Currently, the global incidence of diabetes is increasing, particularly in populous developing regions. In China, over 290 million people are affected by diabetic cardio-cerebrovascular diseases. These diseases account for more than 40% of deaths and impose a significant economic burden on both society and families. Diabetes can result in vascular complications through multiple mechanisms, including cardiovascular and cerebrovascular diseases. Current management guidelines recommend conducting risk assessments before prescribing medications like antihypertensives, hypoglycemics, and lipid-lowering drugs, alongside lifestyle interventions, to help prevent cardio-cerebrovascular diseases. However, pharmacological approaches have several limitations, including adverse drug reactions and variability in patient responses. Chinese herbal medicine (CHM) exerts its therapeutic effects via bioactive metabolites that modulate multiple molecular targets, including enzymes, receptors, and transcriptional regulators, through complex interactions with cellular signaling networks. While modern pharmacological research validates its polypharmacological mechanisms, concerns persist regarding potential botanical drug interactions, toxicological profiles, and pharmacokinetic variability of certain botanicals. Only through a balanced scientific approach can CHM’s unique therapeutic value be fully realized. This review evaluates the efficacy of CHM in mitigating metabolic disorders, focusing on its diverse pharmacological mechanisms, including antioxidant defenses, inflammation suppression, and programmed cell death regulation. It elucidates the role of pivotal signaling cascades, including the glucagon (GLC)/5′-adenosine monophosphate–activated protein kinase (AMPK)/nuclear transcription factor-κB (NF-κB) axis, the GLC/peroxisome proliferator–activated receptor α (PPARα)/PGC-1α pathway, as well as the PI3K/Akt and AMPK/mammalian target of rapamycin (mTOR) signaling pathway, alongside oxidative stress and inflammatory responses. However, future research should prioritize well-structured clinical trials and mechanistic studies to substantiate CHM’s therapeutic potential.
Sachin Shinde, Ramadhani Abdallah Noor, Mary Mwanyika‐Sando
et al.
Abstract In Sub‐Saharan Africa (SSA), adolescents make up around one‐quarter of the population who are growing up in a rapidly urbanizing environment, with its associated risks and benefits, including impacts on health, psychosocial development, nutrition, and education. However, research on adolescents' health and well‐being in SSA is limited. The ARISE (African Research, Implementation Science and Education) Network's Adolescent Health and Nutrition Study is an exploratory, school‐based study of 4988 urban adolescents from five countries: Burkina Faso, Ethiopia, South Africa, Sudan, and Tanzania. A multistage random sampling strategy was used to select the schools and adolescents. Adolescent boys and girls aged 10–15 years were interviewed using a standardized questionnaire by trained enumerators. The questionnaire covered multiple domains including demographic and socioeconomic characteristics, water, sanitation and hygiene practices, antimicrobial resistance, physical activity, dietary behaviours, socioemotional development, educational outcomes, media use, mental health, and menstrual hygiene (only for girls). Additionally, a desk review of health and school meal policies and programs and a qualitative investigation into health and food environments in schools were conducted with students, administrators, and food vendors. In this paper, we describe the study design and questionnaire, present profiles of young adolescents who participated in the study, and share field experiences and lessons learned for future studies. We expect that this study along with other ARISE Network projects will be a first step toward understanding young people's health risks and disease burdens, identifying opportunities for interventions and improving policies, as well as developing potential research capacities on adolescent health and well‐being in the SSA region.
Evangelia Anastasia Tsapou, George Ntourtoglou, Vassilis Dourtoglou
et al.
This study explores the chemical and sensory differentiation of Greek white wines produced from five indigenous grape varieties—Savvatiano, Vidiano, Moschofilero, Assyrtiko, and Malagouzia—across diverse terroirs in Greece. A targeted analytical approach was employed to quantify 12 key volatile aroma compounds derived primarily from amino acid metabolism and lipid degradation, using GC-MS and GC-FID. The selected volatiles, including isoamyl alcohol, phenylethyl alcohol, tyrosol, and hexanoic acid ethyl ester, were chosen for their sensory relevance and their biosynthetic linkage to nitrogenous precursors. Principal Component Analysis (PCA) of wines from the 2019 and 2020 vintages revealed clear varietal clustering, under standardized winemaking conditions. Malagouzia wines were characterized by rich and diverse volatile profiles, particularly long-chain fatty acids and esters, while Vidiano exhibited a consistently restrained aromatic expression. Sensory analysis using sorting and ultra-flash profiling confirmed the chemical clustering, with Moschofilero, Vidiano and Malagouzia wines forming distinct sensory groups. The findings demonstrate that key amino acid-derived volatiles can serve as biochemical markers of varietal typicity and support the use of volatile profiling as a tool for terroir-driven wine classification and quality assessment in Greek white wines.
Nutrition. Foods and food supply, Nutritional diseases. Deficiency diseases
Abstract Food insecurity in Nigeria has persisted for decades, resisting various agricultural policies and programmes since the 1970s. This study investigates the impacts of traditional agriculture (TA), sustainable agriculture (SA), and industrial agriculture (IA) on food security in Nigeria from 2000 to 2022, using a system dynamics modelling approach supported by optimization and linear programming techniques. The model also accounts for climate change and land resource dynamics, integrating data from Nigeria and nine comparable countries to enhance robustness. Results show that while crop yield increased over time, food security fluctuated due to factors including land degradation and uneven food distribution. The findings highlight that improving agricultural productivity alone is insufficient; sustainable practices, climate adaptation, and improved distribution systems are essential for long-term food security in Nigeria.
Nutritional diseases. Deficiency diseases, Public aspects of medicine
Identifying preclinical biomarkers of neurodegenerative diseases remains a major challenge in aging research. In this study, we demonstrate that frequent "Don't know/can't remember" (DK) responses, often treated as missing data in touchscreen questionnaires, serve as a novel digital behavioral biomarker of early cognitive vulnerability and neurodegenerative disease risk. Using data from 502,234 UK Biobank participants, we stratified individuals based on DK response frequency (0-1, 2-4, 5-7, >7) and observed a robust, dose-dependent association with an increased risk of Alzheimer's disease (HR = 1.64, 95% CI: 1.26-2.14) and vascular dementia (HR = 1.93, 95% CI: 1.37-2.72), independent of established risk factors. As DK response frequency increased, participants exhibited higher BMI, reduced physical activity, higher smoking rates, and a higher prevalence of chronic diseases, particularly hypertension, diabetes, and depression. Further analysis revealed a dose-dependent relationship between DK response frequency and the risk of Alzheimer's disease and vascular dementia, with high DK responders showing early neurodegenerative changes, marked by elevated levels of Abeta40, Abeta42, NFL, and pTau-181. Metabolomic analysis also revealed lipid metabolism abnormalities, which may mediate this relationship. Together, these findings reframe DK response patterns as clinically meaningful signals of multidimensional neurobiological alterations, offering a scalable, low-cost, non-invasive tool for early risk identification and prevention at the population level.
Convolutional neural networks are continually evolving, with some efforts aimed at improving accuracy, others at increasing speed, and some at enhancing accessibility. Improving accessibility broadens the application of neural networks across a wider range of tasks, including the detection of eye diseases. Early diagnosis of eye diseases and consulting an ophthalmologist can prevent many vision disorders. Given the importance of this issue, various datasets have been collected from the cornea to facilitate the process of making neural network models. However, most of the methods introduced in the past are computationally complex. In this study, we tried to increase the accessibility of deep neural network models. We did this at the most fundamental level, specifically by redesigning and optimizing the convolutional layers. By doing so, we created a new general model that incorporates our novel convolutional layer named ArConv layers. Thanks to the efficient performance of this new layer, the model has suitable complexity for use in mobile phones and can perform the task of diagnosing the presence of disease with high accuracy. The final model we present contains only 1.3 million parameters. In comparison to the MobileNetV2 model, which has 2.2 million parameters, our model demonstrated better accuracy when trained and evaluated on the RfMiD dataset under identical conditions, achieving an accuracy of 0.9328 versus 0.9266 on the RfMiD test set.
Recent studies on plant disease diagnosis using machine learning (ML) have highlighted concerns about the overestimated diagnostic performance due to inappropriate data partitioning, where training and test datasets are derived from the same source (domain). Plant disease diagnosis presents a challenging classification task, characterized by its fine-grained nature, vague symptoms, and the extensive variability of image features within each domain. In this study, we propose the concept of Discriminative Difficulty Distance (DDD), a novel metric designed to quantify the domain gap between training and test datasets while assessing the classification difficulty of test data. DDD provides a valuable tool for identifying insufficient diversity in training data, thus supporting the development of more diverse and robust datasets. We investigated multiple image encoders trained on different datasets and examined whether the distances between datasets, measured using low-dimensional representations generated by the encoders, are suitable as a DDD metric. The study utilized 244,063 plant disease images spanning four crops and 34 disease classes collected from 27 domains. As a result, we demonstrated that even if the test images are from different crops or diseases than those used to train the encoder, incorporating them allows the construction of a distance measure for a dataset that strongly correlates with the difficulty of diagnosis indicated by the disease classifier developed independently. Compared to the base encoder, pre-trained only on ImageNet21K, the correlation higher by 0.106 to 0.485, reaching a maximum of 0.909.
Mitigation measures are essential for controlling the spread of infectious diseases during pandemics and epidemics, but they impose considerable societal, individual, and economic costs. We developed a general optimization framework to balance costs related to infection and to mitigation. Optimizing the trade-off between mitigation and infection cost, we identified three novel, surprising effects: First, assuming a constant reproduction number $R_0$, the optimal response to an infectious disease requires either strict mitigation or none at all, depending on disease severity, but never does one find an intermediate mitigation level to be optimal. Second, under seasonal variations, optimal mitigation is stricter during winter. Interestingly, a single wave of infections still arises in spring with 3 months delay to the seasonal peak of infectivity, replacing the autumn/winter waves known for classical influenza. Third, during steady vaccination campaigns, even optimal mitigation can result in transient infection waves. Finally, we quantify the cost of delayed mitigation onset and show that even short delays can substantially increase total costs -- if the disease is severe. Overall, our framework is easily applicable to general and complex settings and thereby presents a versatile tool to explore optimal mitigation strategies for endemic and pandemic infectious disease.
Abstract Background Diabetes mellitus (DM) and its associated vascular complications have become a worldwide health concern. The effects and mechanism of vitamin D supplementation on endothelial function under high glucose condition remain elusive. Methods Human umbilical vein endothelial cells (HUVECs) were treated with 35 mM glucose, then 100 nM vitamin D were added. Transwell migration assay, CCK-8, immunofluorescence, flow cytometry, autophagy flux and transmission electric microscope were performed. Results Vitamin D reduced apoptosis, promoted migration and enhanced viability of HUVECs, decreased TIPE1 (Tumor necrosis factor-α-induced protein 8-like 1) under high glucose conditions. Overexpression of TIPE1 reverses the effects of vitamin D by increasing ROS production, inflammation, cell apoptosis, and suppressing autophagy, cell migration and viability. And vitamin D negatively correlated with TIPE1 mRNA level in DM patients. Conclusions Vitamin D reverses the harmful effects of high glucose on HUVECs by reducing TIPE1 expression. And vitamin D supplementation could help to alleviate high glucose-induced injury in type 2 diabetes mellitus patients with microvascular complications.
WHO has declared that more than 2.2 billion people worldwide are suffering from visual disorders, such as media haze, glaucoma, and drusen. At least 1 billion of these cases could have been either prevented or successfully treated, yet they remain unaddressed due to poverty, a lack of specialists, inaccurate ocular fundus diagnoses by ophthalmologists, or the presence of a rare disease. To address this, the research has developed the Hybrid Trio-Network Model Algorithm for accurately diagnosing 12 distinct common and rare eye diseases. This algorithm utilized the RFMiD dataset of 3,200 fundus images and the Binary Relevance Method to detect diseases separately, ensuring expandability and avoiding incorrect correlations. Each detector, incorporating finely tuned hyperparameters to optimize performance, consisted of three feature components: A classical transfer learning CNN model, a two-stage CNN model, and a Siamese Network. The diagnosis was made using features extracted through this Trio-Model with Ensembled Machine Learning algorithms. The proposed model achieved an average accuracy of 97% and an AUC score of 0.96. Compared to past benchmark studies, an increase of over 10% in the F1-score was observed for most diseases. Furthermore, using the Siamese Network, the model successfully made predictions in diseases like optic disc pallor, which past studies failed to predict due to low confidence. This diagnostic tool presents a stable, adaptive, cost-effective, efficient, accessible, and fast solution for globalizing early detection of both common and rare diseases.
Josué Pagán, José L. Risco-Martín, José M. Moya
et al.
Prediction of symptomatic crises in chronic diseases allows to take decisions before the symptoms occur, such as the intake of drugs to avoid the symptoms or the activation of medical alarms. The prediction horizon is in this case an important parameter in order to fulfill the pharmacokinetics of medications, or the time response of medical services. This paper presents a study about the prediction limits of a chronic disease with symptomatic crises: the migraine. For that purpose, this work develops a methodology to build predictive migraine models and to improve these predictions beyond the limits of the initial models. The maximum prediction horizon is analyzed, and its dependency on the selected features is studied. A strategy for model selection is proposed to tackle the trade off between conservative but robust predictive models, with respect to less accurate predictions with higher horizons. The obtained results show a prediction horizon close to 40 minutes, which is in the time range of the drug pharmacokinetics. Experiments have been performed in a realistic scenario where input data have been acquired in an ambulatory clinical study by the deployment of a non-intrusive Wireless Body Sensor Network. Our results provide an effective methodology for the selection of the future horizon in the development of prediction algorithms for diseases experiencing symptomatic crises.
Identification of causal genes and pathways is a critical step for understanding the genetic underpinnings of rare diseases. We propose novel approaches to gene prioritization and pathway identification using DNA language model, graph neural networks, and genetic algorithm. Using HyenaDNA, a long-range genomic foundation model, we generated dynamic gene embeddings that reflect changes caused by deleterious variants. These gene embeddings were then utilized to identify candidate genes and pathways. We validated our method on a cohort of rare disease patients with partially known genetic diagnosis, demonstrating the re-identification of known causal genes and pathways and the detection of novel candidates. These findings have implications for the prevention and treatment of rare diseases by enabling targeted identification of new drug targets and therapeutic pathways.
Real-world data (RWD) and real-world evidence (RWE) have been increasingly used in medical product development and regulatory decision-making, especially for rare diseases. After outlining the challenges and possible strategies to address the challenges in rare disease drug development (see the accompanying paper), the Real-World Evidence (RWE) Scientific Working Group of the American Statistical Association Biopharmaceutical Section reviews the roles of RWD and RWE in clinical trials for drugs treating rare diseases. This paper summarizes relevant guidance documents and frameworks by selected regulatory agencies and the current practice on the use of RWD and RWE in natural history studies and the design, conduct, and analysis of rare disease clinical trials. A targeted learning roadmap for rare disease trials is described, followed by case studies on the use of RWD and RWE to support a natural history study and marketing applications in various settings.
Chrysoula Giourga, Sousana K. Papadopoulou, Gavriela Voulgaridou
et al.
A balanced diet is achieved not only via the consumption of a variety of food products but also by ensuring that we take in sufficient quantities the micronutrients necessary for the adequate functioning of the human body, such as vitamins, an important one of which is vitamin D. Vitamin D has been closely linked to bone health. Vitamin D deficiency has often been associated with negative effects concerning several pregnancy adverse outcomes, the most important of which are the birth of SGA newborns, premature birth, and, finally, preeclampsia, which are discussed in this work. The aim of this review is to critically summarize and scrutinize whether the concentration of vitamin D in the blood serum of pregnant women in all its forms may be correlated with the risk of preeclampsia during pregnancy and whether vitamin D levels could act both as a protective agent or as a risk factor or even a prognostic measure of the disease. The association of vitamin D levels with the onset of preeclampsia was examined by searching the PubMed and Google Scholar databases. A total of 31 clinical trials were identified and included in this review, with the aim of summarizing the recent data concerning vitamin D levels and the risk of preeclampsia. Among them, 16 were published in the last five years, and 13 were published within the last a decade. Most studies showed a significant association between vitamin D deficiency and preeclampsia risk. It was also found that the higher the dose, the lower the risk of disease. Of the 31 articles, only 7 of them did not show a significant difference between vitamin D levels and preeclampsia regardless of comorbidity. The results of this review suggest that there is indeed an association between the concentration of vitamin D during pregnancy and the risk of preeclampsia; however, further studies are strongly recommended to derive conclusive evidence.
Abstract Purpose To investigate the dose-dependent protective effects of statins, specific classes of statins, and different intensities of statin use on sepsis risk in patients with type 2 diabetes mellitus (T2DM). Methods We included patients with T2DM aged ≥ 40 years. Statin use was defined as the use of statin on most days for > 1 months with a mean statin dose of ≥ 28 cumulative defined daily doses (cDDDs) per year (cDDD-year). An inverse probability of treatment-weighted Cox hazard model was used to investigate the effects of statin use on sepsis and septic shock while considering statin use status as a time-dependent variable. Results From 2008 to 2020, a total of 812 420 patients were diagnosed as having T2DM. Among these patients, 118,765 (27.79%) statin nonusers and 50 804 (12.03%) statin users developed sepsis. Septic shock occurred in 42,755 (10.39%) individuals who did not use statins and 16,765 (4.18%) individuals who used statins. Overall, statin users had a lower prevalence of sepsis than did nonusers. The adjusted hazard ratio (aHR) of statin use was 0.37 (95% CI 0.35, 0.38) for sepsis compared with no statin use. Compared with the patients not using statins, those using different classes of statins exhibited a more significant reduction in sepsis, with aHRs (95% CIs) of sepsis being 0.09 (0.05, 0.14), 0.32 (0.31, 0.34), 0.34 (0.32, 0.36), 0.35 (0.32, 0.37), 0.37 (0.34, 0.39), 0.42 (0.38, 0.44), and 0.54 (0.51, 0.56) for pitavastatin, pravastatin, rosuvastatin, atorvastatin, simvastatin, fluvastatin, and lovastatin use, respectively. In the patients with different cDDD-years of statins, multivariate analysis indicated a significant reduction in sepsis, with aHRs of 0.53 (0.52, 0.57), 0.40 (0.39, 0.43), 0.29 (0.27, 0.30), and 0.17 (0.15, 0.19) for Q1, Q2, Q3, and Q4 cDDD-years (P for trend < 0.0001). The optimal daily statin dose of 0.84 DDD was associated with the lowest aHR. Similar trends of higher cDDD-year and specific statin types use were associated with a decrease in septic shock when compared to statin non-users. Conclusion Our real-world evidence demonstrated that the persistent use of statins reduced sepsis and septic shock risk in patients with T2DM and a higher cDDD-year of statin use was associated with an increased reduction of sepsis and septic shock risk in these patients.