Search Results (739)

Schistosomiasis remains a major neglected tropical disease globally and presents particular challenges for countries transitioning from control to elimination. Saudi Arabia represents a unique epidemiological setting, having shifted from historical endemic transmission to very low reported incidence, yet long-term national analyses remain limited. A retrospective longitudinal analysis of national schistosomiasis surveillance data from 2002 to 2024 was conducted to evaluate temporal trends, clinical subtypes, regional distribution, and demographic characteristics. Joinpoint regression was used to identify significant changes in temporal trends, and autoregressive integrated moving average (ARIMA) models were applied to forecast national and regional trajectories. National incidence declined markedly from 5.5 per 100,000 in 2002 to 0.12 per 100,000 in 2024, with a notable change around 2010, followed by sustained low-level incidence. Intestinal schistosomiasis accounted for most cases, with increasing concentration among adult non-Saudi males and near-elimination among children. Regionally, cases were confined to a limited number of western and southwestern regions, particularly Ta’if, Al Baha, Jazan, and Madinah. Forecasting analyses indicated continued low-level detection without evidence of national resurgence. These findings demonstrate a transition to an elimination-maintenance phase and highlight the need for sustained surveillance in historically endemic regions and mobile populations. Full article

Background/Objectives: Osteoporosis is a major global health concern, and early risk assessment plays a crucial role in fracture prevention. Although demographic, clinical, and lifestyle factors are commonly incorporated into screening tools, their relative importance within data-driven prediction frameworks can vary substantially across datasets. Rather than aiming to identify novel predictors, this study evaluates the stability and behavior of established osteoporosis risk factors using statistical inference and machine learning-based feature selection methods across heterogeneous data sources. We further examine whether simplified and near-minimal models can achieve predictive performances comparable to that of full-feature configurations. Methods: An open-access Kaggle dataset (n = 1958) and a retrospective clinical dataset from the University of Phayao Hospital (n = 176) were analyzed. Feature relevance was assessed using logistic regression, likelihood ratio testing, MRMR, ReliefF, and unified importance scoring. Multiple predictor configurations, ranging from full-feature to minimal and near-minimal models, were evaluated using decision tree, support vector machine, k-nearest neighbor, naïve Bayes, and efficient linear classifiers. External validation was performed using hospital-based records. Results: Across all analyses, age consistently emerged as the dominant predictor, followed by corticosteroid use, while other variables showed limited incremental predictive contributions. Simplified models based on age alone or age combined with medication-related variables achieved performances comparable to full-feature models (accuracy ≈91% and AUC ≈ 0.95). In addition, near-minimal models incorporating gender alongside age and medications demonstrated a favorable balance between discrimination and computational efficiency under external validation. Although overall performance declined under distributional shift, naïve Bayes and efficient linear classifiers showed the most stable external behavior (AUC = 0.728–0.787). Conclusions: These findings indicate that stability-driven feature selection primarily reproduces well-established epidemiological risk patterns rather than identifying novel predictors. Minimal and near-minimal models—including those incorporating gender—retain acceptable performances under external validation and are methodologically efficient. Given the limited size and single-center nature of the external cohort, the results should be interpreted as preliminary methodological evidence rather than definitive support for clinical screening deployment. Further multi-center studies are required to assess generalizability and clinical relevance. Full article

Background: Migraine is a prevalent neurological disorder associated with significant morbidity and social burden. Although various triggers for migraine have been identified, the relationship between smoking and migraine remains unclear. This study aimed to compare migraine characteristics between people with and without smoking in Saudi Arabia. Methods: A cross-sectional study using an online survey tool had been conducted in Saudi Arabia. The survey assessed migraine characteristics, smoking behaviour, demographics, and comorbidities. Statistical analyzes were performed to investigate the occurrence of migraine, smoking behaviour, and demographic factors. Descriptive statistics summarized the data, with various statistical tests employed to compare variables between groups. Results: A total of 229 participants were included in the study, with a majority being young adults (48.47%), predominantly females (66.81%), and holding a bachelor’s degree (63.32%). The study found that 19.2% of individuals with migraine were current smokers, with an average smoking duration of 9.7 years. While some reported relief from migraine pain, others experienced increased pain intensity or frequency. No significant differences were found in migraine characteristics between smokers and non-smokers, but younger individuals and males with migraine were more likely to smoke. The study highlights the complex relationship between smoking and migraine, with varying effects on individuals. Conclusions: The study underscores the lack of significant differences in migraine characteristics between smokers and non-smokers, suggesting that smoking does not play a pivotal role in the clinical presentation of migraines. This insight prompts a shift in research focus towards other potential contributors to migraines, such as genetic predispositions, environmental factors, and comorbidities. Understanding these associations can inform public health strategies aimed at alleviating migraine-related burdens. Full article

The COVID-19 pandemic has highlighted the importance of rapid clinical decision-making to facilitate the efficient usage of healthcare resources. Over the past decade, machine learning (ML) has caused a tectonic shift in healthcare, empowering data-driven prediction and decision-making. Recent research demonstrates how ML was used to respond to the COVID-19 pandemic. This paper puts forth new computer-aided COVID-19 disease screening techniques using six classes of ML algorithms (including penalized logistic regression, random forest, artificial neural networks, and support vector machines) and evaluates their performance when applied to a real-world clinical dataset containing patients’ demographic information and vital indices (such as sex, ethnicity, age, pulse, pulse oximetry, respirations, temperature, BP systolic, BP diastolic, and BMI), as well as ICD-10 codes of existing comorbidities, as attributes to predict the risk of having COVID-19 for given patient(s). Variable importance metrics computed using a random forest model were used to reduce the number of important predictors to thirteen. Using prediction accuracy, sensitivity, specificity, and AUC as performance metrics, the performance of various ML methods was assessed, and the best model was selected. Our proposed model can be used in clinical settings as a rapid and accessible COVID-19 screening technique. Full article

The COVID-19 pandemic has been one of the most globally disruptive social events in recent history, bringing widespread lockdowns, restrictions on movement, remote work, mass vaccination campaigns, and millions of deaths worldwide. These unprecedented circumstances have reshaped many aspects of social life, including perceptions of safety and firearm ownership. This study examines changes in reported gun ownership before and during the COVID-19 pandemic in the United States, using binary logistic regression analyses of General Social Survey (GSS) data from 2018 and 2021. Analysis revealed that reported gun ownership remained stable at approximately 35% in both years. However, the demographic and social profile of gun owners shifted significantly. Demographic factors such as sex, US birth, marital status, and income consistently predicted ownership in both years, while race, middle-class identification, and political party affiliation emerged as significant predictors only during the pandemic, with Democrats becoming significantly less likely to report gun ownership. The results demonstrate how social crises can reshape the composition of firearm owners rather than overall rates, with implications for public policy and safety. Full article

This study evaluates the early impact of Central Bank Digital Currencies (CBDCs) on key financial indicators in The Bahamas, focusing on the introduction of the SandDollar—the world’s first fully implemented retail CBDC. Using the Synthetic Control Method (SCM), the analysis constructs counterfactual scenarios to assess the effects of CBDCs on three dependent variables: outstanding loans from commercial banks as a percentage of GDP, outstanding deposits as a percentage of GDP, and the number of deposit accounts per 1000 adults. Three separate SCM models were estimated for the period 2014–2024, incorporating a broad set of control variables reflecting financial infrastructure, economic performance, demographic characteristics, and digital readiness. The findings consistently show that the SandDollar’s implementation is associated with reductions in loan issuance, deposit levels, and deposit account ownership compared to their synthetic counterparts. These results support the hypothesis that direct CBDC models may amplify “deposit substitution” and increase liquidity risks by shifting financial activity away from commercial banks. Although the SCM provides a structured causal framework, the short post-treatment period and potential pandemic-related disruptions limit the scope of a long-term understanding. The study underscores the importance of careful CBDC design, particularly the role of intermediated models in mitigating unintended financial stability risks. Full article

Objective: To analyze demographic traits, clinical complications, and healthcare use in patients with chronic liver disease across major etiologies in a large Romanian cohort. Methods: A retrospective study (2019–2023) of 2359 patients with chronic hepatitis C (CHC), hepatitis associated with alcohol (ALH), cirrhosis associated with alcohol (ALC), or non-alcoholic cirrhosis (NALC). Data on demographics, clinical outcomes, and hospitalizations were analyzed using descriptive statistics, regression modeling, and clustering in IBM SPSS 27.0.1. Results: CHC patients were oldest (mean 67.5 ± 12.3 years), while ALH patients were youngest (56.0 ± 11.0 years). CHC prevalence increased with age (10.0% in ≤30-year-olds to 87.1% in ≥81-year-olds; γ = 0.535, p < 0.001). Females comprised 60–70% of CHC cases, males > 85% of ALH and >78% of ALC. Mean hospitalization duration decreased from 13.80 days (2019) to 9.10 days (2023), yet cirrhotic patients had the longest stays (NALC: 16.37 ± 14.34; ALC: 17.66 ± 12.96) versus CHC (10.38 ± 10.14). Etiology was the strongest predictor of hospitalization length. Portal hypertension (PH) was the most common complication (54.3%), with males bearing more severe hepatic complications (ascites—38.3%; PH—66.8%). Conclusions: Hospital-based Romanian cohort analysis revealed that patient presentation and outcomes are fundamentally shaped by the interplay of etiology, sex, and age. We found a distinct female predominance in CHC, a pronounced male predominance in alcohol-related diseases, and evolving trends in non-alcoholic cirrhosis. These determinants dictate specific epidemiological patterns, hospitalization burdens, and complication risks, underscoring the critical need for a paradigm shift toward personalized, etiology-driven, and sex-tailored clinical management. Full article

Despite its global distribution, the impacts of wild pigs on the environment are poorly understood. However, wild boar (Sus scrofa) is recognized as a pest species, causes extensive damage to agriculture, biodiversity, and forests, and contributes to motor vehicle accidents. This study investigates the causes and mechanisms underlying the demographic explosion of wild boar in Italy. The analysis is based exclusively on official datasets from Italian governmental institutes, allowing quantitative correlations between population dynamics, culling rates, and economic impacts. By integrating historical data, population biology, reproductive physiology, and chemical communication, the study reveals that anthropogenic pressures, counterintuitively driven by wildlife management practices, have significantly contributed to population growth. A shift from a K-strategy to an r-strategy in reproductive behavior, induced by sustained control pressure, has led to increased birth rates and accelerated expansion. Disruptions in species homeostasis trigger harmful changes in ecosystem structure and functionality, delineating a model of environmental damage. These findings highlight the urgency of adopting an integrated wildlife management approach that combines conservation biology and physiological principles with targeted operational interventions to prevent further degradation affecting both the species and the ecosystem. Full article

Background and Objectives: Healthcare-associated infections (HAIs) significantly increase morbidity and mortality in critically ill patients, and their burden became more pronounced during the COVID-19 pandemic. However, data describing the temporal evolution of HAIs, pathogen distribution, and associated risk factors across consecutive pandemic waves remain limited. This study aimed to characterize the epidemiology, microbiology, and outcomes of HAIs in COVID-19 intensive care units (ICU) patients and to identify clinical and laboratory predictors of mortality. Materials and Methods: This retrospective observational study included adult patients with RT-PCR–confirmed COVID-19 who developed at least one HAI ≥ 48 h after ICU admission between March 2020 and December 2020, encompassing the first three pandemic waves in Türkiye, in a tertiary-care ICU. Demographic, clinical, laboratory, and microbiological data were collected. Inflammatory markers and severity scores (SAPS-II, MCCI, and NLR) were analyzed. Receiver operating characteristic (ROC) curve analysis was used to determine optimal cut-off values for mortality prediction. Results: Among the 1656 ICU admissions, 145 patients (8.7%) developed HAIs; after exclusions, 136 patients were included in the final analysis. Bloodstream infections were the most frequent HAI (57%), followed by urinary tract infections (31%), ventilator-associated pneumonia (9%), and surgical site infections (1%). Klebsiella pneumoniae was the predominant pathogen, followed by Candida albicans and Acinetobacter baumannii. Multidrug-resistant organisms, including MRSA and VRE, showed variable distribution across pandemic periods. Overall in-hospital mortality was 74.3%. Non-survivors had significantly higher SAPS-II, MCCI, and NLR values. ROC analysis identified NLR > 38.8 and SAPS-II > 35.5 as mortality-predictive thresholds. Dynamic inflammatory marker patterns correlated with infection timing, and early peaks of CRP, WBC, and IL-6 were associated with worse outcomes. Conclusions: HAIs imposed a substantial clinical burden on critically ill COVID-19 patients, with high mortality driven predominantly by multidrug-resistant bloodstream infections. Severity indices and inflammation-based biomarkers demonstrated strong prognostic value. Temporal shifts in pathogen ecology across pandemic waves underscore the need for adaptive infection-prevention strategies, continuous microbiological surveillance, and strengthened antimicrobial stewardship in critical care settings. Full article

This study examines the evolution and main trends in the number of students, teaching staff and graduates in Romania’s pre-university education system over the period 1990–2024 (and 1990–2023 for graduates), employing ARIMA models to generate forecasts up to the year 2027. The research is grounded in the premise of profound structural transformations within the Romanian educational system, driven by demographic decline, external migration, recurrent reforms, and shifts in resource allocation. The descriptive analysis highlights a pronounced downward trend for all three variables (students, teaching staff and graduates), reflecting the continuous reduction in the school-age population and the restructuring of the educational network. The statistical tests employed, such as Shapiro–Wilk, Augmented Dickey–Fuller, Durbin–Watson, Breusch–Godfrey and ARCH, validate the selected optimal ARIMA models: ARIMA(1,1,1) for teaching staff, ARIMA(4,1,3) for students, and ARIMA(3,1,5) for graduates. The forecasting results indicate that this declining trend is expected to persist through 2027: the number of teaching staff is estimated to decrease to approximately 178,700 individuals, the number of students is estimated to decrease to around 2.78 million, and the number of graduates is projected to fall until 2026, followed by a potential slight stabilization in 2027. The Spearman correlation analysis indicates strong associations among all variables, suggesting that their dynamics are predominantly shaped by demographic and migratory factors. Granger causality analysis shows that changes in birth rates lead to rapid adjustments in teaching staff within 2–3 years. No significant short-term causality is found for the number of students or graduates, though demographic effects appear after 5–6 years for students, indicating long-term impacts on the school population. This study underscores the importance of econometric methods in informing educational policy, particularly in the context of the marked contraction of the school-age population. It also highlights the need for strategic planning regarding human resources in education, per-student funding, the reorganization of the school network, and curriculum adaptation. Full article

Background/Objectives: The prevalence of gastric subepithelial lesions (SELs) is rising. Endoscopic resection (ER) technique provides a minimally invasive alternative to manage gastric SELs. This study aims to evaluate the effectiveness and safety of ER for gastric myogenic tumors, and examine predictors for gastrointestinal stromal tumors (GISTs). Methods: The retrospective study was conducted between 2012 and 2024 at nine tertiary centers in Taiwan. We enrolled patients with endoscopic ultrasound (EUS)-documented gastric myogenic tumors managed by endoscopic muscular dissection (EMD), endoscopic subserosal dissection (ESSD), submucosal tunneling endoscopic resection (STER), and endoscopic full-thickness resection (EFTR). Clinical manifestation, endoscopic features, and outcomes were analyzed. Results: We enrolled 325 patients with 332 lesions [mean EUS size 14.5 mm, 153 (46.1%) leiomyoma, 152 (45.8%) GISTs, 27 (8.1%) other histology]. ER techniques were 193 (58.1%) EMD, 46 (13.9%) ESSD, 28 (8.4%) STER, and 65 (19.6%) EFTR. Technical success, en bloc, and R0 resection rates were 97.0%, 94.3%, and 88.9%, respectively. Twenty-four (9.0%) procedures were shifted to unintentional EFTR, and 21 (6.3%) patients had complications. No recurrence occurred during mean follow-up period of 921.4 days. Two (0.6%) patients died of non-procedure related reasons. Old age, fundus location, heterogeneous echotexture, and exophytic growth pattern were independent risk factors for GIST (all with p < 0.05). Using the above factors, we built a prediction model with sensitivity of 77.0%, specificity of 85.6%, and AUC of 0.8771. Conclusions: ER is an efficient and safe management for gastric myogenic tumors. The histological type could be predicted by demographic characteristics and EUS features. Full article

Deindustrialization has left many industrial buildings inactive, raising questions about their role in contemporary urban life. This article explores how semiotics and psychogeography can reframe such structures as dynamic architectural happenings, shifting emphasis from preservation toward social value and collective experience. This research focuses on Komotini, Greece, where the Technical Chamber Square is reinterpreted through references to the adjacent Tobacco Warehouse. By integrating architectural traces of the past into new recreational and sporting functions, this study demonstrates how heritage can be embedded into everyday practices. Methodologically, this research employs qualitative approaches, including demographic and historical analysis of Komotini’s urban and industrial development, alongside psychogeographic drifting walks. Twenty interviews were conducted with local business owners, residents, and visitors, as well as psychogeographic walks, generating insights into how communities interact with industrial heritage. The findings indicate that semiotics and psychogeography are effective tools for activating public spaces near former industrial sites, enabling the built environment to be understood as a layered record of successive interventions. The study concludes that adaptive redesign offers designers a methodology that can embed industrial fragments into vibrant public realms that sustain diverse communities, catalyze local economies, and honor historical identity through lived practices. Full article

School leaders are central to addressing educational inequality by fostering inclusion and belonging within their school communities. In fast-growth educational environments where enrollment surges and demographic shifts outpace resource capacity, school leaders face complex challenges in developing inclusive structures and cultures for students with disabilities. In this qualitative case study, the authors examined how 18 principals across PreK-12 grade levels in three rapidly expanding Texas districts conceptualized and enacted inclusive leadership. Thematic analysis of semi-structured interviews and focus groups revealed that while principals believed all students are capable of learning, they defined inclusion philosophically but implemented pragmatically limited notions of inclusion shaped by their districts’ organizational structures and continuum of services approaches to special education. Findings illustrate that fast-growth contexts amplify tensions between compliance and care, as school leaders balance external accountability and resource constraints with efforts to cultivate school cultures grounded in empathy, relationships, safety, and belonging. The study presents a complicated picture of how principals navigate pragmatic constraints while pursuing inclusive practices for students receiving special education services. Inclusive educational leadership in fast-growth school environments is more likely to occur when leaders reframe inclusion not as placement, but as a shared commitment to recognizing every student’s capability within a responsive community. Full article

Background and Objectives: Predicting postoperative refractive development after paediatric intraocular lens (IOL) implantation remains challenging due to continued ocular growth and interindividual variability. This scoping review maps current evidence on demographic, biometric, and surgical factors influencing postoperative myopic shift in children undergoing cataract surgery with IOL implantation. Methods and Materials: A systematic literature search was conducted in PubMed and Scopus from the last ten years through October 2025. Eligible studies included children (≤18 years) with congenital or developmental cataract undergoing primary or secondary IOL implantation that reported postoperative refractive change and its predictors. Titles, abstracts, and full texts were screened according to PRISMA-ScR guidelines. Data were charted on study design, age at surgery, follow-up duration, refractive and biometric outcomes, and associated predictors. Results: Twelve studies met the inclusion criteria. Younger age at surgery, shorter preoperative axial length, and unilateral cataract consistently predicted greater postoperative myopic shift. Reported myopic change ranged from approximately −1.8 D after 2 years to −11.6 D after 15 years of follow-up, correlating with the rate of axial elongation. Optical biometry and modern formulas (e.g., Holladay 1) showed lower absolute prediction error than manual A-scan or SRK-II calculations. Postoperative complications, especially glaucoma and visual axis opacification, were associated with greater refractive change. Conclusions: Postoperative myopic shift is a predictable, age-dependent feature of paediatric pseudophakia driven primarily by ocular growth dynamics. Standardised biometry, age-stratified refractive targeting, and integration of longitudinal growth models into IOL calculation algorithms may improve refractive predictability and visual outcomes in children. Full article

Background/Objectives: The extent of initial surgical resection for low-risk papillary thyroid carcinoma (PTC) remains debated. Traditionally, total thyroidectomy (TT) has been the standard approach, although the 2015 American Thyroid Association (ATA) guidelines endorsed a more conservative strategy. The real-world adoption of these recommendations, however, is unclear. This study evaluated changes in the surgical management of low-risk PTC in a high-volume center following the implementation of the ATA guidelines and analyzed the impact on postoperative outcomes. Methods: We conducted a retrospective study of 1644 patients who underwent surgery for localized low-risk PTC < 4 cm between 2014 and 2023. Temporal trends in the initial surgical procedure (TT vs. thyroid lobectomy [TL]) were analyzed overall and by tumor size and patient demographics. The need for completion thyroidectomy after TL and postoperative outcomes were also assessed. Results: The use of TL increased from 0% in 2014 to 59.4% in 2023 (p < 0.001). For microcarcinomas, TL rose from 17.5% in 2016 to 78% in 2023, with similar but less pronounced trends for 1–2 cm tumors. TT remained predominant for nodules > 2 cm. The completion thyroidectomy rate declined from 32% in 2016 to 4% in 2022. Patients undergoing TT experienced higher rates of postoperative complications (12.4% vs. 3.0%), particularly transient hypoparathyroidism (8.9% vs. 0%), and permanent hypoparathyroidism (1.8% vs. 0%), as well as longer operative time and hospital stay (all p < 0.001). The incidence of hypoparathyroidism decreased over time as TL use increased. Conclusions: Adoption of the 2015 ATA guidelines has progressively increased the use of TL in the management of low-risk PTC. This shift in surgical practice is associated with a reduction in the overall postoperative complication burden at the population level, largely driven by decreased hypoparathyroidism. Although guideline uptake has been gradual, current trends suggest increasing acceptance of less aggressive surgical strategies in routine clinical practice. Full article