Abstract: SARS-COV-2 infection has emerged worldwide. To reduce the number of cases and limit the transmission of the virus, health, and local authorities have implemented several strategies. Mass screening is a key strategy for mitigating the damage caused by this pandemic. This strategy is based on the use of qRT-PCR and pooling to diagnose SARS-COV-2 infection. The present work explores the performance and limitations of this strategy for the molecular diagnosis of SARS-CoV-2 infection. Three important technical aspects were retained: the comparison of two commercial extraction kits (BIGFISH & BIOER), the simulation of a non-compliant nasopharyngeal swab, and the evaluation of the pooling strategy. 97 SARS-CoV-2 positive nasopharyngeal samples were used. The comparison of the two extraction kits was based on threshold cycles (Ct) values. The results showed a significant difference (IC=95%) in Ct of the nucleocapsid gene (N; p= 0.0000384) and RNA-dependent RNA polymerase (RdRp; p=0.0254). However, no significant difference was observed between the Internal Control gene (IC; p= 0.0723) and Envelope gene (E; p = 0.150). The Ct values resulting from BIGFISH extraction kit were generally lower than those obtained from BIOER. In terms of sensitivity, the RT-qPCR technique allows for the detection of viral RNA up to 10-3 as a dilution factor. This study demonstrated that the pooling strategy is an effective diagnostic technique. Positive samples remained detectable even in pools of 1000 or even 10000 samples. However, the size of the pool under diagnostic conditions should not exceed a limit that must be dynamically adapted to prevalence to ensure economic and analytical viability.

Abstract: This study proposes a multi-scale LoRA fine-tuning recommendation algorithm based on large language models to address the limitations of traditional recommender systems in semantic understanding, feature redundancy, and parameter transfer efficiency. The method preserves the semantic representation ability of large models while achieving unified modeling of global preferences and local interests through multi-scale semantic decomposition, low-rank parameter adaptation, and cross-scale fusion mechanisms. The model first inputs user-content interaction sequences into a pre-trained language model to obtain context-aware semantic embeddings. Then, a multi-scale semantic pooling structure extracts hierarchical feature information to capture multi-granularity preference relations. Based on this, a multi-scale LoRA module performs low-rank decomposition and cross-scale alignment of weight matrices, significantly reducing parameter size and improving fine-tuning efficiency. Finally, a cross-scale attention fusion layer dynamically reconstructs global and local features to optimize recommendation ranking. Systematic experiments conducted on the MovieLens-1M dataset validate the effectiveness of the proposed method across multiple evaluation metrics. The results show that the model outperforms several baseline algorithms in Precision@K, NDCG@K, Recall@K, and Coverage, demonstrating the advantages of multi-scale structure and LoRA parameterization in enhancing recommendation accuracy, diversity, and generalization. Overall, this research provides a feasible solution for structural optimization and parameter-efficient fine-tuning of large language models in efficient recommendation tasks.

Abstract: Background: Intimate partner violence (IPV) represents a major public health problem in Europe, with significant physical, psychological, and social consequences. Nurses are often the first professionals capable of detecting early signs of IPV, yet lack validated instruments to assess their clinical competency in detection, evaluation, documentation, and intervention. This study aimed to develop and validate the Intimate Partner Violence Nursing Competency Scale (IPVNCS), aligned with the Nursing Intervention Classification (NIC 6403). Methods: A cross-sectional psychometric study was conducted among registered nurses in the Community of Madrid. A 30-item Likert-type self-administered instrument (1–5 scale) was developed based on NANDA, NIC 6403, and NOC frameworks. A total of 202 nurses participated. Reliability was assessed through Cronbach’s alpha. Construct validity was examined using exploratory factor analysis (EFA) with Promax rotation and confirmatory factor analysis (CFA) using AMOS 26. Ethical approval was obtained (CEU San Pablo, code 843/24/104). Results: After item refinement, 26 items remained across four dimensions: (1) Intervention and Referral, (2) Detection and Assessment, (3) Documentation and Recording, and (4) Psychosocial Support. The instrument showed excellent reliability (α = 0.97). KMO was 0.947 and Bartlett’s test was significant (p < 0.001). CFA demonstrated satisfactory fit: χ2/df = 2.066, RMSEA = 0.073, CFI = 0.92, TLI = 0.91, NFI = 0.86. The final model adequately represented the latent structure. The Intimate Partner Violence Competence Scale for Nurses (IPVNCS) was developed and validated. After debugging, its psychometric properties were significantly improved. Four redundant items were eliminated, achieving internal consistency (α = 0.97), a KMO value of 0.947 and a significant Bartlett’s test of sphericity. It showed a better fit, according to χ2/df= (2.066); Parsimony= (720.736); RMR (0.0529; RMSEA (0.073); NFI (0.860); TLI (0.910) and CFI (0.920). The final model provides an adequate representation of the latent structure of the data. Conclusions: The IPVNCS is a valid and reliable tool to assess nursing competencies for clinical management of IPV. It supports structured evaluation across four core nursing domains, enabling improved educational planning, clinical decision-making, and quality of care for victims. The scale fills a gap in clinical nursing assessment tools and can support protocol development in emergency, primary care, and hospital settings.

Abstract: Purpose: To investigate the long-term effects of phacoemulsification by the eight-chop technique on intraocular pressure (IOP) in patients with primary open-angle glaucoma (POAG). Methods: This study comprised the eyes of patients with cataracts who had undergone phacoemulsification and posterior chamber intraocular lens implantation. Patients with corneal disease or opacity, uveitis, pupillary dilation problem, and previous trauma or surgery were excluded. Cataract surgeries were performed using the eight-chop technique. The operative time, phaco time, aspiration time, cumulative dissipated energy, and volume of fluid used were measured intraoperatively. Best-corrected visual acuity and corneal endothelial cell density (CECD) were measured postoperatively at 7 and 19 weeks.Results: In total, 150 eyes of 98 patients were followed up. The POAG group had a CECD loss rate of 1.5% and 1.2% at 7 and 19 weeks postoperatively, respectively. The control group had a CECD loss rate of 0.8% and 1.4% at 7 and 19 weeks postoperatively, respectively. The IOP reduction rate at 1 year postoperatively was 12.7% and 12.5% in the POAG and control groups, respectively. In the subgroups with preoperative IOP below 15 mmHg, IOP decreased significantly (p<.01, paired t-test) at 7 weeks and remained significantly lower at 1 year postoperatively.Conclusions: The eight-chop technique remained effective in lowering IOP after 1 year in the POAG and control groups. This effect did not diminish in patients preoperative IOP below 15 mm Hg. Phacoemulsification using the eight-chop technique may be effective for patients with glaucoma.

Abstract: Arsenic (As) contamination threatens ecosystems and human health, and iron (hydr)oxides-mediated formation of Fe-As composites is a key strategy for arsenic immobilization, while the long-term stability of these composites under complex environmental conditions remains a critical concern. This study systematically investigated the interactive effects of environmental factors (temperature: 5-35°C, pH: 4-8, competing ions: phosphate and citrate) and material intrinsic properties (ferrihydrite aging: 0-60 days, Fe/As molar ratio: 1.875 and 5.66, adsorption time) on Fe-As composite stability using multiscale characterization techniques and theoretical modeling. Results showed that temperature was the dominant controlling factor, with arsenic release increasing by 4.25% per 1°C rise (178% higher at 35°C vs. 20°C) and an exponential relationship model established (R²=0.96). Ferrihydrite aging enhanced stability, as 60-day aged composites (Fh60d-As) exhibited minimal arsenic release (18.83%) at pH 4/20°C, attributed to increase As(V)-O-Fe binding energy (1.2 eV) and -OH group enhancement (12.5%). Phosphate induced 2.4-fold higher arsenic release than citrate, and lower pH (4-6) reduced release via enhanced protonation. A stability prediction model was developed (R²=0.91), and practical remediation strategies were proposed: maintaining temperatures below 25°C in arsenic-containing waste repositories and using pre-aged iron-based materials. This work provides quantitative benchmarks and mechanistic insights for contaminated site rehabilitation.

Abstract: Background: Lhermitte-Duclos disease (LDD), known as dysplastic cerebellar gangliocytoma, is a hamartomatous lesion that causes progressive mass effect in the posterior fossa. Cowden syndrome (CS) is a rare autosomal dominant disorder characterized by an increased risk of developing various systemic malignancies. Both conditions result from mutations in the PTEN gene, which disrupts normal cell growth and proliferation. Some children with PTEN mutations may present with developmental delay or autism spectrum disorder (ASD), potentially associated with CS. Methods: Herein, we present a rare case involving a mother with LDD/CS and her child with ASD. We discuss recommendations for screening and management of LDD/CS patients and PTEN-related ASD and provide a brief literature review. Comprehensive clinical evaluation, diagnostic imaging, and genetic analysis were performed on a female patient with LDD/CS, who underwent surgical treatment and was followed up for 10 years. Genetic testing was also conducted on her 10-year-old child, diagnosed with ASD. Results: The LDD/CS patient underwent successful partial surgical resection of the dysplastic cerebellar gangliocytoma and achieved full neurological recovery. Ten-year follow-up showed no evidence of tumor recurrence. Genetic testing in both the mother and the child confirmed PTEN gene mutations. Conclusions: This case supports a probable association between an LDD/CS-affected parent and an ASD-affected child, linked to PTEN mutations inherited in an autosomal dominant manner. Given the significant risk of malignancy and neurodevelopmental disorders associated with PTEN mutations, patients suspected of having LDD/CS and children with ASD should undergo regular screening for PTEN-related neoplasms and receive appropriate genetic counseling.

Abstract: This paper proposes an idealized, philosophical axiomatization of the absolute infinite in a meta-formal class theory, called MK\( ^{meta} \), that can be back-translated to the formal Morse--Kelley with the axiom of global choice (GC). First, class ordinals and class cardinals are introduced, which avoid the Burali-Forti paradox. Second, GC is assumed to make class cardinals well-orderable. Third, the Hamkinsian multiverse \( M_h \) is defined as the meta-formal collection of all the models \( v \) of any relatively consistent, formal theory. Fourth, a meta-formal theory is rigorously defined by ranging over all the sets \( x\in v\in M_h \). Fifth, \( V^{meta} \) is the unique model of any meta-formal theory. At last, the absolute infinite \( \Omega^{meta}_{card} \) is the proper class cardinality of \( V^{meta} \). Moreover, truth relativism can be countered in a GC-consistent branch of \( M_h \), by accepting the axioms that maximize \( V^{meta} \). Consequently, the definition of \( M_h \) can be used as a rebuttal of both height and width potentialism, when combined with the argument that only the meta-formal level can capture the entire mathematical reality.

Abstract:

Musculoskeletal disorders (MSDs), arising from cumulative exposure to occupational tasks like repetitive motions and heavy lifting, constitute a debilitating health crisis and economic burden across the U.S. workforce. This study aimed to identify and analyze the job-related determinants of these MSDs across U.S. occupations to inform targeted prevention strategies. Utilizing 1,016 observations from publicly available secondary data, including the Survey of Occupational Injuries and Illnesses and the Occupational Information Network, we employed a stepwise regression analysis. The analysis successfully isolated 24 statistically significant MSD predictors, classifying them as either risk amplifiers or mitigators. High-risk sectors, specifically Healthcare Support, Construction and Extraction, Production, and Transportation and Material Moving, accounted for over 86 percent of all MSD cases. Furthermore, approximately 67 percent of these MSD events led to significant work disruptions, including days away from work or job transfers/restrictions, reinforcing the severe operational and economic impact of MSDs. The findings, which move beyond traditional risk factor analysis by integrating detailed occupational profiling data, offer critical insights for informing policy, enhancing the specificity of workplace interventions, and developing more effective, personalized safety protocols.

Abstract:

Urban forests serve as representations of nature within city landscapes. Green Forest, spanning 5,198,412 square meters, has been incorporated into the Municipality of Timișoara’s public domain and designated as a forest park. This fact increased green space per capita and enriched biodiversity within Timișoara’s landscape architecture. This study explores the diversity of Green Forest trees and highlights their contribution to urban landscapes. Statistical methods, including comparative and linear relationships analyses, were employed to assess significant variations in the dendrometric parameters of the analyzed tree species: mean tree height, mean diameter at breast height (DBH), tree age, and stand density. Principal Component Analysis (PCA) and cluster analysis were applied to uncover underlying patterns in the data. Using ArchiCAD and Lumion, high-quality 3D visual representations were developed for an ecological education area, an active recreation region, and a passive recreation area within Green Forest. Due to their morphological characteristics and phenotypic traits, the predominant tree species include Quercus robur, Quercus cerris, Quercus rubra, Fraxinus excelsior, Acer platanoides, Acer pseudoplatanus, Ulmus campestris and Robinia pseudoacacia, contribute to Timișoara’s urban aesthetic. Moreover, the results of the dendrometric analysis provide a foundation for further research in urban ecology. A key practical application of this study is landscape design renderings, which provide detailed and realistic visualizations to effectively communicate the design and functionality of Green Forest’s spaces. If implemented, these developments will encourage public engagement with nature, promoting mental and physical well-being within the community.

Abstract: The incidence of technologies created with the support of federal funding at universities and federal laboratories that are transferred to the private sector is nowhere close to its potential. The literature suggests that technology maturity level can possibly be a useful lever to increase the incidence of technology transfer. Orthodox approaches to technology transfer research have significant limitations that negatively impact their usefulness for investigating this issue. This paper presents a theoretical framework to address this gap and the results of a study that applied this framework in combination with Bayesian analysis to understand whether technology maturity level holds promise as a lever that practitioners and policymakers can use to substantially increase the incidence and societal benefits of technology transfer from universities and federal laboratories. The results of the study indicate that there is about a 55% probability that insufficient maturity is the primary reason that private sector organizations do not pursue 5% or more of available university and federal laboratory technologies. Thus, implementing public policies, programs, and initiatives to further mature technologies created at universities and federal laboratories that private sector firms would otherwise eschew because of insufficient maturity is likely to increase the overall incidence of technology transfer slightly but even a slight increase could produce substantial societal benefits. The potential economic benefits of commercializing such technologies are roughly 1.7 to 2.4 times greater than strategically redistributing the research funding used to create them to induce consumption and spur economic activity.

Abstract: Text-to-video (T2V) generation has recently emerged as a transformative technology within the field of generative AI, enabling the creation of realistic, temporally coherent videos based on natural language descriptions. This paradigm provides significant added value in many domains such as creative media, human-computer interaction, immersive learning, and simulation. Despite its growing importance, systematic discussion of T2V is still limited compared with adjacent modalities such as text-to-image and image-to-video. To alleviate the scarcity of discussions in the T2V field, this paper provides a systematic review of works published from 2024 onward, consolidating fragmented contributions across the field. We survey and categorize the selected literature into three principal areas, namely, T2V methods, datasets, and evaluation practices, and further subdivide each area into subcategories that reflect recurring themes and methodological patterns in the literature. Emphasis will then be placed on identifying key research opportunities and open challenges that need further investigation.

Abstract: Chronic exposure to severe stress is known to erode synapses in prefrontal-limbic circuits, a biological change that can be reversed by treatments that push glutamatergic signalling away from NMDA receptors and toward AMPA receptors. Ketamine infusions confirm the value of that mechanism, yet cost, monitoring demands, and dissociative side-effects make the intravenous route hard to scale.This review looks at a fully oral, four-drug strategy intended to mimic ketamine’s neuroplastic effects with inexpensive, widely available medicines. The proposed stack pairs dextromethorphan for NMDA blockade with a strong CYP2D6 inhibitor (fluoxetine, paroxetine, or bupropion) to keep dextromethorphan active longer; adds piracetam to enhance AMPA throughput; and supplements l-glutamine to replenish presynaptic stores of glutamate.Retrospective reports covering complex PTSD, dissociative fugue, and trauma-linked depression suggest that the combination can ease flashbacks, nightmares, somatic over-arousal, and even suicidality within days to a few weeks. Importantly, these benefits have been observed without the dissociation or blood-pressure spikes that often accompany ketamine infusions. Although the evidence is still limited to small case series, the early signal supports formal trials to test efficacy, safety, and the specific contribution of each component.

Abstract: A brief review of orientational phase transitions and thermodynamic properties of various magnets. These methodological guidelines are intended for students studying the section "Theory of Phase Transitions" in the course "Theory of Solids", as well as the section "Magnetic Phase Transitions" in the course "Theory of Magnetism". They can be used in preparation for laboratory and seminar classes in these courses, and for independent research work by students of the Faculty of Physics.

Abstract: Transitioning to more efficient and digital industrial processes requires plant design methodologies that go beyond traditional approaches and respond to the operational challenges of Industry 4.0. The objective of this study was to integrate Artificial Intelligence (AI) and Augmented Reality (AR) into SLP methodology for the design of a cosmetic emulsion production plant. A case study was developed based on the layout of a previously reported cosmetic plant by creating a preliminary layout using SLP and evaluating it using AI based on technical prompts. Subsequently, the refined model was represented in three dimensions and validated in a real environment using AR. The results show that AI identified opportunities for improvement in operational flows, relationships between critical areas, and space proportions, allowing for precise adjustments without altering the original design logic. Likewise, AI verification and immersive validation using AR confirmed the spatial compatibility of the layout with the selected site, facilitating the early assessment of circulation, access, and volumetric behavior. Thus, the sequential integration of SLP + AI + AR demonstrated its potential to reduce uncertainty in the early stages and move toward modernizing plant design in line with Industry 4.0 principles.

Abstract: Alecrim-do-campo (Baccharis dracunculifolia) is a species of agroindustrial and medicinal relevance that has attracted increasing interest in recent years due to its distinctive chemical profile rich in bioactive compounds. In this context, the present study evaluated the efficiency of different extraction conditions for volatile compounds in alecrim-do-campo, aiming to contribute to the traceability of products that use this species as a source of metabolites. A 2³ factorial design was employed to assess the best conditions for extracting volatiles by headspace solid-phase microextraction (HS‑SPME), using three different semipolar fibers (PDMS/DVB, DVB/CAR/PDMS and CAR/PDMS). Regarding the effect of the variation factors to which the samples were subjected, only the extraction time (min) had a significant effect on compound extraction using the CAR/PDMS fiber. In total, 79 volatile compounds were detected using the three fibers, with CAR/PDMS (43 compounds) and DVB/CAR/PDMS (44 compounds) showing the highest diversity. The nature of this study is important for the industry because it optimizes the search for quality parameters in plant-derived products.

Abstract: Determining the concentration of fatty acid ethyl esters (FAEEs), ethyl sulfate (EtS), and ethyl glucuronide (EtG) is crucial for establishing the actual scale of prenatal alcohol ex-posure (PAE) and the early diagnosis of Fetal Alcohol Spectrum Disorders (FASD). The study’s main objective was to compare two methods of detecting PAE: retrospective sur-veys of maternal alcohol consumption and chromatographic analysis of newborn meco-nium for these alcohol biomarkers. The study involved 478 mothers from the Pomeranian Province, with parallel samples of newborn meconium collected for analysis. Biomarker analysis involved determining the concentration of 9 FAEEs by Gas Chromatography–Mass Spectrometry (GC-MS) and EtG and EtS by Liquid Chromatography–Tandem Mass Spectrometry (LC-MS/MS). The study also aimed to determine the cut-off concentrations and usefulness of these PAE markers. Only approximately 4% of mothers reported drink-ing alcohol during pregnancy, suggesting a significant underestimation of the actual PAE scale compared to objective biomarker analysis. The use of the cumulative biomarker in-dex (CBI2) ≥ 5 suggests that alcohol consumption during pregnancy affected approximate-ly 3% of the studied population, highlighting the low reliability of maternal self-declarations. The most reliable information about PAE was provided by a combination of EtG and EtS biomarkers. The study confirms the difficulty in obtaining a reliable history of alcohol exposure during pregnancy.

Abstract: Generative models play a pivotal role in the field of medical imaging. This paper provides an extensive and scholarly review of the application of generative models in medical image creation and translation. In the creation aspect, the goal is to generate new images based on potential conditional variables, while in translation, the aim is to map images from one or more modalities to another, preserving semantic and informational content. The review begins with a thorough exploration of a diverse spectrum of generative models, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Diffusion Models (DMs), and their respective variants. The paper then delves into an insightful analysis of the merits and demerits inherent to each model type. Subsequently, a comprehensive examination of tasks related to medical image creation and translation is undertaken. For the creation aspect, papers are classified based on downstream tasks such as image classification, segmentation, and others. In the translation facet, papers are classified according to the target modality. A chord diagram depicting medical image translation across modalities, including Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Cone Beam CT (CBCT), X-ray radiography, Positron Emission Tomography (PET), and ultrasound imaging, is presented to illustrate the direction and relative quantity of previous studies. Additionally, the chord diagram of MRI image translation across contrast mechanisms is also provided. The final section offers a forward-looking perspective, outlining prospective avenues and implementation guidelines for future research endeavors.

Abstract: Slow-growing and locally invasive, chordoma is a rare malignant bone tumor that poses significant challenges because of its high recurrence rate and resistance to several standard treatment techniques. All cancers, including chordomas, have altered energy metabolism processes that contribute to their unchecked growth and survival. The significance of non-coding RNAs, particularly circular RNAs (circRNAs), as key regulators at the intersection of cellular metabolism and immune function has been highlighted by recent discoveries. By focusing on important glycolytic enzymes in tumor cells and altering metabolic reprogramming pathways, CircRNAs can influence cancer metabolic adaptability. Furthermore, via influencing immune cell functions as immunological checkpoint signaling and macrophage polarization, circRNAs influence immune evasion in the tumor microenvironment. These frequently happen via regulating important pathway signals, like PI3K/AKT/mTOR and NRF2, or by processes like miRNA sponging, creating a tumor microenvironment that is immunosuppressive and metabolically friendly. The translational pathway of circRNA-targeted therapeutics is promoted as a developing pharmacological entity in this review, which also highlights recent information on the control of circRNA-mediated immunometabolism in chordoma and examines numerous important molecular axes. There are promising opportunities to develop novel precision treatments for chordoma by considering circRNAs as dual regulators of immunological and metabolic networks.

Abstract: This study examines the community integration and One Health strategies employed to fight Ebola virus disease in the Democratic Republic of Congo in the years 2007–2022. We synthesized twelve outbreak reports and conducted qualitative interviews of thirty-six managers and three focus groups and adapted an analytical framework (MATCH) to evaluate three essential dimensions: integration of the One Health approach, community involvement, and bottom-up approaches. This study found evidence of progressive improvement in all domains. The first outbreaks (2007–2009) were marked by moderate community engagement and a One Health approach that was mostly limited to the human health sector, which was deemed suboptimal. The 10th outbreak represented an era of transformation, when the Incident Management System (IMS) was adopted to better manage the response to the virus. The latest outbreaks (13th to 15th) show “optimal” implementation of the “One Health” approach through effective collaboration among those in charge of ensuring human, animal, and environmental health and the community. This study demonstrates that success is largely dependent on bottom-up initiatives where local populations, their leaders (both traditional and religious leaders), community liaisons, and specific groups (women and youth) are involved in the design and implementation of such measures. The inclusion of anthropologists and psychologists in addressing the psychosocial dimensions—fear, stigma, and distress—has been critical in ensuring the success of these initiatives and the degree to which the public trust and accept them. However, there are many issues that still need to be addressed, including poor coordination between sectoral ministries and the partial implementation of IMS at the grassroots level. In summary, the authors of this study propose that these integrated and participatory models are sustainable and imperative to building the resilience of the Congolese health system to future outbreaks.

Abstract: Accurate identification of promoters is essential for deciphering gene regulation but remains challenging due to the complexity and variability of transcriptional initiation signals. Existing deep learning models often fail to simultaneously capture long-range dependencies and precise local motifs in DNA sequences. To address this, we propose DNABERT2-CAMP, a hybrid deep learning framework that integrates global sequence context with localized feature extraction for enhanced promoter recognition in Escherichia coli. The model leverages a pre-trained DNABERT-2 Transformer to encode evolutionary conserved patterns across extended contexts, while a novel CAMP (CNN-Attention-Mean Pooling) module detects fine-grained promoter motifs through convolutional filtering, multi-head attention, and mean pooling. By fusing global embeddings with high-resolution local features, our approach achieves robust discrimination between promoter and non-promoter sequences. Under 5-fold cross-validation, DNABERT2-CAMP attained an accuracy of 93.10% and a ROC AUC of 97.28%. It also demonstrated strong generalization on independent external data, achieving 89.83% accuracy and 92.79% ROC AUC. These results underscore the advantage of combining global contextual modeling with targeted local motif analysis for accurate and interpretable promoter identification, offering a powerful tool for synthetic biology and genomic research.