Coronary Artery Disease (CAD) remains a leading cause of morbidity and mortality worldwide. Early detection is critical to recover patient outcomes and decrease healthcare costs. In recent years, machine learning (ML) advancements have shown significant potential in enhancing the accuracy of CAD diagnosis. This study investigates the application of ML algorithms to improve the detection of CAD by analyzing patient data, including clinical features, imaging, and biomarker profiles. Bi-directional Long Short-Term Memory (Bi-LSTM), Gated Recurrent Units (GRU), and a hybrid of Bi-LSTM+GRU were trained on large datasets to predict the presence of CAD. Results demonstrated that these ML models outperformed traditional diagnostic methods in sensitivity and specificity, offering a robust tool for clinicians to make more informed decisions. The experimental results show that the hybrid model achieved an accuracy of 97.07%. By integrating advanced data preprocessing techniques and feature selection, this study ensures optimal learning and model performance, setting a benchmark for the application of ML in CAD diagnosis. The integration of ML into CAD detection presents a promising avenue for personalized healthcare and could play a pivotal role in the future of cardiovascular disease management.
ABSTRACTObjectiveThis review seeks to provide endocrine clinicians with a comprehensive analysis of breast cancer risk, diagnostic modalities and management strategies in women with endocrine disorders, with particular emphasis on the influence of metabolic factors such as diabetes and obesity, and the role of Menopausal Hormone Therapy (MHT).DesignThe review examines a spectrum of endocrine disorders commonly encountered in clinical practice, including Multiple Endocrine Neoplasia Types 1 (MEN1), 2 (MEN2) and 4 (MEN4), Von Hippel‐Lindau syndrome (VHL), Pheochromocytoma and Paraganglioma (PPGL), Acromegaly, Hyperprolactinaemia, Polycystic Ovary Syndrome (PCOS), Congenital Adrenal Hyperplasia (CAH), Turner Syndrome, alongside metabolic conditions such as diabetes and obesity and the effects of MHT. The review critically appraises each disorder's association with breast cancer risk, screening implications and therapeutic management.PatientsThis analysis focuses on women with the aforementioned endocrine and metabolic disorders, assessing their specific breast cancer risk profiles, informed by the latest clinical evidence and molecular insights.MeasurementsThe review comprehensively evaluates current evidence‐based approaches to screening, diagnostic accuracy and treatment in this patient cohort. Emphasis is placed on the metabolic derangements, hormonal influences and genetic predispositions that modulate breast cancer risk, providing disorder‐specific recommendations for individualised care.ResultsThe findings indicate a significantly elevated breast cancer risk in patients with MEN1, necessitating early initiation of MRI screening by age 40. In MEN2, emerging evidence suggests that combining RET inhibitors with endocrine therapy may yield clinical benefits, although further research is needed to validate this approach. The breast cancer risk associated with MEN4 and VHL syndromes, while documented, remains less well‐characterised, requiring further investigation. Diabetes and obesity are confirmed as major modifiable risk factors, particularly in postmenopausal women, where hyperinsulinemia and metabolic dysfunction contribute to increased incidence and poorer outcomes, notably in triple‐negative breast cancer (TNBC). The role of MHT, particularly combined oestrogen‐progestogen therapy, is strongly associated with increased breast cancer risk, particularly for hormone receptor‐positive malignancies, necessitating cautious use and personalised treatment planning. In contrast, oestrogen‐only MHT appears to confer a reduced risk in women post‐hysterectomy. For patients with PCOS, CAH and Turner Syndrome, while definitive evidence of elevated breast cancer risk is lacking, individualised screening strategies and careful hormone therapy management remain essential due to the complex interplay of hormonal and metabolic factors.ConclusionsThe review highlights the need for personalised breast cancer screening and management protocols in women with endocrine and metabolic disorders. For high‐risk groups such as MEN1 patients, early initiation of MRI screening is warranted. In women with diabetes and obesity, targeted interventions addressing hyperinsulinemia and metabolic dysfunction are critical to mitigating their increased cancer risk. The association between MHT and breast cancer underscores the importance of individualised risk stratification in hormone therapy administration, particularly in women with predisposing genetic or endocrine conditions. Enhanced surveillance tailored to the unique risk profiles of endocrine disorder patients will facilitate early detection and improve clinical outcomes. However, further large‐scale studies are necessary to refine these associations and develop robust, evidence‐based guidelines.
The recent 2024 Endocrine Society Clinical Practice Guideline on Vitamin D for the prevention of diseases has become a source of controversy among medical professionals and the lay public. This Review rebuts the recommendations from this Guideline for infants, children, adolescents, pregnant women, and dark-skinned individuals. It rejects the one-size-fits-all recommendations and provides the data for precision-medicine-guided vitamin D screening and supplementation in these populations.
In recent years, the prevalence of autoimmune thyroid diseases, particularly autoimmune thyroiditis and Graves’ disease, has increased. These conditions sometimes co‑occur as part of an autoimmune ‘overlap’ syndrome. Among the triggers of this lesion, both genetic factors and environmental factors are considered, which under certain conditions contribute to the realization of manifestations of the autoimmune continuum, which can occur either simultaneously or sequentially. Despite the presence of a number of scientific studies, the issues of pathogenesis and clinical course of autoimmune «overlap» syndrome remain still not fully clarified. Objective — to analyze possible triggers of autoimmune «overlap» syndrome and to clarify the features of the clinical course of thyroid diseases using the example of a clinical case. A case from real clinical practice of a patient with autoimmune overlap syndrome is considered. Patient O., 33 years old, works as a manicurist. She complained of palpitations, shortness of breath with light exertion, insomnia, some imbalance, weight loss, fatigue. History of unsuccessful in vitro fertilization; first pregnancy with a frozen fetus at 8 weeks, which was observed in the rudimentary horn of the uterus; second pregnancy, which ended in spontaneous miscarriage at 17—18 weeks. After that, 3 months later, the first manifestation of Graves’ disease occurred, thyreostatic treatment with thiamazole was prescribed for 2.5 months, discontinued due to an allergic reaction, and euthyroidism was achieved. However, a slight increase in antibodies to rTSH was observed against the background of a significant increase in the production of AT‑TPO and AT‑TG. Structural changes in the thyroid gland, characteristic of autoimmune thyroiditis, were also noted. Spontaneous improvement of the condition contributed to the third pregnancy, and the patient gave birth to a healthy boy. She breastfed for 1.2 years, but a year after giving birth, a relapse occurred. Clinical symptoms consistent with diffuse toxic goiter were detected, a decrease in vitamin D levels along with moderate thyroid hyperplasia. She was treated with carbomazole until hormonal balance was achieved and antibodies to rTSH decreased, unfortunately only for 8 months. Almost a year later, a relapse occurred again. It is known that her sister also has thyroid disease. On examination: exophthalmos and ocular symptoms of thyrotoxicosis are absent, tremor of the upper extremities is present. Heart activity is rhythmic, tones are of normal volume, soft systolic murmur at the apex, tachycardia up to 110—120 /min, blood pressure 100/70 mm Hg. A decrease in TSH to 0.285 mU/mL, an increase in vT4 to 2.17 ng/dL, in T3—3.28 pg/mL, as well as an increase in the production of AT‑rTSH, AT‑TPO, AT‑TG, mild anemia. Ultrasound shows slight hyperplasia of the thyroid gland, focally increased echogenicity, the structure is heterogeneous, mosaic, hypervascularization. Considering the existing clinical symptoms of thyrotoxicosis with characteristic changes in the thyroid gland, inherent in both diseases, a diagnosis of autoimmune «overlap» syndrome was made. The patient was treated conservatively with the appointment of carbimazole, B‑complex vitamins, Sorbifer, Anaprilin until the onset of clinical and immunological remission and inhibition of autoaggression processes. Conclusions. The primary triggers of autoimmune ‘overlap’ syndrome include genetic predisposition, especially in young women, along with stress, vitamin D deficiency, exposure to certain chemicals (including acetone‑containing substances), and the use of hormonal medications, particularly progesterone. Autoimmune Overlap syndrome should be considered as a syndrome that combines two thyroid diseases, and the clinical course may be with the initial manifestation of Graves’ disease, and later — the addition of autoimmune thyroiditis with a recurrent course, which requires careful study to develop a personalized rational treatment, giving preference to conservative. The presence of autoimmune «overlap» syndrome contributes to the peculiarities of the clinical manifestations of thyroid diseases, in particular, Graves’ disease is manifested by thyrotoxic cardiomyopathy, neuropathy with moderate hyperplasia and heterogeneity of the thyroid gland structure against the background of a significant increase in autoaggression in the absence of thyrotoxic ophthalmopathy and dermopathy.
Bohua Chen, Lucia Chantal Schneider, Christian Röver
et al.
In the context of clinical research, computational models have received increasing attention over the past decades. In this systematic review, we aimed to provide an overview of the role of so-called in silico clinical trials (ISCTs) in medical applications. Exemplary for the broad field of clinical medicine, we focused on in silico (IS) methods applied in drug development, sometimes also referred to as model informed drug development (MIDD). We searched PubMed and ClinicalTrials.gov for published articles and registered clinical trials related to ISCTs. We identified 202 articles and 48 trials, and of these, 76 articles and 19 trials were directly linked to drug development. We extracted information from all 202 articles and 48 clinical trials and conducted a more detailed review of the methods used in the 76 articles that are connected to drug development. Regarding application, most articles and trials focused on cancer and imaging-related research while rare and pediatric diseases were only addressed in 14 articles and 5 trials, respectively. While some models were informed combining mechanistic knowledge with clinical or preclinical (in-vivo or in-vitro) data, the majority of models were fully data-driven, illustrating that clinical data is a crucial part in the process of generating synthetic data in ISCTs. Regarding reproducibility, a more detailed analysis revealed that only 24% (18 out of 76) of the articles provided an open-source implementation of the applied models, and in only 20% of the articles the generated synthetic data were publicly available. Despite the widely raised interest, we also found that it is still uncommon for ISCTs to be part of a registered clinical trial and their application is restricted to specific diseases leaving potential benefits of ISCTs not fully exploited.
Many rare genetic diseases exhibit recognizable facial phenotypes, which are often used as diagnostic clues. However, current facial phenotype diagnostic models, which are trained on image datasets, have high accuracy but often suffer from an inability to explain their predictions, which reduces physicians' confidence in the model output.In this paper, we constructed a dataset, called FGDD, which was collected from 509 publications and contains 1147 data records, in which each data record represents a patient group and contains patient information, variation information, and facial phenotype information. To verify the availability of the dataset, we evaluated the performance of commonly used classification algorithms on the dataset and analyzed the explainability from global and local perspectives. FGDD aims to support the training of disease diagnostic models, provide explainable results, and increase physicians' confidence with solid evidence. It also allows us to explore the complex relationship between genes, diseases, and facial phenotypes, to gain a deeper understanding of the pathogenesis and clinical manifestations of rare genetic diseases.
Disease progression modeling aims to characterize and predict how a patient's disease complications worsen over time based on longitudinal electronic health records (EHRs). For diseases such as type 2 diabetes, accurate progression modeling can enhance patient sub-phenotyping and inform effective and timely interventions. However, the problem is challenging due to the need to learn continuous-time progression dynamics from irregularly sampled clinical events amid patient heterogeneity (e.g., different progression rates and pathways). Existing mechanistic and data-driven methods either lack adaptability to learn from real-world data or fail to capture complex continuous-time dynamics on progression trajectories. To address these limitations, we propose Temporally Detailed Hypergraph Neural Ordinary Differential Equation (TD-HNODE), which represents disease progression on clinically recognized trajectories as a temporally detailed hypergraph and learns the continuous-time progression dynamics via a neural ODE framework. TD-HNODE contains a learnable TD-Hypergraph Laplacian that captures the interdependency of disease complication markers within both intra- and inter-progression trajectories. Experiments on two real-world clinical datasets demonstrate that TD-HNODE outperforms multiple baselines in modeling the progression of type 2 diabetes and related cardiovascular diseases.
Rachelle P Mendoza, Richard Cody Simon, Nicole A Cipriani
et al.
Objective: This study aims to analyze the diagnostic utility of multiple repeat FNA on thyroid nodules with initially benign diagnosis.
Methods: In a 5-year period, 1658 thyroid nodules with initially benign FNAs were retrospectively reviewed and followed for subsequent resection and repeat biopsy.
Results: Out of 2150 thyroid nodules, 1658 (77.1%) were diagnosed as benign on FNAs. The average age at diagnosis was 57.4 years (range: 11–93 years), and most were females (83.8%). Repeat FNA was performed on 183 benign nodules, of which 141 (8.5%) were sampled a second time and 42 (2.5%) had two or more repeat samplings. For the benign nodules without repeat FNAs, 124 had benign resection. Of cases with one-time repeat FNA, most (n = 101) remained benign on repeat FNAs, 13 of which were benign on resection. Eleven had atypical repeat FNAs, five were resected, four of which were benign and one was atypical follicular neoplasm with HRAS and TERT promoter mutations. Of cases with multiple repeat FNA, most (n = 35) were still benign on repeat FNAs, one had benign resection. Two had atypical repeat biopsies, one was PTC on resection with CCD6::RET fusion. The positive predictive value significantly decreased from 41.1% on single FNA to 8.3% on one-time repeat (P < 0.001) and 16.7% on multiple repeat (P = 0.002). The total cost for the work-up of previously benign nodules was $285,454.
Conclusions: Repeat FNA biopsies did not provide an additional diagnostic value in the evaluation of benign thyroid nodules, and often led to unwarranted follow-up procedures and significantly increased health-care cost.
Diseases of the endocrine glands. Clinical endocrinology
Background: Multiple Sclerosis (MS), an autoimmune disease affecting millions worldwide, is characterized by its variable course, in which some patients will experience a more benign disease course and others a more active one, with the latter leading to permanent neural damage and disability. Methods: This study uses a Markov Chain model to demonstrate the probability of movement across different states on the Expanded Disability Status Scale (EDSS) and attempted to define worsening, improvement, cycling, and stability of these different pathways. Most importantly we were interested in assessing the lack of impermanence of confirmed disability worsening and if it could be estimated from the Markov model. Results: The study identified only 8.1% were considered worsening, 5.6% consistent improving and 86% cyclers and less than 1% consistently stable. More importantly we also found that many (approximately 30%) of participants with confirmed disability worsening (CDW) regressed to stages that were not considered worsening, on subsequent visits after CDW. Conclusions: These finding are similar to what has been reported previously as predictors of worsening, and also for a lack of durability of CDW, but our results suggest that clinical trial endpoints may need to be modified to more accurately capture differences between the treatment and control groups. Further, this suggests that the rate of worsening in trials that use time to CDW are overestimating the extent of CDW. The trials remain valid since the regressing applies to both treatment and control groups, but that the results may be underestimating the treatment benefit due to misclassification.
Karthik Gopinath, Douglas N. Greve, Colin Magdamo
et al.
Surface-based analysis of the cerebral cortex is ubiquitous in human neuroimaging with MRI. It is crucial for cortical registration, parcellation, and thickness estimation. Traditionally, these analyses require high-resolution, isotropic scans with good gray-white matter contrast, typically a 1mm T1-weighted scan. This excludes most clinical MRI scans, which are often anisotropic and lack the necessary T1 contrast. To enable large-scale neuroimaging studies using vast clinical data, we introduce recon-all-clinical, a novel method for cortical reconstruction, registration, parcellation, and thickness estimation in brain MRI scans of any resolution and contrast. Our approach employs a hybrid analysis method that combines a convolutional neural network (CNN) trained with domain randomization to predict signed distance functions (SDFs) and classical geometry processing for accurate surface placement while maintaining topological and geometric constraints. The method does not require retraining for different acquisitions, thus simplifying the analysis of heterogeneous clinical datasets. We tested recon-all-clinical on multiple datasets, including over 19,000 clinical scans. The method consistently produced precise cortical reconstructions and high parcellation accuracy across varied MRI contrasts and resolutions. Cortical thickness estimates are precise enough to capture aging effects independently of MRI contrast, although accuracy varies with slice thickness. Our method is publicly available at https://surfer.nmr.mgh.harvard.edu/fswiki/recon-all-clinical, enabling researchers to perform detailed cortical analysis on the huge amounts of already existing clinical MRI scans. This advancement may be particularly valuable for studying rare diseases and underrepresented populations where research-grade MRI data is scarce.
Clinical reasoning refers to the cognitive process that physicians employ in evaluating and managing patients. This process typically involves suggesting necessary examinations, diagnosing patients' diseases, and deciding on appropriate therapies, etc. Accurate clinical reasoning requires extensive medical knowledge and rich clinical experience, setting a high bar for physicians. This is particularly challenging in developing countries due to the overwhelming number of patients and limited physician resources, contributing significantly to global health inequity and necessitating automated clinical reasoning approaches. Recently, the emergence of large language models (LLMs) such as ChatGPT and GPT-4 have demonstrated their potential in clinical reasoning. However, these LLMs are prone to hallucination problems, and the reasoning process of LLMs may not align with the clinical decision path of physicians. In this study, we introduce a novel framework, In-Context Padding (ICP), designed to enhance LLMs with medical knowledge. Specifically, we infer critical clinical reasoning elements (referred to as knowledge seeds) and use these as anchors to guide the generation process of LLMs. Experiments on two clinical question datasets demonstrate that ICP significantly improves the clinical reasoning ability of LLMs.
The clinical trial process, a critical phase in drug development, is essential for developing new treatments. The primary goal of interventional clinical trials is to evaluate the safety and efficacy of drug-based treatments for specific diseases. However, these trials are often lengthy, labor-intensive, and expensive. The duration of a clinical trial significantly impacts overall costs, making efficient timeline management crucial for controlling budgets and ensuring the economic feasibility of research. To address this issue, We propose TrialDura, a machine learning-based method that estimates the duration of clinical trials using multimodal data, including disease names, drug molecules, trial phases, and eligibility criteria. Then, we encode them into Bio-BERT embeddings specifically tuned for biomedical contexts to provide a deeper and more relevant semantic understanding of clinical trial data. Finally, the model's hierarchical attention mechanism connects all of the embeddings to capture their interactions and predict clinical trial duration. Our proposed model demonstrated superior performance with a mean absolute error (MAE) of 1.04 years and a root mean square error (RMSE) of 1.39 years compared to the other models, indicating more accurate clinical trial duration prediction. Publicly available code can be found at: https://anonymous.4open.science/r/TrialDura-F196.
Clinical variant classification of pathogenic versus benign genetic variants remains a challenge in clinical genetics. Recently, the proposition of genomic foundation models has improved the generic variant effect prediction (VEP) accuracy via weakly-supervised or unsupervised training. However, these VEPs are not disease-specific, limiting their adaptation at the point of care. To address this problem, we propose DYNA: Disease-specificity fine-tuning via a Siamese neural network broadly applicable to all genomic foundation models for more effective variant effect predictions in disease-specific contexts. We evaluate DYNA in two distinct disease-relevant tasks. For coding VEPs, we focus on various cardiovascular diseases, where gene-disease relationships of loss-of-function vs. gain-of-function dictate disease-specific VEP. For non-coding VEPs, we apply DYNA to an essential post-transcriptional regulatory axis of RNA splicing, the most common non-coding pathogenic mechanism in established clinical VEP guidelines. In both cases, DYNA fine-tunes various pre-trained genomic foundation models on small, rare variant sets. The DYNA fine-tuned models show superior performance in the held-out rare variant testing set and are further replicated in large, clinically-relevant variant annotations in ClinVAR. Thus, DYNA offers a potent disease-specific variant effect prediction method, excelling in intra-gene generalization and generalization to unseen genetic variants, making it particularly valuable for disease associations and clinical applicability.
Timely detection of illnesses is vital to prevent severe infections and ensure effective treatment, as it's always better to prevent diseases than to cure them. Sadly, many patients remain undiagnosed until their conditions worsen, resulting in high death rates. Expert systems offer a solution by automating early-stage diagnoses using a fuzzy rule-based approach. Our study gathered data from various sources, including hospitals, to develop an expert system aimed at identifying early signs of diseases, particularly heart conditions. The diagnostic process involves collecting and processing test results using the expert system, which categorizes disease risks and aids physicians in treatment decisions. By incorporating expert systems into clinical practice, we can improve the accuracy of disease detection and address challenges in patient management, particularly in areas with limited medical resources.
BackgroundAlthough the role of steroid hormones in lipid levels has been partly discussed in the context of separate sexes, the causal relationship between steroid hormones and lipid metabolism according to sex has not been elucidated because of the limitations of observational studies. We assessed the relationship between steroid hormones and lipid metabolism in separate sexes using a two-sample Mendelian randomization (MR) study.MethodsInstrumental variables for dehydroepiandrosterone sulfate (DHEAS), progesterone, estradiol, and androstenedione were selected. MR analysis was performed using inverse-variance weighted, MR-Egger, weighted median, and MR pleiotropy residual sum and outlier tests. Cochran’s Q test, the MR-Egger intercept test, and leave-one-out analysis were used for sensitivity analyses.ResultsThe results showed that the three steroid hormones affected lipid metabolism and exhibited sex differences. In males, DHEAS was negatively correlated with total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and apolipoprotein B (P = 0.007; P = 0.006; P = 0.041, respectively), and progesterone was negatively correlated with TC and LDL-C (P = 0.019; P = 0.038, respectively). In females, DHEAS was negatively correlated with TC (P = 0.026) and androstenedione was negatively correlated with triglycerides and apolipoprotein A (P = 0.022; P = 0.009, respectively). No statistically significant association was observed between the estradiol levels and lipid metabolism in male or female participants.ConclusionsOur findings identified sex-specific causal networks between steroid hormones and lipid metabolism. Steroid hormones, including DHEAS, progesterone, and androstenedione, exhibited beneficial effects on lipid metabolism in both sexes; however, the specific lipid profiles affected by steroid hormones differed between the sexes.
Diseases of the endocrine glands. Clinical endocrinology
Objective. The aim of the present study was to assess insulin-like growth factor 1 (IGF-1) and IGF-binding protein 3 (IGFBP3) as markers of insulin resistance in patients with prediabetes and type 2 diabetes mellitus (TDM2).
Diseases of the endocrine glands. Clinical endocrinology
ObjectiveWe aimed to explore the predictive value of stimulated thyroglobulin (sTg) and pre-ablation antithyroglobulin (pa-TgAb) products for the effect of radioiodine therapy (RAIT) on TgAb-positive differentiated thyroid cancer (DTC) patients.MethodsIn this study, we enrolled 265 patients with TgAb-positive DTC who underwent RAIT after total thyroidectomy (TT). Based on the last follow-up result, the patients were divided into two groups: the excellent response (ER) group and the non-excellent response (NER) group. We analyzed the factors related to the effect of RAIT.ResultsThe ER group consisted of 197 patients. The NER group consisted of 68 patients. For the univariate analysis, we found that the maximal tumor diameter, whether with extrathyroidal extension (ETE), bilateral or unilateral primary lesion, multifocality, preoperative TgAb (preop-TgAb), pa-TgAb, sTg × pa-TgAb, initial RAIT dose, N stage, and surgical extent (modified radical neck dissection or not), showed significant differences between the ER group and NER group (all p-values <0.05). The receiver operating characteristic (ROC) curves showed that the cutoff value was 724.25 IU/ml, 424.00 IU/ml, and 59.73 for preop-TgAb, pa-TgAb, and sTg × pa-TgAb, respectively. The multivariate logistic regression analysis results indicated that pa-TgAb, sTg × pa-TgAb, initial RAIT dose, and N stage were independent risk factors for NER (all p-values <0.05). For the Kaplan–Meier analysis of disease-free survival (DFS), the median DFS of the patients with sTg × pa-TgAb < 59.73 and initial RAIT dose ≤ 100 mCi was significantly longer than that of the patients with sTg × pa-TgAb ≥ 59.73 (50.27 months vs. 48.59 months, p = 0.041) and initial RAIT dose >100 mCi (50.50 months vs. 38.00 months, p = 0.030).ConclusionWe found the sTg and pa-TgAb conducts is a good predictor of the efficacy of RAIT in TgAb-positive DTC patients. It can play a very positive and important role in optimizing treatment, improving prognosis, and reducing the burden of patients.
Diseases of the endocrine glands. Clinical endocrinology
Rehab H. Werida, Aalaa Ramzy, Youssri Nassief Ebrahim
et al.
Abstract Background and objective Type 2 diabetes mellitus (T2DM) is caused by insulin resistance or tissue insensitivity to insulin, as well as relative insulin insufficiency. Diabetes that is uncontrolled for an extended period of time is linked to substantial comorbidities and organ damage. The purpose of the current study is to assess the effect of coadministration of omega-3 fatty acids with glimepiride on blood glucose, lipid profile, serum irisin, and sirtuin-1 levels in T2DM patients. Methods This clinical trial involved 70 type 2 diabetic patients randomly assigned to glimepiride 3 mg with either omega-3 capsules contained fish oil 1000 mg, 13% of eicosapentaenoic acid (EPA) and 9% docosahexaenoic acid (DHA) (omega-3 group, n = 35) or placebo capsules contained corn oil and linoleic acid (control group, n = 35) daily for three months. Blood samples were obtained at the start of the study and 12 weeks later for biochemical examination of HbA1c%, FBG, fasting insulin, and lipid profile. In addition, the atherogenic index of plasma (AIP) was calculated. Human enzyme-linked immunosorbent assay (ELISA) kits were utilized for assessing serum irisin and sirtuin-1 levels before and after the intervention. Results Compared to the control group, omega-3 fatty acids decreased serum fasting blood glucose (FBG, p < 0.001), glycated hemoglobin percent (HbA1C%, p < 0.001), total cholesterol (TC, p < 0.001), triglycerides (TGs, p = 0.006), low density lipoprotein (LDL, p = 0.089), and Homeostatic Model Assessment for Insulin Resistance (HOMA-IR, p = 0.021) after three months of intervention. However, a significant increase was reported in serum irisin and high density lipoprotein (HDL) between both groups after intervention (p = 0.026 and p = 0.007, respectively). The atherogenic index of plasma (AIP) increased in the control group but decreased in the omega-3 group, with significant differences between the two groups (p < 0.001). Conclusion The present study found that supplementing with omega-3 fatty acids might dramatically enhance blood irisin levels, as well as improve glycemic control and lipid profile in type 2 diabetes mellitus patients using glimepiride. Trial Registration This study is registered on ClinicalTrials.gov under identifier NCT03917940 . (The registration date: April 17, 2019).
Diseases of the endocrine glands. Clinical endocrinology
ABSTRACT Aims/Introduction The association between serum uric acid (SUA) levels and prediabetes risk remains poorly understood. The aim of this longitudinal retrospective study was to evaluate the association between SUA levels and prediabetes progression in Japanese individuals through sex‐specific analysis. Materials and Methods We enrolled 20,743 participants (11,916 men and 8,827 women) who underwent annual medical health checkups in 2017 (baseline) and 2022. None of the participants had diabetes and prediabetes or were taking SUA‐lowering medications at baseline. Participants were divided into four groups according to the quartiles of SUA levels at baseline. Multivariable‐adjusted Cox regression analysis was conducted to examine the risk of prediabetes progression. In addition, multivariate restricted cubic spline analysis was conducted to investigate the dose–response risk. Results In women, compared with the lowest SUA quartile (Q1) group, the adjusted hazard ratios (95% confidence intervals) of prediabetes in the Q2, Q3, and Q4 groups were 1.03 (0.86–1.25), 1.41 (1.18–1.68), and 1.55 (1.30–1.84), respectively. However, in men, no significant association in the risk of prediabetes was found across quartiles of SUA. Furthermore, in women, restricted cubic spline analysis revealed the dose–response relationship between SUA and progression to prediabetes. Conclusions The results indicate that elevated serum SUA levels might be positively and independently associated with an increased risk of progression to prediabetes in Japanese women.
Diseases of the endocrine glands. Clinical endocrinology
Shikha Dubey, Tushar Kataria, Beatrice Knudsen
et al.
With the advent of digital scanners and deep learning, diagnostic operations may move from a microscope to a desktop. Hematoxylin and Eosin (H&E) staining is one of the most frequently used stains for disease analysis, diagnosis, and grading, but pathologists do need different immunohistochemical (IHC) stains to analyze specific structures or cells. Obtaining all of these stains (H&E and different IHCs) on a single specimen is a tedious and time-consuming task. Consequently, virtual staining has emerged as an essential research direction. Here, we propose a novel generative model, Structural Cycle-GAN (SC-GAN), for synthesizing IHC stains from H&E images, and vice versa. Our method expressly incorporates structural information in the form of edges (in addition to color data) and employs attention modules exclusively in the decoder of the proposed generator model. This integration enhances feature localization and preserves contextual information during the generation process. In addition, a structural loss is incorporated to ensure accurate structure alignment between the generated and input markers. To demonstrate the efficacy of the proposed model, experiments are conducted with two IHC markers emphasizing distinct structures of glands in the colon: the nucleus of epithelial cells (CDX2) and the cytoplasm (CK818). Quantitative metrics such as FID and SSIM are frequently used for the analysis of generative models, but they do not correlate explicitly with higher-quality virtual staining results. Therefore, we propose two new quantitative metrics that correlate directly with the virtual staining specificity of IHC markers.