Endocrinology

Multi-Omics Analyses Reveal BPA Accelerates Glioma Progression via Cell Cycle Dysregulation.

Bisphenol A (BPA), a pervasive environmental endocrine disruptor, its role in glioma progression is unclear. We sought to elucidate how BPA influences glioma malignancy by integrating multi-omics analyses with experimental validation. Candidate targets of BPA were gathered from the ChEMBL database and predicted using the SwissTargetPrediction and Similarity Ensemble Approach (SEA) tools. Glioma-as...

An intelligent MRI-based all-in-one diagnostic strategy for axillary lymph node status in breast cancer.

An integrated diagnostic strategy of preoperative identification of sentinel lymph node (SLN) metastasis, SLN metastatic burden, and non-SLN (NSLN) metastasis in breast cancer remains to be developed to guide axillary surgery de-escalation. Here we develop a magnetic resonance imaging-based hierarchical multitask deep learning model, breast cancer axillary lymph node network (BCALN-Net) to predict...

Transcriptomic identification of key genes influencing m7G modification in thyroid carcinoma and experimental validation.

BACKGROUND: N7-methylguanosine (m7G) modification plays a critical role in RNA metabolism and is increasingly recognized for its implications in cance...

Burden trajectories of stroke attributable to lipid-glucose dysfunction and biomarker validation: evidence from the global burden of disease (GBD) 2023 and the China health and retirement longitudinal study (CHARLS).

BACKGROUND: Metabolic dysfunction is an important contributor to stroke burden and may also help identify individuals at higher future risk of stroke....

An interpretable machine learning model for predicting postoperative hypotension in type 2 diabetes mellitus undergoing non‑cardiac surgery.

BACKGROUND: Postoperative hypotension (POH) is a common and serious complication in patients with type 2 diabetes mellitus (T2DM) undergoing non‑cardi...

Case study application of high-throughput new approach methodologies for exposure to the interpretation of matched biomarker and indoor media measurements.

BACKGROUND: The U.S. Environmental Protection Agency, under its exposure forecasting (ExpoCast) project, has developed new approach methodologies for ...

Precision neuroprotection in cardiac surgery: a structured narrative review of multimodal, data-driven perioperative care.

Neurological injury remains a major contributor to morbidity, mortality, and long-term cognitive decline in patients undergoing cardiac surgery, despi...

Biases of large language models in diagnosing Cushing's syndrome.

The diagnosis of endogenous Cushing's syndrome (CS) can be complicated and often delayed, given its low incidence (estimated globally at 1.8 cases to ...

Association between visceral adiposity index and delayed union/nonunion after tibial or femoral shaft fractures: a single-center retrospective cohort study.

BACKGROUND: Delayed union and nonunion remain clinically important complications after tibial and femoral shaft fractures. Although traditional risk f...

Interpretable integration of SEM and SVM for reliable thyroid nodule classification.

Hepatic Steatosis Severity Prediction in Nonobese Individuals: Machine Learning Model Development and Validation.

BACKGROUND: Steatotic liver disease affects 40% of nonobese individuals, but existing screening tools inadequately detect and stage disease severity i...

Interpretable machine learning and incremental prognostic model for all-cause and cardiovascular mortality in obesity with MASLD population: Comorbidity insights from triglyceride-glucose and its obesity-related derivatives.

OBJECTIVE: To investigate comorbid correlation and prognostic value of triglyceride-glucose (TyG) and its obesity-related derivatives for all-cause an...

Benchmarking deep learning models for laryngeal cancer staging using the LaryngealCT dataset.

Laryngeal cancer imaging research lacks standardised public datasets to enable reproducible deep learning (DL) model development. We present Laryngeal...

A Risk Score for Polycystic Ovary Syndrome Based on Meta-Analysis and Machine Learning of Gut Microbiota Signatures.

Polycystic Ovary Syndrome (PCOS) is a prevalent endocrine and metabolic disorder among reproductive-age women, in which emerging evidence suggests a s...

Body composition radiomics integrated with machine learning to predict prognosis in advanced NSCLC treated with first-line immunochemotherapy and concurrent radiotherapy.

OBJECTIVE: Adipose tissue is a highly heterogeneous and plastic endocrine and immune organ whose characteristics have been linked to prognosis in NSCL...

Pancreatic organoids and organoid-on-a-chip platforms: from disease modeling to precision therapy.

BACKGROUND: Pancreatic diseases, including diabetes, pancreatic ductal adenocarcinoma, pancreatitis, and cystic fibrosis, impose a substantial clinica...

Incorporating dietary information to enhance polygenic prediction models with applications to body mass index and type 2 diabetes.

BACKGROUND: Polygenic predictors can enhance screening for metabolism-related traits such as body mass index (BMI) and type 2 diabetes (T2D). However,...

Risk stratification for reoperation outcomes in recurrent or persistent papillary thyroid carcinoma: development and validation of a machine learning model.

BACKGROUNDS: Reoperation is a key therapeutic strategy for recurrent or persistent papillary thyroid carcinoma (PTC), but its outcomes remain highly h...

Large-scale transcriptomic data mining using explainable XGBoost and SHAP reveals shared biomarkers and molecular mechanisms between type-2 diabetes and triple-negative breast cancer for drug repurposing.

Type 2 diabetes (T2D) is considered as a risk factor of triple-negative breast cancer (TNBC). So, there is a significant chance of their co-existence....

Comparative predictive accuracy of machine learning versus traditional statistical methods for diabetes-related complications: A systematic review and analysis.

AIMS: To compare the predictive accuracy of machine learning models versus traditional statistical models for predicting and detecting long-term compl...