Hasil untuk "Diseases of the endocrine glands. Clinical endocrinology"

Naomi Sasaya, Shigefumi Kishida, Ryo Kikuchi et al.

Digital traces of daily activities, such as e-commerce (EC) purchase histories, provide scalable signals for public health surveillance, yet their epidemiological validity remains unclear. This study validates a behavioral proxy for disease onset, defined as transitions from regular to therapeutic diets, by comparing large-scale EC data (N=55,645) against independent insurance-derived clinical records. Using feline lower urinary tract disease (FLUTD) as a case study, the proxy showed strong agreement with clinical data for ingredient-level risk patterns (r=0.74) and seasonal dynamics (r=0.82). Furthermore, analysis using EC data alone reproduced the established protective association of wet food consumption. These results demonstrate that validated behavioral signals from EC data can serve as cost-effective complements to traditional surveillance, with potential applicability to monitoring lifestyle-related diseases in human populations.

Bodrun Naher Siddiquea, Bodrun Naher Siddiquea, Dianna J. Magliano et al.

ObjectivesTo assess the prevalence and determinants of macrovascular complications (coronary artery disease, stroke, and diabetic foot) among adults living with T2DM in rural Bangladesh.MethodsA population-based cross-sectional study was conducted between December 2023 and September 2024, involving 1094 adults with diagnosed T2DM from rural areas of three regions/divisions in Bangladesh. Data were collected through household interviews, physical examination, and medical record reviews. Macrovascular complications were identified using clinical criteria and documented diagnosis. The leverage of six machine learning (ML) algorithms were applied in identifying influential variables associated with these complications.ResultsThe prevalence of coronary artery disease (CAD), stroke, and diabetic foot was 11.2%, 5.3%, and 9.1%, respectively. The Light Gradient Boosting Machine algorithm performed best for CAD and diabetic foot, with ROC values of 98.8% and 92.6%, respectively, while Random Forest showed the best performance for stroke with a ROC of 99%. These models also outperformed others across accuracy, precision, F1 score, and calibration. Across models, common predictors included older age, longer diabetes duration, diabetes onset at age 45 years or above, and smoking. Hypertension and elevated cholesterol were linked to CAD and stroke. Coexisting microvascular complications were also identified.ConclusionsThis study identified a substantial burden of macrovascular complications among rural adults with T2DM, with CAD, stroke, and diabetic foot emerging as the most prevalent outcomes. Advanced age, longer duration of diabetes, smoking, hypertension, and elevated cholesterol were consistently associated with these complications, highlighting the need for intensified cardiometabolic risk control within primary care. These findings underscore the urgency of strengthening integrated diabetes–cardiovascular management in rural Bangladesh to reduce the progression and impact of these major vascular outcomes.

Junhyeok Lee, Han Jang, Kyu Sung Choi

Precise delineation of meningiomas is crucial for effective radiotherapy (RT) planning, directly influencing treatment efficacy and preservation of adjacent healthy tissues. While automated deep learning approaches have demonstrated considerable potential, achieving consistently accurate clinical segmentation remains challenging due to tumor heterogeneity. Interactive Medical Image Segmentation (IMIS) addresses this challenge by integrating advanced AI techniques with clinical input. However, generic segmentation tools, despite widespread applicability, often lack the specificity required for clinically critical and disease-specific tasks like meningioma RT planning. To overcome these limitations, we introduce Interactive-MEN-RT, a dedicated IMIS tool specifically developed for clinician-assisted 3D meningioma segmentation in RT workflows. The system incorporates multiple clinically relevant interaction methods, including point annotations, bounding boxes, lasso tools, and scribbles, enhancing usability and clinical precision. In our evaluation involving 500 contrast-enhanced T1-weighted MRI scans from the BraTS 2025 Meningioma RT Segmentation Challenge, Interactive-MEN-RT demonstrated substantial improvement compared to other segmentation methods, achieving Dice similarity coefficients of up to 77.6\% and Intersection over Union scores of 64.8\%. These results emphasize the need for clinically tailored segmentation solutions in critical applications such as meningioma RT planning. The code is publicly available at: https://github.com/snuh-rad-aicon/Interactive-MEN-RT

Christine A March, Elissa Naame, Ingrid Libman et al.

BackgroundSchool-partnered interventions may improve health outcomes for children with type 1 diabetes, though there is limited evidence to support their effectiveness and sustainability. Family, school, or health system factors may interfere with intervention usability and implementation. ObjectiveTo identify and address potential implementation barriers during intervention development, we combined methods in user-centered design and implementation science to adapt an evidence-based psychosocial intervention, the collaborative care model, to a virtual school-partnered collaborative care (SPACE) model for type 1 diabetes between schools and diabetes medical teams. MethodsWe recruited patient, family, school, and health system partners (n=20) to cocreate SPACE through iterative, web-based design sessions using a digital whiteboard (phase 1). User-centered design methods included independent and group activities for idea generation, visual voting, and structured critique of the evolving SPACE prototype. In phase 2, the prototype was evaluated with the usability evaluation for evidence-based psychosocial interventions methods. School nurses reviewed the prototype and tasks in cognitive walkthroughs and completed the Intervention Usability Scale (IUS). Two members of the research team independently identified and prioritized (1-3 rating) discrete usability concerns. We evaluated the relationship between prioritization and the percentage of nurses reporting each usability issue with Spearman correlation. Differences in IUS scores by school nurse characteristics were assessed with ANOVA. ResultsIn the design phase, the partners generated over 90 unique ideas for SPACE, prioritizing elements pertaining to intervention adaptability, team-based communication, and multidimensional outcome tracking. Following three iterations of prototype development, cognitive walkthroughs were completed with 10 school nurses (n=10, 100% female; mean age 48.5, SD 9.5 years) representing different districts and years of experience. Nurses identified 16 discrete usability issues (each reported by 10%-60% of participants). Two issues receiving the highest priority (3.0): ability to access a virtual platform (n=3, 30% of participants) and data-sharing mechanisms between nurses and providers (n=6, 60% of participants). There was a moderate correlation between priority rating and the percentage of nurses reporting each issue (ρ=0.63; P=.01). Average IUS ratings (77.8, SD 11.1; 100-point scale) indicated appropriate usability. There was no difference in IUS ratings by school nurse experience (P=.54), student caseload (P=.12), number of schools covered (P=.90), or prior experience with type 1 diabetes (P=.83), suggesting that other factors may influence usability. The design team recommended strategies for SPACE implementation to overcome high-priority issues, including training users on videoconferencing applications, establishing secure forms for school data reporting, and sharing glucose data in real-time during SPACE meetings. ConclusionsCross-sector interventions are complex, and perceived usability is a potential barrier to implementation. Using web-based cocreation methods with community partners promoted high-quality intervention design that is aligned with end-user priorities. Quantitative and qualitative assessments indicated appropriate degree of usability to move forward with pilot-testing.

K. Shchubelka, Walter W. Wolfsberger, O. Oleksyk et al.

Type 1 diabetes (T1D) is a complex autoimmune disorder with a strong genetic component. While genome-wide association studies have identified over 90 loci associated with T1D, the contribution of rare, coding, and population-specific variants remains poorly explored. A new international collaborative effort, based in Ukraine in partnership with Uzhhorod National University and coordinated with Oakland University, was launched to enrol 20,000 individuals (10,000 clinically confirmed T1D cases and 10,000 ethnically/geographically matched controls) from Ukraine and neighbouring Poland, generate high-coverage whole-exome sequences (WES) and genome-wide genotypes, and create an open, GDPR-compliant data resource. Genomics of Type 1 Diabetes in Ukraine initiative established a nationwide network of endocrinologists linked to a –80 °C biobank and REDCap phenotyping platform at Uzhhorod National University (Uzhhorod, Ukraine). Peripheral-blood DNA is extracted locally; WES (Illumina NovaSeq X, Twist capture) and array genotyping are performed at the Regeneron Genetics Center (NY, USA), with joint calling and quality control on the HPC cluster (Oakland University, MI, USA). Despite wartime logistical constraints, more than 12,000 volunteer participants were recruited across Ukraine, and high-quality exome and genotype data were generated for 10,000 samples for a case-control genome-wide association study. Association testing is underway to confirm the known and identify several new Eastern-European-specific coding variants; re-analysis of exomes also enabled molecular re-diagnosis of monogenic diabetes in multiple families. A de-identified web portal (genes.uzhnu.edu.ua) and mirror deposition in the European Nucleotide Archive provide tiered access to raw and summary data. Pilot long-read HLA sequencing, stool metagenomics, and whole-blood RNA-seq are underway to extend the resource and enable future collaborations. The project has demonstrated the feasibility of large-scale genomics in a resource-constrained setting impacted by the ongoing war and delivers the first substantial T1D variant catalogue from Eastern Europe and will scale to 20,000 participants and multi-omics integration, is poised to refine genetic risk prediction, illuminate novel disease pathways, and strengthen precision-medicine infrastructure in the region.

Vinni Faber Rasmussen, Mathilde Thrysøe, Páll Karlsson et al.

Conclusion: In conclusion, adolescents with diabetic autonomic neuropathy who also reported autonomic symptoms had lower well-being and impaired social inclusion. Adolescents with symptoms of neuropathy and females appear to be at higher risk of lower well-being, and using standardized screening tools helps to identify the subjects at risk.

M.I. Shved, I.O. Yastremska, R.M. Ovsiychuk et al.

Background. There is a lack of scientific data on the me­chanisms of influence of type 2 diabetes mellitus (T2DM) and meta­bolic syndrome (MS) on the incidence of complications in patients with myocardial infarction; no effective methods of their correction have been developed. The purpose of the study was to evaluate the incidence of reperfusion complications and the effectiveness of upstream therapy in patients with acute myocardial infarction combined with T2DM and MS by including L-carnitine/L-arginine and dapagliflozin, a sodium-glucose cotransporter-2 (SGLT2) inhibitor, in the treatment program. Materials and methods. We examined 38 patients with acute ST-elevation myocardial infarction (STEMI) on the background of T2DM and MS (study group) and 38 patients with STEMI without T2DM and MS (control group). In addition to the use of general clinical methods, detailed laboratory and instrumental examinations were performed: biochemical blood tests, evaluation of glucose, insulin, HOMA-IR, lipidogram, coagulogram, creatine kinase-MB, troponin T, electrocardiography, transthoracic echocardiography and coronary angiography. The risk of in-hospital mortality was predicted by the GRACE score. All patients with STEMI underwent urgent coronary angiography with subsequent balloon angioplasty and the infarct-dependent internal carotid artery stenting, as well as standard drug therapy according to the Ministry of Health protocol and were additionally prescribed dapagliflozin 10 mg/day and 5 intravenous infusions of L-arginine-L-carnitine mixture (4.2 and 2.0 g, respectively) in 100 ml of solvent. Results. STEMI on the background of T2DM and MS is significantly more common in middle-aged men. The development of STEMI was associated with the presence of comorbid conditions: hypertension, T2DM and MS, chronic kidney disease, and combined risk factors for coronary heart disease. The severity of the patient’s clinical condition was due to congestive heart failure III–IV and the presence of life-threatening complications of the acute period of myocardial infarction such as ventricular arrhythmias (45.3 %), conduction disorders (23.8 %), pulmonary edema (17.3 %), and acute left ventricu­lar aneurysm (13.3 %). Compensation of carbohydrate metabolism in patients with myocardial infarction combined with T2DM and MS using dapagliflozin was accompanied by a significant reduction in the incidence of life-threatening complications: the incidence of rhythm disturbances in patients of the study group decreased from 87.5 to 50.0 %, heart failure (Killip class II–III) — from 70.0 to 12.5 %, which was signi­ficant compared with the control group. The inclusion of parenteral arginine (4.2 g/day) and L-carnitine (2.0 g/day) in the protocol therapy program contributed to a decrease in postinfarction cardiac remodeling and an increase in ejection fraction by 7 %. In this situation, cytoprotective therapy against the background of treatment with the SGLT2 inhibitor dapagliflozin acts as a pathogenetic upstream therapy. Conclusions. In patients with STEMI combined with T2DM and MS, hyperglycemia, insulin resistance and severe abnormalities of morphological and functional parameters of the heart with its systolic-diastolic dysfunction are observed at baseline, which are triggers of the complicated course of this pathology, with the deve­lopment of heart failure syndrome, rhythm and conduction disorders. The comprehensive treatment with the inclusion of L-carnitine and L-arginine against the background of using the SGLT2 inhibitor dapagliflozin as upstream therapy helps restore tissue sensitivity to insulin, improve carbohydrate meta­bolism, central cardiovascular hemodynamics that is accompanied by a significant reduction in the frequency and severity of acute left ventricular failure and life-threa­tening reperfusion arrhythmias.

Suqin Jin, Siyu Zhao, Xiaoyu Yue et al.

Abstract Background Pathological changes in the arterial vasculature play a pivotal role in the development of macrovascular and microvascular complications of diabetes mellitus (DM). Compared with traditional measurements of carotid artery intima-media thickness, separate measurements of the thickness of the intima and the media using high-resolution ultrasonography could reveal vascular anatomical changes more precisely. Homocysteine (HCY) is closely related to vascular complications in DM patients. This study aimed to explore the thickness of the intima and media separately in the carotid, radial, and dorsalis pedis arteries in DM patients, to examine their diagnostic value for DM with complications and their relationship with HCY. Methods This was a cross-sectional study. A total of 123 DM patients and 102 healthy controls were enrolled. Arterial ultrasonography was performed using a 24-MHz probe to measure the thickness of the intima and media in the carotid, radial, and pedal arteries. Serum levels of fasting glucose, low-density lipoprotein cholesterol, HCY, and clinical information were also collected. Multivariate linear regression was performed to investigate the association between ultrasonographic parameters and risk factors, and binary logistic regression was used to explore the diagnostic value of combination model for DM with complications. Results Carotid, radial, and pedal artery intima thickness were substantially thicker in DM patients than controls. Compared with DM patients without macrovascular complications, those with macrovascular complications exhibited a thicker media in all three arteries, a thicker carotid intima, and a thicker carotid artery intima-media thickness. The relative difference was greatest for carotid artery media thickness (28.4%). HCY positively correlated with all MTs and CIT in DM patients. CIT was associated with traditional risk factors including age, systolic blood pressure and HCY. Combination model of age, SBP and CIT provides a satisfactory diagnostic value for DM patients with macrovascular complications (area under the curve, 0.827). Conclusions Measurement of arterial intima and media thickness using high-resolution ultrasonography might be a promising tool to reveal arterial pathological changes in DM patients.

Himanshu Pandey, Akhil Amod, Shivang et al.

Artificial Intelligence (AI) and Large Language Models (LLMs) hold significant promise in revolutionizing healthcare, especially in clinical applications. Simultaneously, Digital Twin technology, which models and simulates complex systems, has gained traction in enhancing patient care. However, despite the advances in experimental clinical settings, the potential of AI and digital twins to streamline clinical operations remains largely untapped. This paper introduces a novel digital twin framework specifically designed to enhance oncology clinical operations. We propose the integration of multiple specialized digital twins, such as the Medical Necessity Twin, Care Navigator Twin, and Clinical History Twin, to enhance workflow efficiency and personalize care for each patient based on their unique data. Furthermore, by synthesizing multiple data sources and aligning them with the National Comprehensive Cancer Network (NCCN) guidelines, we create a dynamic Cancer Care Path, a continuously evolving knowledge base that enables these digital twins to provide precise, tailored clinical recommendations.

Yubo Zhang, Shudi Hou, Mingyu Derek Ma et al.

Large language models (LLMs) are increasingly applied to clinical decision-making. However, their potential to exhibit bias poses significant risks to clinical equity. Currently, there is a lack of benchmarks that systematically evaluate such clinical bias in LLMs. While in downstream tasks, some biases of LLMs can be avoided such as by instructing the model to answer "I'm not sure...", the internal bias hidden within the model still lacks deep studies. We introduce CLIMB (shorthand for A Benchmark of Clinical Bias in Large Language Models), a pioneering comprehensive benchmark to evaluate both intrinsic (within LLMs) and extrinsic (on downstream tasks) bias in LLMs for clinical decision tasks. Notably, for intrinsic bias, we introduce a novel metric, AssocMAD, to assess the disparities of LLMs across multiple demographic groups. Additionally, we leverage counterfactual intervention to evaluate extrinsic bias in a task of clinical diagnosis prediction. Our experiments across popular and medically adapted LLMs, particularly from the Mistral and LLaMA families, unveil prevalent behaviors with both intrinsic and extrinsic bias. This work underscores the critical need to mitigate clinical bias and sets a new standard for future evaluations of LLMs' clinical bias.

Yingzhou Lu, Yaojun Hu, Chenhao Li

Bringing a novel drug from the original idea to market typically requires more than ten years and billions of dollars. To alleviate the heavy burden, a natural idea is to reuse the approved drug to treat new diseases. The process is also known as drug repurposing or drug repositioning. Machine learning methods exhibited huge potential in automating drug repurposing. However, it still encounter some challenges, such as lack of labels and multimodal feature representation. To address these issues, we design DrugCLIP, a cutting-edge contrastive learning method, to learn drug and disease's interaction without negative labels. Additionally, we have curated a drug repurposing dataset based on real-world clinical trial records. Thorough empirical studies are conducted to validate the effectiveness of the proposed DrugCLIP method.

Tianqi Wei, Zhi Chen, Xin Yu

Plant disease recognition is a critical task that ensures crop health and mitigates the damage caused by diseases. A handy tool that enables farmers to receive a diagnosis based on query pictures or the text description of suspicious plants is in high demand for initiating treatment before potential diseases spread further. In this paper, we develop a multimodal plant disease image retrieval system to support disease search based on either image or text prompts. Specifically, we utilize the largest in-the-wild plant disease dataset PlantWild, which includes over 18,000 images across 89 categories, to provide a comprehensive view of potential diseases relating to the query. Furthermore, cross-modal retrieval is achieved in the developed system, facilitated by a novel CLIP-based vision-language model that encodes both disease descriptions and disease images into the same latent space. Built on top of the retriever, our retrieval system allows users to upload either plant disease images or disease descriptions to retrieve the corresponding images with similar characteristics from the disease dataset to suggest candidate diseases for end users' consideration.

Liu Xingnan, Zhang Na

PurposeThis study aims to create and validate a clinical model that predict the probability of fertilization failure in routine in-vitro fertilization (IVF) cycles.MethodsThis study employed a retrospective methodology, gathering data from 1770 couples that used reproductive center’s of the Fourth Hospital of Hebei Medical University standard IVF fertilization between June 2015 and June 2023. 1062 were in the training set and 708 were in the validation set when it was randomly split into the training set and validation set in a 6:4 ratio. The study employed both univariate and multivariate logistic regression analysis to determine the factors those influence the failure of traditional in vitro fertilization. Based on the multiple regression model, a predictive model of traditional IVF fertilization failure was created. The calibration and decision curves were used to assess the effectiveness and therapeutic usefulness of this model.ResultsThe following factors independently predicted the probability of an unsuccessful fertilization: infertility years, basal oestrogen, the rate of mature oocytes, oligoasthenozoospermia, sperm concentration, sperm vitality, percentage of abnormal morphological sperm, and percentage of progressive motility (PR%).The receiver operating characteristic curve’s area under the curve (AUC) in the training set is 0.776 (95% CI: 0.740,0.812), while the validation set’s AUC is 0.756 (95% CI: 0.708,0.805), indicating a rather high clinical prediction capacity.ConclusionOur generated nomogram has the ability to forecast the probability of fertilization failure in couples undergoing IVF, hence can assist clinical staff in making informed decisions.

Saurav Sagar, Mohammed Javed, David S Doermann

The agricultural sector plays an essential role in the economic growth of a country. Specifically, in an Indian context, it is the critical source of livelihood for millions of people living in rural areas. Plant Disease is one of the significant factors affecting the agricultural sector. Plants get infected with diseases for various reasons, including synthetic fertilizers, archaic practices, environmental conditions, etc., which impact the farm yield and subsequently hinder the economy. To address this issue, researchers have explored many applications based on AI and Machine Learning techniques to detect plant diseases. This research survey provides a comprehensive understanding of common plant leaf diseases, evaluates traditional and deep learning techniques for disease detection, and summarizes available datasets. It also explores Explainable AI (XAI) to enhance the interpretability of deep learning models' decisions for end-users. By consolidating this knowledge, the survey offers valuable insights to researchers, practitioners, and stakeholders in the agricultural sector, fostering the development of efficient and transparent solutions for combating plant diseases and promoting sustainable agricultural practices.

Julia Camps, Lucas Arantes Berg, Zhinuo Jenny Wang et al.

Cardiac in silico clinical trials can virtually assess the safety and efficacy of therapies using human-based modelling and simulation. These technologies can provide mechanistic explanations for clinically observed pathological behaviour. Designing virtual cohorts for in silico trials requires exploiting clinical data to capture the physiological variability in the human population. The clinical characterisation of ventricular activation and the Purkinje network is challenging, especially non-invasively. Our study aims to present a novel digital twinning pipeline that can efficiently generate and integrate Purkinje networks into human multiscale biventricular models based on subject-specific clinical 12-lead electrocardiogram and magnetic resonance recordings. Essential novel features of the pipeline are the human-based Purkinje network generation method, personalisation considering ECG R wave progression as well as QRS morphology, and translation from reduced-order Eikonal models to equivalent biophysically-detailed monodomain ones. We demonstrate ECG simulations in line with clinical data with clinical image-based multiscale models with Purkinje in four control subjects and two hypertrophic cardiomyopathy patients (simulated and clinical QRS complexes with Pearson's correlation coefficients > 0.7). Our methods also considered possible differences in the density of Purkinje myocardial junctions in the Eikonal-based inference as regional conduction velocities. These differences translated into regional coupling effects between Purkinje and myocardial models in the monodomain formulation. In summary, we demonstrate a digital twin pipeline enabling simulations yielding clinically-consistent ECGs with clinical CMR image-based biventricular multiscale models, including personalised Purkinje in healthy and cardiac disease conditions.

A. L. De Sousa-Coelho, A. L. De Sousa-Coelho, A. L. De Sousa-Coelho et al.

Yuka Ohmachi, Yuka Ohmachi, Shin Urai et al.

Kenny–Caffey syndrome type 2 (KCS2) is an extremely rare skeletal disorder involving hypoparathyroidism and short stature. It has an autosomal dominant pattern of inheritance and is caused by variants in the FAM111 trypsin-like peptidase A (FAM111A) gene. This disease is often difficult to diagnose due to a wide range of more common diseases manifesting hypoparathyroidism and short stature. Herein, we present the case of a 56-year-old female patient with idiopathic hypoparathyroidism and a short stature. The patient was treated for these conditions during childhood. Upon re-evaluating the etiology of KCS2, we suspected that the patient had the disorder because of clinical manifestations, such as cortical thickening and medullary stenosis of the bones, and lack of intellectual abnormalities. Genetic testing identified a heterozygous missense variant in the FAM111A gene (p.R569H). Interestingly, the patient also had bilateral sensorineural hearing loss and vestibular dysfunction, which have been rarely described in previous reports of pediatric cases. In KCS2, inner ear dysfunction due to Eustachian tube dysfunction may progress in middle age or later. However, this disease is now being reported in younger patients. Nevertheless, our case may be instructive of how such cases emerge chronically after middle age. Herein, we also provide a literature review of KCS2.

Kaiser Wani, Kaiser Wani, Shakilur Rahman et al.

Wei Yin, Jie Zhang, Yao Guo et al.

Premenstrual syndrome (PMS), a recurrent and moderate disorder that occurs during the luteal phase of the menstrual cycle and quickly resolves after menstruation, is characterized by somatic and emotional discomfort that can be severe enough to impair daily activities. Current therapeutic drugs for PMS such as selective serotonin reuptake inhibitors are not very satisfying. As a critical pineal hormone, melatonin has increasingly been suggested to modulate PMS symptoms. In this review, we update the latest progress on PMS-induced sleep disturbance, mood changes, and cognitive impairment and provide possible pathways by which melatonin attenuates these symptoms. Moreover, we focus on the role of melatonin in PMS molecular mechanisms. Herein, we show that melatonin can regulate ovarian estrogen and progesterone, of which cyclic fluctuations contribute to PMS pathogenesis. Melatonin also modulates gamma-aminobutyric acid and the brain-derived neurotrophic factor system in PMS. Interpreting the role of melatonin in PMS is not only informative to clarify PMS etiology but also instructive to melatonin and its receptor agonist application to promote female health. As a safe interaction, melatonin treatment can be effective in alleviating symptoms of PMS. However, symptoms such as sleep disturbance, depressive mood, cognitive impairment are not specific and can be easily misdiagnosed. Connections between melatonin receptor, ovarian steroid dysfunction, and PMS are not consistent among past studies. Before final conclusions are drawn, more well-organized and rigorous studies are recommended.