Search Results (885)

High-dimensional prediction problems with complex non-linear feature interactions present significant algorithmic challenges in machine learning, particularly when dealing with imbalanced datasets and multicollinearity issues. This study proposes an innovative Shapley Additive Explanations (SHAP)-enhanced machine learning framework that integrates SHAP with advanced ensemble methods for interpretable financialization prediction. The methodology simultaneously addresses high-dimensional feature selection using 40 independent variables (19 CSR-related and 21 financialization-related), multicollinearity issues, and model interpretability requirements. Using a comprehensive dataset of 25,642 observations from 3776 Chinese A-share companies (2011–2022), we implement nine optimized machine learning algorithms with hyperparameter tuning via the Hippopotamus Optimization algorithm and five-fold cross-validation. XGBoost demonstrates superior performance with 99.34% explained variance, achieving an RMSE of 0.082 and R² of 0.299. SHAP analysis reveals non-linear U-shaped relationships between key predictors and financialization outcomes, with critical thresholds at approximately 10 for CSR_SocR, 1.5 for CSR_S, and 5 for CSR_CV. SOE status, EPU, ownership concentration, firm size, and housing prices emerge as the most influential predictors. Notable shifts in factor importance occur during the COVID-19 pandemic period (2020–2022). This work contributes a scalable, interpretable machine learning architecture for high-dimensional financial prediction problems, with applications in risk assessment, portfolio optimization, and regulatory monitoring systems. Full article

Non-polio enteroviruses continue to cause numerous epidemics world-wide that range from mild to severe disease, including acute flaccid paralysis, meningitis, severe respiratory infections and encephalitis. Using publicly available data we present a comprehensive global and regional temporal distribution of non-polio enteroviruses, with a focus on highly prevalent genotypes. We found that regional distribution did vary compared to global prevalence where the top prevalent genotypes included CVA6 and EV-A71 in Asia, EV-D68 in North America and CVA13 in Africa, while E-30 was prevalent in Europe, South America and Oceania. In 2020, the COVID-19 pandemic did interrupt non-polio enterovirus detections globally, and cases rebounded in subsequent years, albeit at lower prevalence and with decreased genotype diversity. Environmental surveillance for non-polio enteroviruses does occur and has been used in some regions as an early-warning system; however, further development is needed to effectively supplement potential gaps in clinical surveillance data. Overall, monitoring for non-polio enteroviruses is critical to identify true incidence, improve understanding of genotype circulation, provide an early warning system for emerging/re-emerging genotypes and allow for better outbreak control. Full article

Background: Emerging evidence indicates that some individuals recovering from COVID-19 develop persistent symptoms, including fatigue, pain, cognitive difficulties, and psychological distress, commonly known as Long COVID. These symptoms often overlap with those seen in Chronic Fatigue Syndrome/Myalgic Encephalomyelitis (CFS/ME), underscoring the need for integrative, non-pharmacological interventions. This Phase II controlled trial aimed to evaluate the feasibility and preliminary efficacy of Heart Rate Variability Biofeedback (HRV-BF) in individuals with Long COVID who meet the diagnostic criteria for CFS/ME. Specific objectives included assessing feasibility indicators (drop-out rates, side effects, participant satisfaction) and changes in fatigue, depression, anxiety, pain, and health-related quality of life. Methods: Participants were assigned alternately and consecutively to the HRV-BF intervention or Treatment-as-usual (TAU), in a predefined 1:1 sequence (quasirandom assignment). The intervention consisted of 10 HRV-BF sessions, held twice weekly over 5 weeks, with each session including a 10 min respiratory preparation and 40 min of active training. Results: The overall drop-out rate was low (5.56%), and participants reported a generally high level of satisfaction. Regarding side effects, the mean total Simulator Sickness Questionnaire score was 24.31 (SD = 35.42), decreasing to 12.82 (SD = 15.24) after excluding an outlier. A significantly greater improvement in severe fatigue was observed in the experimental group (H = 4.083, p = 0.043). When considering all outcomes collectively, a tendency toward improvement was detected in the experimental group (binomial test, p < 0.0001). Conclusions: HRV-BF appears feasible and well tolerated. Findings support the need for Phase III trials to confirm its potential in mitigating fatigue in Long COVID. Full article

In recent years, the persistent emergence of novel infectious pathogens (epitomized by the global coronavirus disease-2019 (COVID-2019) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)) has propelled nucleic acid testing (NAT) into an unprecedented phase of rapid development. As a key technology in modern molecular diagnostics, NAT achieves precise pathogen identification through specific nucleic acid sequence recognition, establishing itself as an indispensable diagnostic tool across diverse scenarios, including public health surveillance, clinical decision-making, and food safety control. The reliability of NAT systems fundamentally depends on reference materials (RMs) that authentically mimic the biological characteristics of natural viruses. This critical requirement reveals significant limitations of current RMs in the NAT area: naked nucleic acids lack the structural authenticity of viral particles and exhibit restricted applicability due to stability deficiencies, while inactivated viruses have biosafety risks and inter-batch heterogeneity. Notably, pseudovirus has emerged as a novel RM that integrates non-replicative viral vectors with target nucleic acid sequences. Demonstrating superior performance in mimicking authentic viral structure, biosafety, and stability compared to conventional RMs, the pseudovirus has garnered substantial attention. In this comprehensive review, we critically summarize the engineering strategies of pseudovirus platforms and their emerging role in ensuring the reliability of NAT systems. We also discuss future prospects for standardized pseudovirus RMs, addressing key challenges in scalability, stability, and clinical validation, aiming to provide guidance for optimizing pseudovirus design and practical implementation, thereby facilitating the continuous improvement and innovation of NAT technologies. Full article

Background: Neurodevelopmental disorders, including autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), are increasingly recognized as conditions arising from multifaceted interactions among genetic predisposition, environmental exposures, and epigenetic modifications. Among epigenetic mechanisms, non-coding RNAs (ncRNAs), including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and PIWI-interacting RNAs (piRNAs), have gained attention as pivotal regulators of gene expression during neurodevelopment. These RNA species do not encode proteins but modulate gene expression at transcriptional and post-transcriptional levels, thereby influencing neuronal differentiation, synaptogenesis, and plasticity. Objectives: This systematic review critically examines and synthesizes the most recent findings, particularly in the post-COVID transcriptomic research era, regarding the role of ncRNAs in the pathogenesis, diagnosis, and potential treatment of neurodevelopmental disorders. Methods: A comprehensive literature search was conducted to identify studies reporting on the expression profiles, functional implications, and clinical relevance of ncRNAs in neurodevelopmental disorders, across both human and animal models. Results: Here, we highlight that multiple classes of ncRNAs are differentially expressed in individuals with ASD and ADHD. Notably, specific miRNAs and lncRNAs demonstrate potential as diagnostic biomarkers with high sensitivity and specificity. Functional studies further reveal that ncRNAs actively contribute to pathogenic mechanisms by modulating neuronal gene networks. Conclusions: Emerging experimental data indicate that the exogenous administration of certain ncRNAs may reverse molecular and behavioral phenotypes, supporting their therapeutic promise. These findings broaden our understanding of neurodevelopmental regulation and open new avenues for personalized diagnostics and targeted interventions in clinical neuropsychiatry. Full article

The rapid spread of COVID-19 increased the need for speedy diagnostic tools, which led scientists to conduct extensive research on deep learning (DL) applications that use chest imaging, such as chest X-ray (CXR) and computed tomography (CT). This review examines the development and performance of DL architectures, notably convolutional neural networks (CNNs) and emerging vision transformers (ViTs), in identifying COVID-19-related lung abnormalities. Individual ResNet architectures, along with CNN models, demonstrate strong diagnostic performance through the transfer protocol; however, ViTs provide better performance, with improved readability and reduced data requirements. Multimodal diagnostic systems now incorporate alternative methods, in addition to imaging, which use lung ultrasounds, clinical data, and cough sound evaluation. Information fusion techniques, which operate at the data, feature, and decision levels, enhance diagnostic performance. However, progress in COVID-19 detection is hindered by ongoing issues stemming from restricted and non-uniform datasets, as well as domain differences in image standards and complications with both diagnostic overfitting and poor generalization capabilities. Recent developments in COVID-19 diagnosis involve constructing expansive multi-noise information sets while creating clinical process-oriented AI algorithms and implementing distributed learning protocols for securing information security and system stability. While deep learning-based COVID-19 detection systems show strong potential for clinical application, broader validation, regulatory approvals, and continuous adaptation remain essential for their successful deployment and for preparing future pandemic response strategies. Full article

Mental health disorders, particularly depression and anxiety, have become increasingly prevalent among elite athletes, exacerbated by factors such as competitive pressure and the Coronavirus Disease 19 (COVID-19) pandemic. This study analyzes trends in the use of antidepressants, anxiolytics, and cannabinoids (delta-9-tetrahydrocannabinol (THC)/cannabidiol (CBD)) among Italian athletes from 2011 to the first half of 2023 (FH2023), referring to anti-doping reports published by the Italian Ministry of Health. Data from 13,079 athletes were examined, with a focus on non-prohibited medications, banned substances, and regulatory impacts, including threshold adjustments for THC since 2013 and the legalization of CBD. The results show fluctuating use of antidepressants/anxiolytics, with peaks in 2021 and the FH2023, coinciding with post-pandemic awareness. Positive THC cases rose following regulatory changes, reflecting socio-cultural trends. Gender disparities emerged, with THC use predominantly among males (e.g., nine males vs. one female in 2013), though female athletes were underrepresented in testing. This study highlights the need for personalized, evidence-based strategies that balance therapeutic efficacy and anti-doping compliance. Clinicians should carefully consider prescribing selective serotonin reuptake inhibitors (SSRIs) and benzodiazepines to address depression and anxiety and should monitor the risks of CBD contamination. Future research should adopt longitudinal, gender-sensitive approaches to refining guidelines and combating stigma in professional sports. Full article

Background: Bacterial infections are frequent complications in critically ill COVID-19 patients, and are associated with increased morbidity, antibiotic use, and healthcare burden. Early and accurate identification of infection remains challenging. Pancreatic Stone Protein (PSP) has emerged as a promising biomarker of infection. In this study, PSP was evaluated alongside C-reactive protein (CRP). Methods: We conducted a prospective study including 105 critically ill COVID-19 patients admitted to the intensive care unit (ICU). Blood samples were collected at admission to measure PSP and CRP. A LASSO Least Absolute Shrinkage and Selection Operator (LASSO) regression model was used to identify independent predictors of proven or suspected bacterial infection. Mixed-effects models were applied to account for repeated measures and clinical confounders. Results: Among 105 patients, 57 (54%) developed bacterial infections. PSP levels were significantly higher in infected patients (median 100 ng/mL) than in non-infected patients (median 37 ng/mL, p < 0.001). CRP was also elevated in infected patients (median 125 vs. 70 mg/L, p = 0.015). The LASSO model retained PSP as the most informative predictor. In mixed-effects logistic regression, PSP remained significantly associated with infection (OR 1.017, 95% CI 1.006–1.027, p = 0.001). The AUC for PSP was 0.87. Conclusion: PSP appears to be a useful biomarker for early detection of bacterial infection in critically ill COVID-19 patients. Its integration into infection surveillance protocols could support antibiotic stewardship efforts and improve clinical decision-making. Full article

Infectious disease outbreaks amplify the influence of stressors on psychological conditions. The purpose of this study was to analyze the disturbing influence of COVID-19 outbreak-related information and the influence of trust on the Serbian healthcare system and COVID-19 preventive measures on anxiety and depression. An anonymous online questionnaire assessing the demographic information, disturbance level and causes, and levels of anxiety and depression has been distributed to the participants, divided into student and non-student groups. The non-student group was further divided into healthcare, military, and education workers. Anxiety and depression levels, as well as the level of decreased trust in COVID-19-related preventive measures, were higher among students compared to non-students (p = 0.011). Higher anxiety and depression levels, and higher influence of the COVID-19 outbreak on those levels, were observed in education and healthcare workers, compared to military personnel. Medical doctors reported a higher level of trust in the healthcare system compared to nurses (p = 0.023). Trust in the healthcare system increased more frequently compared to the pre-vaccination period among medical doctors, compared to nurses (p = 0.040). Higher anxiety and depression and lower public trust levels in students and workers in education and the healthcare sector indicate a need to focus on these important society members during public health emergencies. Full article

Background: he surge in the use of Pre-hospital Emergency Medical Systems (EMS) and Emergency Departments (ED) has become a pressing issue worldwide after the COVID-19 pandemic. To address this challenge, we developed an experimental and innovative care pathway supported by telemedicine. The aim of this study is to describe the activity of the Integrated Medical Center (CMI): a new telemedicine-based care model for patients referring to the Emergency Medical System. Methods: A prospective observational study was conducted from January 2022 to December 2022. The CMI was established to manage patients referring to the Emergency Medical System. Results: From January to December 2022, a total of 8680 calls were managed by CMI, with an average of 24 calls per day. 6243 patients (71.9%) were managed without ED access of whom 4884 patients (78.2%) were managed through telemedicine evaluation only, and 1359 (21.8%) with telemedicine evaluation and dispatch of the Home Rapid Response Team (HRRT). The population treated by the HRRT exhibited a higher age. The mean satisfaction score was 9.1/10. Conclusions: Telemedicine evaluation allowed for remote assessments, treatment prescriptions, and teleconsultation for HRRT and was associated with high patient satisfaction. This model could be useful in future pandemics for managing patients with non-urgent illnesses at home, preventing hospital admissions for potentially infectious patients, and thereby reducing in-hospital transmission. Full article

The COVID-19 pandemic and recent viral outbreaks have highlighted the need for viral diagnostics that balance accuracy with accessibility. While traditional laboratory methods remain essential, point-of-care solutions are critical for decentralized testing at the population level. However, a gap persists between academic proof-of-concept studies and clinically viable tools, with novel technologies remaining inaccessible to clinics due to cost, complexity, training, and logistical constraints. Recent advances in surface functionalization, assay simplification, multiplexing, and performance in complex media have improved the feasibility of both optical and non-optical sensing techniques. These innovations, coupled with scalable manufacturing methods such as 3D printing and streamlined hardware production, pave the way for practical deployment in real-world settings. Additionally, software-assisted data interpretation, through simplified readouts, smartphone integration, and machine learning, enables the broader use of diagnostics once limited to experts. This review explores improvements in viral diagnostic approaches, including colorimetric, optical, and electrochemical assays, showcasing their potential for democratization efforts targeting the clinic. We also examine trends such as open-source hardware, modular assay design, and standardized reporting, which collectively reduce barriers to clinical adoption and the public dissemination of information. By analyzing these interdisciplinary advances, we demonstrate how emerging technologies can mature into accessible, low-cost diagnostic tools for widespread testing. Full article

Mycoplasma pneumoniae, a cell wall-deficient pathogen, primarily affects children and adolescents, causing Mycoplasma pneumoniae pneumonia (MPP). Following the relaxation of non-pharmaceutical interventions (NPIs) post COVID-19, there has been a global increase in MPP cases and macrolide-resistant strains. Vaccination against M. pneumoniae is being explored as a promising approach to reduce infections, limit antibiotic misuse, and prevent the emergence of drug-resistant variants. We developed an mRNA vaccine, mRNA-SP+P1, incorporating a eukaryotic signal peptide (tissue-type plasminogen activator signal peptide) fused to the C-terminal region of the P1 protein. Targeting amino acids 1288 to 1518 of the P1 protein, the vaccine was administered intramuscularly to BALB/c mice in a three-dose regimen. To evaluate immunogenicity, we quantified anti-P1 IgG antibody titers using enzyme-linked immunosorbent assays (ELISAs) and assessed cellular immune responses by analyzing effector memory T cell populations using flow cytometry. We also tested the functional activity of vaccine-induced sera for their ability to inhibit adhesion of the ATCC M129 strain to KMB17 cells. The vaccine’s protective efficacy was assessed against the ATCC M129 strain and its cross-protection against the ST3-resistant strain. Transcriptomic analysis was conducted to investigate gene expression changes in peripheral blood, aiming to uncover mechanisms of immune modulation. The mRNA-SP+P1 vaccine induces P1 protein-specific IgG antibodies and an effector memory T-cell response in BALB/c mice. Adhesion inhibition assays demonstrated that serum from vaccinated mice attenuatesthe adhesion ability of ATCC M129 to KMB17 cells. Furthermore, three doses of the vaccine confer significant and long-lasting, though partial, protection against the ATCC M129 strain and partial cross-protection against the ST3 drug-resistant strain. Transcriptome analysis revealed significant gene expression changes in peripheral blood, confirming the vaccine’s capacity to elicit an immune response from the molecular level. Our results indicate that the mRNA-SP+P1 vaccine appears to be an effective vaccine candidate against the prevalence of Mycoplasma pneumoniae. Full article

The COVID-19 pandemic has disrupted the progress toward Sustainable Development Goal 3, particularly in developing countries, exacerbating existing health disparities and creating new challenges for health systems worldwide. This study explores the role of university research in advancing SDG 3 targets in a post-pandemic context using the University of the Western Cape as a case study. Through qualitative data analysis of research titles and abstracts registered between 2020 and 2022, we applied the WHERETO model of McTighe and Bloom’s Taxonomy to categorize research according to the SDG 3 targets and indicators. This approach provides insight into which health priorities were addressed through scholarly research at UWC in alignment with the UN 2030 Agenda, particularly during pandemic recovery. Our findings indicate that research priorities largely corresponded with South Africa’s health challenges, with the highest concentration of studies addressing non-communicable diseases and mental health (Target 3.4), infectious diseases (Target 3.3), and medicine development (Target 3.b). These priorities align with the National Health Research Committee’s identified health priorities for disadvantaged communities in the Western Cape. Notably, research on mental health and emergency preparedness (Target 3.d) increased significantly during the pandemic period, reflecting shifting priorities in response to COVID-19. This study offers critical insights into how university research shifted priorities adapted during the pandemic and identifies areas requiring focused attention to support post-pandemic recovery. By highlighting research gaps and opportunities, our findings provide a foundation for developing more comprehensive approaches to health research that address the disparities exacerbated by COVID-19 while advancing the 2030 agenda. This model could inform research prioritization at other institutions facing similar challenges in both local and global contexts. Full article

Antimicrobial resistance (AMR) is considered a global healthcare emergency in the 21st century. Although the evolution of microorganisms through Darwinian mechanisms and antibiotic misuse are established drivers, the structural socioeconomic factors of AMR remain insufficiently explored. This review takes on an analytical perspective, drawing upon a wide spectrum of evidence to examine the extent to which socioeconomic factors contribute to the global proliferation of AMR, with an emphasis on low- and middle-income countries (LMICs). The analytical review at hand was carried out through a search for relevant articles and reviews on PubMed, Google Scholar, the Centers for Disease Control and Prevention, and the World Health Organization database using combinations of the keywords “antimicrobial resistance,” “socioeconomic factors,” “low- and middle-income countries,” “surveillance,” “healthcare access,” and “agriculture.” Preference was given to systematic reviews, high-impact primary studies, and policy documents published in peer-reviewed journals or by reputable global health organizations. Our analysis identifies a complex interplay of systemic vulnerabilities that accelerate AMR in resource-limited settings. A lack of regulatory frameworks regarding non-prescription antibiotic use enables the proliferation of multi-drug-resistant microorganisms. Low sewer connectivity facilitates the environmental dissemination of resistance genes. Proper antibiotic selection is hindered by subpar healthcare systems and limited diagnostic capabilities to deliver appropriate treatment. Additionally, gender disparities, forced migration, and climate-driven zoonotic transmission compound the burden. During the COVID-19 pandemic, antimicrobial misuse surged, further amplifying resistance trends. AMR is not solely a biological phenomenon, but a manifestation of global inequity. Mitigation requires a transformation of policy directed toward a “One Health” strategy that incorporates socioeconomic, environmental, and health system reforms. Strengthening surveillance, investing in infrastructure, regulating pharmaceutical practices, and promoting health equity are essential to curb the rising tide of resistance. Full article

This article investigates predicted future developments in fine dining using a mixed-methods approach rooted in German gastronomic culture. By conducting an inductive media content analysis and ten semi-structured expert interviews with leading figures in Germany’s high-end food sector, we applied a qualitative mixed-methods approach. The study was based exclusively on data collected in 2018 and 2019, deliberately excluding pandemic-related developments in order to focus on long-term structural and cultural trends in fine dining. We identified two core thematic clusters: one related to sustainable food practices (ecology/sustainability, regionality, seasonality, from-farm-to-table, and vegetarianism/veganism) and the other to experiential dimensions of dining (experience, topic-based concept, and storytelling). Our findings contribute to the academic discussion on culinary futures and provide grounded insights into how fine dining is likely to evolve in response to broader societal, environmental, and cultural shifts. This study fills a significant research gap by systematically mapping emerging restaurant concepts based on non-COVID data, making it a valuable reference for scholars and practitioners alike. Full article