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

Megan A. Witherow, Michael L. Evans, Ahmed Temtam et al.

Alzheimer's disease (AD), defined as an abnormal buildup of amyloid plaques and tau tangles in the brain can be diagnosed with high accuracy based on protein biomarkers via PET or CSF analysis. However, due to the invasive nature of biomarker collection, most AD diagnoses are made in memory clinics using cognitive tests and evaluation of hippocampal atrophy based on MRI. While clinical assessment and hippocampal volume show high diagnostic accuracy for amnestic or typical AD (tAD), a substantial subgroup of AD patients with atypical presentation (atAD) are routinely misdiagnosed. To improve diagnosis of atAD patients, we propose a machine learning approach to distinguish between atAD and non-AD cognitive impairment using clinical testing battery and MRI data collected as standard-of-care. We develop and evaluate our approach using 1410 subjects across four groups (273 tAD, 184 atAD, 235 non-AD, and 685 cognitively normal) collected from one private data set and two public data sets from the National Alzheimer's Coordinating Center (NACC) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). We perform multiple atAD vs. non-AD classification experiments using clinical features and hippocampal volume as well as a comprehensive set of MRI features from across the brain. The best performance is achieved by incorporating additional important MRI features, which outperforms using hippocampal volume alone. Furthermore, we use the Boruta statistical approach to identify and visualize significant brain regions distinguishing between diagnostic groups. Our ML approach improves the percentage of correctly diagnosed atAD cases (the recall) from 52% to 69% for NACC and from 34% to 77% for ADNI, while achieving high precision. The proposed approach has important implications for improving diagnostic accuracy for non-amnestic atAD in clinical settings using only clinical testing battery and MRI.

Biraja Ghoshal, William Woof, Bhargab Ghoshal et al.

Predicting clinical severity from genotype remains a fundamental challenge in molecular medicine, particularly for enzymes whose function depends on sub-atomic-scale geometry. Mutations in the \textit{RPE65} isomerohydrolase cause Leber Congenital Amaurosis (LCA) and related retinal diseases; however, the kinetic mechanisms connecting sub-atomic-scale perturbations to blindness remain unclear. In this study, we demonstrate that mutations in the human visual isomerase RPE65 are governed by a quantum-mechanical threshold effect arising from proton tunneling in the active site. We established a hybrid quantum-classical structure-to-phenotype pipeline combining AlphaFold structure prediction with \textit{ab initio} quantum simulation using the Variational Quantum Eigensolver (VQE) to analyze minimal proton-coupled electron transfer in the visual cycle. Our analysis reveals that many pathogenic mutations do not merely occlude the active site, but rather strongly reduce the quantum probability of proton tunneling. We observed a sharp non-linear effect, termed the "Quantum Cliff," where minute structural changes (below 0.1 Å) reduce the reaction rate by multiple orders of magnitude. Based on these findings, we introduce a dimensionless Relative Quantum Activity Score (RQAS) that isolates the geometry-controlled exponential sensitivity of the reaction rate and successfully distinguishes between mild and severe patient phenotypes. These results suggest that RPE65 operates near a quantum-critical point, where sub-Angstrom structural perturbations induce a catastrophic loss of function. Furthermore, our findings establish quantum tunneling as a predictive mechanistic link between atomic structure and clinical phenotype, proposing a general framework for quantum-structural disease modeling.

Huu-Huy-Hoang Tran, Gia-Bao Duong, Quoc-Viet-Anh Tran et al.

Extracting drug use information from unstructured Electronic Health Records remains a major challenge in clinical Natural Language Processing. While Large Language Models demonstrate advancements, their use in clinical NLP is limited by concerns over trust, control, and efficiency. To address this, we present NOWJ submission to the ToxHabits Shared Task at BioCreative IX. This task targets the detection of toxic substance use and contextual attributes in Spanish clinical texts, a domain-specific, low-resource setting. We propose a multi-output ensemble system tackling both Subtask 1 - ToxNER and Subtask 2 - ToxUse. Our system integrates BETO with a CRF layer for sequence labeling, employs diverse training strategies, and uses sentence filtering to boost precision. Our top run achieved 0.94 F1 and 0.97 precision for Trigger Detection, and 0.91 F1 for Argument Detection.

Pedro Iglesias

Skeletal muscle, traditionally recognized for its motor function, has emerged as a key endocrine organ involved in metabolic regulation and interorgan communication. This narrative review addresses the dual role of muscle as a target tissue for classical hormones—such as growth hormone (GH), insulin-like growth factor type 1 (IGF-1), thyroid hormones, and sex steroids—and as a source of myokines, bioactive peptides released in response to muscle contraction that exert autocrine, paracrine, and endocrine effects. Several relevant myokines are discussed, such as irisin and Metrnl-like myokines (Metrnl), which mediate exercise-associated metabolic benefits, including improved insulin sensitivity, induction of thermogenesis in adipose tissue, and immunometabolic modulations. It also examines how muscle endocrine dysfunction, caused by chronic inflammation, hormone resistance, or sedentary lifestyle, contributes to the development and progression of metabolic diseases such as obesity, type 2 diabetes, and sarcopenia, highlighting the importance of muscle mass in the prognosis of these pathologies. Finally, the therapeutic potential of interventions aimed at preserving or enhancing muscle function—through physical exercise, hormone therapy and anabolic agents—is highlighted, together with the growing research on myokines as biomarkers and pharmacological targets. This review expands the understanding of muscle in endocrinology, proposing an integrative approach that recognizes its central role in metabolic health and its potential to innovate the clinical management of endocrine–metabolic diseases.

Si Woo Lee, Hyunki Park, Minyoung Lee et al.

Background Sodium-glucose cotransporter type 2 (SGLT2) inhibitors, such as dapagliflozin, are primarily used to lower glucose in type 2 diabetes. Recent studies suggest broader metabolic effects, particularly in the liver. This study explores the molecular mechanisms by which dapagliflozin influences hepatic glucose and lipid metabolism, hypothesizing that it activates the 5’-adenosine monophosphate-activated protein kinase (AMPK)-sirtuin 1 (Sirt1) pathway to promote gluconeogenesis and reduce lipid accumulation via autophagy. Methods HepG2 hepatocellular carcinoma cells were treated with dapagliflozin, and Western blotting, quantitative reverse transcription polymerase chain reaction, and fluorescence microscopy were used to assess gluconeogenic enzyme expression and autophagy. In vivo, mice with liver-specific autophagy related 7 (Atg7) deletion and those on a high-fat diet were used to evaluate glucose regulation, lipid metabolism, and autophagy. Results Dapagliflozin significantly increased expression of gluconeogenic enzymes like phosphoenolpyruvate carboxykinase (PEPCK) in HepG2 cells and enhanced autophagic flux, evidenced by increased light chain 3B (LC3B)-II levels and autophagosome formation. AMPK-Sirt1 activation was confirmed as the underlying mechanism. Additionally, dapagliflozin reduced fatty acid synthesis by suppressing enzymes such as acetyl-CoA carboxylase and fatty acid synthase, while promoting fatty acid degradation via carnitine palmitoyltransferase 1α (CPT1α) upregulation. In high-fat diet mice, dapagliflozin increased hepatic gluconeogenesis and reduced lipid accumulation, though serum cholesterol and triglyceride levels were unaffected. Conclusion Dapagliflozin enhances hepatic gluconeogenesis and reduces steatosis by activating the AMPK-Sirt1 pathway and promoting autophagy. These findings suggest that SGLT2 inhibitors could offer therapeutic benefits for managing hepatic lipid disorders, beyond glycemic control.

Qiaoli Cui, Zhenming Zhang, Lang Qin et al.

ABSTRACT Aims/Introduction Diabetic foot ulcer (DFU) is a prevalent complication of diabetes characterized by heightened inflammation and impaired wound‐healing processes. Interleukin‐37 (IL‐37) is a natural suppressor of innate inflammation. Here, we aim to investigate the potential of IL‐37 in enhancing the healing process of diabetic wounds. Materials and Methods The skin samples of DFU and non‐diabetic patients during foot and ankle orthopedic surgery were collected. The IL‐37 transgenic mice (IL‐37Tg) were created using CRISPR/Cas‐mediated genome engineering. Mice were administered streptozotocin (STZ, 150 mg/kg) to induce a diabetic model. After 4 weeks, an equidistant full‐thickness excisional wound measuring 8 mm was created on the central back of each mouse and allowed to heal naturally. Body weight and blood glucose levels were measured weekly. The wound area was measured, and skin samples were collected on Day 10 for further Quantitative polymerase chain reaction (qPCR) and WB detection and RNA sequencing analysis. Results The proinflammation cytokines such as TNF‐α and IL‐1β and the MAPK signaling pathway were significantly increased in the wound margin of DFU patients. Compared with diabetic mice, diabetic IL‐37Tg mice showed a significantly accelerated healing process. The enriched signaling pathways in RNA sequencing included cytokine–cytokine receptor interaction, TNF signaling pathway, and NOD‐like receptor signaling pathway. Through QPCR and WB detection, we found that IL‐37 could inhibit the activated MAPK and NOD‐like signaling pathway, reducing TNF‐α, IL‐1β, and NLRP3 expression in the diabetic wound. Conclusions IL‐37 promotes skin wound healing in diabetic mice, providing a new possible target for treating diabetic wounds.

I.M. Fomina, T.V. Gavrish, K.S. Malikov et al.

Background. Chronic pancreatitis (CP) is a complex progressive disease of the pancreas, which is accompanied by significant metabolic disorders, exocrine insufficiency, maldigestion, and malabsorption. Patients with CP often face nutritional deficiencies, which include protein, vitamin, and mineral deficiencies. This leads to weight loss, anemia, and deterioration in the quality of life. One of the key elements in the treatment of such patients is diet therapy aimed at correcting nutrient deficiencies and compensating for impaired digestive system functions. In modern gastroenterological practice, functional food products manufactured using extrusion technology are attracting increasing attention. It allows creating products with high bioavailability, easy digestibility, and an optimal balance of proteins, fats, and carbohydrates. In particular, thermoplastically extruded products based on wheat, buckwheat, and rice cereals, enriched with chicken fillet, carrots, apples, and pumpkin, show high potential in correcting the nutritional status of patients with CP. The purpose of the study was to assess the effectiveness of using thermoplastic extruded products in diet therapy of patients with CP. Materials and methods. The study involved 110 patients, who were divided into two groups: the main (70 participants with CP who received the proposed mixture for enteral nutrition) and the comparison one (40 people with isolated CP who were fed a standard diet). The assessment of nutritional status included determining the level of albumin, hemoglobin, body mass index, as well as the content of pancreatic elastase-1 in feces. For 12 weeks, patients in the main group received extruded products as the main element of the diet. Results. A significant improvement in the nutritional status of the patients was noted. The albumin level increased from 32.1 ± 1.8 g/l to 38.5 ± 2.1 g/l, and hemoglobin from 112 ± 5 g/l to 125 ± 6 g/l. The patients’ body mass index increased 1.7 times. All changes were statistically significant. The organoleptic evaluation of the products showed that 91 % of the patients rated them as “tasty” or “very tasty”, which ensures a high level of the diet acceptability. In addition, the products are well tolerated, which increases compliance with therapy. Conclusions. The use of thermoplastic extruded products in diet therapy for patients with CP is a promising direction in the treatment of this disease. It allows to ensure the correction of nutritional status, improve the quality of life, and also contribute to reducing the risk of developing complications associated with metabolic disorders.

Satya Narayana Panda, Vaishnavi Kukkala, Spandana Iyer

Dermatological conditions affect 1.9 billion people globally, yet accurate diagnosis remains challenging due to limited specialist availability and complex clinical presentations. Family history significantly influences skin disease susceptibility and treatment responses, but is often underutilized in diagnostic processes. This research addresses the critical question: How can AI-powered systems integrate family history data with clinical imaging to enhance dermatological diagnosis while supporting clinical trial validation and real-world implementation? We developed a comprehensive multi-modal AI framework that combines deep learning-based image analysis with structured clinical data, including detailed family history patterns. Our approach employs interpretable convolutional neural networks integrated with clinical decision trees that incorporate hereditary risk factors. The methodology includes prospective clinical trials across diverse healthcare settings to validate AI-assisted diagnosis against traditional clinical assessment. In this work, validation was conducted with healthcare professionals to assess AI-assisted outputs against clinical expectations; prospective clinical trials across diverse healthcare settings are proposed as future work. The integrated AI system demonstrates enhanced diagnostic accuracy when family history data is incorporated, particularly for hereditary skin conditions such as melanoma, psoriasis, and atopic dermatitis. Expert feedback indicates potential for improved early detection and more personalized recommendations; formal clinical trials are planned. The framework is designed for integration into clinical workflows while maintaining interpretability through explainable AI mechanisms.

Mubaraq Yakubu, Navodini Wijethilake, Jonathan Shapey et al.

Purpose: Accurate segmentation of both the pituitary gland and adenomas from magnetic resonance imaging (MRI) is essential for diagnosis and treatment of pituitary adenomas. This systematic review evaluates automatic segmentation methods for improving the accuracy and efficiency of MRI-based segmentation of pituitary adenomas and the gland itself. Methods: We reviewed 34 studies that employed automatic and semi-automatic segmentation methods. We extracted and synthesized data on segmentation techniques and performance metrics (such as Dice overlap scores). Results: The majority of reviewed studies utilized deep learning approaches, with U-Net-based models being the most prevalent. Automatic methods yielded Dice scores of 0.19--89.00\% for pituitary gland and 4.60--96.41\% for adenoma segmentation. Semi-automatic methods reported 80.00--92.10\% for pituitary gland and 75.90--88.36\% for adenoma segmentation. Conclusion: Most studies did not report important metrics such as MR field strength, age and adenoma size. Automated segmentation techniques such as U-Net-based models show promise, especially for adenoma segmentation, but further improvements are needed to achieve consistently good performance in small structures like the normal pituitary gland. Continued innovation and larger, diverse datasets are likely critical to enhancing clinical applicability.

Peng Zhang, Divya Chaudhary

Alzheimer's Disease (AD) early detection is critical for enabling timely intervention and improving patient outcomes. This paper presents a DMV framework using Llama3-70B and GPT-4o as embedding models to analyze clinical notes and predict a continuous risk score associated with early AD onset. Framing the task as a regression problem, we model the relationship between linguistic features in clinical notes (inputs) and a target variable (data value) that answers specific questions related to AD risk within certain topic categories. By leveraging a multi-faceted feature set that includes geolocation data, we capture additional environmental context potentially linked to AD. Our results demonstrate that the integration of the geolocation information significantly decreases the error of predicting early AD risk scores over prior models by 28.57% (Llama3-70B) and 33.47% (GPT4-o). Our findings suggest that this combined approach can enhance the predictive accuracy of AD risk assessment, supporting early diagnosis and intervention in clinical settings. Additionally, the framework's ability to incorporate geolocation data provides a more comprehensive risk assessment model that could help healthcare providers better understand and address environmental factors contributing to AD development.

Shatha Abudalou

Inter reader variability and cross site domain shift challenge the automatic segmentation of prostate anatomy using T2 weighted MRI images. This study investigates whether transformer models can retain precision amid such heterogeneity. We compare the performance of UNETR and SwinUNETR in prostate gland segmentation against our previous 3D UNet model [1], based on 546 MRI (T2weighted) volumes annotated by two independent experts. Three training strategies were analyzed: single cohort dataset, 5 fold cross validated mixed cohort, and gland size based dataset. Hyperparameters were tuned by Optuna. The test set, from an independent population of readers, served as the evaluation endpoint (Dice Similarity Coefficient). In single reader training, SwinUNETR achieved an average dice score of 0.816 for Reader#1 and 0.860 for Reader#2, while UNETR scored 0.8 and 0.833 for Readers #1 and #2, respectively, compared to the baseline UNets 0.825 for Reader #1 and 0.851 for Reader #2. SwinUNETR had an average dice score of 0.8583 for Reader#1 and 0.867 for Reader#2 in cross-validated mixed training. For the gland size-based dataset, SwinUNETR achieved an average dice score of 0.902 for Reader#1 subset and 0.894 for Reader#2, using the five-fold mixed training strategy (Reader#1, n=53; Reader#2, n=87) at larger gland size-based subsets, where UNETR performed poorly. Our findings demonstrate that global and shifted-window self-attention effectively reduces label noise and class imbalance sensitivity, resulting in improvements in the Dice score over CNNs by up to five points while maintaining computational efficiency. This contributes to the high robustness of SwinUNETR for clinical deployment.

Kesheng Wang, Xiaoyu Chen, Chunlei He et al.

In the medical image analysis field, precise quantification of curve tortuosity plays a critical role in the auxiliary diagnosis and pathological assessment of various diseases. In this study, we propose a novel framework for tortuosity quantification and demonstrate its effectiveness through the evaluation of meibomian gland atrophy uniformity,serving as a representative application scenario. We introduce an information entropy-based tortuosity quantification framework that integrates probability modeling with entropy theory and incorporates domain transformation of curve data. Unlike traditional methods such as curvature or arc-chord ratio, this approach evaluates the tortuosity of a target curve by comparing it to a designated reference curve. Consequently, it is more suitable for tortuosity assessment tasks in medical data where biologically plausible reference curves are available, providing a more robust and objective evaluation metric without relying on idealized straight-line comparisons. First, we conducted numerical simulation experiments to preliminarily assess the stability and validity of the method. Subsequently, the framework was applied to quantify the spatial uniformity of meibomian gland atrophy and to analyze the difference in this uniformity between \textit{Demodex}-negative and \textit{Demodex}-positive patient groups. The results demonstrated a significant difference in tortuosity-based uniformity between the two groups, with an area under the curve of 0.8768, sensitivity of 0.75, and specificity of 0.93. These findings highlight the clinical utility of the proposed framework in curve tortuosity analysis and its potential as a generalizable tool for quantitative morphological evaluation in medical diagnostics.

J. D. Martins, Henrique Pina, Débora Silveira et al.

Abstract Introduction Down syndrome (DS) is the most common chromosomal condition among live-born infants. It is associated with intellectual disability as well as medical issues ranging from congenital heart disease, obstructive sleep apnea, celiac disease, to endocrinopathies, namely thyroid disorders. The spectrum of thyroid dysfunction in patients with DS include congenital hypothyroidism, subclinical hypothyroidism, acquired hypothyroidism, and hyperthyroidism. Identifying medical comorbidities in these patients can be crucial to optimize their quality of life. Clinical Case This is the case of a male patient, 30 years old, caucasian, institutionalized, with DS and known medical history of esophageal dilation, macroglossia, valvular heart disease with mild aortic insufficiency and left ventricular moderated hypertrophy, macrocytic anemia, and pulmonary embolism 10 months earlier. Due to pallor, somnolence, fatigue, thin and fragile hair, and bradycardia, blood tests were requested and revealed an elevated TSH (385.20 mUI/L, N 0.27-4.20) with undetectable free T4 levels (<0.5 pmol/L, N 12.0-22.0), macrocytic anemia (Hb 11.4 g/dL; MCV 102 fL), renal dysfunction (creatinine 1.73 mg/dL) and hypercholesterolemia (total cholesterol 310 mg/dL; LDL 236 mg/dL). The chest X-ray revealed an heart silhouette enlargement and the echocardiogram showed left ventricular hypertrophy with preserved ejection fraction, diastolic dysfunction, and mild pericardial effusion. The thyroid ultrasound revealed a globally reduced gland, with diffuse hyperechogenicity and heterogeneity, suggesting thyroiditis. The patient was referred to the emergency department and the additional study revealed the presence of anti-TPO and anti-TG antibodies and a severe CK elevation (9280 UI/L, N 46-171). The patient, although lethargic, presented with normal blood pressure, heart rate, and body temperature during the hospital admission. Searching our national health system database, it was found the patient already had TSH elevation with undetectable free T4 levels at least 12 years before, in another hospital, but had lost follow-up there after the blood tests, and therefore remaining undiagnosed and untreated. Oral levothyroxine reposition was started, first with 50 micrograms and then 100 micrograms and the patient was discharged after 5 days with follow-up at the Endocrinology department. Two months later, there were already clinical and laboratory changes, with improved level of consciousness and normalized free T4 levels. Conclusion Although hypothyroidism is a common and widely recognized condition, this case emphasizes how challenging the diagnosis can be in a person with DS. Knowing that this syndrome is associated with several organic dysfunctions, namely endocrine disorders with non-specific clinical manifestations, there must be a high level of suspicion in order to screen, diagnose, and treat the comorbidities that may appear.

Wen Wu, Chao Zhang, Philip C. Woodland

Speech-based automatic detection of Alzheimer's disease (AD) and depression has attracted increased attention. Confidence estimation is crucial for a trust-worthy automatic diagnostic system which informs the clinician about the confidence of model predictions and helps reduce the risk of misdiagnosis. This paper investigates confidence estimation for automatic detection of AD and depression based on clinical interviews. A novel Bayesian approach is proposed which uses a dynamic Dirichlet prior distribution to model the second-order probability of the predictive distribution. Experimental results on the publicly available ADReSS and DAIC-WOZ datasets demonstrate that the proposed method outperforms a range of baselines for both classification accuracy and confidence estimation.

Vida Adeli, Soroush Mehraban, Irene Ballester et al.

This study investigates the application of general human motion encoders trained on large-scale human motion datasets for analyzing gait patterns in PD patients. Although these models have learned a wealth of human biomechanical knowledge, their effectiveness in analyzing pathological movements, such as parkinsonian gait, has yet to be fully validated. We propose a comparative framework and evaluate six pre-trained state-of-the-art human motion encoder models on their ability to predict the Movement Disorder Society - Unified Parkinson's Disease Rating Scale (MDS-UPDRS-III) gait scores from motion capture data. We compare these against a traditional gait feature-based predictive model in a recently released large public PD dataset, including PD patients on and off medication. The feature-based model currently shows higher weighted average accuracy, precision, recall, and F1-score. Motion encoder models with closely comparable results demonstrate promise for scalability and efficiency in clinical settings. This potential is underscored by the enhanced performance of the encoder model upon fine-tuning on PD training set. Four of the six human motion models examined provided prediction scores that were significantly different between on- and off-medication states. This finding reveals the sensitivity of motion encoder models to nuanced clinical changes. It also underscores the necessity for continued customization of these models to better capture disease-specific features, thereby reducing the reliance on labor-intensive feature engineering. Lastly, we establish a benchmark for the analysis of skeleton-based motion encoder models in clinical settings. To the best of our knowledge, this is the first study to provide a benchmark that enables state-of-the-art models to be tested and compete in a clinical context. Codes and benchmark leaderboard are available at code.

Olivera Stanojlović, Dragan Hrnčić, Danijela Vojnović-Milutinović et al.

Anastasia-Konstantina Sakali, Alexandra Bargiota, Maria Papagianni et al.

Anastasia-Konstantina Sakali, Alexandra Bargiota, Maria Papagianni et al.

Qingqing Yin, Qingqing Yin, Yan Gao et al.

Eugene Han, Kyung-Do Han, Yong-ho Lee et al.

Background This study investigated the changes of fatty liver disease prevalence in general Korean population. Methods This study analyzed data from the Korean National Health Insurance Service from 2009 to 2017 that included individuals aged 20 years or older who had undergone a medical health examination. Fatty liver disease was assessed using the fatty liver index (FLI). The disease severity was defined by FLI cutoff, ≥30 as moderate, and ≥60 as severe fatty liver disease. Results The prevalence of Korean adults aged 20 years or over with fatty liver disease (FLI ≥60) increased from 13.3% in 2009 to 15.5% in 2017 (P for trend <0.001). The increase in fatty liver disease prevalence was prominent in men (from 20.5% to 24.2%) and the young age (20 to 39 years) group (from 12.8% to 16.4%) (P for interaction <0.001). The prevalence of fatty liver disease was the highest in type 2 diabetes mellitus (T2DM, 29.6%) population compared to that of prediabetes or normoglycemia (10.0% and 21.8%) in 2017. The prevalence of fatty liver disease had statistically increased in individuals with T2DM and prediabetes (P for trend <0.001). Its prevalence increased more steeply in the young-aged population with T2DM, from 42.2% in 2009 to 60.1% in 2017. When applying a lower FLI cutoff (≥30) similar results were observed. Conclusion The prevalence of fatty liver disease in the Korean population has increased. Individuals who are young, male, and have T2DM are vulnerable to fatty liver disease.