Background: Previous neuroimaging studies have identified brain regions related to respiratory motor control and perception. However, little is known about the resting-state functional connectivity (FC) associated with respiratory impairment. We aimed to determine the FC involved in mild respiratory impairment without changing the saturation of percutaneous oxygen. Methods: We obtained resting-state functional magnetic resonance imaging data from 36 healthy volunteers during normal respiration and mild respiratory impairment induced by resistive load (effort breathing). ROI-to-ROI and seed-to-voxel analyses were performed using Statistical Parametric Mapping 12 and the CONN toolbox. Results: Compared to normal respiration, effort breathing activated FCs within and between the sensory perceptive area of respiration (the postcentral gyrus, anterior insular cortex (AInsula), and anterior cingulate cortex) and visual cortex (the visual occipital, occipital pole (OP), and occipital fusiform gyrus). Graph theoretical analysis showed strong centrality in the visual cortex. A significant positive correlation was observed between the dyspnoea score (modified Borg scale) and FC between the left AInsula and right OP. Conclusions: These results suggested that the FCs within the respiratory sensory area via the network hub may be neural mechanisms underlying effort breathing and modified Borg scale scores.

Neurodegeneration is a major problem in neuroscience and medicine as it causes and worsens many neurological and psychiatric disorders. It involves gradual loss of neurons at different levels of the brain, affecting cognition, emotion, behavior, consciousness, and attention. Understanding the causes and consequences of neurodegeneration is crucial for identifying the risk factors, biomarkers, and treatments. However, the current therapies are mostly symptomatic and ineffective. Therefore, new and innovative methods, such as noninvasive brain stimulation (NIBS), are needed to modulate brain activity and plasticity in a safe and reversible manner. This field is rapidly developing, with a focus on exploring new clinical applications, mechanisms, and combinations of NIBS and drugs. This special issue covers the latest progress and challenges in the study of neurodegeneration in cognitive impairment and mood disorders from the experimental, clinical, and translational perspectives. It addresses the complexity and diversity of these conditions, the lack of disease-modifying treatments, the ethical and practical issues of conducting research in vulnerable and diverse populations, the integration and interpretation of data from multiple sources and modalities, and the development and validation of novel methods such as NIBS, artificial intelligence, and drug repurposing. Collaborative efforts among various stakeholders are needed to improve our knowledge and treatment of neurodegeneration underlying cognitive impairment and mood disorders.

(1) Background The SKT is a short cognitive performance test designed to assess impairments in memory and cognitive abilities such as attention and speed of information processing. In 2019, new regression-based norms for the English version of the SKT were calculated applying data from older cognitively healthy adults living in the US, Ireland and Australia (the norming sample) The study has two aims: to establish valid cut-offs for distinguishing between no cognitive impairment, mild cognitive impairment (MCI) and dementia (1), and to cross-validate the new norms for detecting MCI and dementia (2) in a community sample of older adults with clinical diagnoses; (2)Methods: The validation sample included 142 older adults (mean age=87.7, SD=3.55) from the Sydney Memory and Aging Study (MAS Study). Participants were classified as having normal cognition, MCI, or dementia through a consensus diagnosis as well as by the SKT; (3)Results: The sensitivity of the SKT for the differentiation of cognitive impairment (MCI or dementia) from normal cognition was 80.6%. The convergence between the SKT and the consensus diagnoses was 60.1% for all three diagnostic groups. The correlation between the SKT and the Addenbrooke’s Cognitive Examination III (ACE-III), a well validated measure of cognitive ability in older adults, was r = 0.3;. (4) Conclusions: In sum, it can be stated that the SKT is a valid tool for detecting early stages of cognitive impairment, performing very well in discriminating between no cognitive impairment and cognitive impairment (MCI or dementia).

Telomeres, located at the ends of each chromosome play a vital role in determining cellular fate and aging. Accelerated shortening of telomeres may also result in accelerated aging and occurrence of age-related degenerative diseases. Anti-aging treatments may help in increasing the telomere length along with improving quality of life, reducing dependency for daily life activities, increasing life expectancy and reducing the risks associated with aging. In this study, we evaluated the effect of anti-aging integrative therapy in 36 individuals. The treatment included a combination of HBOT, ozone therapy, colon hydrotherapy, etc. Telomere length was used as a biomarker for objectively quantifying the outcome of the treatment. The effect of treatment was also assessed on various aging symptoms. At the end of the treatment, it was found that that 97.22% individuals showed an increase in telomere length with an average increase of 0.43 Kbp and 94.44% individuals showed reduced biological age with an average decrease of 3.8 years. These improvements were statistically significant. Improvements were also recorded in the majority of the symptoms. No major adverse events were observed. These results indicate that antiaging integrative therapy has the potential to reverse aging symptoms, cellular aging and improve quality of life.

A central hypothesis on brain functioning is that plasticity regulates the signals transfer function by modifying the efficacy of synaptic transmission. In the cerebellum, granular layer has been shown to control the gain of signals transmitted through the mossy fiber pathway. Until now, the impact of plasticity on incoming activity patterns was analyzed by combining electrophysiological recordings in acute cerebellar slices and computational modeling, unraveling a broad spectrum of different forms of synaptic plasticity in the granular layer, often along with forms of intrinsic excitability changes. Here, we attempt to provide a brief overview of the most prominent forms of plasticity at excitatory synapses formed by mossy fibers onto principal neurons (granule cells, Golgi cells and unipolar brush cells) in the granular layer. Specifically, we will highlight current understanding of the mechanisms and their functional implications of the synaptic and intrinsic plasticity, providing valuable insights into how inputs are processed and reconfigured at the cerebellar input stage.

Asymmetric lithium salts, such as Lithium (difluoromethanesulfonyl)(trifluoromethanesulfonyl)imide (LiDFTFSI), have been demonstrated to surpass traditional symmetric lithium salts with improved Li+ conductivity and the capacity to generate stable solid electrolyte interphase (SEI) while maintaining compatibility with aluminum (Al0) current collector. However, the intrinsic reductive mechanism through which LiDFTFSI influences battery performance remains unclear and under debate. Herein, the detailed SEI reactions of LiDFTFSI-based electrolytes were investigated by combining density functional theory and molecular dynamics, aiming to clarify the formation process and atomic structure of SEI. Our results show that asymmetric DFTFSI– weakens the interaction between carbonate solvents and Li+, and substantially alter the solvation structure, exhibiting a well-balanced coordination capacity to bis(trifluoromethanesulfonyl)imide (TFSI–). Nanoseconds hybrid molecular dynamics simulation further reveals that the preferential decomposition of LiDFTFSI produces sufficient LiF and Li2O to facilitate a robust SEI. Moreover, the abundant F– generated from LiDFTFSI decomposition accumulates on the Al surface and subsequently combines with Al3+ from the current collector to form AlF3, potentially inhibiting corrosion of the current collector. Overall, these findings elucidate how LiDFTFSI regulates the solvation sheath and SEI structure, advancing the development of high-performance electrolytes compatible with current collector.

Short video addiction refers to a psychological dependency on short video applications, characterized by an intense attachment and an inability to control usage, leading to typical addiction symptoms. While its definition bears similarities to substance addiction, significant differences also exist. According to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), published in 2013, short video addiction has not yet been officially classified as an addiction disorder. To facilitate more in-depth and standardized research on short video addiction, it is essential to synthesize and organize existing studies on its theoretical foundations and mechanisms.

The AUC-ROC is a widespread measure to assess the performance of a binary classification model across various discrimination thresholds. Yet, it is not apt for unbalanced class problems and is misleading for early retrieval. For instance, link prediction in complex networks is an unbalanced early retrieval problem, whose goal is to offer a ranking of the nonobserved network links, prioritizing a small cohort of positive links (for which we are sure the labels are reliable) on top of a list largely populated by unlabeled links (for which the labels are uncertain). Differently from binary classification, here the evaluation focuses on how the predictor prioritizes the positive class, because the negative class does not exist and is replaced by an unlabeled class. For decades, scholars have been investigating the necessity to correct and to generalize the AUC-ROC to work for both unbalanced classification and early retrieval evaluation, this is requested in many applied domains of computational science. Here we propose the area under the magnified ROC (AUC-mROC), a new measure that adjusts the standard AUC-ROC to work for unbalanced early retrieval problems such as link prediction. Finally, we introduce the area under the generalized ROC (AUC-gROC), which unifies both AUC-mROC and AUC-ROC to provide a universal and reliable evaluation measure that can work for both classification and early retrieval problems, regardless of class proportion.This discovery represents a critical achievement, culminating in success after decades of contentious research. Its solution promises to significantly impact various domains within applied computational science.

Inflammation and insulin resistance are recognized as central features in the pathology of psoriasis, profoundly affecting the lives of those affected by this condition. The relationship between lifestyle choices and psoriasis is well-documented, highlighting the potential benefits of dietary modifications and physical activity in managing the condition during exacerbations. Furthermore, growing evidence emphasizes the significant impact of nutritional interventions on various skin disorders, particularly those characterized by inflammation and insulin dysregulation. Notably, the role of dietary fats is of particular interest, with their influence on inflammation and insulin resistance in psoriasis garnering increasing attention. Moreover, the association between excessive adiposity and heightened psoriatic severity underscores the importance of weight management strategies in disease management. This review explores the intricate relationship between specific dietary fats and inflammation in psoriasis, offering a comprehensive analysis of their implications for disease progression and management.

Chip pad alignment inspection is of great importance in the industrial field. However, due to the fact that chip pads are usually small, problems such as misdetection and missed detection often occur. When applying deep learning methods for chip pad detection, it is necessary to ensure accurate detection of small target chips while meeting the requirements of lightweight detection models for industrial needs. To solve the above problems, this paper proposes a lightweight model based on improved YOLOv5s. Firstly, the feature extraction part is improved to increase the network's focus on the target. Secondly, the feature fusion layer is improved to double the resolution of the prediction head, and the context-aware network is designed to enhance the context-capture ability of key features of small targets. Finally, SIoU is adopted as the loss function to improve the speed and accuracy of the regression frame. The experimental results show that the improved YOLOv5s algorithm improves the detection accuracy by 2.3% and reduces the network parameters by 81.8% compared to the original algorithm. The improved algorithm is combined with image processing techniques to design correction methods for alignment anomalies and realize real-time alignment anomaly correction in industry.

Brain pathological changes impair cognition early in disease etiology. There is an urgent need for understanding aging-linked mechanisms of early memory loss to develop therapeutic strategies and prevent the development of cognitive impairment. Tusc2, also named Fus1, is a mitochondrial-resident protein regulating Ca2+ fluxes to and from mitochondria impacting overall health. We reported that Tusc2-/- female mice develop chronic inflammation and age prematurely, causing age- and gender-dependent spatial memory deficits at 5 m.o. Therefore, we investigated Tusc2-dependent mechanisms of memory impairment in mice, comparing changes in resident and brain-infiltrating immune cells. Interestingly, Tusc2-/- female mice demonstrated proinflammatory increase in astrocytes, IFN-γ expression in CD4+T cells and Granzyme-B in CD8+T cells. We also found fewer FOXP3+ T-regulatory cells and Ly49G+ NK and Ly49G+ NKT cells in female Tusc2-/- brain, suggesting a weakened anti-inflammatory response. Moreover, Tusc2-/- hippocampi exhibited Tusc2- and sex-specific protein changes associated with brain plasticity, including mTOR activation, and Calbindin and CamKII dysregulation affecting intracellular Ca2+ dynamics. Overall, data suggest that dysregulation of Ca2+-dependent processes and proinflammatory brain microenvironment heighten in Tusc2-/- mice could underlie cognitive impairment. Thus, strategies to modulate the brain’s Tusc2-, Ca2+, and mitochondria-dependent pathways should be explored to improve cognitive health.

The aim of this retrospective study was to compare the clinical outcomes of treating isolated surgical neck fractures of the humerus using either the intramedullary nail or the MIROS (Minimally Invasive Reduction and Osteosynthesis System®) system. The study included 42 patients who underwent surgical treatment at the Polyclinic from January 2014 to June 2020. The results indicated that both treatments were effective; however, the MIROS system exhibited a higher complication rate. Intramedullary nailing was more reliable, thus recommended as the primary treatment method. This study underscores the importance of selecting the appropriate fixation technique to minimize complications and enhance patient outcomes.

The National Institute of Health Stroke Scale (NIHSS) is used worldwide to classify stroke severity as ‘mild’, ‘moderate’ or ‘severe’ based on neurological impairment. Yet stroke survivors argue that the classification of ‘mild’ does not represent the holistic experience and impact of stroke on their daily lives. In this observational cohort study, we aimed to identify different types of impairment among stroke survivors classified as mild. We used mild stroke survivors’ data from the START longitudinal stroke cohort (n=73) with measures related to sensorimotor, cognition, depression, functional disability, physical activity, work and social adjustment over 12 months. Given the multi-source, multi-granular and unlabelled nature of data, we utilised a structure-adapting, unsupervised machine learning approach, the Growing Self-organising Map (GSOM) algorithm to generate distinct clinical profiles. These diverse impairment profiles revealed that mild stroke survivors experience varying degrees of impairment and impact (cognitive, depression, physical activity, work/social adjustment) at different time points, despite the uniformity implied by their NIHSS-classified ‘mild’ stroke. This emphasises the necessity of creating a holistic and comprehensive representation of mild stroke survivors’ needs over the first-year post-stroke to improve rehabilitation and post-stroke care.

Sparse training (ST) aims to ameliorate deep learning by replacing fully connected artificial neural networks (ANNs) with sparse or ultra-sparse ones, such as brain networks are, therefore it might benefit to borrow brain-inspired learning paradigms from complex network intelligence theory. Here, we launch the ultra-sparse advantage challenge, whose goal is to offer evidence on the extent to which ultra-sparse (around 1% connection retained) topologies can achieve any leaning advantage against fully connected. Epitopological learning is a field of network science and complex network intelligence that studies how to implement learning on complex networks by changing the shape of their connectivity structure (epitopological plasticity). One way to implement Epitopological (epi- means new) Learning is via link prediction: predicting the likelihood of non-observed links to appear in the network. Cannistraci-Hebb learning theory inspired the CH3-L3 network automata rule for link prediction which is effective for general-purpose link prediction. Here, starting from CH3-L3 we propose Epitopological Sparse Meta-deep Learning (ESML) to apply Epitopological Learning to sparse training. In empirical experiments, we find that ESML learns ANNs with ultra-sparse hyperbolic (epi-)topology in which emerges a community layer organization that is meta-deep (meaning that each layer also has an internal depth due to power-law node hierarchy). Furthermore, we discover that ESML can in many cases automatically sparse the neurons during training (arriving even to 30% neurons left in hidden layers), this process of node dynamic removal is called percolation. Starting from this network science evidence, we design Cannistraci-Hebb training (CHT), a 4-step training methodology that puts ESML at its heart. We conduct experiments on 7 datasets and 5 network structures comparing CHT to dynamic sparse training SOTA algorithms and the fully connected counterparts. The results indicate that, with a mere 1% of links retained during training, CHT surpasses fully connected networks on VGG16, GoogLeNet, ResNet50, and ResNet152. This key finding is an evidence for ultra-sparse advantage and signs a milestone in deep learning. CHT acts akin to a gradient-free oracle that adopts CH3-L3-based epitopological learning to guide the placement of new links in the ultra-sparse network topology to facilitate sparse-weight gradient learning, and this in turn reduces the convergence time of ultra-sparse training. Finally, CHT offers the first examples of parsimony dynamic sparse training because, in many datasets, it can retain network performance by percolating and significantly reducing the node network size. Our code is available at: https://github.com/biomedical-cybernetics/Cannistraci-Hebb-training.

Background: Preserving health and physical fitness is critical to ensure independent living across the lifespan. Lower levels of physical fitness are associated with age-related cognitive decline and a higher prevalence of mild cognitive impairment (MCI). Thus, this study investigates the influence of a six-month dance intervention on selected measures of physical fitness in older adults with MCI. Methods: In this randomized controlled trial 55 patients with MCI were randomized into a sportive dance training (IG; n=26; Age: 70.7 +/- 5.6 years; 62% female) or an inactive control group (CG; n=24; Age: 69.1 +/- 6.8 years; 46% female).The dance group received two 90-minute dance training sessions per week over a duration of six-months, which focused on learning dance movement patterns. During the training sessions, heartrate was measured to control exercise intensity. Physical fitness was assessed using cardiopulmonary exercise testing (CPET), lower limb functional fitness via sit-to-stand test, handgrip strength and heart rate variability (HRV). Results: We observed that the dance intervention preserved the cardiorespiratory fitness as measured by maximal oxygen uptake (VO2max) during CPET, which decreased in the CG. Furthermore, participants in the IG demonstrated increases in leg and handgrip strength although not statistically significant. HRV displayed a nonsignificant decrease following the intervention. Conclusion: The results of this randomized controlled trial suggest that sportive dance training can preserve physical fitness in older adults with MCI. Although improvements in the other parameters were statistically non-significant, likely due to the small sample size, stabilizing muscular fitness and preventing age-related decline in older adults with MCI is important for maintaining functional independence.

In this paper, a novel microwave catalyst Ni@SiC/CNFs (NSC), C/Ni (NC), and C/SiC (SC) were successfully synthesized by electrostatic spinning method, and proved to degrade methylene blue with high efficiency under microwave irradiation. The results imply that the catalyst Ni@SiC/CNFs with double shell structure gave a 99.99 % removal rate in 90 s for the degradation of methylene blue under microwave irradiation, outperformed the C/Ni and C/SiC and most other reported catalysts in similar studies. On the one hand, the possible mechanism of the methylene blue degradation should be ascribed to that double shell structure increases the polarization source of the material, resulting in excellent microwave absorption properties; and on the other, the in-situ generation of active species OH• and O2 - under microwave radiation and the synergistic coupling effect of metal plasma greatly improved the degradation efficiency of methylene blue. The findings of this study could provide a valuable reference for the green degradation of industrial dye wastewater and its sustainable development process.

Cisplatin is a widely used anticancer drug for treating solid tumors, but it is associated with severe side effects, including nephrotoxicity. Various studies have suggested that the nephrotoxicity of cisplatin could be overcome; nonetheless, an effective adjuvant drug has not yet been established. Oleanolic acid acetate (OAA), a triterpenoid isolated from Vigna angularis, is commonly used to treat inflammatory and allergic diseases. This study aimed to investigate the protective effects of OAA against cisplatin-induced apoptosis and necroptosis using TCMK-1 cells and a mouse model. In cisplatin-treated TCMK-1 cells, OAA treatment significantly reduced Bax and cleaved-caspase3 expression, whereas increased Bcl-2 expression. Moreover, in cisplatin-induced kidney injury mouse model, OAA treatment alleviated weight loss in the body and major organs and also relieved cisplatin-induced nephrotoxicity symptoms. RNA sequencing analysis of kidney tissues identified lipocalin-2 as the most upregulated gene by cisplatin. Additionally, necroptosis-related genes such as receptor interacting protein kinase (RIPK) and mixed lineage kinase domain like (MLKL) were identified. In vitro study, the phosphorylation of RIPKs and MLKL was reduced by OAA pretreatment in both cisplatin-treated cells and cells boosted by co-treatment with z-VAD-FMK. In conclusion, OAA could protect the kidney from cisplatin-induced nephrotoxicity and may serve as an anticancer adjuvant.

Dynamic sparse training is an effective strategy to alleviate the training and inference demands of artificial neural networks. However, current sparse training methods face a challenge in achieving high levels of sparsity while maintaining performance comparable to that of their fully connected counterparts. The Cannistraci-Hebb training (CHT) method produces an ultra-sparse advantage compared to fully connected training in various tasks by using a gradient-free link regrowth method, which relies solely on the network topology. However, its rigid selection based on link prediction scores may lead to epitopological local minima, especially at the beginning of the training process when the network topology might be noisy and unreliable. In this article, we introduce the Cannistraci-Hebb training soft rule (CHTs), which applies a flexible approach to both the removal and regrowth of links during training, fostering a balance between exploring and exploiting network topology. Additionally, we investigate the network topology initialization using several approaches, including the bipartite scale-free and bipartite small-world network models. Empirical results show that CHTs can surpass the performance of fully connected networks with MLP architecture by using only 1% of the connections (99% sparsity) on the MNIST, EMNIST, and Fashion MNIST datasets and can provide remarkable results with only 0.1% of the links (99.9% sparsity). In some MLPs for image classification tasks, CHTs can reduce the active neuron network size to 20% of the original nodes (neurons), demonstrating a remarkable ability to generalize better than fully connected architectures, reducing the entire model size. This represents a relevant result for dynamic sparse training. Finally, we present evidence from larger network models such as Transformers, with 10% of the connections (90% sparsity), where CHTs outperform other prevalent dynamic sparse training methods in machine translation tasks.

Aim. In the current study, we aimed at identifying resting-state brain networks, which are different in patients with depression compared to healthy individuals. Moreover, we analyzed the potential for clinical use of different network measures that could discriminate the two groups and thus help the diagnostic process. Method and subjects. We recruited 90 subjects: 49 healthy controls (HC) and 41 patients with a major depressive episode (MDE). All subjects underwent clinical evaluation and functional resting-state MRI. The data were processed to investigate functional connectivity network measures across the two groups using Brain Connectivity Toolbox. The statistical inferences were made at the functional networks level, using false discovery rate method. Independent samples t-test was performed on the network measures mean values to reveal differences between HC and MDE groups. Permutation-based statistical testing was used to test the significance of the difference between the distributions of the network measures by nodes for HC and MDE groups. Linear discriminant analysis was used to differentiate between the groups. Results and discussion. Significant differences in FC between depressed patients and healthy controls were found with the most prominent changes encompassing within-region as well as between-region connectivity of occipital lobe areas such as precuneus (PreCu), cuneus (Cu), superior occipital gyrus (SOG), lingual gyrus (LG), fusiform gyrus (FG), cerebellum, along with limbic structures including the hippocampus (Hipp) and cingulate gyrus. Linear discriminant analysis demonstrated that the full connectivity matrices, as well as those with only the significant connections identified in advance, were the most precise in differentiating between depression and health. These measures reached precision levels of 97% and 94%, respectively. Conclusion. Our study delivered further evidence about impairment of functional connectivity networks in MDE that may contribute to differentiate patients with depression from healthy subjects.

Major depressive disorder (MDD) is a prevalent mental health condition with substantial social and economic consequences. This study focuses on the interplay between genetic and environmen-tal factors in MDD, with particular emphasis on epigenetic mechanisms such as stochastic epige-netic mutations (SEMs), epigenetic age acceleration, and epigenetic drift. We conducted a meta-analysis of DNA methylation patterns across six datasets (n=1125 MDD cases, 398 controls in whole blood; n=95 MDD cases, 96 controls in brain tissues) from the Gene Expression Omnibus. The analysis did not reveal significant global methylation differences between MDD cases and controls. However, a brain-specific probe (cg25801113) near the SHF gene consistently exhibited hypomethylation, suggesting its potential relevance to MDD. SEMs were assessed, revealing a dis-tinct burden at the gene level in MDD cases, though no significant findings emerged for epigenetic age acceleration, indicating it may not play a central role in MDD. The study also identified rare epivariations in specific genes unique to MDD cases. In blood tissue, 51 genes were associated with these unique epivariations, while 1 gene was highlighted in the brain tissue. These results un-derscore the importance of exploring rare epivariations in understanding the molecular mecha-nisms underlying MDD and offer potential targets for further investigation.

This study investigates the effects of chanting Nam-Myoho-Renge-Kyo by a long-term Nichiren Shoshu Buddhist practitioner on sound frequencies (A), brain activity (B), and microbial metabolism(C).A. Analysis of sound frequencies: Spectral analysis of chanting revealed peaks at 8 Hz and 116 Hz, corresponding to the first Schumann resonance and a frequency linked to chloride ion movements, respectively. Additionally, five peaks (417, 528, 639, 741, 852 Hz) corresponding to solfeggio frequencies were identified. These frequencies are known to exert a positive impact on the human endocrine and autonomous nervous systems; improve survival of human brain cells; decrease anxiety in rats; reverse cognitive and endocrine deficits in zebra fish; reduce total concentration of reactive oxidative species in brain tissue. B. Study of Brain activity: Brain activity changes were measured using functional near-infrared spectroscopy. Prefrontal cortex activity increased modestly during chanting compared to a pre-chanting baseline, but significantly increased afterwards. This suggests a shift towards focused attention during chanting and enhanced activity afterward. C. Effects on microbial metabolism: Probiotic cultures exposed to chanting, either directly or indirectly, showed increased metabolic activity. This suggests local and non-local effects, similar to previously reported phenomena. Importantly, this study clarifies that the spiritual practice of Nichiren Shoshu Buddhism transcends mere neurophysiological explanations. While brain activity and other bodily processes may be correlated with the practice, the religious and spiritual experience of chanting Nam-Myoho-Renge-Kyoencompasses a mystical dimension that, though not contradicting science, cannot be fully explained by scientific methods alone.

Diacylglycerol kinase (DGK) is a lipid kinase that phosphorylates diacylglycerol (DAG) to generate phosphatidic acid (PA). Based on converting one important signaling molecule (DAG) to another (PA), DGK plays an important role in plant responses to abiotic stress including waterlogging stress. However, no studies have been reported on the characterization of the DGK gene family in the waterlogging-tolerant kiwifruit germplasm, Actinidia valvata Dunn. Here, we identified 18 AvDGK genes in the A. valvata genome. The phylogenetic analysis showed that AvDGKs can be classified into three clusters, and members within the same cluster have similar domain distribution, exon-intron structures and conserved motif compositions. Chromosome localization analysis revealed that all the AvDGK genes are located across 18 different chromosomes. There were 29 duplicated gene pairs in kiwifruit and all had undergone purifying selection during evolution. Promoter cis-element analysis revealed that the cis-elements within AvDGK genes are associated with multiple functions, including phytohormone signal transduction, stress response, and plant growth and development. The expression pattern analyses indicated that AvDGK play an important role in the fruit development and plant response to waterlogging stress. AvDGKs gene family in the tetraploid A. valvata genome might promote PA synthesis and subsequent signal transduction both under short-term and long-term waterlogging stress. These results provide information regarding the structural characteristics and potential function of DGK genes within kiwifruit and lay a fundamental basis for further research into breeding for enhancing the kiwifruit’s tolerance to waterlogging stress.

The origin of neurodegenerative diseases is linked to the abnormal folding of disease-associated proteins. The traditional amyloid hypothesis has been a focal point of research, but recent findings suggest a more complex picture. This review explores the role of physiological conformation of aggregation-prone domain (also known as prion-like low complexity domain) and multiple co-misfolded proteins in neurodegenerative diseases. We highlight the importance of targeting multi-heterotypic misfolded proteins for effective treatments and emphasize the irreversible neurodegeneration stemming from the loss of physiological folding in prion-like disease-causing proteins underscores the criticality of refolding. Additionally, we introduce the concept of ReproFold Therapeutics, a novel approach aimed at restoring the cellular prion-like folding of disease-causing proteins to rebuild normal cross-β interactomes and mitigate neurodegeneration. Baicalein, a potential drug candidate for this approach, shows promise in vitro and in animal models. Moreover, prion-like low complexity domains implicated in neurodegeneration are also associated with other disorders such as diabetes, cancers, and mental illnesses. Understanding the core pathology of neurodegenerative diseases and the broader implications of prion-like LC domains could revolutionize healthcare approaches and policies worldwide.

In this paper, we study the constrained minimization problem for an energy functional which is related to the following Kirchhoff type equation \begin{equation*} -\Big(\eta+b\big(\int_{\R^{3}}|\nabla u|^{2}dx\big)^{s}\Big)\Delta u+V(x)u=\mu u +\lambda|u|^{p}u,\end{equation*} where $b$ is a positive constant, parameters $\eta\geq0, \lambda>0$, exponents $s>0$, $0

The kinase pathway plays a crucial role in blood vessel function. Particular attention is paid to VEGFR-type 2 angiogenesis and vascular morphogenesis as the tyrosin kinase pathway is preferentially activated. In silico studies were performed on several peptides that affect VEGFR2 in both stimulating and inhibitory ways. This investigation aims to examine the molecular properties of VEGFR2, a molecule primarily involved in the processes of vasculogenesis and angiogenesis. These relationships were defined by the interactions between vascular endothelial growth factor receptor 2 (VEGFR2) and the structural features of the systems. The chemical space of the inhibitory peptides and stimulators was described using topological and energetic properties. Furthermore, chimeric models of stimulating and inhibitory proteins (for VEGFR2) were computed using the protein system structures. The interaction between the chimeric proteins and VEGFR was computed. The chemical space was further characterized using complex manifolds and high-dimensional data visualization. The results show that a slightly similar chemical area is shared by VEGFR2 and is shared by stimulating and inhibitory proteins. On the other hand, the stimulator peptides and the inhibitors have distinct chemical spaces.

Abstract: Background: Adolescents frequently self-report pain, according to epidemiological research. The knee is one of the sites where the pain is most common. One of the main factors contributing to the number of years people spend disabled and having substantial personal, societal, and economic burdens globally is musculoskeletal disorders. Insidious, slowly building onset that neither the adolescent nor the parent is aware of [5]. Patellofemoral Pain (PFP) is a clinical knee pain commonly affecting adolescents. In the present study, the authors aimed to estimate the frequency of knee pain in Saudi adolescents. Methods: This cross-sectional survey carried out from June to November 2022 included 676 adolescents, ages 10 to 18. The data are presented as frequencies and percentages for categorical variables. Analysis of variance (ANOVA) was used to compare means between groups, while the chi-square test was used for the comparison of categorical variables. Statistical significance was set at p < 0.05. Results: Adolescents were invited to take part in the study; 57.5% were female and 42.5% were male, and 68.8% were aged between 15 and 18. The prevalence of knee pain was considerably high among females (26%), while it was 19.2% among males. Moreover, age was a significant predictor for knee pain, and BMI was as well. We also found a significant difference between BMI classification and stiffness of the knee (p = 0.008). There is a difference between adolescents who do physical activities in leisure and difficulty bending (p = 0.03). Conclusion: Our study noted a high rate of knee pain among adolescents, so we need to raise awareness about risk factors. Adolescent knee pain can be prevented with conservative methods and some minor lifestyle/activity modifications.

In our country, the aging society is rapidly progressing, with an increase in patients with chronic kidney disease (CKD) and those undergoing dialysis, coupled with the aging of the population. As a result, the number of individuals with cognitive impairment (CI) is rising, and addressing this issue has become an urgent problem. One characteristic of dementia in CKD patients is the high frequency of vascular dementia, making the prevention through the management of classical risk factors such as hypertension, diabetes mellitus, dyslipidemia, smoking, etc., associated with atherosclerosis and arteriosclerosis. Other effective measures, including the use of renin-angiotensin system inhibitors, addressing anemia, exercise therapy, and lifestyle improvements, have been reported. Incidence and progression of CI may also be influenced by the type of dialysis or kidney replacement therapy, with reports suggesting that long-duration dialysis, low-temperature hemodialysis, peritoneal dialysis, and kidney transplantation (KTx) can have a possible preferable effect on preserving cognitive function.

Recognizing highly occluded objects is believed to arises from the interaction between the brain's vision and cognition controlling areas, although supporting neuroimaging data is currently limited. To explore the neural mechanism during this activity, we conducted an occlusion object recognition experiment using functional magnetic resonance imaging (fMRI). During magnet resonance examinations, 66 subjects engaged in object recognition tasks with three different occlusion degrees. Generalized linear model (GLM) analysis showed that the activation degree of occipital lobe (inferior occipital gyrus, middle occipital gyrus, and occipital fusiform gyrus) and dorsal anterior cingulate cortex (dACC) was related to the occlusion degree of the objects. Multivariate pattern analysis (MVPA) further unearthed a considerable surge in classification precision when dACC activation was incorporated as a feature. This suggested the combined role of dACC and occipital lobe in occluded object recognition tasks. Moreover, psychophysiological interaction (PPI) analysis disclosed that functional connectivity (FC) between the dACC and the occipital lobe was enhanced with increased occlusion, highlighting the necessity of FC between these two brain regions in effectively identifying exceedingly occluded objects. In conclusion, these findings contribute to understanding the neural mechanisms of highly occluded objects recognition, augmenting our appreciation of how the brain manages incomplete visual data.

Molecular analysis of growing teratoma syndrome has not been extensively studied. Here, we report a 14-year-old boy with a growing mass during treatment for a mixed germ cell tumor of the pineal region. Tumor markers were negative; thus, growing teratoma syndrome was suspected. A radical resection via the occipital transtentorial approach was performed, and histopathological examination revealed a teratoma with malignant features. Methylation classifier analysis confirmed the diagnosis of teratoma, and DMRT1 loss and 12p gain were identified by copy number variation analysis, potentially elucidating the cause of growth and malignant transformation of the teratoma. The patient remains in remission after intense chemoradiation treatment as a high-risk germ cell tumor.

Sensory integration is an essential function of human life, and its decline causes difficulties in the quality of life, especially in older adults. In this study, we proposed an arm-reaching task based on a virtual reality head-mounted display (VR-HMD) system to assess sensory integration in daily life, and examined whether reaching task performance was associated with age and resting-state functional connectivity (rsFC) between the brain regions of interest involved in sensory integration. We hypothesized that performance during a reaching task with multiple cognitive loadings would be affected by aging. The gap angle between the reaching hand and target positions was compared between older and middle-aged adults as a measure of sensory integration function. In addition, rsFC, which was significantly correlated with the gap angles of individual participants in the older adult group, was identified using resting-state functional magnetic resonance imaging data. The mean gap angle was significantly larger in the older adults than in the middle-aged group and was also significantly correlated with connections between the left primary motor area and the left inferior temporal gyrus, temporo-occipital part. Because the connectivity between the areas is known to be related to visuomotor integration, the results suggest the involvement of visuomotor integration in the decline of sensory integration function in old age and the validity of the gap angle during the proposed VR-HMD task as an index for the decline in function.

Extracellular vesicles (EVs) hold enormous potential in the diagnosis and treatment of diseases, benefit from their roles in intercellular signaling, as well as their plentiful presence and stability in body fluids. However, conventional EV isolation methods are labor-intensive, and harvest EVs with low purity and compromised recovery. In addition, the drawbacks, such as limited sensitivity and specificity of traditional EV analysis methods hinder the application of EVs in clinical use. Therefore, it is urgent to develop effective and standardized methods for isolating and detecting EVs. Microfluidics technology is a powerful and rapidly developing technology that has been introduced as a potential solution for the above bottlenecks. It holds the advantages of high integration, short analysis time, low consumption of samples and reagents. In this review, we summarize the traditional techniques alongside microfluidic-based methodologies for the isolation and detection of EVs. We emphasize the separation advantages of microfluidic technology in enhancing EV capture efficiency and precise targeting, while summarizing its analytical role in targeted detection and discussing the significance of EV detection in the tumor microenvironment, providing a direction for achieving automated and high-throughput EV detection in clinical samples.

Regardless of the SARS-CoV-2 pandemic containment, it remains paramount to comprehensively understand its underlying mechanisms to mitigate potential future health and economic impacts, comparable to those experienced throughout the course of the pandemic. The angiotensin-converting enzyme 2 (ACE2) provide anchorage for SARS-CoV-2 binding, thus implicating that ACE and ACE2 might contribute to the variability in infection severity. This study aimed to elucidate predisposing factors influencing the disease course among people infected by SARS-CoV-2, focusing on angiotensin-converting enzyme (ACE) and ACE2 polymorphisms. Notably, despite similar demographics and comorbidities, COVID-19 patients exhibit substantial differences in prognosis. Genetic polymorphisms in ACE and ACE2 have been implicated in disease progression, prompting our investigation into their role in COVID-19 evolution. Using next-generation sequencing (NGS), we analyzed ACE and ACE2 genes in a sample group comprising six subjects infected by SARS-CoV-2. Our findings revealed a correlation between specific polymorphisms and COVID-19 outcomes. Specifically, ACE and ACE2 intronic deletions were observed in all deceased patients, suggesting a potential association with mortality. These results highlight the significance of genetic factors in shaping the clinical course of COVID-19, emphasizing the importance of further research into the impact of genetic variations on COVID-19 severity.

A frequent co-morbidity of cerebrovascular pathology and Alzheimer's disease pathology has been observed over past decades. Accordingly, much evidence has been reported which indicates that microvascular endothelial dysfunction, due to cerebrovascular risk factors (e.g., atherosclerosis, obesity, diabetes, smoking, hypertension, aging), precedes cognitive decline in Alzheimer's disease and contributes to its pathogenesis. By incorporating appropriate drug(s) into biomimetic (lipid cubic phase) nanocarriers, one obtains a multitasking combination therapeutic which targets certain cell-surface scavenger receptors, mainly class B type I (i.e., SR-BI), and crosses the blood-brain barrier (BBB). Such targeting allows for various Alzheimer's-related cell types to be simultaneously searched out, in vivo, for localized drug treatment. This in vivo targeting advantage may be particularly important for repurposing an FDA-approved drug, especially one which has shown the added ability to restore some cognitive functions in certain animal models of Alzheimer's disease (e.g., the anticancer drug bexarotene); this (candidate repurposing) drug up to now, by itself (i.e, without nanocarrier), displayed poor CNS penetration in human subjects.

This study explores innovative strategies to enhance hands-on learning experiences in a Database Management Systems (DBMS) course while addressing challenges related to software installation complexities and financial barriers. The investigation focuses on an online course format catering to students majoring in information systems, healthcare administration, business administration, and marketing. Traditional software installation hurdles, especially for complex systems like Microsoft Access and MySQL, can impede students' engagement and access. To overcome these challenges, the study proposes leveraging cloud-based solutions and clientless remote desktop gateways like Apache Guacamole. Results indicate that these approaches effectively eliminate software installation obstacles, ensuring seamless access to essential tools for all students. The study underscores the importance of innovative technological solutions in promoting inclusive and impactful learning environments in higher education.

The representation of intelligence is achieved by patterns of connections among neurons in brains and machines. Brains grow continuously, such that their patterns of connections develop through activity-dependent specification with the continuing ontogenesis of individual experience. The theory of active inference proposes that the developmental organization of sentient systems reflects general processes of informatic self-evidencing, through the minimization of free energy, that may be described in information terms that are not dependent on a specific physical substrate. At a certain level of complexity, self-evidencing of living (self-organizing) information systems becomes hierarchic and reentrant, such that effective consciousness emerges as the consequence of a good regulator. These principles imply that an adequate reconstruction of the computational dynamics of an individual human brain is possible with a sufficient neuromorphic emulation.

The landscape of business intelligence (BI) is marked by rapid growth, extensive research, and practical implementations, reflecting its sustained relevance in modern business strategies. This paper delves into the dynamics of BI, particularly focusing on the interplay between maturity and agility. While BI maturity emphasizes optimization and predefined goals, BI agility prioritizes adaptability and response to change. The paper highlights the contradictions between these concepts, emphasizing the need for a balanced approach in BI strategy formulation.The role of organizational culture in fostering BI agility is explored through literature review and interviews with BI professionals. Key insights include the necessity of an open culture for effective BI utilization, clear strategic vision, decision delegation, information sharing, business-IT cooperation, and a learning-oriented mindset. These cultural factors contribute significantly to BI effectiveness and the ability to navigate dynamic business landscapes. The paper proposes pillars of agile maturity supported by an open organizational culture, including efficient communication, information integration, creative problem-solving, and close collaboration between business and IT. These pillars underscore the importance of a flexible and open mindset in navigating BI challenges and ensuring sustained value. Empirical validation and further research are suggested to deepen our understanding of the impact of organizational culture on BI agility and effectiveness.

In this work, the properties of a composition based on synthetic rubber ethylene-propylene ter-polymer and polymers with functional groups of the mixture are studied. Low efficiency of some compositions based on ethylene and propylene copolymer limits their use in the production of tires and rubber products. Therefore, we conducted a study to improve the disadvantages of SREPT-60 using butyl rubber + choro polyisoprene + butadiene-nitrile + butadiene styrene rubber and adding a plasticizer to the composition = α, methacrylate-(bis-diethylene glycol phthalate) -(btegf-9) The data obtained showed that a number of its indicators of rubber based on them were improved. Tear resistance, resistance to repeated stretching increased, adhesion of the composition to metal, chemical resistance increased, the degree of swelling in benzene and gasoline-benzene mixture decreased. Compositions based on the mixture SREPT-60 using butyl rubber + choro polyisoprene + butadiene-nitrile + butadiene styrene rubber and adding a plasticizer to the composition = α, methacrylate-(bis-diethylene glycol phthalate) -(btegf-9) +Dioctyl phthalate: (70:20:10) have high resistance to swelling in an aggressive environment (Benzene, gasoline, alcohol, acid, oil, etc.) mixture.

The intricate architecture of dendritic arborization is fundamental to the formation and functionality of neural networks, serving as the primary site for synaptic integration and signal propagation. This study presents a pioneering computational model that simulates dendritic growth dynamics, employing exponential and logarithmic scaling over an extended developmental period of 720 days. This model offers a valuable tool for investigating the implications of diverse dendritic growth patterns on neural development, synaptic connectivity, and ultimately, cognitive functions. The computational framework incorporates biologically plausible parameters, allowing for the systematic exploration of dendritic branching patterns and their potential impact on neuronal information processing. By simulating exponential and logarithmic growth scales, the model captures the inherent complexity and diversity of dendritic morphologies observed in various neuronal populations across different brain regions. The findings from this study hold significant implications for our understanding of neural circuit assembly, synaptic integration, and the potential functional consequences of aberrant dendritic growth patterns observed in neurodevelopmental disorders. Furthermore, the model's ability to simulate extended developmental trajectories over a prolonged period of 720 days offers insights into the dynamic interplay between dendritic growth and synaptic pruning, which is crucial for the refinement and optimization of neural networks.

Aileen Oeberst and Roland Imhoff’s remarkable paper (Perspectives on Psychological Science 2023; 18: 1464-87). should be compulsory reading for all intelligence analysts. It offers a tantalisingly parsimonious explanation comprising the combination of strongly held prior 'beliefs' coupled with subsequent 'belief-consistent information processing' (akin to confirmation bias) - an explanation that simplifies understanding of a vast array of cognitive biases, and thereby suggests that all of these might be attenuated through the application of accessible analytical and assessment practices. What this means for intelligence analysis will be determined by the explicit and implicit 'beliefs' (or, more generally, ‘value[s]’) attributed to any evidence available, and any insight generated, to reduce decision-makers’ uncertainty and unsubstantiated certainty, and thereby offer them future advantage. Analysts are well aware that different sources of evidence (whether empirical, theoretical or entirely speculative) can assign different types and amounts of ‘prior value’ to the information available for intelligence analysis and assessment. Acknowledging such value as a potential driver of subsequent confirmation bias should help analysts guard against any inherent tendency to preference evidence solely on the basis that this was initially considered most ‘valuable’. Instead they should subject all evidence and all insight – regardless of perceived ‘value’ – to a consistent battery of systematic, rigorous and robust evaluation.

Diabetes mellitus (DM) is the most common metabolic disease in humans, and its prevalence is increasing worldwide in parallel with the obesity pandemic. A lack of insulin or insulin resistance, and consequently hyperglycemia, leads to many systemic disorders, among which diabetic encephalopathy (DE) is a long-term complication of the central nervous system (CNS), characterized by cognitive impairment and motor dysfunctions. The role of oxidative stress and neuroinflammation in the pathomechanism of DE has been proven. Fractalkine (CX3CL1) has unique properties as an adhesion molecule and chemoattractant, and by acting on its only receptor, CX3CR1, it regulates the activity of microglia in physiological states and neuroinflammation. Depending on the clinical context, CX3CL1-CX3CR1 signaling may have neuroprotective effects by inhibiting the inflammatory process in microglia or, conversely, maintaining/intensifying inflammation and neurotoxicity. This review discusses the evidence supporting that the CX3CL1-CX3CR1 pair is neuroprotective and other evidence that it is neurotoxic. Interrupting the vicious cycle within neuron-microglia interactions may be a therapeutic goal in DE by limiting the inflammatory response.

Continual learning, a crucial facet of machine learning, involves the perpetual acquisition of valuable insights from incoming data, sans the necessity for full dataset access. Esteemed as a fundamental goal in artificial intelligence, continual learning grapples with an enduring challenge—catastrophic forgetting. A proficient model must exhibit adaptability for new data assimilation and robustness to retain existing knowledge. Class-incremental learning (CIL) aids the gradual integration of knowledge from newly introduced classes, forming a universal classifier. However, directly training the model with fresh class instances triggers a problem—forgetting distinguishing features of prior classes, causing a performance decline. Addressing such issues in machine learning, this survey aims to delineate significant challenges, outcomes, and recent advancements, including our contributions to CIL techniques, especially in image classification.

Humulus lupulus, or hops, is a vital ingredient in brewing, contributing bitterness, flavor, and aroma. The female plants produce strobiles rich in essential oils and acids, along with bioactive compounds like polyphenols, humulene, and myrcene, which offer health benefits. This study examined the aromatic profiles of five hop varieties grown in Brazil versus their countries of origin. Fifty grams of pelletized hops from each strain were collected and analyzed using HS-SPME/GC-MS to identify volatile compounds, followed by statistical analysis with PLS-DA and ANOVA. The study identified 330 volatile compounds and found significant aromatic differences among hops from different regions. For instance, H. Mittelfrüher grown in Brazil has a fruity and herbaceous profile, while the German-grown variety is more herbal and spicy. Similar variations were noted in Magnum, Nugget, Saaz, and Sorachi Ace varieties. The findings underscore the impact of terroir on hop aromatic profiles, with Brazilian-grown hops displaying distinct profiles compared to their counterparts from their countries of origin, including variations in aromatic notes and α-acid content.

With the rapid growth of large language models (LLMs), there is increasing demand for memory and computation in LLMs. Recent efforts on post-training pruning of LLMs aim to reduce the model size and computation requirements, yet the performance is still sub-optimal. In this paper, we present a plug-and-play solution for post-training pruning of LLMs. The proposed solution has two innovative components: 1) Relative Importance and Activations (RIA), a new pruning metric that jointly considers the weight and activations efficiently on LLMs; and 2) Channel Permutation, a new approach to maximally preserve important weights under N:M sparsity. The two proposed components can be readily combined to further enhance the N:M semi-structured pruning of LLMs. Our empirical experiments show that RIA alone can already surpass all existing post-training pruning methods on prevalent LLMs, e.g., LLaMA ranging from 7B to 65B. Furthermore, N:M semi-structured pruning with channel permutation can even outperform the original LLaMA2-70B on zero-shot tasks, together with practical speed-up on specific hardware. Our code is available at: https://github.com/biomedical-cybernetics/Relative-importance-and-activation-pruning

A growing number of studies focus on methods to estimate and analyze the functional connectome of the human brain. Graph theoretical measures are commonly employed to interpret and synthesize complex network-related information. While resting state functional MRI (rsfMRI) is often employed in this context, is known to exhibit poor reproducibility, a key factor which is commonly neglected in typical cohort studies using connectomics-related measures as biomarkers. We aimed to fill this gap by analyzing and comparing inter- and intra- subject variability of connectivity matrices as well as graph-theoretical measures in a large (n=1003) database of young healthy subjects which underwent four consecutive rsfMRI sessions. We analyzed both directed (Granger Causality and Transfer Entropy) and undirected (Pearson Correlation and Partial Correlation) time-series association measures and related global and local graph-theoretical measures. While matrix weights exhibit a higher reproducibility in undirected as opposed to directed methods, this difference disappears when looking at global graph metrics and, in turn, exhibits strong regional dependence in local graphs metrics. Our results warrant caution in the interpretation of connectivity studies, and serve as a benchmark for future investigations by providing quantitative estimates for the inter- and intra- subject variabilities in both directed and undirected connectomic measures.

Injury to the peritoneum during surgery is followed by a healing process that frequently results in the attachment of adjacent organs by a fibrous mass, referred to commonly as adhesions. Because injuries to the peritoneum during surgery are inevitable, one must understand the mechanisms of adhesion formation to prevent its occurrence. This requires a thorough understanding of the molecular sequence that results in the attachment of injured peritoneum and the development of fibrous tissue. Recent data show that fibroblasts from the injured peritoneum may play a critical role in forming adhesion tissues. Therefore, identifying changes in gene expression patterns in the peritoneal fibroblasts during the process may provide clues to the mechanisms by which adhesion develops. This review presents many papers discussing genes implied in peritoneal adhesion.

This study examines the contribution of early executive functions (EFs) in the association between fluency in reading and arithmetic. Kindergarten children (N = 1,185) were assessed on Executive Functions skills and on reading and arithmetic fluency in Grade 1 and Grade 3. The analysis revealed that beyond the connection within each domain there is a unidirectional effect between fluency measures, with Grade 1 reading fluency significantly influencing the development of arithmetic fluency in Grade 3. Furthermore, the findings indicate that kindergarten EFs significantly contribute to arithmetic fluency at both time points and to reading fluency in the first grade. Early EF skills also emerged as a mediator of associations between fluency performance in reading and arithmetic, suggesting that the influence of EFs extends beyond individual academic domains. These findings have implications for understanding the cognitive mechanisms that underlie the relations between these academic skills.

The epidemic of obesity has increased worldwide and is associated with comorbidities as diabetes as cardiovascular disease. Strategies that modulate body weight and improves glycemic metabolism has increased, and the bariatric surgeries as Sleeve Gastrectomy (SG) has been highlighted in obesity treatment. However, the mechanism by which SG reduces body weight and improves glycemic control are unknow. C57BL mice were submitted to a 10 weeks high fat-diet protocol and submitted to SG. Food intake, fed/fasted glycemia and hypothalamic anorexigenic and orexigenic gene expression were evaluated. SG reduces body weight, fasting glycemia, insulinemia and leptinemia in obese mice. Moreover, SG reduces food and total calorie intake with no alterations in the expression of anorexigenic and orexigenic gene expression. These data indicates that SG improves obesity associated alterations, at least in part, by reduction on food intake. This effect is not associated with the classic food intake pathway in hypothalamus, indicating the involvement of supplementary pathways in this process.

Background: Cognitive-behavioral alterations can occur after an acquired brain injury. It is a stressful situa-tion for patient and relatives. Objectives: To develop and evaluate a synchronous online training program on emotional intelligence (EI) for caregivers of adult patients with cognitive-behavioral impairment due to acquired brain injury. Methods: A quasi-experimental study was designed, a target population of ten caregivers attended to a one-month virtual synchronous course about EI. The emotional status of caregivers was registered one-month-previous and one-month-post program using comparative measures: The Trait Meta-Mood Scale (TMMS-24) and the Positive and Negative Affect Schedule (PANAS). Results: Median age of the ten caregivers was 48 years, 80% of women with a median care-time of 6 years. 50% of them were spouses of the patients. 60% of the patients were affected by stroke (hemorrhagic or ischemic cause). The main cognitive impairment of the patients reported by relatives was memory deficit. After re-ceiving training, favorable changes were found regarding emotional affect measured with the PANAS, both positive (increase) and negative (decrease), as well as with the TMMS-24-mood-repair area (p < 0.05). Conclusions: Training in EI helps caregivers to make their mood more positive and improve aspects of their emotional intelligence such as emotional regulation.

Cancer is one of the leading causes of death, making timely diagnosis and prognosis very important. Utilization of AI (artificial intelligence) enables providers to organize and process patient data in a way that can lead to better overall outcomes. This review paper aims to look at the varying uses of AI for diagnosis and prognosis and clinical utility. PubMed and EBSCO databases were utilized for finding publications from January 1, 2013, to December 22, 2023. Articles were collected using key search terms such as “artificial intelligence” and “machine learning.” Included in the collection were studies of the application of AI in determining cancer diagnosis and prognosis using multi-omics data, radiomics, pathomics, clinical and laboratory data. The resulting 89 studies were categorized into eight sections based on the type of data utilized and then further subdivided into two subsections focusing on cancer diagnosis and prognosis, respectively. 8 studies integrated more than one form of omics, namely genomics, transcriptomics, epigenomics, and proteomics. Incorporating AI into cancer diagnosis and prognosis alongside omics and clinical data represents a significant advancement. Given the considerable potential of AI in this domain, ongoing prospective studies are essential to enhance algorithm interpretability and to ensure safe clinical integration.

As the most serious of the many worse new pathological changes caused by diabetes, there are many risk factors for the occurrence and development of DR. It mainly includes hyperglycemia, hypertension, hyperlipidemia and so on. Among them, hyperglycemia is the most critical cause, which plays a key role in the pathological changes of DR.High-sucrose diets(HSDs) lead to elevated blood glucose levels in vivo, which, through oxidative stress, inflammation, the production of AGEs and VEGF, causes plenty of pathological damages to the retina and ultimately bring about loss of vision. The existing therapies for DR primarily target the terminal stage of the disease, when irreversible visual impairment has appeared. Therefore, early prevention is particularly critical. Early prevention of DR-related vision loss requires adjustments to dietary habits, mainly by reducing sugar intake. This article primarily discusses the risk factors, pathophysiological processes, and molecular mechanisms associated with the development of DR caused by HSDs. It aims to raise awareness of the crucial role of diet in the occurrence and progression of DR, promote timely changes in dietary habits, prevent vision loss, and improve the quality of life. The aim is to make people aware of the importance of diet in the occurrence and progression of DR. According to the dietary modification strategies we give, patients can change their poor eating habits timely, avoid the retinopathy that can be avoided theoretically, and obtain a excellent outcome.

To characterize the fecal microbiota of lactating Bactrian camels, this study employed a random selection of camels, assigning them to three groups: the first parity group, the third parity group, and the fifth parity group. The fecal microbial community of lactating camels was assessed using 16S rRNA amplicon sequencing, and the resulting library was sequenced on an Illumina NovaSeq platform. The first parity group was characterized by Verrucomicrobiaceae as a distinctive bi-omarker, whereas Clostridiaceae exhibited a similar role to the third parity group. In terms of functional analysis, the relative abundance of the Steroid biosynthesis pathway was highest in the first parity group, whereas the fifth parity group demonstrated a higher relative abundance of the Tuberculosis pathway compared to the other groups. The first parity group exhibited the greatest presence of seven types of enzymes, whereas the fifth parity group displayed the highest levels of two specific enzymes. Additionally, a positive association was observed between the relative abundance of the Steroid biosynthesis pathway and Verrucomicrobiaceae. Furthermore, the relative abundance of the Tuberculosis pathway displayed positive correlations with 14 enzymes. In conclusion, this study revealed that different parity was associated with distinct fecal microbial ecologies in lactating Bactrian camels.

The nuclear magnetic resonance (NMR) metabolomic analysis was applied to investigate the differences within nineteen Sicilian Nocellara del Belice mono varietal Extra Virgin Olive Oils (EVOO), grown in two zones different for altitude and soil composition. Several classes of endogenous olive oil metabolites were quantified through a nuclear magnetic resonance (NMR) three-experiment protocol coupled with a yet developed data-processing called MARA-NMR. This method, taking around one-hour experimental time per sample, faces the possible quantification of different class of compounds at different concentration’s range which would require at least three alternative traditional methods. NMR results were compared with the data of traditional analytical methods to quantify free fatty acidity (FFA), fatty acid methyl esters (FAMEs), and total phenol content. The presented NMR methodology is compared with traditional analytical practices and its consistency is also tested through slightly different data treatment. Despite the rich literature about the NMR of EVOOs, the paper points out that there are still several advances potentially improving this general analysis and overcoming the other cumbersome and multi-devices analytical strategies. Monovarietal EVOO’s composition is mainly affected by pedoclimatic conditions in turn relaying upon the nutritional properties, quality, and authenticity. Data collection, analysis and statistical processing are discussed touching the important issues related to the climate changes in Sicily and to the specific influence of pedoclimatic conditions.

Background: This study was designed to adapt the Delis-Kaplan Executive System (D-KEFS) version of the Color-Word Interference (CWIT) and Trail making Tests (TMT) in Greek adult population from 20 to 49 years old, since it is of research as well as clinical importance to detect executive functions’ impairment in young adults with neurological or/and psychiatric conditions. Aims: Norms for the Greek adult population have been calculated to be available for neuropsychologists and health professionals who work in relevant settings. Methods: The study sample consisted of 101 healthy adults (41% male and 60% female), aged 20 to 49 years (M=32.16, SD=11.57) with education from 12 to 19 years of schooling (M=14.51, SD= .89). Pearson correlation test as well as chi square test were conducted to examine potential associations between gender, age, education, and participants’ performance. Afterwards, we calculated normative data using raw scores and transformed them to percentile scores. Finally, Greek norms were compared to the original raw scores, which were transformed in scaled scores by Delis et al. (2001). Results: The findings showed that age was the only variable which affected CWIT, whereas level of education as well as age were predictive factors for most TMT conditions, except for the visual scanning test (Condition 1). Gender did not affect both tests. Finally, D-KEFS norms for CWIT and TMT are available for Greek adult population to help clinicians detect executive functions’ deficits and therefore adjust tailored therapeutic strategies. Additionally, it is of great importance to use these tests for research purposes. Conclusion: Given that executive functions are assumed as high- level skills, which are highly related to everyday functionality, adapted tests contribute not only to assess the progression of any existing neurological as well as psychiatric disorders, but they can also be used to evaluate patients’ ability to live independently, as well as their access to work.

Researchers are exploring solutions to meet the growing demand for protein due to the expected increase in global population by 2050. Interest in alternative protein sources like insects has risen, driven by concerns about environmental impact and the need for sustainable food production. This study aimed to develop and evaluate the physicochemical properties of soy protein-based burger enriched with insect protein from Alphitobius diaperinus. Three formulations were developed: a control (B0), and burgers with 5% (B5) and 10% (B10) insect protein - Whole Buffalo Powder (WBP). Results showed that adding insect protein decreased the burger analogue's pH. There was observed a clear trend of increasing total lipids and SFA, and decreasing MUFA and PUFA, as the WBP concentration increases from 0% to 10%. No significant differences with increasing WBP concentration in the protein content of the burger analogue, as well as the cooking yield, were noted. The WBP addition had a notable effect on the color change, especially decrease in brightness (L*). It was shown that as the WBP concentration increased, there were no significant differences in the texture profile of the burger analogues. The formulation with 5% WBP concentration was the most acceptable in sensory analysis.

Honey is a sweet substance extensively consumed in the world for its nutritional and healthy properties. However, residues of pesticides and environmental contaminants can compromise its quality. For this reason, the physicochemical parameters, and the organic contamination of monofloral and multifloral honeys from three regions of Algeria (Tiaret, Laghouat and Tindouf) were monitored to evaluate the quality of honey and the safety for consumers. In general, the results obtained from the physicochemical analyses were in line with the EU standards. In terms of contamination, pesticides authorised and used in Algerian agriculture (matalaxyl-M and cyromazine), as well as banned pesticide (carbaryl), were found in almost all the samples. However, only the concentration of cyromazine was higher than the relative EU maximum residue levels. PCB 180, PCB 189, anthracene, fluorene and phenanthrene were mainly detected. All the honeys show traces of DiBP, DBP, DEHP and DEHT, but no traces of bisphenols were found. Moreover, the dietary exposure assessment shows that a small amount of Algerian honey can be safely consumed. Overall, data from this study should motivate the Algerian government to enhance their monitoring activities in beekeeping and to find solutions for implementing more sustainable agricultural practices harmonizing with the international legislation.

To enhance the applicability of dual-phase high-entropy alloys (HEAs) like Fe32Cr33Ni29Al3Ti3, fabricated via selective laser melting (SLM), a focus on improving their mechanical properties is essential. As part of this effort, heat treatment was explored. This study compares the microstructure and mechanical properties of the as-printed sample with those cooled in water after undergoing heat treatment at temperatures ranging from 1000 to 1200 ℃ for 1 hour. Both pre- and post-treatment samples reveal a dual-phase microstructure comprising FCC and BCC phases. Although heat treatment led to a reduction in tensile and yield strength, it significantly increased ductility compared to the as-printed sample. This strength-ductility trade-off is related to changes in grain sizes with ultrafine grains enhancing strength and micron grains optimizing ductility, also the influence of the content of FCC/BCC phases and dislocation density. In particular, the sample heat-treated at 1000 °C for 1 hour and then water-cooled exhibited a better combination of strength and ductility, a yield strength of 790 MPa, and an elongation of 13%. This research offers innovative perspectives on crafting dual-phase HEA of Fe32Cr33Ni29Al3Ti3, allowing for tailorable microstructure and mechanical properties through a synergistic approach involving SLM and heat treatment.

IBD, encompassing CD and UC, are characterized by chronic gastrointestinal inflammation due to abnormal immune responses to gut microflora. The bidirectional communication between the gut and the brain, known as the GBA, is disrupted in IBD, leading to neurobiological imbalances and affective symptoms. Systemic inflammation in IBD affects the brain's inflammatory response system, hormonal axis, and BBB integrity, influencing gut microbiota and clinical responses to probiotics. This review aims to explore the links between dysregulations in the GBA, serum biomarkers, and the development of MCI and NDDs. The objective is to identify potential correlations and propose future research directions to understand the impact of altered microbiomes and intestinal barrier functions on NDDs. Studies suggest a potential link between IBD and extraintestinal complications such as MCI and NDDs. Mechanisms include systemic inflammation, BBB dysfunction, autoimmune responses, GBA dysfunction, medication effects, and comorbidities. Serum biomarkers, including vitamin D3, C-reactive protein, high-sensitivity C-reactive protein, vitamin B12, homocysteine, serum amyloid, neuron-specific enolase, neurofilament light chain, S100 proteins, brain-derived neurotrophic factor, IL-6, LP-2 associated phospholipase, prostaglandin E2, IL-1β, and TNF-α, have been investigated for their potential to predict MCI but current results are lacking clarity.

The triad of vascular impairment, muscle atrophy, and cognitive decline represents critical age-related conditions that significantly impact health. Vascular impairment disrupts blood flow, precipitating muscle mass reduction seen in sarcopenia and the decline of neuronal functions characteristic of neurodegeneration. Our limited understanding of the intricate relationships within this triad hinders accurate diagnosis and effective treatment strategies. This review ana-lyzes the interrelated mechanisms that contribute to these conditions, with a specific focus on ox-idative stress, chronic inflammation, and impaired nutrient delivery. The aim is to understand the common pathways involved and to suggest comprehensive therapeutic approaches. Vascular dysfunctions hinder the circulation of blood and the transportation of nutrients, resulting in sar-copenia characterized by muscle atrophy and weakness. Vascular dysfunction and sarcopenia have a negative impact on physical function and quality of life. Neurodegenerative diseases exhibit comparable pathophysiological mechanisms that affect cognitive and motor functions. Preventive and therapeutic approaches encompass lifestyle adjustments, addressing oxidative stress, in-flammation, and integrated therapies that focus on improving vascular and muscular well-being. Better understanding of these links can refine therapeutic strategies and yield better patient out-comes. This study emphasizes the complex interplay between vascular dysfunction, muscle de-generation, and cognitive decline, highlighting the necessity for multidisciplinary treatment ap-proaches. Advances in this domain promise improved diagnostic accuracy, more effective thera-peutic options, and enhanced preventive measures, all contributing to a higher quality of life for the elderly population.

The behavioral variant of frontotemporal dementia (bvFTD) has a devastating effect on multiple domains of daily living. The purpose of this PRISMA-compliant systematic review is to summarize the most important factors associated with functional impairment in this clinical group by critically analyzing the existing literature spanning the period from 2000 to 2023. To be included in the review, the study had to investigate any kind of correlates of functional status in bvFTD patients, using a previously validated instrument of functional assessment. Out of 40 articles assessed for eligibility, 18 met the inclusion criteria. The anatomical pattern of cerebral atrophy at baseline appeared to be the strongest predictor of the rate of functional decline over time, with the frontal-dominant anatomical subtype being associated with a faster rate of functional impairment. Additionally, executive dysfunction as well as apathy appeared to contribute significantly to functional disability in bvFTD patients. A comparative examination of bvFTD in relation to other clinical subtypes of FTD and other types of dementia in general suggests that it’s the predominant atrophy of the frontal lobes along with the subsequent unique combination of cognitive and neuropsychiatric manifestations that account for the pronounced functional limitations observed in these individuals, even from the early stages of the disease.

To better understand the shape memory materials and self-healing materials, a new series of liquid-crystalline shape memory polyurethane (LC-SMPU) composites, named SMPU-OOBAm, were successfully prepared by incorporating 4-octyldecyloxybenzoic acid (OOBA) into the PEG-based SMPU. The effect of OOBA on the structure, morphology and properties has been carefully investigated. The results demonstrate that SMPU-OOBAm have liquid crystalline properties, triple-shape memory properties and self-healing properties. The incorporated OOBA promotes the crystallizability of both soft and hard segments of SMPU, and the crystallization rate of the hard segment of SMPU decreases when the OOBA-content increases. Additionally, the SMPU-OOBAm forms a two-phase separated structure (SMPU phase and OOBA phase), and it shows two-step modulus changes upon heating. Therefore, the SMPU-OOBAm shows triple-shape memory behavior, and the shape recovery ratio decreases with an increase in the OOBA content. Finally, SMPU-OOBAm shows self-healing properties. The new mechanism can be ascribed to the heating-induced “bleeding” of OOBA in the liquid crystalline state and the subsequent re-crystallization upon cooling. This successful combination of liquid crystalline properties, triple-shape memory properties and self-healing properties make the SMPU-OOBAm composites with many promise applications in smart optical devices, smart electronic devices and smart sensors.

Complexity science studies physical phenomena that cannot be explained by the mere analysis of the single units of a system but requires to account for their interactions. A feature of complexity in connected systems is the emergence of mesoscale patterns in a geometric space, such as groupings in bird flocks. These patterns are formed by groups of points that tend to separate from each other, creating mesoscale structures. When multidimensional data or complex networks are embedded in a geometric space, some mesoscale patterns can appear respectively as clusters or communities, and their geometric separability is a feature according to which the performance of an algorithm for network embedding can be evaluated. Here, we introduce a framework for the definition and measure of the geometric separability (linear and nonlinear) of mesoscale patterns by solving the travelling salesman problem (TSP), and we offer experimental evidence on embedding and visualization of multidimensional data or complex networks, which are generated artificially or are derived from real complex systems. For the first time in literature the TSP’s solution is used to define a criterion of nonlinear separability of points in a geometric space, hence redefining the separability problem in terms of the travelling salesman problem is an innovation which impacts both computer science and complexity theory.

Task fMRI has played a critical role in recognizing the specific functions of the different regions of human brain during various cognitive activities. This study aimed to investigate group analysis and functional connectivity in the Faradarmangars brain during the Faradarmani CF (FCF) connection. Using task functional MRI (task-fMRI), we attempted the identification of different activated and deactivated brain regions during the Consciousness Filed connection. Clusters that showed significant differences in peak intensity between task and rest group were selected as seeds for seed-voxel analysis. Connectivity of group differences in functional connectivity analysis was determined following each activation and deactivation network. In this study, we report the fMRI-based representation of the FCF connection at the human brain level. The group analysis of FCF connection task revealed activation of frontal lobe (BA6/BA10/BA11). Moreover, seed based functional connectivity analysis showed decreased connectivity within activated clusters and posterior Cingulate Gyrus (BA31). Moreover, we observed an increased connectivity within deactivated clusters and frontal lobe (BA11/BA47) during the FCF connection. Activation clusters as well as the increased and decreased connectivity between different regions of the brain during the FCF connection, firstly, validates the significant effect of the FCF and secondly, indicates a distinctive pattern of connection with this non-material and non-energetic field, in the brain.

In the realm of metal steel and iron manufacturing, there is a constant drive to optimize production efficiency and enhance the quality of the final products. This abstract explores a paradigm shift in this industry through the utilization of Computational Fluid Dynamics (CFD) as a powerful tool for achieving these goals .The study focuses on various critical aspects of the manufacturing process. Firstly, it delves into three-phase processes such as slag and gas entrainment in liquid steel, which have a significant impact on the overall product quality. Understanding and optimizing these processes is crucial to ensure the desired chemical composition and physical properties of the final metal.The abstract investigates the role of CFD in vacuum degassing, a process that plays a pivotal role in removing impurities, such as hydrogen and oxygen, from the liquid steel. By leveraging CFD to simulate and analyze the fluid flow patterns, researchers and engineers can design more efficient vacuum degassing systems, leading to improved product purity and reduced waste.Another crucial aspect explored in this abstract is the alloy melt and mixing process. By employing CFD simulations, researchers can gain insights into the movement of different alloy components and optimize their distribution within the melt. This aids in achieving the desired alloy composition, ensuring uniformity, and enhancing the mechanical properties of the final product.In addition, the study delves into the movement and flotation of inclusions, which are unwanted impurities present in the metal. CFD can be utilized to model and analyze the behavior of these inclusions, enabling engineers to design efficient strategies for their removal, thereby enhancing the purity and integrity of the final product.Moreover, the abstract considers the impact of melt temperature losses, which can occur during different stages of the manufacturing process. By employing CFD, it becomes possible to understand and mitigate these temperature losses, leading to improved energy efficiency and better control over the manufacturing parameters.Part 1 of this abstract critically evaluates the use of CFD in iron making, analyzing its benefits and limitations. It highlights how CFD can aid in process optimization, improving the understanding of complex phenomena involved in iron making, and ultimately leading to enhanced production efficiency.Part 2 focuses on the application of CFD in steel making and steel operation. It explores the fluid flow dynamics during different stages of steel production, including refining, casting, and rolling. By leveraging CFD, engineers can optimize these processes, leading to improved product quality, reduced defects, and increased productivity.This abstract demonstrates the transformative potential of Computational Fluid Dynamics in metal, steel, and iron manufacturing. By utilizing CFD as a tool to model and analyze complex fluid flow phenomena, researchers and engineers can revolutionize the production process, maximizing efficiency, and ensuring high-quality end products.

: Background: Brain-derived neurotrophic factor (BDNF) is a crucial mediator of neuronal plasticity. Here, we investigated the effects of controlled normobaric hypoxia (NH) combined with physical inactivity on BDNF blood levels and executive functions. Methods: Twenty-five healthy adults (25.8±3.3 years, 15 female) were analyzed in a randomized, controlled cross-over study. Each intervention began with a 30 min resting phase under normoxia (NOR), followed by a 90 min continuation of NOR or NH (peripheral oxygen saturation [SpO₂] 85-80%). Serum and plasma samples were collected every 15 min. Heart rate and SpO₂ were continuously measured. Before and after each exposure, cognitive tests were performed and after 24 h another follow-up blood sample was taken. Results: NH decreased SpO₂ (p<.001, η_p²=.747) and increased heart rate (p=.006, η_p²=.116) significantly. The 30-min resting phase under NOR led to a significant BDNF reduction in serum (p <.001, η_p²=.581) and plasma (p <.001, η_p²=.362). Continuation of NOR further significantly reduced BDNF after another 45 min (p=.018) in serum and after 30 min (p=.040) and 90 min (p=.005) in plasma. There was no significant BDNF decline under NH. 24 h Follow-up examination showed a significant decline of serum BDNF, both after NH and NOR. Conclusion: Our results show that NH has the potential to counteract physical inactivity-induced BDNF decline. Therefore, our study emphasizes the need for a physically active lifestyle and its positive effects on BDNF. This study also demonstrates the need for a standardized protocol for future studies to determine BDNF in serum and plasma.

Surface initiated atom transfer radical polymerization (SI-ATRP) is one of the most versatile technique to modify the surface properties of material. Recent developed metal free SI-ATRP makes such technique more widely applicable. Herein photo-induced metal-free SI-ATRP of methacrylates, such as methyl methacrylate, N-isopropanyl acrylamide, and N,N- dimethylaminoethyl methacrylate, on the surface of SBA-15 was reported to fabricate organic-inorganic hybrid materials. SBA-15 based polymeric composite with adjustable graft ratio was obtained. The structure evolution during the SI-ATRP modification of SBA-15 was monitored and verified by FT-IR, XPS, TGA, BET, and TEM. The obtained polymeric composite showed enhanced adsorption ability for the model compound toluene in aqueous. This procedure provides a low cost, ready availability, and facile modification way to synthesize the polymeric composites without the contamination of metal.

Alzheimer’s disease (AD) poses a significant public health challenge due to its irreversible and progressive nature while lack of a cure. As a potential strategy to combat AD, its prevention becomes increasingly attractive. Mild Cognitive Impairment due to AD (MCI-AD) represents a critical transitional stage between normal age-related cognitive decline and more severe AD conditions, occurring just before dementia onset. Unfortunately, there is currently no established animal model that accurately recapitulates MCI-AD characteristics. While many laboratories have traditionally used normally aged wild-type animals as experimental models, this approach falls short in representing the inherently worse state of MCI-AD compared to normal aging. To address this gap, we introduce an animal model—a transgenic mouse line with genetic inactivation of G protein-coupled receptor kinase-5 (GRK5), commonly known as the GRK5 knockout (GRK5KO) mouse. These GRK5KO mice exhibit amnesia, cognitive deficits, increased β-amyloid levels, neurofibrillary tangle (NFT) immunopositive axonopathy, and hippocampal neurodegenerative changes. Importantly, these pathological alterations predominantly impact the entorhinal, transentorhinal, and hippocampal cortices, aligning with human MCI-AD criteria and Braak stage II of human AD progression. Notably, female GRK5KO mice show approximately 2.5 times more NFT-positive axonopathy than males, mirroring the higher prevalence of AD cases in women. Collectively, existing data strongly supports the GRK5KO mouse as a qualified animal model for studying MCI-AD.

To elucidate mesenchymal stromal cells (MSC)-tumor intricate and ambiguous interactions, the reliable cell labeling and tracking techniques are imperative. Traditional methods of cell labelling have limitations mostly relevant to need for genetic modification what demands the exploration for alternative strategies. Here, we propose using fluorescent photoconvertible microcapsules containing Rhodamine B (RhB) to study mMSC migration in brain tumors. These 3 μm sized microcapsules are readily internalised by cells and undergo photoconversion under 561 nm laser exposure with fluorescence blue shift on demand. Optimal photoconversion was achieved with a laser irradiation duration of 0.4 ms, which provide a maximal brightness of photoconverted label without an excessive laser exposure on cells. Capsules modified with polyallylamine hydrochloride demonstrated the best potential for intracellular uptake without significantly affecting cell viability, motility, or proliferation. The optimal ratio of 20 capsules per mMSC was determined for labeling. Moreover, migration of individual mMSCs within 2D and 3D glioblastoma cell (EPNT-5) colonies over 2 days and in vivo tumor settings over 7 days was tracked. Our study unveils a robust platform for investigating MSC-tumor dynamics, offering insights into therapeutic strategies. Photoconvertible microcapsules could also become an indispensable tool for studying complex fundamental processes of cell-cell interactions for a wide range of problems in biology and medicine.

Arguably the most important parameter in treating cognitive decline associated with Alzheimer’s disease is the length of time in which improvement, if achieved at all, is sustained. However, monotherapies such as donepezil and memantine are associated with a more rapid decline than no treatment in patients over multi-year follow-ups {Kennedy, 2018 #535}. Furthermore, anti-amyloid antibody treatment, which at best simply slows decline, is associated with accelerated cerebral atrophy [2], resulting in earlier dementia-associated brain volumes for those treated at the MCI stage than untreated patients. In contrast, a precision medicine approach, in which the multiple potential drivers of cognitive decline are identified for each patient and then targeted with a personalized protocol (such as ReCODE), has led to documented improvement in patients with cognitive decline [3]; [4]; [5], but long-term follow-up (>5 years) has not been reported previously. Therefore, we report here sustained cognitive improvement, in some cases for over a decade, in patients treated with a precision medicine protocol—something that has not been reported in patients treated with anti-cholinesterase, glutamate receptor inhibitory, anti-amyloid, or other therapeutic methods. These case studies warrant long-term cohort studies to determine how frequently such sustained cognitive improvements occur in patients treated with precision medicine protocols.

Background: The purpuse was to investigate the influence induced by magnesium chloride (MgCl2) and zinc gluconate (ZnG) supplementation on liver and kidney injuries experimentally-induced with acetaminophen (AAPh) and potentiated by ciprofloxacin addition in rats. Material and methods: The experiment was performed on 5 animals groups: group 1 – control, treated for 6 weeks with normal saline, 1mL/kg; group 2 – AAPh, treated for 6 weeks with AAPh, 100 mg/ kg/day; group 3 – AAPh+C, treated AAPh 100 mg/kg/ day for 6 weeks and ciprofloxacin, 50mg /kg/day, only in the last 14 days of the experiment; group 4 – AAPh+C+Mg, with the same treatment as group 3, but, and in the last 14 days, MgCl2 10 mg/ kg/day was added; group 5 – AAPh+C+Zn, with the same treatment as group 3, but, and in the last 14 days, zinc gluconate (ZnG),10 mg/kg/day was added. All administrations were performed by oral gavage. At the end of the experiment, the animals were sacrificed and blood samples were collected for biochemistry examinations. Results: Treatment with AAPh for 6 weeks determined an alteration of the liver function (increases in alanine aminotransferase, aspartate aminotransferase, lactic dehydrogenase and, and gamma-glutamyl transferase) and of renal function (increases in serum urea and creatinine) (P < 0.001 group 2 vs. group 1 for all mentioned parameters). Also, the antioxidant defense capacity was impaired in group 2 vs. group 1 (superoxide dismutase and glutathione peroxidase activity decreased in group 2 vs. group 1, 0.001 < P < 0.01, respectively 0.01 < P < 0.05). The addition of ciprofloxacin, 50 mg/kg/day during the last 14 days resulted in further increases in alkaline phosphatase, alanine aminotransferase, aspartate aminotransferase, urea and creatinine (0.01 < P < 0.05, group 3 vs. group 2). MgCl2 provided a slight protection against liver enzymes increase and a more pronounced protection against the increase in serum urea and creatinine (0.001 < P < 0.01 group 4 vs. group 3). MgCl2 provided a slight protection against the decrease in superoxide dismutase (0.01 < P < 0.05 group 4 vs. group 3), but not against decrease of glutathione peroxidase. The improvement of mentioned parameters could also be seen in case of ZnG, to a higher extent especially in the case of alanine aminotransferase and lactic dehydrogenase (0.01 < P < 0.05 group 5 vs. group 4). Conclusions: This study represents a furthermore proof for beneficial effect of magnesium and zinc salts against toxicity induced by different agents, including antibacterials added to the analgesic and antipyretic acetaminophen; the protection is proven on the liver and kidney’s function; the antioxidant profile improvement has a key role, especially in case of zinc gluconate.

YOLOv8, as an efficient object detection method, can swiftly and precisely identify objects within images. However, traditional algorithms encounter difficulties when detecting small targets in remote sensing images, such as missing information, background noise, and interactions among multiple objects in complex scenes, which may affect performance. To tackle these challenges, we propose an enhanced algorithm optimized for detecting small objects in remote sensing images, named HP-YOLOv8. Firstly, we design the C2f-D-Mixer (C2f-DM) module as a replacement for the original C2f module. This module integrates both local and global information, significantly improving the ability to detect features of small objects. Secondly, we introduce a feature fusion technique based on attention mechanisms, named Bi-Level Routing Attention in Gated Feature Pyramid Network (BGFPN). This technique utilizes an efficient feature aggregation network and reparameterization technology to optimize information interaction between different scale feature maps, and through the Bi-level Routing Attention (BRA) mechanism, it effectively captures critical feature information of small target objects. Finally, we propose the Smooth Mean Perpendicular Distance Intersection over Union (SMPDIoU) loss function. The method comprehensively considers the shape and size of detection boxes, enhances the model's focus on the attributes of detection boxes and provides a more accurate bounding box regression loss calculation method. To demonstrate our approach's efficacy, we conduct comprehensive experiments across the RSOD, NWPU VHR-10, and VisDrone2019 datasets. The experimental results show that the HP-YOLOv8 achieves 95.11%, 93.05%, and 53.49% in the mAP@0.5 metric, and 72.03%, 65.37%, and 38.91% in the more stringent mAP@0.5:0.95 metric, respectively.

Neurological involvement following COronaVIrus Disease 19 (COVID-19) is thought to have a neuroinflammatory etiology. Co-ultraPEALut (an antiinflammatory molecule) and luteolin (an antioxidant) yielded promising results as neuroinflammation antagonists. This study aimed at describing cognitive impairment in post-COVID patient treated with co-ultraPEALut. Montreal Cognitive Assessment (MoCA), Prospective-Retrospective Memory Questionnaire (PRMQ), Fatigue Severity Scale (FSS) and a subjective evaluation were administered at baseline and after 10 months. Co-ultraPEALut-treated patients were retrospectively compared with controls. 26 co-ultraPEALut-treated patients showed significant improvement in PRMQ (T0: 51.94±10.55, T1: 39.67±13.02, p

In this paper, carbon nanotubes modified screen-printed electrode (CNTs/SPE) was prepared and the CNTs/SPE was employed for the electrochemical determination of antioxidant substance Chlorogenic acids (CGAs). A pair of well-defined redox peak of CGA was observed at the CNTs/SPE in 0.10 mol∙L^-1 acetic acid-sodium acetate buffer (pH 6.2) and electrode process is adsorption-controlled. Cyclic voltammetry (CV) and differential pulse voltammetry (DPV) method for the determination of CGA were proposed based on the CNTs/SPE. Under the optimal conditions, the proposed method exhibited linear ranges from 4.73×10^-7 to 4.45×10^-5 mol∙L^-1, the linear regression equation was Ipa(µA) = 4.1993 C (mol∙L^-1)+1.1039 (r = 0.9976) and the detection limit for CGA could reach 3.25×10^-6 mol∙L^-1. The recovery of matrine was 94.74~106.65% (RSD = 2.92%) in coffe beans. The proposed method is quick, sensitive, reliable, and can be used for the determination of CGA.

The current study was designed to investigate the anti-inflammatory effect of salidroside (SDS) and the underlying mechanism by using lipopolysaccharide (LPS)-stimulated RAW 264.7 macrophages in vitro and a mouse model of binge drinking induced liver injury in vivo. SDS downregulated protein expression of toll-like receptor 4 (TLR4) and CD14. SDS inhibited LPS-triggered phosphorylation of LPS-activated kinase 1 (TAK1), p38, c-Jun terminal kinase (JNK), and extracellular signal-regulated kinase (ERK). Degradation of IκB-α and nuclear translocation of nuclear factor (NF)-κB were effectively blocked by SDS. SDS concentration-dependently suppressed LPS mediated inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2) protein levels, as well as their downstream products, NO. SDS significantly inhibited protein secretion of interleukin (IL)-1β. Additionally C57BL/6 mice were orally administrated SDS for continuous 5 days, followed by three gavages of ethanol every 30 min. Alcohol binge drinking caused the increasing of hepatic lipid accumulation and serum transaminases levels. SDS pretreatment significantly alleviated liver inflammatory changes and serum transaminases levels. Further investigation indicated that SDS markedly decreased protein level of IL-1β in serum. Taken together, these data implied that SDS inhibits liver inflammation both in vitro and in vivo, and may be a promising candidate for the treatment of inflammatory liver injury.

The Universe is treated as an unbound thermodynamic system in which two forces of Nature compete: Attractive gravity loss and repulsive entropic gain. Entropic gain, although not presently considered a force of Nature, is otherwise well known and can be expressed with the gas laws. The Universe at the time of last scattering was all gas. Its instant gain is expressed as gas pressure. When the Gibbs equation is properly applied to this unbound gas, the resulting atomic Hubble parameter is found to be exclusively dependent on baryon density, giving a constant and perpetual two-to-one gain/loss ratio. Instant loss to gravity cannot offset gas pressure in this “thermal equation”. By contrast, the Friedmann equation, a ΛCDM foundation, treats baryon mass as accreted, having little if any entropic gain. These two equations are actually equivalent. Expanding accreted matter at its comoving critical density is herein shown to behave exactly like a freely expanding gas. However, neither equation includes all of today’s Universal kinetic energy, and cannot by themselves fully account for observed stellar movement. The present paper proposes the plausible existence of highly energetic or “suprathermal” free electrons which comprise about half of the intergalactic medium’s kinetic total. It is this suprathermal energy which Λ expresses. The fluid equation, another ΛCDM foundation, eliminates entropic gain from the Gibbs equation. Additionally, rest mass’s Einstein energy E=Mc² is treated as a thermal variable. These two assumptions, isoentropy and energy conflation, led astronomers to populate Λ as they followed the prior art.

Prenylated flavonoids of Humulus lupulus L. have gained attention for their potential health benefits. In this study, Amber ALE and Russian Imperial Stout beers were prepared. In order to increase the content of prenylated flavonoids in beer, there was added ethanol extract of Polaris hops, they were dry hopping with Polaris hops or pure xanthohumol was added. In unfiltered beers, the highest total content of polyphenols (3.60 ± 0.32 g/L gallic acid) was determined on the eighth day of fermentation and maturation in Amber ALE beer without the addition of extract. The highest total antioxidant capacity was determined in Russian Imperial Stout beer with the addition of xanthohumol on the eighth day of fermentation and maturation (0.675 ± 0.008 mmol/L Trolox). This type of beer also had a high antioxidant efficiency (85.96 - 88.00%). Xanthohumol (2.05 mg/L) and isoxanthohumol (2.819 mg/L) were detectable only until the thirteenth day of fermentation and beer maturation. Therefore, the beers were filtered, and the ethanol extract of the Polaris was added to them for 24 hours. This procedure was used to prepare a functional beer of the Amber ALE type (15.210 ± 0.053 mg xanthohumol in 1 L and 0.922 ± 0.077 mg isoxanthohumol in 1 L) and a functional beer of the Russian Imperial Stout type (32.375 ± 0.032 mg xanthohumol in 1 L and 1.877 ± 0.043 mg isoxanthohumol in 1 L).

This study explores novel techniques to improve the detection accuracy of skarn iron deposits using advanced image processing methodologies. Leveraging the capabilities of Aster image, Band Ratio (BR) images, and Principal Component Analysis (PCA), alongside the power of 3D Convolutional Neural Networks (3D-CNN), the research aims to enhance the precision and efficiency of ore detection in complex geological environments. The proposed method employs a specific 3D-CNN architecture, accepting input as a 7×7×C image patch, where C represents the combined number of selected Aster image bands, Principal Component (PC) bands, and computed BR images. To evaluate the accuracy of the proposed method, five distinct image bands combinations, including the proposed bands combination, were tested and evaluated based on the overall accuracy (OA), average accuracy (AA), and kappa coefficient. The results demonstrated that while the incorporation of BR images alongside Aster bands initially seemed promising, it introduced significant confusion in certain classifications, leading to unexpected mis-classification rates. Surprisingly, utilizing solely Aster bands as input parameters yielded higher accuracy rates (OA= 93.13%, AA = 91.96%, Kappa = 90.91%) compared to scenarios involving the integration with band ratios (OA = 87.02%, AA = 79.15, and Kappa = 82.60%) or the integration of BR images to PC bands (OA= 87.78%, AA = 82.39%, Kappa = 83.81%). However, the amalgamation of Aster bands with selected PC bands showed slight improvements in accuracy (OA= 94.65%, AA = 92.93%, Kappa = 93.45%), although challenges in accurately classifying certain features persisted. Ultimately, the proposed combination of Aster bands, PC bands, and BR images (proposed bands combination) presented the most visually appealing and statistically accurate results (OA = 96.95%, AA= 94.87%, and Kappa = 95.93%), effectively addressing misclassifications observed in the other combinations. These findings underscore the synergistic contributions of each of the Aster bands, PC bands, and BR images, with Aster bands proving pivotal for optimal skarn classification, PC bands enhancing intrusions classification accuracy, and BR images strengthening wall rock classification accuracy. In conclusion, the proposed combination of input image bands emerges as a robust and comprehensive methodology, demonstrating unparalleled accuracy in the remote sensing detection of skarn iron minerals.

(1) Background: Alzheimer’s disease (AD) presents a significant challenge in terms of discovering novel diagnostic methods and therapies. Previous research has shed light on the neuroprotective properties of Apelin-13 in neurodegenerative disorders. However, elucidating the mechanism underlying its efficacy in combating AD-related nerve injury is imperative. In this study, we aimed to investigate the mechanism of action of Apelin-13 in an in vivo model of AD induced by streptozocin (STZ); (2) Methods: We utilized an STZ-induced nerve injury model of AD in mice to investigate the effects of Apelin-13 administration. The Apelin-13 was administered intranasally, and cognitive impairment was assessed using standardized behavioral tests. The main methods included behavioral assessment, histological analysis, and biochemical assays to evaluate synaptic plasticity and oxidative stress signaling pathways; (3) Results: Our findings indicate that intranasal administration of Apelin-13 ameliorated cognitive impairment in the STZ-induced AD model. Furthermore, we observed that this effect was potentially mediated through the enhancement of synaptic plasticity and attenuation of oxidative stress signaling pathways; (4) Conclusions: The results of this study suggest that intranasal administration of Apelin-13 holds promise as a therapeutic strategy for preventing neurodegenerative diseases such as AD. By improving synaptic plasticity and mitigating oxidative stress, Apelin-13 may offer a novel approach to neuroprotection in AD and related conditions.

To investigate the intricate dynamics of brain activity when interacting with a dynamic environment, it is imperative to continuously generate and update expectations regarding forthcoming events and their corresponding sensory and motor responses. This study aims to explore the interconnectivity in time perception across predictable and unpredictable conditions. The necessary data for this study were acquired from EEG signals, sourced from an existing database that involved an experiment conducted on a group of healthy participants. The individuals were subjected to two distinct conditions, predictable and unpredictable, encompassing various time delays. The functional connectivity between brain regions was estimated using a method known as the phase lag index. This method was employed to discern disparities in time perception between the two conditions. A comprehensive comparison of the two conditions across different delays demonstrated noteworthy variations, particularly in the gamma, beta, and theta frequency bands. The differences between delays were more pronounced in the predictable condition. Subsequently, an in-depth analysis was conducted to scrutinize the dissimilarities between the conditions within each delay. Notably, significant differences were observed across all delays. In the unpredictable condition, an increase in connectivity was detected within the alpha band during the 400-ms delay, specifically between occipital and temporal regions. Moreover, the mean connectivity in the unpredictable condition surpassed that of the predictable condition. In the delta band, distinct connectivity patterns were observed across different delays, involving connections between central and frontal regions. Specifically, a heightened connectivity between central and prefrontal regions was noted during the 83-ms delay. Notably, the right hemisphere of the prefrontal cortex played a vital role in time perception. Furthermore, a decline in connectivity across the delta, theta, and beta bands was observed in both conditions during the longest delay (800 ms) when compared to other delays

Background: Critical states present scale-free dynamics, optimizing neuronal complexity. We explored electroencephalogram (EEG) criticality in amnesic Mild Cognitive Impairment (aMCI) patients with clinical improvement in working memory, verbal memory, verbal fluency and overall executive functions after the completion of a 6-month prospective memory training. (2) Methods: We compared “before” and “after” stationary resting-state EEG records of right-handed MCI patients (n = 17; 11 females), using the method of critical fluctuations and Haar wavelet analysis. (3) Results: Significant criticality enhancement was present in frontotemporal electrodes [mean dif: 0.10; Z = 7, p = .019] and isolated electrodes T6 [mean dif: 0.204; t (10) = - 2.3, p = .044] and F4 [mean dif: 0.0194; t (10) = - 2.82; p = .018]. (4) Conclusions: EEG criticality agreed with clinical improvement, consisting a possible monitoring biomarker in MCI and Alzheimer’s disease. Further evaluation is needed in patients under cognitive training or even drug-modifying therapies.

One of the most prevalent illnesses, diabetes does not directly result in patient mortality. But, it increases the risk of death. Any disease that may be predicted in its early stages can lessen its fatal effects while also enhancing the quality of the healthcare system. For the early-stage prediction of diabetes or such types of non-communicable diseases, we need the proper set of influential features. This research has developed a machine learning-based disease prediction model to identify the influential features for diabetes prediction and give a near-perfect classification accuracy. This model includes Min-Max normalization for data normalization, Isolation Forest (iForest) for outlier removal, Synthetic Minority Oversampling Technique (SMOTE) for oversampling, Random Forest based Recursive Feature Elimination (RFE-RF) test, Chi-Square test, and Minimum Redundancy Maximum Relevancy (mRMR) test based feature selection methods for identifying the influential features, and Support Vector Machine (SVM), K Nearest Neighbor (KNN), and Naive Bayes (NB) for the classification. The results clarify that the proposed model outperforms the previous models and studies. The SVM has attained an accuracy of 99.58% in classification using the five features chosen from the Chi-Square test. Lastly, SHAP, an explainable AI model, has been used to assess the classifier model’s performance. These selected features and the classifier model can be used for early-stage diabetes prediction.

Involving industrial waste into economic circulation, in particular flotation waste from coal enrichment, is an urgent task. The coal preparation waste of ArcelorMittal Temirtau JSC was studied with a view to the possibility of using it as an alternative substitute for coke breeze in the sintering process. During a thermoanalytical study on a synchronous thermal analyzer STA 449 NETZSCH in dynamic mode with a heating rate of 10, 20, 30 °C/min in an air flow of 30 cm³/min in the temperature range 30–1000 °C, we determined the lower heat of combustion of carbon-containing material, change mass during calcination, change and rate of change in the mass of the sample, thermal effect of the process, the temperature of the beginning, end and maximum development of thermal effects. It has been established that the greatest mass loss is observed in the temperature range 580 °C- 610 °C, which is about 65–67% of the total mass of the organic component of coal. The maximum burnout rate corresponds to a temperature of 540–563 °C. The lowest calorific value of the studied waste was 15.9 MJ/kg. The results of thermal analytical research and technical analysis substantiate the possibility of using flotation waste in a mixture of up to 10% with coke breeze for combustion in the sintering layer for the production of sinter. Analysis of the composition of flotation waste showed that they have a high specific surface area (56.8∙10³ m²/kg), are highly dispersible, soggy, and capable of swelling, and the predominant amount of clay substance makes them a plastic material. The possibilities of using coal flotation waste as an alternative substitute for coke breeze have been studied after the regimes of its preparation for sintering have been developed and a rational regime of layer-by-layer combustion in the sintering layer has been established. The optimal ratio of coke and OPC in the fuel mixture of the sintering charge was determined.

The goal of the project was to understand how topography/bathymetry is imported and used in GeoFlood with an aim towards improving efficiency and computational cost of bathymetry handling in GeoFlood.GeoFlood is a software to simulate overland flooding basing on shallow water equations.Numerical methods and programming were used for this study.The GeoFlood was ran on a given data set (Malpasset data) and it was found out that indeed topography handling during simulations has to be improved to minimise the computational time and also improve the efficiency.Through this research, we also looked at the the shallow water equations, and the numerical techniques used to solve the equations

Species of the genus Drosophila have served as favorite models in speciation studies; however, genetic factors of interspecific reproductive incompatibility are underinvestigated. Here we performed the analysis of hybrid female sterility by crossing Drosophila melanogaster females and Drosophila simulans males. Using transcriptomic data analysis, molecular, cellular, and genetic approaches, we analyzed differential gene expression, transposable element (TE) activity, piRNA biogenesis, and functional defects of oogenesis in hybrids. Premature germline stem cell loss was the most prominent defect of oogenesis in hybrid ovaries. Due to the differential expression of genes encoding piRNA pathway components, rhino and deadlock, the functional RDCmel complex in hybrid ovaries was not assembled. However, the activity of the RDCsim complex was maintained in hybrids, independent of the genomic origin of piRNA clusters. Despite the identification of a cohort of overexpressed TEs in hybrid ovaries, we found no evidence that their activity can be considered the main cause of hybrid sterility. We revealed a complicated pattern of Vasa protein expression in the hybrid germline, including partial AT-chX piRNA targeting of the vasasim allele and a significant zygotic delay in vasamel expression. We arrived at the conclusion that the hybrid sterility phenotype was caused by intricate multi-locus differences between the species.

This study aimed to investigate the relationship between sleep duration, dementia severity, age, and ANS regulation. This cross-sectional pilot study included 27 older patients with probable Alzheimer’s disease, hospitalized at a psychiatric center. We measured heart rate variability to assess ANS regulation at night, evaluated dementia severity using the Clinical Dementia Rating scale, and obtained sleep duration data from sleep diaries maintained by psychiatric nurses. The data were analyzed using repeated-measures generalized linear models with age, sex, dementia severity, and medication (antipsychotics) as covariates. A sleep duration of 7–8 h per night was associated with better ANS regulation than other sleep durations, as indicated by sympathovagal balance (p = 0.005). ANS dysregulation was characterized by a 20.7% increase in sympathetic nervous system activity and a 14.8% decrease in parasympathetic nervous system (PSNS) activity. We found a significant correlation between dementia severity and PSNS activity (p = 0.04) and observed an inverted U-shaped association between sleep duration and ANS regulation. This pilot study demonstrated that a sleep duration of 7–8 h per night may be critical in maintaining optimal ANS regulation. Bidirectional associations between sleep duration, inadequate sleep duration, aging, and ANS regulation warrant further investigation.

The aim of this study was to examine the oxidation of human serum albumin (HSA) caused by oxidative stress after an exhaustive exercise such as ultra-marathon race. Thus, blood samples from 12 adult runners who underwent a 103 km mountain ultra-marathon race were collected pre- and 24, 48 and 72 h post race. HSA was partially purified using affinity chromatography and then was subjected to Western blot analysis for disulfide dimers determination, indicating oxidation. The results were correlated with those from a previous study in which the same samples were analyzed using different oxidative stress markers and a good correlation with protein carbonyls (PC) at all time points was observed. Moreover there was a significant correlation with static oxidation-reduction potential (sORP) at 24 h, and a negative correlation with capacity oxidation-reduction potential (cORP) at 24 and 48 h. In addition, an individual analysis of albumin dimers exhibited great inter-individual differences. This inter-individual variability in the oxidation of HSA may suggest different interventions (e.g. through diet) in order to confront the effects on athletes’ organism after a strenuous exercise. In conclusion, this study supported the importance of the assessment of albumin dimers as a predictive marker for exercise-induced oxidative stress.

Accurate classification of objects in 3D point clouds is a significant problem in several applications, such as autonomous navigation and augmented/virtual reality scenarios, which has become a research hot spot. In this paper, we presented a deep learning strategy for 3D object classification in augmented reality. The proposed approach is a combination of the GRU and LSTM. LSTM networks learn longer dependencies well, but due to the number of gates, it takes longer to train; on the other hand, GRU networks have a weaker performance than LSTM, but their training speed is much higher than GRU, which is The speed is due to its fewer gates. The proposed approach used the combination of speed and accuracy of these two networks. The proposed approach achieved an accuracy of 0.99 in the 4,499,0641 points dataset, which includes eight classes (unlabeled, man-made terrain, natural terrain, high vegetation, low vegetation, buildings, hardscape, scanning artifacts, cars). Meanwhile, the traditional machine learning approaches could achieve a maximum accuracy of 0.9489 in the best case.

Background: Sexual dysfunction is the most common side effect encountered by cervical cancer patients during and after treatment, and rehabilitation strategies are needed to improve their quality of life. A variety of rehabilitation strategies have been reported in previous studies, varying by country, race and even continent. However, the common rehabilitation strategies used by cervical cancer survivors in Tanzania and their outcomes are unknown. The study aimed to unfold sexual rehabilitation strategies and outcome practiced by cervical cancer survivors who experienced sexual dysfunction during and after treatment in North-western Tanzania. Methods: Descriptive qualitative study were employed. Phenomenology study design used with a sample of 7 participants and data corrected via in-depth interview guided by 11 questions. Audiotaped and transcribed to get main themes and sub themes through deductive analysis. Results: Most participants reported inability to achieve an orgasm and low libido and dyspareunia after treatment. Most participants were eagerly to have their sexual activity recovered. However, they were not familiar with any rehabilitation strategy after the experience of sexual dysfunction. Even though, some of the participants attempted various interventions, but all were unsatisfactory leading to a significant decline in sexual interest and activity. Conclusion: Cervical cancer survivors are not familiar with any sexual rehabilitation strategies. Health education concerning various sexual rehabilitation strategies should be provided to survivors during and even after treatment.

Abstract: Brassinosteroids (BR) are essential plant hormones that play a crucial role in regulating plant growth and development, as well as their resistance to abiotic and biotic stresses, including pathogen infections. In the context of plant im-munity, BR influence various processes such as the expression of genes related to pathogen defense, the production of antimicrobial compounds, and the re-inforcement of the plant cell structure. The aim of work is understand the role of BR in plant defense against abiotic and biotic stresses. BR are involved in the regulation of metabolic processes, water economy, and photosynthesis, contributing to increased plant resistance to abiotic stresses. The study discusses various mechanisms of BR action on plant immunity, including the regulation of gene expression related to defense, activation of the plant defense system, in-teraction with other plant hormones, regulation of water and salt balance, and influence on photosynthetic processes. In the context of plant immunity, BR influence various processes, including the expression of genes related to pathogen defense, the production of antimicrobial compounds, and the reinforcement of the plant cell structure. Additionally, BR are involved in the regulation of metabolic processes, water economy, and photosynthesis, con-tributing to increased plant resistance to abiotic and biotic stresses. The paper emphasizes the importance of further research on the role and mechanisms of action of BR in plant immunity, which may contribute to the development of more effective strategies for the use of BR in agriculture, horticulture, and plant protection.

In this paper, we introduce a novel controlled quantum communication protocol utilizing a quantum random walk involving one sender, one receiver, and multiple controllers. Inspired by classical random walk theory, quantum random walk serves as the foundation of our proposed protocol. With this protocol, we demonstrate the ability to transfer any n- dimensional quantum state to any party facilitated by any m number of controllers. Furthermore, any of the (m+1) individuals has the freedom to accept the role of the receiver. Rigorous testing of the protocol’s performance is conducted through quantum state tomography. We have run different variations of the circuits in IBMQ QASM simulator. Additionally, we explore the effectiveness of a weak measurement-based protocol designed to mitigate amplitude-damping noise’s detrimental effects on quantum states. By analyzing fidelity versus amplitude damping noise-strength plots for scenarios with and without the weak measurement protocol, we provide valuable insights into its protective capabilities across various levels of noise. These find- ings illuminate the protocol’s potential applications in quantum information processing

Introduction: Migraine is a highly disabling neurological condition with substantial variability in its clinical presentation. This study aimed to explore the influence of familial transmission of comorbid medical conditions on the phenotype of chronic and episodic migraine. Methods: A total of 500 patients who presented to a private tertiary headache clinic were categorized into episodic and chronic migraine groups based on the International Classification of Headache Disorders-3 criteria. Data on demographics, clinical symptoms, and familial medical history were collected. Statistical analysis included chi-square and independent sample t-tests to evaluate the impact of familial comorbidities on migraine phenotypes. Results: Of the participants, 63% were diagnosed with episodic migraine and 37% with chronic migraine. Significant findings included higher rates of medication overuse (63.31%) and allodynia (58.82%) in patients with chronic migraine. Notably, patients with a maternal history of hypercholesterolemia showed a higher prevalence of chronic migraine (78.6%), whereas those with both parents affected tended to have episodic migraine. Educational levels are inversely correlated with the prevalence of chronic migraine, suggesting a socioeconomic influence on migraine chronification. Discussion: This study highlights significant associations between familial health conditions and migraine phenotypes. The findings suggest that the maternal transmission of comorbidities, particularly hypercholesterolemia, may influence the development of chronic migraine. These results underscore the importance of considering familial medical history in the management and understanding of migraine.

This review explores the nuanced decision-making surrounding postoperative radiotherapy for diffuse low-grade glioma (DLGG) patients, emphasizing the need for personalized treatment to balance the benefits of tumor control with the risks of long-term side effects. Radiotherapy is a cornerstone of DLGG treatment, offering significant improvements in progression-free survival, but its potential for cognitive decline and vascular lesions necessitates careful consideration of individual patient factors. The review delves into the complexities of radiotherapy timing, revealing that while early radiotherapy enhances progression-free survival, it does not significantly impact overall survival and may lead to earlier onset of cognitive decline. Conversely, delayed radiotherapy may mitigate cognitive decline but may result in shorter progression-free survival and the onset of neurological symptoms. The review advocates for a patient-centered approach, emphasizing the importance of weighing individual risks and benefits, and highlights the potential of artificial intelligence in predicting cognitive decline and the need to revisit previous research findings in light of the 2021 WHO (World Health Organization) classification update, underscoring the evolving nature of DLGG treatment and the ongoing quest for optimal therapeutic strategies.

Intraprocedural rupture (IPR) is a severe complication of coil embolization (CE) for intracranial aneurysms, yet the clinical and hemodynamic features of rupture risk areas remain unclear. From January 2013 to December 2023, 435 saccular cerebral aneurysms (316 unruptured, 119 ruptured) underwent CE at our institution. Included were cases where extravasation or coil protrusion occurred during CE. The rupture point was determined from postoperative data, and computational fluid dynamics (CFD) analysis was conducted to assess hemodynamic features. IPR occurred in six aneurysms (1.3%; three ruptured, three unruptured; dome size: 4.7±1.8 mm, D/N: 1.5±0.5, locations: four internal carotid artery [ICA], one anterior cerebral artery, one middle cerebral artery). ICA aneurysms were treated using adjunctive techniques (three balloon-assisted, one stent-assisted). Two aneurysms (M1M2 and A1) were treated simply, yet had relatively small and misaligned domes. CFD analysis identified the rupture point as a flow impingement zone with maximum pressure (Pmax) in five aneurysms (83.3%). Time-averaged wall shear stress (WSS) was locally reduced around this area (1.3±0.7 [Pa]). Hemodynamically unstable areas indicated fragile, thin walls with rupture risk. Microcatheter insertion along the inflow zone, directed towards the caution area, was practiced. The presence of a flow impingement zone with Pmax and low WSS suggests potential aneurysm rupture in CE, particularly with adjunctive techniques. Small aneurysms, especially those with axial misalignment, warrant careful consideration due to the risk of excessive flow impact stress on the aneurysm wall.

The blood-brain barrier (BBB) acts as a structural and functional barrier for brain homeostasis. This review highlights the pathological contribution of BBB dysfunction to neuroimmunological diseases, including multiple sclerosis (MS), neuromyelitis optica spectrum disorder (NMOSD), myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD), autoimmune encephalitis (AE) and paraneoplastic neurological syndrome (PNS). The transmigration of massive lymphocytes across the BBB caused by the activation of cell adhesion molecules is involved in the early phase of MS, and dysfunction of the cortical BBB is associated with the atrophy of gray matter in the late phase of MS. In the onset of NMOSD, increased permeability of the BBB causes the entry of circulating AQP4 autoantibodies into the central nervous system (CNS). Recent reports have shown the importance of glucose-regulated protein (GRP) autoantibodies as BBB-reactive autoantibodies in NMOSD, inducing antibody-mediated BBB dysfunction. BBB breakdown has also been observed in MOGAD, NPSLE, and AE with anti-NMDAR antibodies. Our recent report demonstrated the presence of GRP78 autoantibodies in patients with MOGAD and the molecular mechanism responsible for GRP78 autoantibody-mediated BBB impairment. Disruption of the BBB may explain the symptoms in the brain and cerebellum in the development of PNS, as it induces the entry of pathogenic autoantibodies or lymphocytes into the CNS by autoimmunity against tumors in the periphery. GRP78 autoantibodies were detected in paraneoplastic cerebellar degeneration and Lambert-Eaton myasthenic syndrome and were associated with cerebellar ataxia with anti-P/Q type voltage-gated calcium channel antibodies. This review reports that therapies affecting the BBB that are currently available for disease-modifying therapies for neuroimmunological diseases have the potential to prevent BBB damage.

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.

A facilitative transport system exists on the blood brain barrier (BBB) that has been tacitly assumed to be a path for glutamate entry to brain. But glutamate is a non-essential amino acid whose brain content is much greater than plasma, and studies in vivo show that glutamate does not enter brain in material quantities except in those small regions with fenestrated capillaries (circumventricular organs). The situation became understandable when luminal (blood facing) and abluminal (brain facing) membranes were isolated and studied separately. Facilitative transport of glutamate and glutamine exist only on the luminal membranes whereas Na+-dependent transport systems for glutamate, glutamine and some other amino acids are present only on the abluminal membrane. The Na+-dependent cotransporters of the abluminal membrane are in a position to actively transport amino acids from the extracellular fluid (ECF) into the endothelial cells of the BBB. These powerful secondary active transporters couple the energy of the Na+-gradient to move glutamate and glutamine into the ECF whereupon glutamate can exit to blood on the luminal facilitative glutamate transporter. Glutamine may also exit brain on a separate facilitative transport system that exists on the luminal membranes or glutamine can be hydrolyzed to glutamate within the BBB thereby releasing ammonia that is freely diffusible. The γ-glutamyl participate cycle participates indirectly by producing oxoproline (pyroglutamate) that stimulates almost all secondary active transporters yet discovered in the abluminal membranes of the BBB.

Background: This study investigated the incidence, characteristics and functional outcomes associated with unplanned Acute Care Unit Readmissions (ACUR) during inpatient traumatic brain injury (TBI) rehabilitation in an Asian cohort. Methods: Retrospective review of electronic medical records from single rehabilitation unit was conducted from 1 Jan 2012 to 31 Dec 2014. Inclusion criteria were first TBI, aged &gt;18 years, admitted &lt; 6 months of TBI. ACUR were characterized into either of neurological, medical or neurosurgical subtypes. The main outcome measure was discharge Functional Independence Measure (FIM). Secondary outcomes included rehabilitation length of stay (RLOS). Results: Of 121 eligible TBI records, the incidence of ACUR was 14% (n=17), comprising neurologic (76.5%) and medical (23.5%) subtypes, occurring at median of 13 days (IQR 6, 28.5) after rehabilitation admission. Patients without ACUR had significantly higher admission mean (SD) FIM score compared to those with ACUR (FIM ACUR-negative 63.4 (21.1) vs FIM ACUR-positive 50.53(25.4), P =0.026). Significantly lower discharge FIM was noted in those with ACUR compared to those without. (FIM ACUR-positive 65.8(31.4) vs FIM ACUR-negative 85.4 (21.1), P = 0.023) Furthermore, a significant near-doubling of RLOS was noted in ACUR patients compared to non-ACUR counterparts [ACUR-positive median 55 days (IQR 34.50, 87.50) vs ACUR-negative median 28 days (IQR 16.25, 40.00), P = 0.002]. Conclusion: This study highlights the significant negative functional impact and lengthening of rehabilitation duration of ACUR on inpatient rehabilitation outcome for TBI.

Based on functional magnetic resonance imaging and multilayer dynamic network model, the quantified temporal stability of brain network has shown potentials in predicting altered brain functions. The present review focuses on summarizing current knowledge on the commonly-used measures of brain network’s temporal stability and the clinical research progress on them. There are a variety of widely used measures of temporal stability such as the variance/standard deviation of dynamic functional connectivity strengths, the temporal variability, the flexibility (switching rate), and the temporal clustering coefficient, while there is no consensus to date that which measure is the best. The temporal stability of human brain networks may be associated with several factors such as sex, age, cognitive functions, head motion, and data preprocessing/analyzing strategies, which should be considered in the clinical studies. Multiple common psychiatric diseases such as schizophrenia, major depressive disorder, and bipolar disorder have been found to be related to altered temporal stability, especially during the resting state; generally, both excessively decreased and increased temporal stabilities were thought to reflect disease-related brain dysfunctions. However, the measures of temporal stability are still far from applications in clinical diagnoses for neuropsychiatric diseases partly because of the divergent results, and further studies are warranted.

The nature of the interaction between Th17 cells and the blood-brain barrier (BBB) is critical for the development of autoimmune inflammation in the central nervous system (CNS). TNF-a or IL-17 stimulation is known to enhance the adherence of Th17 cells to the brain endothelium. The brain endothelial cells (bEnd.3) express VCAM-1, the receptor responsible for inflammatory cell adhesion, which binds VLA-4 on migrating effector lymphocytes at the early stage of brain inflammation. The present study examines the effect of the pro-inflammatory cytokines TNF-a and IL-17 on the adherence of Th17 cells to bEnd.3 The bEnd.3 cells were found to increase production of CCL2 and CXCL1 after stimulation by pro-inflammatory cytokines, while CCL2, CCL5, CCL20 and IL17 induced Th17 cell migration through a bEnd.3 monolayer. This interaction between Th17 cells and the brain endothelium appears to be mediated by VCAM-1 and some chemotactic cytokines. This observation may suggest potential therapeutic targets for the prevention of autoimmune neuroinflammation development in the CNS.

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.

Spermatogenesis is critical for insect reproduction and is regulated by many different genes. In this study, we found that Forkhead transcription factor Fd59a functions as a key factor in spermatogenesis of Drosophila melanogaster. Fd59a contains a conversed Forkhead domain, and it is clustered to the FoxD subfamily with other FoxD members from some insect and vertebrate species. Mutation in Fd59a caused swelling in the apical region of the testis. More importantly, only a few mature sperm were present in the seminal vesicle of Fd59a mutant flies compared to the control flies, and the fertility of Fd59a2/2 mutant males was significantly lower than that of the control flies. Immunofluorescence staining showed that the homeostasis of testis stem cell niche in Fd59a2/2 mutant and Fd59a RNAi flies was disrupted, and apoptosis of sperm bundles was increased. Furthermore, results from RNA-sequencing and qRT-PCR suggest that Fd59a can regulate expression of genes related to reproductive process and cell death. Taken together, our results indicated that Fd59a plays a key role in the spermatogenesis of Drosophila.