Search Results (17,038)

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a worldwide challenge. There are an estimated 17–24 million patients worldwide, with an estimated 60 percent or more who have not been diagnosed. Without a known cure, no specific curative medication, disability lasting years to being life-long, and disagreement among healthcare providers as to how to most appropriately treat these patients, ME/CFS patients are in need of assistance. Appropriate healthcare provider education would increase the percentage of patients diagnosed and treated; however, in-school healthcare provider education is limited. To address the latter issue, the New Jersey Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Association (NJME/CFSA) has developed an independent, incentive-driven, learning program for students of the health professions. NJME/CFSA offers a yearly scholarship program in which applicants write a scholarly paper on an ME/CFS-related topic. The efficacy of the program is demonstrated by the 2024–2025 first place scholarship winner’s essay, which addresses the biological basis of ME/CFS and how the healthcare provider can improve the quality of life of ME/CFS patients. For the reader, the essay provides an update on what is known regarding the biological underpinnings of ME/CFS, as well as a medical student’s perspective as to how the clinician can provide care and support for ME/CFS patients. The original essay has been slightly modified to demonstrate that ME/CFS is a worldwide problem and for publication. Full article

This study presents and discusses evidence on the value of biomarker testing and precision medicine in Latin America through a health equity lens. It is essential to explore how to harness the benefits of precision medicine to narrow the health equity gap, ensuring all patients have access to the best cancer treatment. The methodology employed to develop this document consists of a non-systematic literature review, followed by a process of validation and feedback with a group of experts in relevant fields. Precision medicine could help reduce health inequities in Latin America by providing better diagnosis and treatment for everyone with cancer. However, its success in achieving this depends on the implementation of policies that promote equitable access. Findings indicate that the current policy landscape in the Latin American region is not conducive to improving access, reach, quality, or outcome-related problems in cancer care, nor to realizing the full potential of precision medicine. The study explores how precision medicine can advance health equity, concluding with an analysis of the challenges and recommendations for overcoming them. Full article

Background and Objectives: Clostridioides difficile infection (CDI) poses a major public health problem worldwide. Materials and Methods: In this retrospective study, we included 274 patients with CDI, who were hospitalized in Internal Medicine Departments in Evangelismos General Hospital in Athens, Greece, during the past decade. Demographic, clinical and laboratory parameters of the patients were recorded. Statistical analysis revealed an association between older age and mortality as well as heart failure and mortality among patients with CDI. Results: Notably, WBC (white blood count), neutrophils, NLR (neutrophil-to-lymphocyte ratio), dNLR (derived NLR), SII (systemic immune–inflammation index) and hs-CRP (high-sensitivity C-reactive protein) demonstrated a positive association with mortality, whereas serum albumin levels and PNR (platelet-to-neutrophil ratio) exhibited an inverse relationship with mortality. We propose that the aforementioned biomarkers may be used as prognostic parameters regarding mortality from CDI. Conclusions: Large scale studies among patients with CDI with the advent of AI (artificial intelligence) may incorporate demographic, clinical and laboratory features into prognostic scores to further characterize the global CDI threat. Full article

The rapid growth of electric vehicles (EVs) and new energy systems has put lithium-ion batteries at the center of the clean energy change. Nevertheless, to achieve the best battery performance, safety, and sustainability in many changing circumstances, major innovations are needed in Battery Management Systems (BMS). This review paper explores how artificial intelligence (AI) and digital twin (DT) technologies can be integrated to enable the intelligent BMS of the future. It investigates how powerful data approaches such as deep learning, ensembles, and models that rely on physics improve the accuracy of predicting state of charge (SOC), state of health (SOH), and remaining useful life (RUL). Additionally, the paper reviews progress in AI features for cooling, fast charging, fault detection, and intelligible AI models. Working together, cloud and edge computing technology with DTs means better diagnostics, predictive support, and improved management for any use of EVs, stored energy, and recycling. The review underlines recent successes in AI-driven material research, renewable battery production, and plans for used systems, along with new problems in cybersecurity, combining data and mass rollout. We spotlight important research themes, existing problems, and future drawbacks following careful analysis of different up-to-date approaches and systems. Uniting physical modeling with AI-based analytics on cloud-edge-DT platforms supports the development of tough, intelligent, and ecologically responsible batteries that line up with future mobility and wider use of renewable energy. Full article

Smartphones have revolutionized our daily lives, becoming portable pocket computers with easy internet access. India, the second-highest smartphone and internet user, experienced a significant rise in smartphone usage between 2013 and 2024. Prolonged smartphone use, exceeding 20 min at a time, can lead to physical and mental health issues, including psychophysiological disorders. Digital devices and their extended exposure to blue light cause digital eyestrain, sleep disorders and visual-related problems. This research examines the impact of 1 h smartphone usage on visual fatigue among young Indian adults. A portable, low-cost system has been developed to measure visual activity to address this. The developed visual activity measurement system measures blink rate, inter-blink interval, and pupil diameter. Measured eye activity was recorded during 1 h smartphone usage of e-book reading, video watching, and social-media reels (short videos). Social media reels show increased screen variations, affecting pupil dilation and reducing blink rate due to continuous screen brightness and intensity changes. This reduction in blink rate and increase in inter-blink interval or pupil dilation could lead to visual fatigue. Full article

The problem of spreading harmful infections through contaminated surfaces has become more acute during the recent coronavirus pandemic. The design of self-cleaning materials, which can continuously decompose biological contaminants, is an urgent task for environmental protection and human health care. In this study, the surface of blended cotton/polyester fabric was functionalized with N-doped TiO₂ (TiO₂-N) nanoparticles using titanium(IV) isopropoxide as a binder to form durable photoactive coating and additionally decorated with Cu species to promote its self-cleaning properties. The photocatalytic ability of the material with photoactive coating was investigated in oxidation of acetone vapor, degradation of deoxyribonucleic acid (DNA) fragments of various lengths, and inactivation of PA136 bacteriophage virus and Candida albicans fungi under visible light and ultraviolet A (UVA) radiation. The kinetic aspects of inactivation and degradation processes were studied using the methods of infrared (IR) spectroscopy, polymerase chain reaction (PCR), double-layer plaque assay, and ten-fold dilution. The results of experiments showed that the textile fabric modified with TiO₂-N photocatalyst exhibited photoinduced self-cleaning properties and provided efficient degradation of all studied contaminants under exposure to both UVA and visible light. Additional modification of the material with Cu species substantially improved its self-cleaning properties, even in the absence of light. Full article

Drosophila melanogaster is one of the most known and used organisms worldwide, not just to study general biology problems but above all for modeling complex human diseases. During the decades, it has become a central tool to understand the genetics of human disease, how mutations alter the behavior and health of cells, tissues, and organs, and more recently to test new compounds with a potential therapeutic use. But how did this small insect become so crucial in genetics? And how is it currently used in the study of human conditions affecting millions of people? In this review, we retrace the historical origins of its adoption in genetics laboratories and list all the advantages it provides to scientific research, both for its daily usage and for the fine tuning of gene regulation through genetic engineering approaches. We also provide some examples of how it is used to study human diseases such as cancer, neurological and infectious diseases, and its importance in drug discovery and testing. Full article

Background/Objectives: Maternal overweight and obesity are critical factors increasing the risk of various pregnancy complications. Maternal obesity can lead to fetal macrosomia and a heightened risk of intrauterine death, with long-term implications for the child’s health. This study aimed to analyze the incidence of obesity and its impact on pregnancy outcomes in women who delivered by cesarean section at the University Hospital “St. George”, Plovdiv. Methods: A single-center retrospective cohort study was conducted. The documentary method was used for gathering data. Records were randomly selected. The statistical methods used included mean values, confidence intervals (of mean), frequency, and the Kolmogorov–Smirnov test for normality of distribution. Data comparisons were performed using the Mann–Whitney test. Mean values of numerical variables were compared using the independent samples t-test. Results: In total, 46.36% of women in this study were affected by obesity to varying degrees, and the proportion of women who were overweight at the end of their pregnancy was 37.85%. In the studied cohort, 15.99% of women were affected by hypertensive complications. This significant prevalence of obesity highlights concerns regarding body weight among women of reproductive age. This study emphasized a strong correlation between maternal obesity, particularly severe obesity, and the occurrence of preeclampsia. Conclusions: In this study among women who delivered by cesarean section, a significant proportion of them were affected by overweight and obesity. Data for our country are insufficient, and a more in-depth study of this problem is needed. Future research should explore the long-term impacts of maternal obesity on the health of the mother and the newborn. Full article

The purpose of the present research is to analyse the negative outcomes associated with the excessive Instagram dependency of those users that access the application through their smartphones. An empirical study was conducted through online interviews using structured questionnaires, resulting in 342 valid responses, with the target population being young users over 18 years old who access Instagram daily. Research shows that dependency on Instagram is primarily driven by individuals’ need for orientation and understanding, with entertainment being a secondary motivation. The results indicate that dependency on the social network is positively associated with excessive use, addiction, and Instastress. Furthermore, excessive use contributes to personal and social problems and increases both stress levels and mindfulness related to the platform. In turn, this excessive use intensifies addiction, which functions as a mediating variable between overuse and Instastress, mindfulness, and emotional exhaustion. This study offers valuable insights for academics, mental health professionals, and marketers by emphasizing the importance of fostering healthier digital habits and developing targeted interventions. Full article

To address the inefficiency of traditional concrete crack detection methods and the heavy reliance of supervised learning on extensive labeled data, in this study, an intelligent assessment method of concrete damage based on pseudo-label semi-supervised learning and fractal geometry theory is proposed to solve two core tasks: one is binary classification of pixel-level cracks, and the other is multi-category assessment of damage state based on crack morphology. Using three-channel RGB images as input, a dual-path collaborative training framework based on U-Net encoder–decoder architecture is constructed, and a binary segmentation mask of the same size is output to achieve the accurate segmentation of cracks at the pixel level. By constructing a dual-path collaborative training framework and employing a dynamic pseudo-label refinement mechanism, the model achieves an F1-score of 0.883 using only 50% labeled data—a mere 1.3% decrease compared to the fully supervised benchmark DeepCrack (F1 = 0.896)—while reducing manual annotation costs by over 60%. Furthermore, a quantitative correlation model between crack fractal characteristics and structural damage severity is established by combining a U-Net segmentation network with the differential box-counting algorithm. The experimental results demonstrate that under a cyclic loading of 147.6–221.4 kN, the fractal dimension monotonically increases from 1.073 (moderate damage) to 1.189 (failure), with 100% accuracy in damage state identification, closely aligning with the degradation trend of macroscopic mechanical properties. In complex crack scenarios, the model attains a recall rate (Re = 0.882), surpassing U-Net by 13.9%, with significantly enhanced edge reconstruction precision. Compared with the mainstream models, this method effectively alleviates the problem of data annotation dependence through a semi-supervised strategy while maintaining high accuracy. It provides an efficient structural health monitoring solution for engineering practice, which is of great value to promote the application of intelligent detection technology in infrastructure operation and maintenance. Full article

Introduction: Night shift work (NSW) has been increasingly addressed in the scientific literature, as it is considered a probable carcinogen. In this study, we investigated the association of NSW with health problems, unhealthy behaviors, and occupational carcinogens. Methods: Cross-sectional study with a sample of 47,953 workers from the 2019 National Health Survey. NSW prevalence was estimated according to sociodemographic characteristics. To investigate the associations of NSW with all study variables, gender stratified logistic regression models were used. The odds-ratio and 95% confidence intervals were estimated. Results: Among men, there was a significant association of NSW with sleep disorders (OR = 1.39; 95% CI: 1.17–1.65), tiredness (OR = 1.68; 95% CI: 1.41–2.00), obesity (OR = 1.41; 95% CI: 1.20–1.66), unhealthy food consumption (OR = 1.28; 95% CI: 1.12–1.46), handling of radioactive material (OR = 2.45; 95% CI: 1.61–3.72), and biological material (OR = 3.18; 95% CI: 3.15–4.80). Among females, NSW was associated with the same variables except obesity, but depressive feelings (OR = 1.35 95% CI: 1.09–1.67), frequent alcohol intake (OR = 1.48; 95% CI: 1.23–1.78), handling of chemical substances (OR = 1.54; OR = 1.54; 95% CI: 1.20–1.97), and passive smoking at work (OR = 1.45; 95% CI: 1.12–1.86) were highly significant. Conclusion: Night shift workers are more vulnerable to occupational carcinogen exposure, experience greater impacts on their well-being, and are more likely to engage in unhealthy behaviors. These findings should be considered in managing and organizing night work in the workplace. Actions to promote healthy work environments should be encouraged to protect workers’ health. Full article

Background: Schizophrenia affects patients’ organizational thinking, as well as their ability to identify problems. The main objective of this study was to explore healthcare consultants’ application of AI mind maps to educate patients with schizophrenia regarding their perceptions of family function, social support, quality of life, and loneliness, and to help these patients think more organizationally and understand problems more effectively. Methods: The study used a survey research design and purposive sampling method to recruit 66 participants with schizophrenia who attended the psychiatric outpatient clinic of a hospital in central Taiwan. They needed to be literate, able to respond to the topic, and over 18 years old (inclusive), and they attended individual and group health education using AI mind maps over a 3-month period during regular outpatient clinic visits. Results: The study results show that patients’ family function directly affects their quality of life (p < 0.05) and loneliness (p < 0.05), satisfaction with social support affects quality of life and loneliness directly (p < 0.05), and satisfaction with social support is a mediating factor between family function and quality of life (p < 0.05), as well as a mediating factor between family function and loneliness (p < 0.05). Conclusions: Therefore, this study confirms the need to provide holistic, integrated mental health social care support for patients with schizophrenia, showing that healthcare consultants can apply AI mind maps to empower patients with schizophrenia to think more effectively about how to mobilize their social supports. Full article

Metabolic syndrome is a global health crisis. However, there are no effective therapeutic strategies for metabolic syndrome. Therefore, this study was conducted to find out a novel silkworm-based metabolic syndrome model that bridges microbial ecology and metabolic dysregulation by integrating hemolymph lipids and midgut microbiota. Our results showed that the levels of HDL-C in the hemolymph of the lean silkworm strain were significantly higher than that in the obese silkworm strain. Furthermore, correlation analysis revealed that Lactococcus and Oceanobacillus were positively related to HDL-C levels, while SM1A02 and Pseudonocardia were negatively associated with HDL-C levels. These relationships between the identified bacteria in the midgut and HDL-C, known as the “good” lipid, in the hemolymph could help guide the development of new treatments for obesity and metabolic problems like high cholesterol in humans. Overall, our results not only established a framework for understanding microbiota-driven lipid dysregulation in silkworms but also offered potential probiotic targets and a bacterial biomarker for obesity and metabolic dysfunction intervention in humans. Full article

This paper addresses the problem of optimizing obesity, which has been a challenging issue in the last decade based on recent data revealed in 2024 by the World Health Organization (WHO). The current work introduces a new mathematical model of the dynamics of weight over time with embedded control parameters to optimize the number of obese, overweight, and comorbidity populations. The mathematical formulation of the model is developed under certain sufficient conditions that guarantee the positivity and boundedness of solutions over time. The model structure exhibits inherent symmetry in population group transitions, particularly around the equilibrium state, which allows the application of analytical tools such as the Routh–Hurwitz and Metzler criteria. Then, the analysis of local and global stability of the obesity-free equilibrium state is discussed based on these criteria. Based on the Pontryagin maximum principle (PMP), the deviation from the obesity-free equilibrium state is controlled. The model’s effectiveness is demonstrated through simulation using the Forward–Backward Sweeping algorithm with parameters derived from recent research in human health. Incorporating symmetry considerations in the model enhances the understanding of system behavior and supports balanced intervention strategies. Results suggest that the model can effectively inform strategies to mitigate obesity prevalence and associated health risks. Full article