Search Results (1,812)

Non-communicable diseases (NCDs) such as Diabetes Mellitus (DM) and Sickle Cell Disease (SCD) pose an escalating health challenge in Africa, underscored by diagnostic deficiencies, inadequate surveillance, and limited health system capacity that contribute to late diagnoses and consequent preventable complications. This review adopts a comparative framework that considers DM and SCD as complementary indicator diseases, both metabolic and genetic, and highlights intersecting diagnostic, infrastructural, and governance hurdles relevant to AI-enabled screening in resource-constrained environments. The study synthesizes epidemiological data across both African and high-income regions and methodically catalogs machine learning (ML) and deep learning (DL) research by clinical application, including risk prediction, image-based diagnostics, remote patient monitoring, privacy-preserving learning, and governance frameworks. Our key observations reveal significant disparities in disease detection and health outcomes, driven by underdiagnosis, a lack of comprehensive newborn screening for SCD, and fragmented diabetes surveillance systems in Africa, despite the availability of effective diagnostic technologies in other regions. The reviewed literature on ML/DL shows high algorithmic accuracy, particularly in diabetic retinopathy screening and emerging applications in SCD microscopy. However, most studies are constrained by small, single-site datasets that lack robust external validation and do not align well with real-world clinical workflows. The review identifies persistent implementation challenges, including data scarcity, device variability, limited connectivity, and inadequate calibration and subgroup analysis. By integrating epidemiological insights into AI diagnostic capabilities and health system realities, this work extends beyond earlier surveys to offer a comprehensive, Africa-centric, implementation-focused synthesis. It proposes actionable operational and policy recommendations, including offline-first deployment strategies, federated learning approaches for low-bandwidth scenarios, integration with primary care and newborn screening initiatives, and enhanced governance structures, to promote equitable and scalable AI-enhanced diagnostics for NCDs. Full article

Background: The human gut microbiome is highly complex, and its composition is strongly influenced by dietary patterns. Alterations in microbiome structure have been associated with a range of diseases, including type 2 diabetes mellitus. However, the underlying mechanisms for this remain poorly understood. In this study, a novel in vitro approach was utilized to investigate the interplay between gut bacteria, dietary metabolites, and metabolic dysfunction. Methods: Two representative gut bacterial species—Bacteroides thetaiotaomicron and Lactobacillus fermentum—were isolated from human faecal samples and subjected to controlled dietary manipulation to mimic eubiotic and dysbiotic conditions. Metabolites produced under these conditions were extracted, characterized, and quantified. To assess the functional impact of these metabolites, we utilized the INS-1 832/3 insulinoma cell line, evaluating insulin sensitivity through glucose-stimulated insulin secretion and ERK1/2 activation. Results: Our findings demonstrate that metabolites derived from high-carbohydrate/high-fat diets exacerbate metabolic dysfunction, whereas those generated under high-fibre conditions significantly enhance insulin secretion and glucose-dependent ERK1/2 activation in co-culture compared to monocultures. Conclusions: This work systematically disentangles the complex interactions between gut microbiota, diet, and disease, providing mechanistic insights into how microbial metabolites contribute to the onset of metabolic disorders. Full article

Hard-to-heal wounds remain a significant challenge for healthcare professionals, particularly in aging populations. Although most chronic wounds are associated with diabetes or chronic venous insufficiency, rare etiologies should also be considered. One such cause is envenomation by Phoneutria spp. (native to South America, rare in Europe). Their venom contains potent neurotoxins. While systemic manifestations are more commonly reported, localized necrotic skin lesions may also occur. This case report presents a rare chronic wound following a suspected Phoneutria spider bite and highlights the importance of an individualized, multimodal treatment approach. A 61-year-old male patient with a progressive thigh wound following a spider bite sustained during work. Despite initial self-treatment and pharmacotherapy the wound deteriorated. The patient was admitted to the authors’ facility, where surgical treatment included necrosectomy and a sandwich graft using an acellular dermal matrix combined with a split-thickness skin graft. Adjunctive therapies included negative pressure wound therapy and hyperbaric oxygen therapy. After discharge, outpatient wound care was continued. Treatment was monitored with photographic documentation and serial microperfusion measurements. Complete wound closure was achieved after 4 months of specialized therapy. Management of chronic wounds requires a multidisciplinary and individualized approach with surgical intervention, advanced wound care and specialized outpatient follow-up. Full article

Background/Objectives: Telemedicine has emerged as a pivotal approach to improving access to diabetic retinopathy (DR) screening, diagnosis, management, and monitoring. Over the past two decades, rapid advancements in digital imaging, mobile health technologies, and artificial intelligence have substantially expanded the role of teleophthalmology in DR, resulting in a large volume of pertinent publications. This study aimed to provide a scientific overview of telemedicine applied to DR through bibliometric analysis. Methods: A search of the Web of Science Core Collection was conducted on 15 November 2025 to identify English-language original research and review articles regarding telemedicine for DR. Bibliographic data from relevant publications were extracted and underwent quantitative analysis and visualization using the tools Bibliometrix and VOSviewer. Results: A total of 515 articles published between 1998 and 2025 were included in our analysis. During this period, the research field of telemedicine for DR exhibited an annual growth rate of 13.14%, with publication activity markedly increasing after 2010 and peaking in 2020–2021. Based on the number of publications, United States, China, and Australia were the most productive countries, while Telemedicine and e-Health, Journal of Telemedicine and Telecare, and British Journal of Ophthalmology were the most relevant journals in the field. Keyword co-occurrence analysis revealed three major thematic clusters within the broader topic of telemedicine and DR, namely, public health-oriented work, telehealth service models, and applications of artificial intelligence technologies. Conclusions: The role of telemedicine in DR detection and care represents an expanding multidisciplinary field of research supported by contributions from multiple authors and institutions worldwide. As technological capabilities continue to evolve, ongoing innovation and cross-domain collaboration could further advance the applications of teleophthalmology for DR, promoting more accessible, efficient, and equitable identification and management of this condition. Full article

Timely recognition of type 1 diabetes (T1D) in children and adolescents is crucial to prevent acute complications such as diabetic ketoacidosis (DKA). This narrative review examines the pathophysiology, clinical presentation, and diagnostic challenges of childhood T1D, including the young age of onset, clinician training gaps, and overlapping symptomatology between T1D and other common pediatric illnesses. Despite increased awareness, a significant proportion of children still present with DKA at diagnosis due to misinterpretation of symptoms, such as polydipsia, polyuria, and weight loss. This work emphasizes the importance of early recognition, timely intervention, and the use of structured management algorithms for primary care clinicians. Strategies to reduce DKA incidence, based on existing literature, successful real-world examples, and current guidelines, include enhanced screening for high-risk populations, educational initiatives, and improved diagnostic protocols. By implementing systematic approaches and public health campaigns, healthcare providers can improve early T1D detection and prevent severe DKA complications, ultimately enhancing patient outcomes and reducing long-term morbidity. Full article

Background: Insulin resistance (IR) is a key metabolic abnormality underlying type 2 diabetes and cardiometabolic diseases. Although lifestyle and sociodemographic determinants are well described, the role of psychosocial constructs—such as purpose in life—remains insufficiently characterized. No prior study in large occupational samples has examined the associations between purpose in life and IR when evaluated through three complementary indices: the triglyceride–glucose index (TyG), the Single-Point Insulin Sensitivity Estimator for Insulin Resistance (SPISE-IR), and the metabolic score for insulin resistance (METS-IR). Objectives: To analyze the cross-sectional associations between purpose in life and IR indicators in a large working population and determine whether these associations persist after accounting for sociodemographic and lifestyle factors. Methods: A cross-sectional study was conducted among 93,077 Spanish workers aged 20–69 years undergoing routine occupational health examinations. IR was estimated using TyG, SPISE-IR, and METS-IR indices. Purpose in life was assessed using the 10-item Purpose in Life Test and categorized into three groups based on the empirical distribution of scores. Multinomial logistic regression models adjusted for age, sex, social class, smoking, Mediterranean diet adherence, physical activity, and BMI were used to examine associations. Results: Lower purpose in life was consistently associated with higher IR categories across all indices. Compared with individuals reporting high purpose, those with low purpose had higher odds of belonging to the high IR category (TyG ORa 1.59; 95% CI 1.45–1.74; SPISE-IR ORa 1.94; 95% CI 1.76–2.13; METS-IR ORa 2.21; 95% CI 1.98–2.47). Adding purpose in life to sociodemographic and lifestyle models modestly improved discrimination for identifying high IR categories. Conclusions: In this large occupational cohort, purpose in life was independently associated with insulin resistance as measured by three metabolic indices. These findings highlight the relevance of psychosocial factors in metabolic health. Longitudinal studies are needed to clarify temporal pathways and assess whether purpose-oriented approaches may contribute to improved metabolic profiles. Full article

Due to the complexity of blood glucose dynamics and the high variability of the physiological structure of diabetic patients, implementing a safe and effective insulin dosage control algorithm to keep the blood glucose of diabetic patients within the normal range (70–180 mg/dL) is currently a challenging task in the field of diabetes treatment. Deep reinforcement learning (DRL) has proven its potential in diabetes treatment in previous work, thanks to its strong advantages in solving complex dynamic and uncertain problems. It can address the challenges faced by traditional control algorithms, such as the need for patients to manually estimate carbohydrate intake before meals, the requirement to establish complex dynamic models, and the need for professional prior knowledge. However, reinforcement learning is essentially a highly exploratory trial-and-error learning strategy, which is contrary to the high-safety requirements of clinical practice. Therefore, achieving safer control has always been a major challenge for the clinical application of DRL. This paper addresses this challenge by combining the advantages of DRL and the traditional control algorithm—model predictive control (MPC). Specifically, by using the blood glucose and insulin data generated during the interaction between DRL and patients in the learning process to learn a blood glucose prediction model, the problem of MPC needing to establish a patient’s blood glucose dynamic model is solved. Then, MPC is used for forward-looking prediction and simulation of blood glucose, and a safety controller is introduced to avoid unsafe actions, thus restricting DRL control to a safer range. Experiments on the UVA/Padova glucose kinetics simulator approved by the US Food and Drug Administration (FDA) show that the time proportion of adult patients within the healthy blood glucose range under the control of the model proposed in this paper reaches 72.51%, an increase of 2.54% compared with the baseline model, and the proportion of severe hyperglycemia and hypoglycemia events is not increased, taking an important step towards the safe control of blood glucose. Full article

Accurate prediction of Blood Glucose (BG) levels is essential for effective diabetes management and the prevention of adverse glycemic events. This study introduces a novel designed hybrid Gated Recurrent Unit-Transformer (GRU-Transformer) model tailored to forecast BG levels at 15, 30, 45, and 60 min horizons using only Continuous Glucose Monitoring (CGM) data as input. The proposed approach integrates advanced CGM feature extraction step. The extracted features are statistically the mean, the median, the maximum, the entropy, the autocorrelation and the Detrended Fluctuation Analysis (DFA). In addition, in order to define more enhanced and specific features, the custom 3-points monotonicity score, the sinusoidal time encoding, and the workday/weekend binary features are proposed in this work. This approach enables the model to capture physiological dynamics and contextual temporal patterns of Type 1 Diabetes (T1D) with great accuracy. To thoroughly assess the performance of the proposed method, we relied on several well-established metrics, including Root Mean Squared Error (RMSE), Coefficient of Determination (R²), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Percentage Error (RMSPE). Experimental results demonstrate that the proposed method achieves superior predictive accuracy for both short-term (15–30 min) and long-term (45–60 min) forecasting. Specifically, the model attained the lowest average RMSE values, with 4.00 mg/dL, 6.65 mg/dL, 7.96 mg/dL, and 8.91 mg/dL and yielding consistently high R² scores for the respective prediction horizons. This new method distinguishes itself by continuously exceeding current prediction models, reinforcing its potential for real-time CGM and clinical decision support. Its high accuracy and adaptability make it a favorable tool for improving diabetes management and personalized glycemic control. Full article

The context of food science and biotechnology is undergoing a profound transformation, characterized by an evolutionary shift from conventional large-scale fermentation to precision biomanufacturing, positioning Lactic Acid Bacteria (LAB) as versatile cellular biofactories for next-generation functional foods. This review analyzes the evolutionary role of LAB, their utilization as probiotics, and the technological advances driving this shift. This work also recognizes the fundamental contributions of pioneering women in the field of biotechnology. The primary methodology relies on the seamless integration of synthetic biology (CRISPR-Cas editing), Multi-Omics analysis, and advanced Artificial Intelligence/Machine Learning, enabling the precise, rational design of LAB strains. This approach has yielded significant findings, including successful metabolic flux engineering to optimize the biosynthesis of high-value nutraceuticals such as Nicotinamide Mononucleotide and N-acetylglucosamine, and the development of Live Biotherapeutic Products using native CRISPR systems for the expression of human therapeutic peptides (e.g., Glucagon-like Peptide-1 for diabetes). From an industrial perspective, this convergence enhances strain robustness and supports the digitalized circular bioeconomy through the valorization of agri-food by-products. In conclusion, LAB continue to consolidate their position as central agents for the development of next-generation functional foods. Full article

Diabetic foot ulcers (DFUs) represent 6.3% of the various complications of type 2 diabetes mellitus, with a risk of development of up to 34%. Several factors contribute to the formation of ulcers, which are very difficult to treat as they hinder efficient wound healing. Patients experience persistent pain, which leads to the consumption of various medications (polypharmacy) due to the lesions not resolving. Conversely, this can increase the risk of various factors, including a chronic inflammatory state, which hinders the body’s own regenerative processes. Until now, treatment options have been limited to washing the wound and stimulating new tissue growth, but this is a painful and unsuccessful process. One of the treatment options is therefore cell therapy with mesenchymal stem cells, which have immunomodulatory characteristics and allow tissue regeneration, although the effect directly in pain is not totally clear. We have previously reported in our working group that patients with ulcers treated with mesenchymal stem cells (MSCs) have been able to integrate into their daily lives, although the pain related to the inflammatory state and polypharmacy has not been studied. Objective: This study investigates how the local administration of MSCs improves the condition of an ulcer by inducing tissue regeneration. It also shows how the concentration of systemic inflammatory biomarkers is modified in direct correlation with pain and the consumption of medications over time. Methods: Local administration of MSCs at 7 and 14 days, measuring pro- and anti-inflammatory cytokines relative to the healthy control group, evaluating wound healing, and monitoring the medications taken by the patient in conjunction with pain perception. Results: Cell administration showed that inflammatory molecules were reduced and anti-inflammatory molecules increased. This is reflected in the consumption of Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) in relation to wound improvement, with a decrease in pain medication consumption of less than 50%. We provide evidence that locally administered mesenchymal stem cells influence systemic inflammatory processes necessary for tissue recovery, impacting patients’ polypharmacy consumption due to reduced perceived pain. Conclusions: This report establishes a direct link between mesenchymal stem cells and pain relief in type 2 diabetes ulcers, potentially paving the way for new pain therapies. Full article

Background: Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful analytical tool in metabolomics, but it is often hindered by the high cost and technical complexity of the machines, limiting its clinical and point-of-care applications. Recent advances in benchtop NMR technology have sought to overcome these barriers by providing more compact, affordable, and user-friendly instruments. This systematic review aims to assess the potential of benchtop NMR in clinical metabolomics, highlighting its practical advantages, current applications, and technological challenges relative to high-field systems. Methods: For this systematic review we searched Web of Science and PubMed databases to identify studies employing benchtop NMR spectroscopy in clinical and biomedical applications. The review focuses on works that evaluated metabolic profiling in human and animal disease contexts, compared benchtop and high-field performance, and utilized advanced data analysis methods, including multivariate and machine learning approaches. Results: Among the 74 records identified, 15 research articles were eligible, including 11 studies involving human biospecimens and 4 studies concerning animal samples. The selected works were published between 2018 and 2025. These studies demonstrated the potential clinical utility of low-field NMR in differentiating disease states such as tuberculosis, type 2 diabetes, neonatal sepsis, and chronic kidney disease, achieving diagnostic accuracies comparable to high-field instruments. Conclusions: Although limited by lower sensitivity and spectral resolution, benchtop NMR represents a significant step toward the democratization of NMR-based metabolomics. Continued hardware development, improved pulse sequences, and the integration of artificial intelligence for spectral processing and modeling are expected to enhance its analytical power and accelerate its clinical adoption. Full article

Parkinson’s disease (PD) is a complex neurodegenerative disorder characterised by progressive motor and non-motor impairment, in which current therapies remain symptomatic and fail to halt dopaminergic neuron loss. Growing evidence linking metabolic dysfunction, type 2 diabetes, and neurodegeneration has renewed interest in glucagon-like peptide 1 (GLP-1) receptor agonists as potential disease-modifying agents. While several recent reviews have explored the role of incretin-based therapies, the present work provides an integrative perspective by combining a mechanistic analysis of GLP-1 signalling pathways with a model-specific synthesis of preclinical findings and an appraisal of clinical translational relevance. We consolidate evidence across PI3K/Akt, MAPK/ERK, cAMP/PKA–CREB, and AMPK pathways, emphasising their convergence on mitochondrial homeostasis, proteostasis, neuroinflammation, and synaptic resilience. To enhance translational clarity, we summarise preclinical studies across major PD models, evaluate dose comparability and blood–brain barrier penetration, and identify pharmacokinetic and mechanistic factors that may explain divergent clinical outcomes. We also compare the therapeutic potential of key GLP-1 agonists, including exendin-4, liraglutide, semaglutide, lixisenatide, and emerging dual agonists. By integrating biochemical, preclinical, and clinical domains, this review provides a comprehensive framework for interpreting the current evidence and guiding the future development of incretin-based neuroprotective strategies in PD. Full article

Distinguishing subcutaneous adipose tissue (SAT) from intermuscular adipose tissue (IMAT) is clinically important because IMAT infiltration is strongly associated with age-related functional decline, sarcopenia, diabetes, cardiovascular disease, and obesity. Current assessments rely on MRI or CT, which are stationary, costly, and labor-intensive. Portable ultrasound-based solutions could enable broader, proactive screening. This model study investigated the feasibility of differentially assessing SAT and IMAT using features extracted from propagating ultrasound signals. Twenty-five phantoms were constructed using gelatin as a muscle-mimicking matrix and oil as the SAT and IMAT compartments, arranged to provide gradual variations in fat fractions ranging from 0% to 50%. Ultrasound measurements were collected at 0.8 MHz and 2.2 MHz, and multiple evaluation criteria were computed, including ultrasound velocity and parameters derived from the signal intensity. Classification domains were then generated from intersecting decision rules associated with these criteria. In parallel, artificial neural networks (ANN/LSTM) were trained and tested on identical phantom subsets to evaluate data-driven classification performance. Both the rule-based and ANN/LSTM approaches achieved diagnostically meaningful separation of SAT and IMAT. The aim of this work was to perform an experimental proof-of-concept study on idealized tissue models to demonstrate that ultrasound measurements can reliably differentiate SAT and IMAT, supporting the development of future screening devices. Full article

Background: Obesity is a chronic disease that has negative health consequences for children. Peer support models have been used to manage chronic diseases like diabetes; however, little is known about how a peer support intervention might promote healthy infant growth to prevent pediatric obesity. The aim of this project was to explore parental perspectives on how a peer support intervention might be developed to support healthy infant weight gain and nutrition. Methods: Data were collected from November 2022 to October 2023 at a single pediatric primary care clinic. Semi-structured interviews explored parents’ perspectives of how a peer parent coach could promote healthy infant nutrition and growth. Interviews focused on (1) common infant feeding and nutrition questions, (2) the role and importance of peer support during the newborn period, and (3) strategies for addressing and facilitating connections to food-related resources and addressing food insecurity. Results: A total of 18 interviews were conducted. Average parental age was 32.1 years (range 20–46 years). Thirty-three percent of the participants identified as Black, 28% identified as White, 11% identified as Asian, and the remaining identified as Other or preferred not to report. Half of the sample reported a household income of <$20,000, 67% reported having public insurance, and 11% reported household food insecurity. Themes that emerged included: peer parent coaches can (1) provide emotional support to families with young infants, (2) education focused on infant nutrition, and (3) facilitate connections with nutrition resources. Participants also noted the importance of understanding a family’s unique culture when counseling on infant growth and nutrition. Conclusions: Multiple themes were identified about how a peer support intervention could support healthy infant nutrition and growth. Future work should test the feasibility and acceptability of a peer support intervention to promote healthy infant weight gain. Full article

Physical inactivity, which currently dominates the lifestyles of most people, is linked to chronic metabolic diseases such as obesity, type 2 diabetes (T2D), hypertension, metabolic syndrome, and cardiovascular disease, all of which share insulin resistance as a common pathogenic mechanism. Both epidemiological and experimental intervention studies have consistently shown that physical activity and exercise can reduce the incidence of these diseases and significantly improve their clinical outcomes, resulting in enhanced quality of life and well-being. This approach includes various forms of aerobic and anaerobic/resistance training, either individually or in combination, leading to reduced insulin resistance and visceral fat, regardless of the weight loss achieved through diet. It also lowers inflammatory responses and oxidative stress, a harmful mechanism that leads to cellular damage, and positively impacts immunological regulation. Regarding timing, physical activity/exercise appears to produce better outcomes for metabolic control, particularly in individuals with T2D, when performed after dinner compared to other times of the day. In addition to organized physical activity/exercise sessions, practices such as interrupting prolonged sitting with frequent breaks every 30 min that involve muscular contractions and increased energy expenditure may also benefit metabolic health. Minimizing physical inactivity, prolonged sitting at work or during leisure time, can decrease the frequency of metabolic illness, enhance health and quality of life, and avert premature death. However, intense exercise may not always be the most beneficial option for health, and the relationship between adverse events and the intensity of physical activity or exercise resembles a U-shaped or J-shaped curve. Physical activity/exercise should be performed at a suitable intensity that aligns with personal capability. In this primarily clinically focused review, we discuss the effects of insulin on target tissues, the significance of insulin sensitivity in metabolic regulation, how physical inactivity contributes to insulin resistance, the different types of exercise and their impact on insulin effectiveness, and the importance of physical activity and exercise in managing metabolic diseases. Full article