Search Results (10,215)

Background: Neurogenic bowel dysfunction (NBD) is a major chronic sequela of spinal cord injury (SCI) with substantial implications for rehabilitation and long-term management. However, population-level evidence describing how gastrointestinal (GI) diagnostic codes are used following SCI, particularly within administrative healthcare systems, remains limited. Methods: We conducted a nationwide retrospective cohort study using administrative claims data from the Korean National Health Insurance Service (NHIS). A total of 584,266 adults with trauma-related SCI encounters between 2009 and 2019 were identified. GI diagnostic codes—paralytic ileus (K56), irritable bowel syndrome (K58), and functional bowel disorders (K59)—were evaluated as administrative proxies for bowel dysfunction. Demographic characteristics, disability status, regional factors, and health behaviors were analyzed using multivariable logistic regression. Results: GI diagnostic codes were frequently recorded after SCI, most commonly irritable bowel syndrome (approximately 30%) and functional bowel disorders (approximately 37%), whereas paralytic ileus was uncommon. Greater disability severity, female sex, older age, and rural residence were consistently associated with higher odds of GI diagnostic coding. Physical activity showed robust inverse associations across all models. Inverse associations observed with smoking and alcohol consumption were interpreted as reflecting residual confounding or health-related selection, rather than biological protective effects. Conclusions: Patterns of GI diagnostic coding after SCI likely reflect the clinical burden and management needs of neurogenic bowel dysfunction within healthcare systems, rather than the development of new gastrointestinal diseases. These findings underscore the importance of individualized bowel management, incorporation of structured physical activity into rehabilitation programs, and equitable access to SCI rehabilitation services, particularly for individuals with greater disability or those living in rural areas. Full article

The intensification of aquaculture practices has been accompanied by an increased incidence of bacterial diseases, leading to a greater reliance on antibiotics for disease control. Consequently, the widespread and often indiscriminate use of these compounds has contributed to the emergence and dissemination of antibiotic-resistant bacteria within aquaculture systems, posing a serious threat to animal health, environmental sustainability, and public health. In this regard, research efforts have focused on developing alternative strategies to reduce antibiotic use. Natural compounds have gained particular attention due to their well-documented antimicrobial and antibiofilm activities. In this context, the combined application of antibiotics and natural compounds has emerged as a promising approach to enhance antimicrobial efficacy while potentially mitigating the development of resistance. This review synthesizes the current knowledge on antibiotic resistance in aquaculture, highlights the role of biofilm formation as a key resistance mechanism, and critically examines the potential of antibiotic–natural compound combinations against major aquaculture pathogens, with particular emphasis on bacterial growth inhibition, biofilm disruption, and virulence attenuation. Collectively, the evidence discussed underscores the potential of synergistic strategies as a sustainable tool for improving disease management in aquaculture while supporting efforts to limit antibiotic resistance. Full article

Liver fibrosis, the progressive accumulation of scar tissue resulting from chronic liver disease, is increasingly recognized as a multi-system condition, the effects of which extend beyond the liver, affecting brain health. Dementia, characterized by progressively impaired cognition sufficient to impede daily functioning, is a major global health issue with incompletely defined risk factors and pathogenic precursors. To examine the relationship between liver fibrosis and cognitive outcomes, we conducted a comprehensive PubMed literature search, and human studies published in English were included. Evidence is synthesized on the pathophysiology and clinical significance of liver fibrosis, types of dementia, and studies supporting the association between liver fibrosis and cognitive impairment. Meta-analytic data indicate that liver fibrosis is associated with an approximately 30% increased risk of incident dementia (pooled hazard ratio ~1.3), with progressively higher risks across more advanced fibrosis stages. Putative pathomechanisms, potentially modulated by age and sex, include chronic systemic and neuro-inflammation, insulin resistance, vascular dysfunction, and a perturbed intestinal microbiota–liver–brain axis. Non-invasive liver fibrosis diagnostics, advanced neuroimaging, and biomarkers represent key tools for assessing risk. In conclusion, liver fibrosis is a systemic condition that can affect brain health. Early detection, thorough risk assessment and interventions, such as lifestyle changes, metabolic therapies, and antifibrotic treatments, may help protect neural function. Key research gaps are identified, with suggestions for improving understanding of liver fibrosis’s connection to dementia or cognitive impairment. Full article

Cervical cancer is a largely preventable disease, with over 90% of cases caused by persistent infection with human papillomavirus (HPV). Despite the availability of HPV vaccination and cervical screening, incidence rates in Canada have been rising since 2015, particularly among underserved populations. This study investigates contributing factors behind cervical cancer diagnoses in Eastern Ontario over a two-year period to identify gaps leading to failures in prevention and screening. A retrospective chart review was conducted for cervical cancer cases diagnosed between January 2022 and December 2023 at two regional cancer centres in Eastern Ontario. Cases were categorized as screen-detected, inadequately screened, or system failure, based on prior screening history and care processes. Data was collected on patient, screening, and cancer characteristics. Of 132 cases, 22 (16.7%) were screen-detected, 73 (55.3%) were inadequately screened, and 37 (28.0%) were attributed to healthcare system failure. Later-stage disease was significantly more common in the latter two groups. Thirty-one (23.5%) cases presented with palliative diagnoses, and 18 (13.6%) individuals died within 2.5 years. Inadequate screening was associated with rurality, deprivation, and lack of a primary care provider. System failures included false-negative Pap tests, loss to follow-up, and misapplication of screening guidelines. This study evaluated failures in cervical cancer prevention, which led to cervical cancer diagnoses in Eastern Ontario. Gaps included suboptimal screening participation, lack of access to care, health care system breakdowns, and limitations of the Pap test. Findings provide concrete suggestions for eliminating cervical cancer in Canada. Full article

Piscine lactococcosis is an emerging bacterial disease that threatens freshwater and marine aquaculture in the Mediterranean region. This study characterized isolates of Lactococcus garvieae and Lactococcus petauri from farmed fish through molecular identification, genomic typing and antimicrobial susceptibility testing. A total of 39 bacterial strains were analyzed using species-specific real-time PCR assays, multilocus sequence typing and broth microdilution to determine minimum inhibitory concentrations. Results suggest a temporal shift in freshwater systems, where L. garvieae predominated in earlier isolates (mainly ST13, CC4), while L. petauri (ST14, CC14) appears as the dominant species in recent years. In marine fish, only L. garvieae was detected, mainly ST95 (CC95), a lineage previously reported in Europe. Molecular variability was found in both species with lineages capable of infecting livestock and humans. Amoxicillin displayed promising results; florfenicol showed moderate activity, while flumequine exhibited no inhibitory effect. Oxytetracycline and trimethoprim–sulfamethoxazole showed variable results requiring prudent use. These region-specific susceptibility profiles provide updated baseline data to guide empirical antimicrobial therapy while awaiting laboratory confirmation, highlighting the evolution of lactococcosis in aquaculture and emphasizing the need for molecular surveillance, antimicrobial stewardship, and vaccine updates within a One Health framework to mitigate impacts on Mediterranean aquaculture and public health. Full article

Non-communicable diseases (NCDs) such as Diabetes Mellitus (DM) and Sickle Cell Disease (SCD) pose an escalating health challenge in Africa, underscored by diagnostic deficiencies, inadequate surveillance, and limited health system capacity that contribute to late diagnoses and consequent preventable complications. This review adopts a comparative framework that considers DM and SCD as complementary indicator diseases, both metabolic and genetic, and highlights intersecting diagnostic, infrastructural, and governance hurdles relevant to AI-enabled screening in resource-constrained environments. The study synthesizes epidemiological data across both African and high-income regions and methodically catalogs machine learning (ML) and deep learning (DL) research by clinical application, including risk prediction, image-based diagnostics, remote patient monitoring, privacy-preserving learning, and governance frameworks. Our key observations reveal significant disparities in disease detection and health outcomes, driven by underdiagnosis, a lack of comprehensive newborn screening for SCD, and fragmented diabetes surveillance systems in Africa, despite the availability of effective diagnostic technologies in other regions. The reviewed literature on ML/DL shows high algorithmic accuracy, particularly in diabetic retinopathy screening and emerging applications in SCD microscopy. However, most studies are constrained by small, single-site datasets that lack robust external validation and do not align well with real-world clinical workflows. The review identifies persistent implementation challenges, including data scarcity, device variability, limited connectivity, and inadequate calibration and subgroup analysis. By integrating epidemiological insights into AI diagnostic capabilities and health system realities, this work extends beyond earlier surveys to offer a comprehensive, Africa-centric, implementation-focused synthesis. It proposes actionable operational and policy recommendations, including offline-first deployment strategies, federated learning approaches for low-bandwidth scenarios, integration with primary care and newborn screening initiatives, and enhanced governance structures, to promote equitable and scalable AI-enhanced diagnostics for NCDs. Full article

Background: Libya, a conflict-affected North African country, has a fragile health system and poor surveillance, leaving it largely underrepresented in global estimates. Earlier Libyan reviews were descriptive, lacking breakpoint standardization, isolate-level pooling, or AMR-attributable mortality and DALY estimates. To our knowledge, this study represents the first comprehensive report that integrates phenotypic and genotypic data to estimate deaths and DALYs attributable to AMR-induced mortality and morbidity, describe spatiotemporal patterns, and model future trajectories. Methods: We performed a meta-analysis according to the PRISMA 2020 guideline of Libyan studies reporting phenotypic or genotypic resistance among clinical bacterial isolates (1970–2024), combined with microbiology records from hospitals and national surveillance systems (preregistered in PROSPERO ID: CRD420251066018). Susceptibility results were standardized to CLSI/EUCAST and deduplicated using WHO GLASS first-isolate rules. We used random-effects meta-regression to estimate pooled resistance, and the counterfactual approach of Global Burden of Disease (GBD) was applied to estimate AMR-attributable DALYs. Molecular data on resistance genes, sequence types, and tuberculosis mutations were systematically collected. Results: We included 62 eligible studies together with national and facility-level surveillance datasets, providing isolate-level susceptibility data for 31,439 clinical isolates from Libya. In 2024, we estimated 2183 deaths (95% UI 1752–2614) attributable to AMR, representing 9.7% (95% UI 7.8–11.6) of total deaths with a mortality rate of 15.2 per 100,000 (12.2–18.2). DALYs attributable to AMR increased from 14,628 (95% UI 11,702–17,554) in 1970 to 96,715 (95% UI 77,372–116,058). The highest pooled resistance involved carbapenem-resistant/MDR A. baumannii, third-generation cephalosporin- and fluoroquinolone-resistant Enterobacterales, and carbapenem-resistant P. aeruginosa. Molecular data showed widespread ESBLs, OXA-/NDM-type carbapenemases, plasmid-mediated colistin resistance, high-risk E. coli ST131 and K. pneumoniae ST147 lineages, and canonical drug-resistant M. tuberculosis mutations. Conclusions: Combined with global and regional evidence, our findings suggest a high and increasing burden of AMR in Libya. These findings emphasize the need for rapid expansion of data collection systems, GLASS-aligned surveillance, diagnostic capacities, and infection control measures. Full article

Background and Objectives: Payment parity laws require commercial health plans to pay for telehealth on the same basis as in-person care. We systematically reviewed open-access empirical studies to identify and synthesize empirical U.S. studies that explicitly evaluated state telehealth payment parity (distinct from coverage-only parity) and to summarize reported effects on telehealth utilization, modality mix, quality/adherence, equity/access, and expenditures. Methods: Following PRISMA 2020, we searched PubMed/MEDLINE, Scopus, and Web of Science for U.S. studies that explicitly modeled state payment parity or stratified results by payment parity vs. coverage-only vs. no parity. We included original quantitative or qualitative studies with a time or geographic comparator and free full-text availability. The primary outcome was telehealth utilization (share or odds of telehealth use); secondary outcomes were modality mix, quality and adherence, equity and access, and spending. Because designs were heterogeneous (interrupted time series [ITS], difference-in-differences [DiD], regression, qualitative), we used structured narrative synthesis. Results: Nine studies met inclusion criteria. In community health centers (CHCs), payment parity was associated with higher telehealth use (42% of visits in parity states vs. 29% without; Δ = +13.0 percentage points; adjusted odds ratio 1.74, 95% CI 1.49–2.03). Among patients with newly diagnosed cancer, adjusted telehealth rates were 23.3% in coverage + payment parity states vs. 19.1% in states without parity, while cross-state practice limits reduced telehealth use (14.9% vs. 17.8%). At the health-system level, parity mandates were linked to a +2.5-percentage-point telemedicine share in 2023, with mental-health (29%) and substance use disorder (SUD) care (21%) showing the highest telemedicine shares. A Medicaid coverage policy bundle increased live-video use by 6.0 points and the proportion “always able to access needed care” by 11.1 points. For hypertension, payment parity improved medication adherence, whereas early emergency department and hospital adoption studies found null associations. Direct spending evidence from open-access sources remained sparse. Conclusions: Across ambulatory settings—especially behavioral health and chronic disease management—state payment parity laws are consistently associated with modest but meaningful increases in telehealth use and some improvements in adherence and perceived access. Effects vary by specialty and are attenuated where cross-state practice limits persist, and the impact of payment parity on overall spending remains understudied. Full article

Mobile edge-AI plant monitoring systems enable automated disease control in greenhouses and open fields, reducing dependence on manual inspection and the variability of visual diagnostics. This paper proposes a lightweight two-stage edge-AI system for strawberries, in which a YOLOv10n detector on board a mobile agricultural robot locates leaves affected by seven common diseases (including Leaf Spot) with real-time capability on an embedded platform. Patches are then automatically extracted for leaves classified as Leaf Spot and transmitted to the second module—a compact MobileViT-S-based classifier with ordinal output that assesses the severity of Leaf Spot on three levels (S1—mild, S2—moderate, S3—severe) on a specialised set of 373 manually labelled leaf patches. In a comparative experiment with lightweight architectures ResNet-18, EfficientNet-B0, MobileNetV3-Small and Swin-Tiny, the proposed Ordinal MobileViT-S demonstrated the highest accuracy in assessing the severity of Leaf Spot (accuracy ≈ 0.97 with 4.9 million parameters), surpassing both the baseline models and the standard MobileViT-S with a cross-entropy loss function. On the original image set, the YOLOv10n detector achieves an mAP@0.5 of 0.960, an F1 score of 0.93 and a recall of 0.917, ensuring reliable detection of affected leaves for subsequent Leaf Spot severity assessment. The results show that the “YOLOv10n + Ordinal MobileViT-S” cascade provides practical severity-aware Leaf Spot diagnosis on a mobile agricultural robot and can serve as the basis for real-time strawberry crop health monitoring systems. Full article

Chronic obstructive pulmonary disease (COPD) is a major health burden worldwide and in Taiwan, ranking as the third leading cause of death globally, and its prevalence in Taiwan continues to rise. Readmission within 14 days is a key indicator of disease instability and care efficiency, driven jointly by patient-level physiological vulnerability (such as reduced lung function and multiple comorbidities) and healthcare system-level deficiencies in transitional care. To mitigate the growing burden and improve quality of care, it is urgently necessary to develop an AI-based prediction model for 14-day readmission. Such a model could enable early identification of high-risk patients and trigger multidisciplinary interventions, such as pulmonary rehabilitation and remote monitoring, to effectively reduce avoidable early readmissions. However, medical data are commonly characterized by severe class imbalance, which limits the ability of conventional machine learning methods to identify minority-class cases. In this study, we used real-world clinical data from multiple hospitals in Kaohsiung City to construct a prediction framework that integrates data generation and ensemble learning to forecast readmission risk among patients with chronic obstructive pulmonary disease (COPD). CTGAN and kernel density estimation (KDE) were employed to augment the minority class, and the impact of these two generation approaches on model performance was compared across different augmentation ratios. We adopted a stacking architecture composed of six base models as the core framework and conducted systematic comparisons against the baseline models XGBoost, AdaBoost, Random Forest, and LightGBM across multiple recall thresholds, different feature configurations, and alternative data generation strategies. Overall, the results show that, under high-recall targets, KDE combined with stacking achieves the most stable and superior overall performance relative to the baseline models. We further performed ablation experiments by sequentially removing each base model to evaluate and analyze its contribution. The results indicate that removing KNN yields the greatest negative impact on the stacking classifier, particularly under high-recall settings where the declines in precision and F1-score are most pronounced, suggesting that KNN is most sensitive to the distributional changes introduced by KDE-generated data. This configuration simultaneously improves precision, F1-score, and specificity, and is therefore adopted as the final recommended model setting in this study. Full article

This paper examines the contrasting yet complementary approaches of the Constrained Disorder Principle (CDP) and Stefan Hell’s deterministic optical nanoscopy for managing noise in complex systems. The CDP suggests that controlled disorder within dynamic boundaries is crucial for optimal system function, particularly in biological contexts, where variability acts as an adaptive mechanism rather than being merely a measurement error. In contrast, Hell’s recent breakthrough in nanoscopy demonstrates that engineered diffraction minima can achieve sub-nanometer resolution without relying on stochastic (random) molecular switching, thereby replacing randomness with deterministic measurement precision. Philosophically, these two approaches are distinct: the CDP views noise as functionally necessary, while Hell’s method seeks to overcome noise limitations. However, both frameworks address complementary aspects of information extraction. The primary goal of microscopy is to provide information about structures, thereby facilitating a better understanding of their functionality. Noise is inherent to biological structures and functions and is part of the information in complex systems. This manuscript achieves integration through three specific contributions: (1) a mathematical framework combining CDP variability bounds with Hell’s precision measurements, validated through Monte Carlo simulations showing 15–30% precision improvements; (2) computational demonstrations with N = 10,000 trials quantifying performance under varying biological noise regimes; and (3) practical protocols for experimental implementation, including calibration procedures and real-time parameter optimization. The CDP provides a theoretical understanding of variability patterns at the system level, while Hell’s technique offers precision tools at the molecular level for validation. Integrating these approaches enables multi-scale analysis, allowing for deterministic measurements to accurately quantify the functional variability that the CDP theory predicts is vital for system health. This synthesis opens up new possibilities for adaptive imaging systems that maintain biologically meaningful noise while achieving unprecedented measurement precision. Specific applications include cancer diagnostics through chromosomal organization variability, neurodegenerative disease monitoring via protein aggregation disorder patterns, and drug screening by assessing cellular response heterogeneity. The framework comprises machine learning integration pathways for automated recognition of variability patterns and adaptive acquisition strategies. Full article

Background: In recent years, electronic patient-reported outcome (ePRO) systems on electronic devices, such as smartphones, have been employed to collect patients’ self-assessments and symptom reports. However, these studies were limited to younger populations and patients with severe diseases. Objective: This study aimed to evaluate the ease of use and response continuity of an ePRO system used by healthy middle-aged and older adults. Methods: This prospective observational study included participants aged 40–74 years undergoing specific health checkups. The System Usability Scale (SUS) was used to assess ePRO usability. Response continuity was evaluated by assessing EuroQol 5-Dimensional 5-Level responses once a month for up to 3 months after the health checkup date. Results: Eleven participants, aged 47–73 years, participated in the study. The mean SUS on the screening date was 59.1 (95% CI: 50.0–68.1; a cut-off of 70 indicated “useful”). However, only one participant failed to complete the ePRO at one and two months post-examination, and responses were obtained from all participants at three months. Conclusions: Due to the small sample size, usability as measured by the SUS should be interpreted descriptively. While initial onboarding appeared to be a major implementation barrier, sustained monthly ePRO reporting over 3 months was achievable among participants who completed registration with support, suggesting the conditional feasibility of response continuity in this preventive health checkup setting. Full article

Weed management has become a critical agricultural practice, as weeds compete with crops for nutrients, host pests and diseases, and cause major economic losses. The invasive weed Ambrosia artemisiifolia (common ragweed) is particularly problematic in Hungary, endangering crop productivity and public health through its fast proliferation and allergenic pollen. This review examines the current challenges and impacts of A. artemisiifolia while exploring sustainable approaches to its management through precision agriculture. Recent advancements in unmanned aerial vehicles (UAVs) equipped with advanced imaging systems, remote sensing, and artificial intelligence, particularly deep learning models such as convolutional neural networks (CNNs) and Support Vector Machines (SVMs), enable accurate detection, mapping, and classification of weed infestations. These technologies facilitate site-specific weed management (SSWM) by optimizing herbicide application, reducing chemical inputs, and minimizing environmental impacts. The results of recent studies demonstrate the high potential of UAV-based monitoring for real-time, data-driven weed management. The review concludes that integrating UAV and AI technologies into weed management offers a sustainable, cost-effective, mitigate the socioeconomic impacts and environmentally responsible solution, emphasizing the need for collaboration between agricultural researchers and technology developers to enhance precision agriculture practices in Hungary. Full article

Mathematical models for infectious diseases, particularly autonomous ODE models, are generally known to possess simple dynamics, often converging to stable disease-free or endemic equilibria. This paper investigates the dynamic consequences of a crucial, yet often overlooked, component of pandemic response: the saturation of public health testing. We extend the standard SIR model to include compartments for ‘Confirmed’ (C) and ‘Monitored’ (M) individuals, resulting in a new SICMR model. By fitting the model to U.S. COVID-19 pandemic data (specifically the Omicron wave of late 2021), we demonstrate that capacity constraints in testing destabilize the testing-free endemic equilibrium ($E_1$). This equilibrium becomes an unstable saddle-focus. The instability is driven by a sociological feedback loop, where the rise in confirmed cases drive testing effort, modeled by a nonlinear Holling Type II functional response. We explicitly verify that the eigenvalues for the best-fit model satisfy the Shilnikov condition (${\lambda }_u>{\lambda }_s$), demonstrating the system possesses the necessary ingredients for complex, chaotic-like dynamics. Furthermore, we employ Stochastic Differential Equations (SDEs) to show that intrinsic noise interacts with this instability to generate ’noise-induced bursting,’ replicating the complex wave-like patterns observed in empirical data. Our results suggest that public health interventions, such as testing, are not merely passive controls but active dynamical variables that can fundamentally alter the qualitative stability of an epidemic. Full article

Chronic, low-grade inflammation is a characteristic of metabolic diseases like type 2 diabetes. Despite recommendations to select low- or non-fat dairy foods over full-fat dairy foods for metabolic health, recent research suggests potential anti-inflammatory benefits of dairy fat consumption. We aimed to compare the systemic inflammatory tone (i.e., circulating inflammatory biomarker concentrations and ex vivo peripheral blood mononuclear cell inflammatory responses) of individuals with prediabetes after consuming diets with full-fat (3.25%) or non-fat yogurt. We hypothesized that short-term consumption of three daily full-fat yogurt servings beneficially affects inflammatory tone. Thirteen participants aged 45–75 years completed an eight-week randomized, double-masked, controlled crossover study. The two, three-week experimental diets comprised three daily servings of full-fat or non-fat yogurt and were each preceded by a one-week run-in diet. Following each diet, circulating inflammatory biomarkers and cytokine concentrations in the supernatants of peripheral blood mononuclear cells under control or lipopolysaccharide-stimulated conditions were measured. Compared with non-fat yogurt intake, circulating immature granulocyte concentrations were lower following full-fat yogurt intake, but there were no other differences in leukocyte concentrations. Circulating concentrations of cytokines or other inflammatory markers did not differ by diet. Cell supernatant interleukin-1β concentrations were lower following the full-fat yogurt diet under unstimulated conditions but were not different between diets under stimulated conditions. There were no differences by diet in supernatant concentrations of other cytokines under unstimulated or stimulated conditions. Together, minimal differences in inflammatory tone were observed following the short-term consumption of three daily servings of full-fat or non-fat yogurt in individuals with prediabetes. Full article