Search Results (30,039)

Background: Visual impairment (VI) continues to be a significant global public health concern, especially in underserved rural communities. Objectives: This study aims to assess the prevalence of VI and refractive errors, as well as to identify the causes and risk factors associated with VI in Duhknah, a rural area in Qassim Province, Saudi Arabia. Methods: This cross-sectional study, conducted in May 2024, included 929 participants aged 6–90 years from Duhknah, a rural area in Qassim Province, Saudi Arabia. Refractive errors (REs) were measured using a non-cycloplegic autorefractometer. Anterior and posterior eye examinations were performed using slit lamp biomicroscopy, direct ophthalmoscopy, and 90 D fundus biomicroscopy. VI was classified based on the International Classification of Diseases 11th revision (ICD-11), 2018. Results: The findings revealed that 671 (72.2%) participants had never undergone an eye examination. The overall prevalence of presenting VI was 370 (39.8%), comprising 21.6% with mild VI, 11.0% moderate, 4.1% severe, and 3.1% classified as blind. The prevalence of hyperopia, myopia, and astigmatism was 20.6%, 36.9%, and 13.2%, respectively. Uncorrected REs were the most common cause of VI (81.4%), followed by amblyopia (13.5%) and cataracts (3.2%). Regression analysis showed that women had 1.58 times higher odds of VI (p = 0.001). Participants with eye examinations for one year or more had 3.64 times higher odds (p < 0.001). Additionally, the risk of VI was significantly lower among older participants (ages 18–90) compared to younger ones (ages 6–17), (p < 0.001). Conclusions: This study found most participants had never had an eye exam, and VI was highly prevalent in the rural community. These findings underscore the need to strengthen primary eye care in rural Saudi Arabia. Regular vision screening, particularly for children, and better access to refractive services could significantly reduce VI and support the goals of Saudi Vision 2030. Full article

Sex-based immunological differences significantly influence the outcome of vaccination, yet the molecular mediators underpinning these differences remain largely elusive. MicroRNAs (miRNAs), key post-transcriptional regulators of gene expression, have emerged as critical modulators of innate and adaptive immune responses. In this study, we investigated the expression profile of selected circulating miRNAs as potential biomarkers of sex-specific humoral responses to the mRNA COVID-19 vaccine in a cohort of health care workers. Plasma samples were collected longitudinally at a defined time point (average 71 days) post-vaccination and analyzed using RT-qPCR to quantify a panel of immune-relevant miRNAs. Anti-spike (anti-S) IgG titers were measured by chemiluminescent immunoassays. Our results revealed sex-dependent differences in miRNA expression dynamics, with miR-221-3p and miR-148a-3p significantly overexpressed in vaccinated female HCWs and miR-155-5p overexpressed in vaccinated males. MiR-148a-3p showed a significant association with anti-S/RBD (RBD: receptor binding domain) IgG levels in a sex-specific manner. Bioinformatic analysis for miRNA targets indicated distinct regulatory networks and pathways involved in innate and adaptive immune responses, potentially underlying the differential immune activation observed between males and females. These findings support the utility of circulating miRNAs as minimally invasive biomarkers for monitoring and predicting sex-specific vaccine-induced immune responses and provide mechanistic insights that may inform tailored vaccination strategies. Full article

Sexually transmitted infections (STIs) are a significant global health concern, affecting millions of people worldwide, particularly sexually active adolescents and young adults. These infections, caused by various pathogens, including bacteria, viruses, parasites, and fungi, can have profound implications for women’s reproductive health and fertility. This review explores the role of vaginal and uterine infections in women’s infertility, focusing on the most common pathogens and their impact on reproductive outcomes. Bacterial infections, such as those caused by intracellular bacteria (Mycoplasma, Ureaplasma, and Chlamydia), Neisseria gonorrhoeae, and bacterial vaginosis, are among the most prevalent causes of infertility in women. Studies have shown that these infections can lead to pelvic inflammatory disease, tubal occlusion, and endometrial damage, all of which can impair fertility. Mycobacterium tuberculosis, in particular, is a significant cause of genital tuberculosis and infertility in high-incidence countries. Viral infections, such as Human papillomavirus (HPV) and Herpes simplex virus (HSV), can also affect women’s fertility. While the exact role of HPV in female infertility remains unclear, studies suggest that it may increase the risk of endometrial implantation issues and miscarriage. HSV may be associated with unexplained infertility. Parasitic infections, such as trichomoniasis and schistosomiasis, can directly impact the female reproductive system, leading to infertility, ectopic pregnancy, and other complications. Fungal infections, such as candidiasis, are common but rarely have serious outcomes related to fertility. The vaginal microbiome plays a crucial role in maintaining reproductive health, and alterations in the microbial balance can increase susceptibility to STIs and infertility. Probiotics have been proposed as a potential therapeutic strategy to restore the vaginal ecosystem and improve fertility outcomes, although further research is needed to establish their efficacy. In conclusion, vaginal and uterine infections contribute significantly to women’s infertility, with various pathogens affecting the reproductive system through different mechanisms. Early diagnosis, appropriate treatment, and preventive measures are essential to mitigate the impact of these infections on women’s reproductive health and fertility. Full article

Salmonella is a major foodborne pathogen, yet real-time data on duck-derived strains in China remain scarce. This study investigated the epidemiology, antimicrobial resistance (AMR), gene profiles, and PFGE patterns of 114 Salmonella isolates recovered from 397 deceased ducks (2021–2024) across nine provinces (isolation rate: 28.72%). Fourteen serotypes were identified, with S. Typhimurium (23.68%), S. Indiana (21.93%), S. Kentucky (18.42%), and S. Enteritidis (12.28%) being predominant. Most isolates showed high resistance to β-lactams, tetracyclines, quinolones, and sulfonamides, with extensive multidrug resistance (MDR) observed—especially in S. Indiana, S. Typhimurium, and S. Kentucky. Among the 23 detected resistance genes, tet(B) had the highest prevalence (75.44%), particularly in S. Indiana. Biofilm formation was observed in 99.12% of isolates, with 84.21% demonstrating moderate to strong capacity. Eighteen virulence genes were detected; S. Enteritidis carried more spvB/C, sipB, and sodC1, while S. Indiana had higher cdtB carriage. PFGE revealed substantial genetic diversity among strains. This comprehensive analysis highlights the high AMR and biofilm potential of duck-derived Salmonella in China, emphasizing the urgent need for enhanced surveillance and control measures to mitigate public health risks. Full article

Background and Objectives: Periodontitis is a chronic inflammatory disease that leads to progressive destruction of periodontal tissues and remains a significant global health burden. While conventional therapies such as scaling and root planning offer short-term improvements, they often fall short in maintaining long-term microbial control, underscoring the need for adjunctive strategies. This study evaluated the clinical and microbiological effects of a novel essential oil (EO)-based gel—SmartGel OV—formulated with Origanum vulgare. Materials and Methods: Thirty adults with periodontitis were enrolled in a 4-month observational study, during which SmartGel OV was applied daily via gingival massage. Clinical outcomes and bacterial profiles were assessed through probing measurements and real-time PCR analysis. Additionally, a pilot AI-based tool was explored as a supplemental method to monitor inflammation progression through intraoral images. Results: Significant reductions were observed in Fusobacterium nucleatum and Capnocytophaga spp., accompanied by improvements in clinical markers, including probing depth, bleeding on probing, and plaque index. The AI framework successfully identified visual inflammation changes and supported early detection of non-responsiveness. Conclusions: SmartGel OV demonstrates promise as a natural adjunctive treatment for periodontitis and AI monitoring was included as an exploratory secondary tool to assess feasibility for future remote tracking. Full article

This study investigates the presence, origin, and associated ecological and human health risks of polycyclic aromatic hydrocarbons (PAHs) in soils from uncultivated lands and beach sediments within the Yellow River Delta (YRD), China. The measured concentrations of 16 priority PAHs in soils spanned 24.97–326.42 ng/g (mean: 130.88 ng/g), while concentrations in sediments ranged from 46.17 to 794.32 ng/g, averaging 227.22 ng/g. In terms of composition, low-molecular-weight PAHs predominated in soil samples, whereas high-molecular-weight compounds were more prevalent in sediments. The positive matrix factorization (PMF) model results suggested that petroleum pollution and fuel combustion were the main sources of PAHs in soils, whereas the contribution in sediments was derived from petroleum and traffic pollution. The ecological risk assessment results indicated that there existed no obvious ecological risk of soil PAHs, but sediment PAHs could negatively impact the surrounding ecological environment, especially in the northern coastal beach area. In addition, soil PAHs posed no potential carcinogenic risk to humans. Further pollution prevention and management measures are required in this region to ensure the safety of the environment. Full article

This paper provides geospatial and correlation analysis of heavy metal distribution in the soil cover of the city of Temirtau and its industrial zones. Based on 25 soil samples taken in 2024, concentrations of nine heavy metals (As, Pb, Zn, Cu, Ni, Co, Mn, Cr, Ba) were determined using X-ray fluorescence analysis. Spatial data interpolation was performed using the Kriging method in the ArcGIS Pro environment. The results showed the presence of localized extreme pollution zones, primarily near the Qarmet JSC metallurgical plant. The most significant exceedances of maximum permissible concentrations (MPC), up to 348× MPC for Cr, 160× MPC for Zn, and 72× MPC for As, were recorded at individual locations. Correlation analysis revealed a moderate positive relationship between several elements, particularly Mn and Cu (r = 0.64). Comparison of the spatial distribution of pollution with population data allowed for the assessment of potential environmental risks. This research emphasizes the need to implement systematic monitoring, sustainable land management practices, ecological maps, and preventive measures to reduce the long-term impact of heavy metals on ecosystems and public health, and to promote environmental sustainability in industrial regions. Full article

Background and Objectives: Surgical smoke generated by energy devices during surgery contains hazardous substances and poses health risks to staff in the operating room. Exposure to surgical smoke must be reduced to minimize the risk of health hazards. Many studies have evaluated surgical smoke qualitatively, but few have performed quantitative assessment. The aim of this study was to quantify the number of particles generated during various breast surgery procedures. Materials and Methods: In this prospective, randomized study, breast surgeries performed at Tokyo Medical and Dental University Hospital (the present Institute of Science Tokyo Hospital) between December 2022 and August 2023 were randomly assigned to two groups: the electrosurgical device group and the electrosurgical device with smoke evacuator group. The number of particles generated by energy devices during surgery was measured using a particle counter. Results: Surgical smoke was generated in all procedures. The number of measured particles was significantly less in the electrosurgical device with smoke evacuator group than in the electrosurgical device group during all procedures (all p < 0.001). Conclusions: All breast surgery procedures produced a significant amount of surgical smoke, which was effectively reduced by using an electrosurgical device with a smoke evacuator. These findings support the routine use of smoke evacuators in breast surgery to reduce occupational exposure to hazardous particles. Implementation of such devices could improve operating room safety and may inform future guidelines and institutional policies regarding surgical smoke management. Full article

The increase in the elderly population globally reflects a change in the population’s mindset regarding preventive health measures and necessitates a rethinking of healthcare strategies. The integration of machine learning (ML)-type algorithms in geriatrics represents a direction for optimizing prevention, diagnosis, prediction, monitoring, and treatment. This paper presents a systematic review of the scientific literature published between 1 January 2020 and 31 May 2025. The paper is based on the applicability of ML techniques in the field of geriatrics. The study is conducted using the Web of Science database for a detailed discussion. The most studied algorithms in research articles are Random Forest, Extreme Gradient Boosting, and support vector machines. They are preferred due to their performance in processing incomplete clinical data. The performance metrics reported in the analyzed papers include the accuracy, sensitivity, F1-score, and Area under the Receiver Operating Characteristic Curve. Nine search categories are investigated through four databases: WOS, PubMed, Scopus, and IEEE. A comparative analysis shows that the field of geriatrics, through an ML approach in the context of elderly nutrition, is insufficiently explored, as evidenced by the 61 articles analyzed from the four databases. The analysis highlights gaps regarding the explainability of the models used, the transparency of cross-sectional datasets, and the validity of the data in real clinical contexts. The paper highlights the potential of ML models in transforming geriatrics within the context of personalized predictive care and outlines a series of future research directions, recommending the development of standardized databases, the integration of algorithmic explanations, the promotion of interdisciplinary collaborations, and the implementation of ethical norms of artificial intelligence in geriatric medical practice. Full article

Background/Objectives: The aim of this study was to evaluate baseline health measurements among transitional housing residents (n = 29) participating in an 8-week pilot wellness intervention. Methods: Researchers measured anthropometrics, body composition, muscular strength, cardiovascular indicators, physical activity, diet quality, and health-related perceptions. Researchers analyzed data using descriptive statistics and conventional content analysis. Results: Most participants were male, White, and food insecure. Mean BMI (31.8 ± 8.6 kg/m²), waist-to-hip ratio (1.0 ± 0.1 males, 0.9 ± 0.1 females), body fat percentage (25.8 ± 6.1% males, 40.5 ± 9.4% females), blood pressure (131.8 ± 17.9/85.2 ± 13.3 mmHg), and daily step counts exceeded recommended levels. Absolute grip strength (77.1 ± 19.4 kg males, 53.0 ± 15.7 kg females) and perceived general health were below reference standards. The Healthy Eating Index-2020 score (39.7/100) indicated low diet quality. Common barriers to healthy eating were financial constraints (29.6%) and limited cooking/storage facilities (29.6%), as well as to exercise, physical impediments (14.8%). Conclusions: Residents living in transitional housing have less favorable body composition, diet, and grip strength measures, putting them at risk for negative health outcomes. Wellness interventions aimed at promoting improved health-related outcomes while addressing common barriers to proper diet and exercise among transitional housing residents are warranted. Full article

Sensor selection in IoT-based smart healthcare systems is a complex fuzzy decision-making problem due to the presence of numerous uncertain and interdependent evaluation criteria. Traditional fuzzy multi-criteria decision-making (MCDM) approaches often assume independence among criteria and rely on aggregation operators that impose sharp transitions between preference levels. These assumptions can lead to decision outcomes with insufficient differentiation, limited discriminatory capacity, and potential issues in consistency and sensitivity. To overcome these limitations, this study proposes a novel fuzzy decision-making framework by integrating Quasi-D-Overlap functions into the fuzzy MARCOS (Measurement of Alternatives and Ranking According to Compromise Solution) method. Quasi-D-Overlap functions represent a generalized extension of classical overlap operators, capable of capturing partial overlaps and interdependencies among criteria while preserving essential mathematical properties such as associativity and boundedness. This integration enables a more intuitive, flexible, and semantically rich modeling of real-world fuzzy decision problems. In the context of real-time health monitoring, a case study is conducted using a hybrid edge–cloud architecture, involving sensor tasks such as heartrate monitoring and glucose level estimation. The results demonstrate that the proposed method provides greater stability, enhanced discrimination, and improved responsiveness to weight variations compared to traditional fuzzy MCDM techniques. Furthermore, it effectively supports decision-makers in identifying optimal sensor alternatives by balancing critical factors such as accuracy, energy consumption, latency, and error tolerance. Overall, the study fills a significant methodological gap in fuzzy MCDM literature and introduces a robust fuzzy aggregation strategy that facilitates interpretable, consistent, and reliable decision making in dynamic and uncertain healthcare environments. Full article

Fertility is considered a significant demographic concern, especially in relation to climate change. This study examines how awareness of climate change, measured by five subscales—climate-friendly behavior, knowledge, personal concern, attitude, and multiplicative action—affects fertility intentions, emphasizing the mediating role of threat perception. Data were collected through an online survey administered to a sample of 817 Palestinian citizens aged 18–49 residing in the West Bank. Structural equation modeling (SEM) was utilized for the data analysis. The results revealed that climate change awareness does not directly affect fertility intentions. However, an indirect effect of climate change awareness on fertility intentions was observed, mediated by threat perception as an intervening variable. Individuals exhibiting increased awareness of climate change and perceptions of future risks demonstrated a greater likelihood of reducing their fertility intentions compared to others. Policymakers in the Palestinian territories should prioritize enhancing public awareness regarding climate change and its associated short- and long-term threats. Therefore, incorporating climate education and associated risks into fertility health programs is essential. Full article

Computer vision techniques such as three-dimensional digital image correlation (3D-DIC) and three-dimensional point tracking (3D-PT) have demonstrated broad applicability for monitoring the conditions of large-scale engineering systems by reconstructing and tracking dynamic point clouds corresponding to the surface of a structure. Accurate stereophotogrammetry measurements require the stereo cameras to be calibrated to determine their intrinsic and extrinsic parameters by capturing multiple images of a calibration object. This image-based approach becomes cumbersome and time-consuming as the size of the tested object increases. To streamline the calibration and make it scale-insensitive, a multi-sensor system embedding inertial measurement units and a laser sensor is developed to compute the extrinsic parameters of the stereo cameras. In this research, the accuracy of the proposed sensor-based calibration method in performing stereophotogrammetry is validated experimentally and compared with traditional approaches. Tests conducted at various scales reveal that the proposed sensor-based calibration enables reconstructing both static and dynamic point clouds, measuring displacements with an accuracy higher than 95% compared to image-based traditional calibration, while being up to an order of magnitude faster and easier to deploy. The novel approach has broad applications for making static, dynamic, and deformation measurements to transform how large-scale structural health monitoring can be performed. Full article

Almost one in three firefighters develop mental health disorders at some point during their careers, a rate double that in the general population. Frequent exposures to potentially traumatic situations can contribute to symptoms of these disorders, two of the most common being depression and post-traumatic stress disorder (PTSD). While various psychological interventions have been implemented among this group, reports of their effectiveness include mixed results. To this end, the current study endeavored to test the effectiveness of a 12-week intervention combining occupationally-tailored high-intensity functional training (HIFT) and psychological resilience training (RES) in reducing depressive and post-traumatic stress symptoms (PTSSs), as well as increasing psychological resilience and mental wellbeing, in career firefighters. Thirty career firefighters completed four mental health measurements over 17 weeks while anthropometrics and physical performance (i.e., number of stations completed in 20 min during an eight-station simulated job-task circuit workout [T-CAC]) were measured pre- and post-intervention. Pre to post comparisons were made via repeated-measures t-tests. Significant mean differences were observed for T-CAC stations completed, PTSSs, and psychological resilience between pre- and post-intervention. In future interventions, researchers should actively engage firefighters, maximize integration with daily operations, and employ culturally-relevant practices to explore the links between physical and mental health. Full article

Background: This cross-sectional study examined the association between body mass index (BMI) and executive function (EF) in older adults, with a focus on the moderating role of attitudes toward own aging (ATOA). Method: A total of 431 community-dwelling elderly individuals from Yakumo Town and Kyoto City, Japan, participated between 2023 and 2024. EF was assessed using the Digit Cancellation Test (D-CAT), and ATOA was measured via a validated subscale of the Philadelphia Geriatric Center Morale Scale. Results: Multiple linear regression analyses adjusted for demographic and health covariates revealed a significant interaction between BMI and ATOA in the younger-old cohort. Specifically, higher BMI was associated with lower executive function only in individuals with lower ATOA scores. No such association was observed in those with more positive views on aging. Conclusions: These results indicate that positive psychological constructs, particularly favorable self-perceptions of aging, may serve as protective factors against the detrimental cognitive consequences of increased body mass index in younger-old populations. Full article