Search Results (550)

Teledermatology relies on digital transfer of dermatological images, but compression and resolution differences compromise diagnostic quality. Image enhancement techniques are crucial to compensate for these differences and improve quality for both clinical assessment and AI-based analysis. We developed a customized image degradation pipeline simulating common artifacts in dermatological images, including blur, noise, downsampling, and compression. This synthetic degradation approach enabled effective training of DermaSR-GAN, a super-resolution generative adversarial network tailored for dermoscopic images. The model was trained on 30,000 high-quality ISIC images and evaluated on three independent datasets (ISIC Test, Novara Dermoscopic, PH²) using structural similarity and no-reference quality metrics. DermaSR-GAN achieved statistically significant improvements in quality scores across all datasets, with up to 23% enhancement in perceptual quality metrics (MANIQA). The model preserved diagnostic details while doubling resolution and surpassed existing approaches, including traditional interpolation methods and state-of-the-art deep learning techniques. Integration with downstream classification systems demonstrated up to 14.6% improvement in class-specific accuracy for keratosis-like lesions compared to original images. Synthetic degradation represents a promising approach for training effective super-resolution models in medical imaging, with significant potential for enhancing teledermatology applications and computer-aided diagnosis systems. Full article

Adverse drug events (ADEs) significantly impact patient safety and health outcomes. Manual ADE detection from clinical narratives is time-consuming, labor-intensive, and costly. Recent advancements in large language models (LLMs), including transformer-based architectures such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pretrained Transformer (GPT) series, offer promising methods for automating ADE extraction from clinical data. These models have been applied to various aspects of pharmacovigilance and clinical decision support, demonstrating potential in extracting ADE-related information from real-world clinical data. Additionally, chatbot-assisted systems have been explored as tools in clinical management, aiding in medication adherence, patient engagement, and symptom monitoring. This narrative review synthesizes the current state of LLMs in ADE detection from a clinical perspective, organizing studies into categories such as human-facing decision support tools, immune-related ADE detection, cancer-related and non-cancer-related ADE surveillance, and personalized decision support systems. In total, 39 articles were included in this review. Across domains, LLM-driven methods have demonstrated promising performances, often outperforming traditional approaches. However, critical limitations persist, such as domain-specific variability in model performance, interpretability challenges, data quality and privacy concerns, and infrastructure requirements. By addressing these challenges, LLM-based ADE detection could enhance pharmacovigilance practices, improve patient safety outcomes, and optimize clinical workflows. Full article

Lung nodule detection and segmentation are essential tasks in computer-aided diagnosis (CAD) systems for early lung cancer screening. With the growing availability of CT data and deep learning models, researchers have explored various strategies to improve the performance of these tasks. This review focuses on Multi-Task Learning (MTL) approaches, which unify or cooperatively integrate detection and segmentation by leveraging shared representations. We first provide an overview of traditional and deep learning methods for each task individually, then examine how MTL has been adapted for medical image analysis, with a particular focus on lung CT studies. Key aspects such as network architectures and evaluation metrics are also discussed. The review highlights recent trends, identifies current challenges, and outlines promising directions toward more accurate, efficient, and clinically applicable CAD solutions. The review demonstrates that MTL frameworks significantly enhance efficiency and accuracy in lung nodule analysis by leveraging shared representations, while also identifying critical challenges such as task imbalance and computational demands that warrant further research for clinical adoption. Full article

Medical image registration is an indispensable preprocessing step to align medical images to a common coordinate system before in-depth analysis. The registration precision is critical to the following analysis. In addition to representative image features, the initial pose settings and multiple poses in images will significantly affect the registration precision, which is largely neglected in state-of-the-art works. To address this, the paper proposes a dual-pose medical image registration algorithm based on improved differential evolution. More specifically, the proposed algorithm defines a composite similarity measurement based on contour points and utilizes this measurement to calculate the similarity between frontal–lateral positional DRR (Digitally Reconstructed Radiograph) images and X-ray images. In order to ensure the accuracy of the registration algorithm in particular dimensions, the algorithm implements a dual-pose registration strategy. A PDE (Phased Differential Evolution) algorithm is proposed for iterative optimization, enhancing the optimization algorithm’s ability to globally search in low-dimensional space, aiding in the discovery of global optimal solutions. Extensive experimental results demonstrate that the proposed algorithm provides more accurate similarity metrics compared to conventional registration algorithms; the dual-pose registration strategy largely reduces errors in specific dimensions, resulting in reductions of 67.04% and 71.84%, respectively, in rotation and translation errors. Additionally, the algorithm is more suitable for clinical applications due to its lower complexity. Full article

Ocular diseases can significantly affect vision and overall quality of life, with diagnosis often being time-consuming and dependent on expert interpretation. While previous computer-aided diagnostic systems have focused primarily on medical imaging, this paper proposes VisionTrack, a multi-modal AI system for predicting multiple retinal diseases, including Diabetic Retinopathy (DR), Age-related Macular Degeneration (AMD), Diabetic Macular Edema (DME), drusen, Central Serous Retinopathy (CSR), and Macular Hole (MH), as well as normal cases. The proposed framework integrates a Convolutional Neural Network (CNN) for image-based feature extraction, a Graph Neural Network (GNN) to model complex relationships among clinical risk factors, and a Large Language Model (LLM) to process patient medical reports. By leveraging diverse data sources, VisionTrack improves prediction accuracy and offers a more comprehensive assessment of retinal health. Experimental results demonstrate the effectiveness of this hybrid system, highlighting its potential for early detection, risk assessment, and personalized ophthalmic care. Experiments were conducted using two publicly available datasets, RetinalOCT and RFMID, which provide diverse retinal imaging modalities: OCT images and fundus images, respectively. The proposed multi-modal AI system demonstrated strong performance in multi-label disease prediction. On the RetinalOCT dataset, the model achieved an accuracy of 0.980, F1-score of 0.979, recall of 0.978, and precision of 0.979. Similarly, on the RFMID dataset, it reached an accuracy of 0.989, F1-score of 0.881, recall of 0.866, and precision of 0.897. These results confirm the robustness, reliability, and generalization capability of the proposed approach across different imaging modalities. Full article

Pharmaceutical packaging materials play a crucial role in ensuring the safety and efficacy of medications. This mini-review examines the properties of common packaging materials (glass, plastics, metals, and rubber) and their implications for drug safety. By analyzing 127 research articles from PubMed, Web of Science, and CNKI databases (2000–2025), we also discuss recent regulatory updates in China and highlight emerging technologies, including nanomaterials, sustainable packaging solutions, and intelligent packaging systems that present new opportunities for the pharmaceutical industry. Key findings include the following: (1) The physicochemical properties of packaging materials and potential microbial contamination risks during production significantly impact drug quality and safety, underscoring the need for enhanced research and regulatory oversight. (2) Each material exhibits distinct advantages and limitations: glass demonstrates superior chemical stability but may leach ions; plastics offer versatility but risk plasticizer migration; metals provide exceptional strength yet have limited applications; rubber ensures effective sealing but may release additives compromising drug quality. (3) The pharmaceutical packaging sector is evolving toward intelligent systems and sustainable solutions to address contemporary healthcare challenges. This review can aid pharmaceutical companies in selecting drug packaging and guide manufacturers in developing innovative packaging solutions. Full article

The COVID-19 pandemic has illuminated and intensified pre-existing structural vulnerabilities among older adults living with HIV/AIDS in sub-Saharan Africa, particularly Nigeria. Within already overstretched healthcare infrastructures, these individuals faced heightened economic precarity, disrupted HIV care, and pronounced psychosocial distress. Exploring their lived experiences critically advances an understanding of resilience and informs contextually responsive interventions that can mitigate future health crises. This study employed a narrative qualitative approach to explore the lived experiences of older adults (aged 50 and above) attending the Infectious Diseases Institute (IDI) clinic in Ibadan, Nigeria, during the pandemic lockdown. Purposive sampling guided by maximum variation principles enabled the selection of 26 participants who provided detailed accounts through in-depth interviews. Reflective thematic analysis identified complex narratives illustrating intensified financial hardships, disrupted access to antiretroviral therapy (ART), and heightened psychological distress, including anxiety, depression, and profound isolation. Conversely, participants also articulated experiences of resilience, manifesting in improved medication adherence, strengthened family bonds, and introspective growth fostered by enforced isolation. These nuanced findings highlights the necessity of developing an adaptive, integrated healthcare interventions that addresses economic vulnerabilities, psychosocial wellbeing, and ART continuity, thereby better preparing resource-constrained health systems to support older adults with HIV/AIDS in future public health crises. Full article

Background/Objectives: Artificial intelligence (AI) offers opportunities to enhance healthcare accessibility for people with disabilities (PwDs). However, their application in Saudi Arabia remains limited. This study explores PwDs’ experiences with AI technologies within the Kingdom’s Vision 2030 digital health framework to inform inclusive healthcare innovation strategies. Methods: Semi-structured interviews were conducted with nine PwDs across Riyadh, Al-Jouf, and the Northern Border region between January and February 2025. Participants used various AI-enabled technologies, including smart home assistants, mobile health applications, communication aids, and automated scheduling systems. Thematic analysis following Braun and Clarke’s six-phase framework was employed to identify key themes and patterns. Results: Four major themes emerged: (1) accessibility and usability challenges, including voice recognition difficulties and interface barriers; (2) personalization and autonomy through AI-assisted daily living tasks and medication management; (3) technological barriers such as connectivity issues and maintenance gaps; and (4) psychological acceptance influenced by family support and cultural integration. Participants noted infrastructure gaps in rural areas, financial constraints, limited disability-specific design, and digital literacy barriers while expressing optimism regarding AI’s potential to enhance independence and health outcomes. Conclusions: Realizing the benefits of AI for disability healthcare in Saudi Arabia requires culturally adapted designs, improved infrastructure investment in rural regions, inclusive policymaking, and targeted digital literacy programs. These findings support inclusive healthcare innovation aligned with Saudi Vision 2030 goals and provide evidence-based recommendations for implementing AI healthcare technologies for PwDs in similar cultural contexts. Full article

The One Health approach is rapidly gaining the attention of the scientific community worldwide and is expected to be a major model of scientific reasoning in the 21st century, concerning medical, veterinary and environmental issues. The basic concept of One Health, that humans, animals and their environments are parts of the same natural world affecting each other, is rooted in most ethnic as well as in many religious traditions. Despite this unity and for historical reasons, medical, veterinary and environmental sciences developed independently. The One Health concept tries to reunite these and many other relevant sciences, aiming at a deeper understanding of the interconnection between the natural world, humans and animal health. The dynamic interplay between a host’s microbiome, the microbiomes of other hosts, and environmental microbial communities profoundly influences the host health, given the essential physiological functions the microbiome performs within the organism. The biodiversity of microbiomes is broad and complex. The different areas of the skin, the upper and lower respiratory systems, the ocular cavity, the oral cavity, the gastrointestinal tract and finally the urogenital system of pets and humans alike are niches where a multitude of microorganisms indigenous and transient—commensals and pathogens, thrive in a dynamic antagonistic balance of populations of different phyla, orders, genera and species. The description of these microbiomes attempted in this article is not meant to be exhaustive but rather demonstrative of their complexity. The study of microbiomes is a necessary step towards the One Health approach to pets and humans. Yet, despite the progress made on that subject, the scientific community faces challenges, such as the limitations of studies performed, the scarcity of studies concerning the microbiomes of cats, the multitude of environmental factors affecting the results and others. The two new terms proposed in this article, the “familiome” and the “oikiome”, will aid in the One Health theoretical analysis as well as in its practical approach. The authors strongly believe that new technological breakthroughs, like Big Data Analytics and Artificial Intelligence (AI), will significantly help to overcome these hazards. Full article

Mental illness has emerged as a significant global health crisis, inflicting immense suffering and causing a notable decrease in productivity. Identifying mental health disorders at an early stage allows healthcare professionals to implement more targeted and impactful interventions, leading to a significant improvement in the overall well-being of the patient. Recent advances in Artificial Intelligence (AI) have opened new avenues for analyzing medical records and behavioral data of patients to assist mental health professionals in their decision-making processes. In this study performance of four Randomized Neural Networks (RandNNs) such as Board Learning System (BLS), Random Vector Functional Link Network (RVFLN), Kernelized RVFLN (KRVFLN), and Extreme Learning Machine (ELM) are explored for detecting the type of mental illness a user may have by analyzing the random text of the user posted on social media. To improve the performance of the RandNNs during handling the text documents with unbalanced class distributions, a hybrid feature selection (FS) technique named as TOPSIS-ModCHI is suggested in the preprocessing stage of the classification framework. The effectiveness of the suggested FS with all the four randomized networks is assessed over the publicly available Reddit Mental Health Dataset after experimenting on two benchmark multiclass unbalanced datasets. From the experimental results, it is inferred that detecting the mental illness using BLS with TOPSIS-ModCHI produces the highest precision value of 0.92, recall value of 0.66, f-measure value of 0.77, and Hamming loss value of 0.06 as compared to ELM, RVFLN, and KRVFLN with a minimum feature size of 900. Overall, utilizing BLS for mental health analysis can offer a promising avenue toward improved interventions and a better understanding of mental health issues, aiding in decision-making processes. Full article

Background/Objectives: Major adverse cardiovascular events (MACE)—including heart attacks and strokes—are the leading cause of death in patients with peripheral artery disease (PAD), yet biomarker research for MACE prediction in PAD patients remains limited. Inflammatory proteins play a key role in the progression of atherosclerosis and may serve as useful prognostic indicators for systemic cardiovascular risk in PAD. The objective of this study was to evaluate a broad panel of circulating inflammatory proteins to identify those independently associated with 2-year MACE in patients with PAD. Methods: We conducted a prospective cohort study involving 465 patients with PAD. Plasma concentrations of 15 inflammatory proteins were measured at baseline using validated immunoassays. Patients were followed over a two-year period for the development of MACE, defined as a composite endpoint of myocardial infarction, stroke, or mortality. Protein levels were compared between patients with and without MACE using the Mann–Whitney U test. Cox proportional hazards regression was used to determine the independent association of each protein with MACE after adjusting for baseline demographic and clinical variables, including existing coronary and cerebrovascular disease. To validate the findings, a random forest machine learning model was developed to assess the relative importance of each protein for predicting 2-year MACE. Results: The mean age of the cohort was 71 years (SD 10), and 145 participants (31.1%) were female. Over the two-year follow-up, 84 patients (18.1%) experienced MACE. Six proteins were significantly elevated in PAD patients who developed MACE: interferon gamma (IFN-γ; 42.55 [SD 15.11] vs. 33.85 [SD 12.46] pg/mL, p < 0.001), tumor necrosis factor alpha (TNF-α; 9.00 [SD 5.00] vs. 4.65 [SD 4.29] pg/mL, p < 0.001), chemokine (C-X-C motif) ligand 9 (CXCL9; 75.99 [SD 65.14] vs. 5.38 [SD 64.18] pg/mL, p = 0.002), macrophage inflammatory protein-1 beta (MIP-1β; 20.88 [SD 18.10] vs. 15.67 [SD 16.93] pg/mL, p = 0.009), MIP-1δ (25.29 [SD 4.22] vs. 17.98 [SD 4.01] pg/mL, p = 0.026), and interleukin-6 (IL-6; 12.50 [SD 40.00] vs. 6.72 [SD 38.98] pg/mL, p = 0.035). After adjusting for all baseline covariates, only two proteins—TNF-α (adjusted HR 1.66, 95% CI 1.28–2.33, p = 0.001) and IFN-γ (adjusted HR 1.25, 95% CI 1.12–2.29, p = 0.033)—remained significantly and independently associated with 2-year MACE. These findings were corroborated by the random forest model, where TNF-α and IFN-γ received the highest importance scores for predicting 2-year MACE: (TNF-α: 0.15 [95% CI 0.13–0.18], p = 0.002; IFN-γ: 0.19 [95% CI 0.17–0.21], p = 0.001). Conclusions: From a panel of 15 proteins, TNF-α and IFN-γ emerged as inflammatory biomarkers associated with 2-year MACE in PAD patients. Their measurement may aid in cardiovascular risk stratification, helping to identify high-risk individuals who could benefit from early multidisciplinary referrals to cardiology, neurology, and/or vascular medicine specialists to provide intensified medical therapy. Incorporating these biomarkers into PAD management may improve systemic cardiovascular outcomes through more personalized and targeted treatment approaches. Full article

This review paper addresses the design and testing of ultra-low temperature (ULT) freezers, highlighting their critical functions in various industries, particularly foods, medicine, and research. ULT freezers operating at temperatures of −86 °C and lower have come a long way with improvements in freezing technology, for instance, from traditional vapor compression systems to new multi-stage refrigeration technologies. This progress has added operational reliability and energy efficiency, essential for preserving delicate samples and facilitating groundbreaking research. The article deeply explores the contribution of refrigerants to ULT freezer efficiency and sustainability. With the use of chlorofluorocarbons (CFCs), previously reliant on them, being prohibited due to environmental concerns, the sector opted for environmentally friendly substitutes like hydrofluorocarbons (HFCs), natural refrigerants, and hydrofluoroolefins (HFOs). Regulatory compliance is ensured by rigid validation protocols to guarantee ULT freezers are safe and meet quality requirements without compromising the integrity of the stored material. In addition to their wide-ranging advantages, ULT freezers also have disadvantages, such as energy efficiency, incorporating automation, the integration of IoT and AI for proactive maintenance, and the development of environmentally sustainable refrigerants. Adequate management strategies, including regular employee training and advanced monitoring systems, are vital to counteract threats from temperature variations and reduce long-term diminished performance. Finally, subsequent innovations in ULT freezer technology will not only aid in research and medical initiatives but also support sustainable practices, ensuring their core role as beacons of innovation in preserving the quality of precious biological materials and increasing public health gains. Full article

Trichomonas tenax is an anaerobic flagellate usually found in the oral cavity of humans and domestic animals. It is very likely to be transmitted through kissing, sharing saliva, contaminated utensils, and water. However, research on its transmission dynamics is scarce. Hence, there is a need to identify potential knowledge gaps in T. tenax transmission for future research and emphasize the importance of the One Health approach in controlling the spread of this flagellar protozoan. Trichomonas tenax has been found in humans, dogs, cats, horses, and birds at various body sites, including the lungs and the urogenital tract, in addition to the oral cavity. Its transmission is influenced by environmental factors such as temperature and socioeconomic factors such as age, income, smoking, and public awareness, along with poor oral hygiene and systemic diseases. Direct host-to-host transmission also plays an important role; however, transmission through fomites or contaminated water still needs to be scientifically proven to gain a better understanding of these mechanisms. More studies on this flagellate are warranted, especially using animal models and epidemiological studies, to better understand its transmission dynamics. Prioritizing research in these areas could result in a more comprehensive understanding of T. tenax transmission dynamics and the factors that influence it, ultimately aiding in the development of effective control and prevention strategies. It is also recommended to encourage collaboration between medical and veterinary professionals in addressing this zoonotic protozoan, recognizing that it aligns with the One Health approach. Full article

Background/Objectives: Chronic obstructive pulmonary disease (COPD) is a progressive respiratory disorder characterized by systemic manifestations, including altered body composition, reduced quality of life, and psychological distress. Despite its significance, the relationship between body composition parameters and symptoms of fatigue, anxiety, and depression in patients with COPD remains underexplored. This study aimed to examine the association between detailed body composition metrics and quality of life, fatigue, and anxiety and depression symptoms in male patients with COPD compared to healthy controls. Methods: This cross-sectional study included 49 men with COPD and 51 age-matched healthy controls aged 50–80 years. Body composition was assessed using bioelectrical impedance analysis (BIA). Pulmonary function, dyspnea, activities of daily living, and psychological status were evaluated using spirometry, the Medical Research Council Dyspnea Scale, the London Chest Activity of Daily Living Scale (LCADL), and the Hospital Anxiety and Depression Scale (HADS), respectively. Results: Compared to the controls, patients with COPD exhibited significantly lower forced expiratory volume in one second (FEV₁: 1.1 vs. 2.16 L; p < 0.001), lower fat mass (15.0 vs. 24.3 kg; p < 0.001), and higher muscle mass (53.8 vs. 42.0 kg; p < 0.001). They also reported significantly greater fatigue (Borg scale: 4 vs. 0; p < 0.001), higher anxiety (8 vs. 5; p = 0.006), and depression scores (11 vs. 5; p < 0.001), along with more pronounced limitations in their daily activities. Conclusions: COPD is associated with profound impairments in body composition, physical function, and mental health. Detailed body composition analysis using BIA provides valuable clinical insights and may aid in tailoring individualized interventions to improve quality of life and psychological outcomes in COPD management. Full article

Background/Objectives: Infection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused a global pandemic with far-reaching impacts on human activities. Moreover, direct viral damage and uncontrolled inflammation have been proposed as contributing factors to the severity of SARS-CoV-2 disease. Lipopolysaccharide binding protein (LBP) is also well recognized for its capability to trigger and modulate the host’s innate immune system by attaching to bacterial substances. Nevertheless, the pandemic has further emphasized the critical role of an effective host immune response in controlling viral infection and highlighted the detrimental effect of immune dysregulation. This study aimed to assess plasma levels of LBP and inflammatory biomarkers in SARS-CoV-2 patients with different malnutrition status and severity levels. Methods: This cross-sectional study was carried out in King Khalid University Hospital in Riyadh from December 2020 to December 2021. A total of 166 SARS-CoV-2 patients were recruited including 80 critical and 86 non-critical patients. Medical history, anthropometrical parameters, disease outcome information, and relevant biochemical parameters were extracted from medical records. Plasma samples were collected to test for LBP and inflammatory cytokines. Finally, nutritional risk was assessed by the Nutrition Risk Screening-2002 (NRS-2002) tool. Results: This cross-sectional study found no significant differences in LBP levels between critical and non-critical SARS-CoV-2 patients. However, LBP levels significantly correlated with IL-10, TNF-α and IL-6/IL-10 levels (Spearman’s rho = 0.430, 0.276 and −0.397 respectively; p < 0.001). Conclusions: This study confirmed the elevated inflammatory cytokines in hospitalized SARS-CoV-2 patients and their association with disease severity and malnutrition. These findings may support the mechanism of gut inflammation in order to develop new interventions that lower inflammatory biomarkers, disease severity, and aid in SARS-CoV-2 prevention and management. Full article