium-sized geese aged 0 to 4 weeks, 12 to 13MJ/kg for medium-sized geese aged above 5 weeks, 12.5 to 13MJ/kg for small-sized geese aged 0 to 4 weeks, and 13 to 13.5MJ/kg for mall-sized geese over 5 weeks of age. Future research should be conducted on related diets of large-sized geese, small-sized geese, and higher metabolizable energy levels.

The minimum dominating tree (MDT) problem consists of finding a minimum weight sub-graph from an undirected graph, such that each vertex not in this sub-graph is adjacent to at least one of the vertices in it, and the sub-graph is connected without any ring structures. This paper presents a Dual Neighborhoods Search (DNS) algorithm for solving the MDT problem, which integrates several distinguishing features, such as two neighborhoods collaboratively working for optimizing the objective function, a fast neighborhood evaluation method to boost the searching effectiveness, and several diversification techniques to help the searching process jump out of the local optimum trap thus obtaining better solutions. DNS improves the previous best-known results for 4 public benchmark instances while providing competitive results for the remaining ones. Several ingredients of DNS are investigated to demonstrate the importance of the proposed ideas and techniques.

Background: Neuroendocrine tumors (NET) are a rare group of epithelial neoplasm present in gastrointestinal tract (GI) (67.5%), bronchopulmonary tree (25.3-30%), and in 15% of cases, the primary sites cannot be identified. Although endoscopic screening, improvement in pathological techniques, and early detection have shown improvement in NET survival rates, the prognosis of advanced, metastatic, and poorly differentiated NET is very poor. In this study, we aimed to evaluate the effect of Gastrointestinal and pancreatic (GEPs) NETs grade on overall survival. Method: We searched observational studies describing the overall survival or prognostic factors of primary GEP NETs from May 2011 -May 2021 following PRISMA guidelines. Studies describing the effect of primary grade 3 GEP NETs on overall survival were included. Meta-analysis was performed, and pooled hazard ratio and their 95% confidence interval (95% CI) were obtained. The forest plots were created using random-effects models and sensitivity analysis was performed to account for the heterogeneity. Results: Seven studies with 7692 confirmed patients were included. In our meta-analysis grade 3 GEP NET were associated with higher odds of poor survival (pooled HR: 2.73; 95% CI: 1.36–5.47; p = 0.005), with 92% heterogeneity between studies (p < 0.0001). To account for heterogeneity, sensitivity analysis was performed by removing two outlying studies (Fathi et al. and Foubert et al.) on funnel plots. The results after sensitivity analysis did not change and still showed significant association of grade 3 with poor survival (pooled HR: 4.53; 95% CI: 3.54–5.78; p < 0.00001), with no heterogeneity between studies (p = 0.72; I2 = 0%). Conclusion: Our meta-analysis found that grade 3 GEP NETs are associated with poor survival and additional future studies are needed to identify other risk factors associated with poor survival in GEP NETs to improve mortality.

One of the major areas of research in a business setting has been the effect of the transformational leadership style on workplace engagement. Much debate has taken place on the definitions of both constructs but in recent years, general agreement appears to have been reached on the Multi-Factor Leadership Questionnaire (MLQ) (Avolio and Bass, 2004) as the measure of transformational leadership, and on workplace engagement measured by the Utrecht Work Engagement Scale (UWES) (Schaufeli et al, 2006). However, in the education setting, there is much less agreement on the definition of transformational leadership. Furthermore, there is less of a focus on workplace engagement than in the business field even though available evidence suggests that workplace engagement worldwide is in crisis. This study sought to address both the lack of agreement on the transformational leadership definition and the lack of focus on workplace engagement in educational research by means of a meta-analysis. The meta-analysis resulted in a significant pooled effect size although due recognition is given to the endogeneity problem in causal studies. The endogeneity issue together with the results of the meta-analysis are discussed with a view to furthering educational leadership research.

Major depressive disorder (MDD) is one of the most prevalent and disabling mental disorders worldwide. Among the symptoms of MDD, sleep disturbance such as insomnia is prominent and the first reason patients may seek professional help. However, the underlying pathophysiology of this comorbidity is still elusive. Recently, genome-wide association studies (GWAS) have begun to unveil the genetic background of several psychiatric disorders, including MDD and insomnia. Identifying the shared genomic risk loci between comorbid psychiatric disorders could be a valuable strategy to understand their comorbidity. This study seeks to identify the shared genes and biological pathways between MDD and insomnia based on their shared genetic variants. First, we performed a meta-analysis based on the GWAS summary statistics of MDD and insomnia obtained from Psychiatric Genomics Consortium and UK Biobank, respectively. Next, we associated shared genetic variants to genes using two gene mapping strategies: (a) positional mapping based on genomic proximity and (b) expression quantitative trait loci (eQTL) mapping based on gene expression linkage across multiple tissues. As a result, a total of 719 shared genes were identified. Over half (51%) of them are protein-coding genes. Functional enrichment analysis shows that the most enriched biological pathways are related to epigenetic modification, sensory perception, and immunologic signatures. We also identified druggable targets using a network approach. Together these results may provide insights into understanding the genetic predisposition and underlying biological pathways of comorbid MDD and insomnia symptoms.

Background Reliable and field-applicable diagnosis of schistosome infections in non-human animals is important for surveillance, control, and verification of interruption of human schistosomiasis transmission. This study aimed to summarize uses of available diagnostic techniques through a systematic review and meta-analysis. Methods and principal findings We systematically searched the literature and reports comparing two or more diagnostic tests in non-human animals for schistosome infection. Out of 4,909 articles and reports screened, 18 met our inclusion criteria, four of which were considered in the meta-analysis. A total of 14 techniques (parasitologic, immunologic, and molecular) and nine types of non-human animals were involved in the studies. Notably, four studies compared parasitologic tests (miracidium hatching test (MHT), Kato-Katz (KK), the Danish Bilharziasis Laboratory technique (DBL), and formalin-ethyl acetate sedimentation-digestion (FED-SD)) with quantitative polymerase chain reaction (qPCR), and sensitivity estimates (using qPCR as the reference) were extracted and included in the meta-analyses, showing significant heterogeneity across studies and animals hosts. The pooled estimate of sensitivity was 0.21 (95% confidence interval (CI): 0.03 – 0.48) with FED-SD showing highest sensitivity (0.89, 95% CI: 0.65 – 1.00). Conclusions and significance Our findings suggest that the parasitologic technique FEA-SD and the molecular technique, qPCR, are the most promising field-applicable techniques for schistosome diagnosis in non-human animal hosts. Future studies are needed for validation and standardization of the techniques for real-world field applications.