Search Results (9,957)

Aflatoxins (AFs), harmful secondary metabolites produced by the genus Aspergillus, particularly Aspergillus flavus and Aspergillus parasiticus, are one of the best-known potent mycotoxins, posing a significant risk to public health. The primary type, especially aflatoxin B₁ (AFB₁), is a potent carcinogen associated with liver cancer, immunosuppression, and other health problems. Environmental factors such as high temperatures, humidity, and inadequate storage conditions promote the formation of aflatoxin in staple foods such as maize, peanuts, and rice. Immunocompromised individuals, including those with HIV/AIDS, hepatitis, cancer, or diabetes, are at increased risk due to their reduced detoxification capacity and weakened immune defenses. Chronic exposure to AF in these populations exacerbates liver damage, infection rates, and disease progression, particularly in developing countries and moderate-income populations where food safety regulations are inadequate and reliance on contaminated staple foods is widespread. Biomarkers such as aflatoxin-albumin complexes, urinary aflatoxin M₁, and aflatoxin (AF) DNA adducts provide valuable insights but remain underutilized in resource-limited settings. Despite the globally recognized health risk posed by AF, research focused on monitoring human exposure remains limited, particularly among immunocompromised individuals. This dynamic emphasizes the need for targeted studies and interventions to address the particular risks faced by immunocompromised individuals. This review provides an up-to-date overview of AF exposure in immunocompromised populations, including individuals with cancer, hepatitis, diabetes, malnutrition, pregnant women, and the elderly. It also highlights exposure pathways, biomarkers, and biomonitoring strategies, while emphasizing the need for targeted interventions, advanced diagnostics, and policy frameworks to mitigate health risks in these vulnerable groups. Addressing these gaps is crucial to reducing the health burden and developing public health strategies in high-risk regions. Full article

Hand hygiene is a key measure to prevent healthcare-associated infections (HAIs), yet compliance remains low in Sub-Saharan Africa (SSA), due to limited resources, insufficient training, and behavioral challenges. Simulation-based education offers a promising approach to enhance technical and non-technical skills in safe learning environments, promoting behavioral change and patient safety. This study aimed to develop and pilot a contextually adapted hand hygiene simulation-based learning scenario for nursing students in SSA. Grounded in the Medical Research Council (MRC) Framework and Design-Based Research principles, a multidisciplinary team from European and African higher education institutions (HEIs) co-created this scenario, integrating international and regional hand hygiene guidelines. Two iterative pilot cycles were conducted with expert panels, educators, and students. Data from structured observation and post-simulation questionnaires were analyzed using descriptive statistics. The results confirm the scenario’s feasibility, relevance, and educational value. The participants rated highly the clarity of learning objectives (M = 5.0, SD = 0.0) and preparatory materials (M = 4.6, SD = 0.548), reporting increased knowledge/skills and confidence and emphasizing the importance of clear roles, structured facilitation, and real-time feedback. These findings suggest that integrating simulation in health curricula could strengthen HAI prevention and control in SSA. Further research is needed to evaluate long-term outcomes and the potential for wider implementation. Full article

Groundwater is essential for irrigated agriculture, yet its use remains unsustainable in many regions worldwide. In countries like Pakistan, the situation is particularly pressing. The irrigated agriculture of Pakistan heavily relies on groundwater resources owing to limited canal-water availability. The groundwater quality in the region ranges from good to poor, with the lower-quality water adversely affecting soil structure and plant health, leading to reduced agricultural productivity. The delineation of quality zones with respect to irrigation parameters is thus crucial for optimizing its sustainable use and management. Therefore, this research study was carried out in the Lower Chenab Canal (LCC) irrigation system to assess the spatial distribution of groundwater quality. The geostatistical analysis was conducted using Gamma Design Software (GS+) and the Kriging interpolation method was applied within a Geographic Information System (GIS) framework to generate groundwater-quality maps. Semivariogram models were evaluated for major irrigation parameters such as electrical conductivity (EC), residual sodium carbonate (RSC), and sodium adsorption ratio (SAR) to identify the best fit for various Ordinary Kriging models. The spherical semivariogram model was the best fit for EC, while the exponential model best suited SAR and RSC. Overlay analysis was performed to produce combined water-quality maps. During the pre-monsoon season, 17.83% of the LCC area demonstrated good irrigation quality, while 42.84% showed marginal quality, and 39.33% was deemed unsuitable for irrigation. In the post-monsoon season, 17.30% of the area had good irrigation quality, 44.53% exhibited marginal quality, and 38.17% was unsuitable for irrigation. The study revealed that Electrical Conductivity (EC) was the primary factor affecting water quality, contributing to 71% of marginal and unsuitable conditions. In comparison, the Sodium Adsorption Ratio (SAR) accounted for 38% and Residual Sodium Carbonate (RSC) contributed 45%. Therefore, it is recommended that groundwater in unsuitable zones be subjected to artificial recharge methods and salt-tolerated crops to enhance its suitability for agricultural applications. Full article

Mental health conditions affect many young people in sub-Saharan Africa (SSA), where stigma is high and access to care is limited. Digital tools accessible on basic mobile phones offer a scalable way to promote mental health, but evidence on their effectiveness in SSA is limited. This study evaluated the reach, feasibility, acceptability, and knowledge outcomes of Digital MindSKILLZ, an interactive voice response (IVR) mental health intervention implemented in the Democratic Republic of Congo, Ghana, Nigeria, Rwanda, Uganda, and Zambia. Over seven months, 700,138 people called the platform, and 425,395 (61%) listened to at least one message. Of these users, 63.6% were under 25 and 68.3% were from rural areas. The three content branches—mental health information, mental health skills, and soccer quizzes—were accessed by 36.5%, 46.4%, and 50.9% of users, respectively. Among users who accessed the mental health branch of the intervention, the mean number of messages completed was 7.6 out of 18 messages. In a follow-up survey, 91% of users understood the content, 85% would recommend the intervention, and 38% found the mental health content most helpful. Average knowledge scores were 62%, with lower scores on common disorders and stigma. The intervention showed strong reach and acceptability, but content and implementation improvements are needed to boost engagement and retention. Full article

Background: Air traffic control is a stressful job and vital to aviation safety. Although technological developments have been introduced to enhance and facilitate the tasks of air traffic control officers (ATCOs), ATCOs still experience high levels of job stress. This study explores the influence of mindfulness and social work support (SWS) on the job performance and job stress of ATCOs in Saudi Arabia. Methods: Grounded in Job Demands–Resources (JDR) theory, this study used a cross-sectional design to survey 324 ATCOs, with a 72% response rate. Mindfulness and SWS were treated as individual and situation-specific resources that influence stress and performance outcomes. Results: The results indicated that mindfulness could reduce workplace stress and improve performance. Moreover, SWS was also critical in reducing the adverse impacts of stress on job performance, reflecting the buffering effect posited by JDR theory. Conclusions: This study demonstrates that JDR theory is applicable to the context of ATC since it validates the importance of mindfulness and SWS as critical resources in minimizing stress levels and improving performance. The findings have implications for the viability of mindfulness-based training interventions and peer-support programs in supporting the health of ATCOs and their ability to deal with highly stressful situations. Full article

Depression remains a leading cause of global disability, yet scalable and objective diagnostic tools are still lacking. Speech has emerged as a promising non-invasive modality for automated depression detection, due to its strong correlation with emotional state and ease of acquisition. While convolutional neural networks (CNNs) have achieved state-of-the-art performance in this domain, their high computational demands limit deployment in low-resource or real-time settings. Spiking neural networks (SNNs), by contrast, offer energy-efficient, event-driven computation inspired by biological neurons, but they are difficult to train directly and often exhibit degraded performance on complex tasks. This study investigates whether CNNs trained on audio data from the clinically annotated DAIC-WOZ dataset can be effectively converted into SNNs while preserving diagnostic accuracy. We evaluate multiple conversion thresholds using the SpikingJelly framework and find that the 99.9% mode yields an SNN that matches the original CNN in both accuracy (82.5%) and macro F1 score (0.8254). Lower threshold settings offer increased sensitivity to depressive speech at the cost of overall accuracy, while naïve conversion strategies result in significant performance loss. These findings support the feasibility of CNN-to-SNN conversion for real-world mental health applications and underscore the importance of precise calibration in achieving clinically meaningful results. Full article

Background: T1D is generally associated with increased psychological burden, but evidence from Middle Eastern populations remains scarce. The present study assessed the gender differences in management, prevalence, and risk factors of perceived depression and anxiety among people with T1D in Saudi Arabia. Methods: A cross-sectional online survey was conducted among people withT1D across Saudi Arabia to collect demographic, clinical, and diabetes management data. Perceived depression and anxiety symptoms were assessed using a validated questionnaire. Multinomial logistic regression was used to identify risk factors of severe depression and anxiety. Results: Among 1073 PwT1D (303 males and 770 females), perceived depressive and anxiety symptoms were highly prevalent. Females had higher perceived anxiety compared with males (p = 0.003). Age- and BMI-adjusted regression analysis showed that, overall, higher income (p = 0.008), no neuropathy (p = 0.002), above-average benefit from the diabetes education clinic (p = 0.02), practicing carbohydrate counting (p = 0.002), and HbA1c < 7.0% (p = 0.01) were protective against perceived severe depression. Friends with T1D as the preferred education source (odds ratio [OR] = 2.8, p = 0.04) and a rejected request for continuous glucose monitoring (CGM) (OR = 1.88, p = 0.02) or insulin pump (OR = 2.8, p = 0.001) were significant risk factors. Perceived severe anxiety was associated with insulin pump rejection (OR = 2.4, p < 0.001) and self-reading as the preferred education source (OR = 2.0, p = 0.03). Being male (p = 0.02), no neuropathy (p = 0.01), practicing carbohydrate counting (p < 0.001), and HbA1c < 7.0% (p = 0.001) were protective. Conclusions: Symptoms of depression and anxiety are highly prevalent among people with T1D in Saudi Arabia, with females and socioeconomically disadvantaged individuals at greater risk. The findings highlight an urgent need for integrated mental health support within diabetes care and improved access to resources. Full article

This study presents a comparative evaluation of traditional machine-learning (ML) and deep-learning (DL) approaches for detecting and counting Varroa destructor mites in hyperspectral images. As Varroa infestations pose a serious threat to honeybee health, accurate and efficient detection methods are essential. The ML pipeline—based on Principal Component Analysis (PCA), k-Nearest Neighbors (kNN), and Support Vector Machine (SVM)—was previously published and achieved high performance (precision = 0.9983, recall = 0.9947), with training and inference completed in seconds on standard CPU hardware. In contrast, the DL approach, employing Faster R-CNN with ResNet-50 and ResNet-101 backbones, was fine-tuned on the same manually annotated images. Despite requiring GPU acceleration, longer training times, and presenting a reproducibility challenges, the deep-learning models achieved precision of 0.966 and 0.971, recall of 0.757 and 0.829, and F1-Score of 0.848 and 0.894 for ResNet-50 and ResNet-101, respectively. Qualitative results further demonstrate the robustness of the ML method under limited-data conditions. These findings highlight the differences between ML and DL approaches in resource-constrained scenarios and offer practical guidance for selecting suitable detection strategies. Full article

Background: The COVID-19 pandemic caused lasting disruption to healthcare systems and the mental health of frontline workers. Though the acute crisis has passed, many healthcare workers (HCWs) continue to experience long-term psychological effects, including anxiety, grief, and burnout. This mixed-methods study investigates the enduring effects of the COVID-19 pandemic on the mental health of physicians in a low-resource country. Methods: Drawing on data from the ear, nose, and throat (ENT) or otolaryngology department at a tertiary care hospital in Pakistan, the study employed an explanatory mixed-methods design, combining structured surveys and semi-structured interviews. The Hospital Anxiety and Depression Scale, the Perceived Stress Scale, and the Brief COPE Inventory were administered to 42 ENT specialists, trainees, and house officers, alongside semi-structured interviews with eight ENT physicians. Results: Survey results revealed moderate to high levels of anxiety, depression, and stress that persisted beyond the acute crisis phase of the pandemic. Interviews provided nuanced insights into the emotional burden experienced by physicians, including persistent concerns about contagion risk, professional isolation, and increased workload. Physicians described maladaptive responses and employed active coping strategies, such as seeking peer support and utilizing adaptive problem solving. Conclusions: The COVID-19 pandemic has had enduring effects on the mental well-being of physicians. Targeted interventions and policy reforms that address the ongoing pressures frontline physicians face in resource-constrained environments may help mitigate these burdens, support healthcare professionals more effectively, and improve their mental health. Full article

In today’s fast-moving digital economy, service start-ups are reshaping industries; however, they face intense uncertainty, limited resources, and fierce competition. This study introduces an Artificial Intelligence (AI)-powered process modeling framework designed to give these ventures a competitive edge by combining big data analytics, machine learning, and Business Process Model and Notation (BPMN). While past models often overlook the dynamic, human-centered nature of service businesses, this research fills that gap by integrating AI-Driven Ideation, AI-Augmented Content, and AI-Enabled Personalization to fuel innovation, agility, and customer-centricity. Expert insights, gathered through a two-stage fuzzy Delphi method and validated using DEMATEL, reveal how AI can transform start-up processes by offering real-time feedback, predictive risk management, and smart customization. This model does more than optimize operations; it empowers start-ups to thrive in volatile, data-rich environments, improving strategic decision-making and even health and safety governance. By blending cutting-edge AI tools with process innovation, this research contributes a fresh, scalable framework for digital-age entrepreneurship. It opens exciting new pathways for start-up founders, investors, and policymakers looking to harness AI’s full potential in transforming how new ventures operate, compete, and grow. Full article

Chili peppers, a staple spice in global cuisine, hold substantial economic value due to their diverse pungency levels and distinctive aromatic profiles. In addition to their sensory attributes, Capsicum fruits exhibit notable morphological diversity and potential health benefits. While contemporary Capsicum breeding efforts have focused on the yield, shelf life, and resistance to biotic and abiotic stresses, comparatively less emphasis has been placed on the fruit quality and flavor traits increasingly valued by consumers seeking novel flavors and functional foods. We evaluated seven underutilized Capsicum landraces collected from Peru, Myanmar, and Japan and conducted an integrative analysis of their morphological traits, nutritional composition, pungency, and volatile compounds. Our findings highlight C. chinense from Myanmar and Peru as a particularly diverse species, encompassing accessions with mild to very highly pungent, elevated antioxidant content, and significant contributions to fruity aromatic notes. These findings support the development of flavor-driven chili-pepper-based food products with enhanced nutritional value and tailored pungency. Our identification of beneficial alleles also offers opportunities for interspecific breeding to produce novel cultivars aligned with evolving consumer preferences, thereby supporting the commercialization of traditional varieties, conserving genetic resources, and expanding the market potential of new cultivars. Full article

Background: Functional disability is a growing concern in aging populations with chronic health conditions, yet few studies have compared disability profiles across diagnostic categories using standardized tools. Objectives: This study aimed to characterize the functional profiles of adults with neurological, musculoskeletal, and chronic pain conditions using the World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) and to examine differences by age and sex. Methods: A total of 419 participants (median age = 73 years; 73% female) completed the 36-item WHODAS 2.0. Diagnoses were classified into three groups: neurological (n = 134), musculoskeletal (n = 230), and pain-related (n = 55). Domain-level scores were analyzed using non-parametric tests and Spearman correlations. Results: revealed that neurological conditions were associated with the highest disability levels, particularly in cognition, interpersonal relations, and participation. Musculoskeletal conditions showed greater impairments in mobility and self-care, while pain-related conditions demonstrated variable disability, especially in participation. Women reported higher disability scores in the neurologic group, with significant differences observed in the cognition domain among neurological cases (p = 0.048). Age was positively correlated with disability in self-care and mobility, especially in musculoskeletal conditions. Conclusions: These findings highlight the utility of WHODAS 2.0 in identifying domain-specific limitations across clinical populations. They support the need for individualized, diagnosis- and gender-sensitive rehabilitation strategies, and suggest that WHODAS 2.0 can inform targeted care planning and resource allocation in rehabilitation settings. Future research should incorporate longitudinal designs and explore contextual factors influencing functional outcomes. Full article

Agricultural activities are widely recognized as major sources of water pollution, primarily due to the introduction of heavy metals (HMs) through fertilizers, pesticides, manures, sewage sludge, and irrigation water. Owing to their persistence and non-biodegradability, these metals pose substantial risks to ecosystems and public health. While certain HMs such as cobalt, copper, and zinc are essential micronutrients for crops at low concentrations, others—like arsenic, cadmium, lead, and mercury—enter agricultural systems as contaminants and serve no biological function in plants. This paper explores the complex issue of HM contamination in water resulting from agricultural practices. It reviews the primary sources and pathways through which HMs enter aquatic systems, discusses their ecological and health impacts, and examines analytical methods used for HM detection and monitoring. In response to this challenge, several mitigation strategies are highlighted, including the optimized use of agrochemicals, adoption of sustainable farming practices, and implementation of phytoremediation and bioremediation techniques. Additionally, the importance of community education and regulatory enforcement is emphasized as part of an integrated approach to pollution control. Ultimately, this paper underscores the need for balanced solutions that safeguard water resources while maintaining agricultural productivity. Full article

Over the past two decades, patient safety and clinical risk management have become strategic priorities for healthcare systems worldwide. In this context, the London Protocol has emerged as one of the most influential methodologies for investigating adverse events through a systemic, non-punitive lens. The 2024 edition, curated by Vincent, Adams, Bellandi, and colleagues, represents a significant evolution of the original 2004 framework. It integrates recent advancements in safety science, human factors, and digital health, while placing a stronger emphasis on resilience, proactive learning, and stakeholder engagement. This article critically examines the structure, key principles, and innovations of the London Protocol 2024, highlighting its departure from incident-centered analysis toward a broader understanding of both failures and successes. The protocol encourages fewer but more in-depth investigations, producing actionable and sustainable recommendations rather than generic reports. It also underscores the importance of involving patients and families as active partners in safety processes, recognizing their unique perspectives on communication, care pathways, and system failures. Beyond its strengths—holistic analysis, multidisciplinary collaboration, and cultural openness—the systemic approach presents challenges, including methodological complexity, resource requirements, and cultural resistance in blame-oriented environments. This paper discusses these limitations and explores how leadership, staff engagement, and digital technologies (including artificial intelligence) can help overcome them. Ultimately, the London Protocol 2024 emerges not only as a methodological tool but as a catalyst for cultural transformation, fostering healthcare systems that are safer, more resilient, and committed to continuous learning. Full article

The use of untreated livestock manure in urban agriculture sustains soil fertility but risks disseminating antimicrobial resistance (AMR) in resource-limited settings. This study characterized antibiotic-resistant bacteria (ARB) prevalence across manure–soil–vegetable pathways in Blantyre, Malawi. Using a cross-sectional design, we collected 35 samples (poultry/pig manure, farm/home soils, Brassica rapa subsp. chinensis, Brassica rapa, and Amaranthus spp.) from five livestock farms. Microbiological analysis with API 20E identification and disk diffusion testing revealed clear differences in contamination: Escherichia coli dominated pig manure (52%) and farm soil (35%), with detection in vegetables suggesting possible transfer (e.g., 20% in Brassica rapa subsp. chinensis), while Klebsiella pneumoniae contaminated all sample types (peak: 60% vegetables and 67% home soils). All manure isolates exhibited sulfamethoxazole–trimethoprim resistance, with 50% of pig manure E. coli showing cefotaxime resistance. Soil isolates mirrored these patterns (100% ampicillin resistance in K. pneumoniae and 77% cefotaxime resistance in farm soil E. coli). Vegetables displayed severe multidrug resistance (100% E. coli and 80% K. pneumoniae resistant to ≥3 classes), including critical gentamicin resistance (100% E. coli). Composting for ≤6 weeks, as practiced on the studied farms, did not eliminate ARBs, suggesting that longer durations may be needed. Notably, this study provides the first phenotypic evidence of presumptive Pasteurella-like organisms on edible leafy vegetables, specifically 45% in Amaranthus spp. and 6.1% in Brassica rapa, suggesting a potential zoonotic transmission route from livestock farms that requires molecular confirmation. These findings demonstrate manure-amended farms as AMR reservoirs, necessitating extended composting and antibiotic stewardship to mitigate One Health risks. Full article