Pathology

Supervised artificial intelligence classification of clinker processing conditions by optical microscopy.

Optical microscopy is known to be a powerful technique for the characterization of industrial clinkers, allowing for obtaining valuable information on many aspects of the production process. The time required for the preparation of samples and collection, and interpretation of pictures strongly limits its application on a large scale. Notably, the analysis of samples requires skilled operators and...

Mapping ovarian cellular and molecular landscape across the lifespan of women: a scoping review.

BACKGROUND: With growing interest in ART, fertility preservation, and postmenopausal health of women, reproductive medicine is increasingly focused on characterizing oocytes and ovarian tissue composition, as well as understanding the molecular mechanisms that guide ovarian function throughout its lifecycle. High-throughput omics technologies have enabled the characterization of different molecula...

Protective Effects of p-Coumaric Acid Against Lead-Induced Hepatic Oxidative Stress, Inflammation, and Apoptosis: Integrated Molecular and Machine Learning-Based Analysis.

Lead (Pb) exposure is a major environmental health concern that induces hepatic injury through oxidative stress, inflammation, and apoptosis. This stu...

Current Perspectives on Thyroid Nodule Evaluation: Integrating Cytomorphology, Ultrasound Risk Stratification, and Molecular Testing.

Fine-needle aspiration biopsy (FNAB) remains the cornerstone of preoperative evaluation of thyroid nodules and represents one of the most enduring and...

Artificial Intelligence in Digital Pathology for Prostate Cancer Detection: FDA Clearance and Real-World Implementation.

Artificial intelligence (AI) has emerged as a promising adjunct in surgical pathology, particularly in the diagnosis of prostate cancer, where variabi...

A federated attention-based stacked LSTM framework for interpretable malaria diagnosis under simulated non-IID federated conditions.

Malaria remains a major global health burden, particularly in low-resource regions where microscopic diagnosis relies heavily on expert interpretation...

Predicting placenta accreta spectrum antenatally: a deep learning-radiomics dual-model based on intraoperative clinical and histopathologic criteria.

PURPOSE: Definitive diagnosis of placenta accreta spectrum (PAS) disorders occurs at delivery by using combined intraoperative clinical and histopatho...

Glycosylation-related gene risk model and functional validation of OSTC and TUBA1C in lung adenocarcinoma.

INTRODUCTION: OSTC and TUBA1C drive the malignant progression of lung adenocarcinoma (LUAD) through the modulation of N-glycosylation and the PI3K/AKT...

Towards nano-mechanical simulations of ceramics containing realistic defects via machine-learning potentials: the example of TiB2.

Transition metal diboride (TMB2) ceramics combine high hardness with excellent thermal and chemical stability, making them attractive for protective c...

Follicle Identification in Primate Ovaries Via Machine Learning†.

Studies of ovarian health and aging rely on estimates of the ovarian reserve, i.e. the number of healthy ovarian follicles. We present a machine learn...

Clinical neuroanatomy teaching at Peking University Health Science Center: Evolution, current practice, and future direction.

Neuroanatomy has been considered one of the most challenging subjects in medical education, often leading to "neurophobia" and discouraging students f...

The expanding role of Artificial intelligence in medical diagnosis: from molecular diagnostics and multi-omics to digital health and medical imaging.

INTRODUCTION: The use of Artificial Intelligence (AI), especially Machine learning (ML) and Deep learning (DL), has led to a major shift in medical di...

Noble Metal-Metal Oxide Nanohybrids as a High-Performance LDI-MS Matrix for Machine Learning-Driven Metabolic Diagnosis of Esophageal Cancer.

Esophageal cancer represents a global health challenge with a notably high incidence and poor prognosis, necessitating the development of rapid, nonin...

A Generative Multi-modal Deep Learning Framework for Non-invasive Prediction of Ki-67 Expression in Papillary Thyroid Microcarcinoma Using Preoperative Ultrasound.

Accurate assessment of Ki-67 expression plays a critical role in the risk stratification and prognosis of papillary thyroid microcarcinoma (PTMC). How...

Precision Oncology in Northern Germany - Integrating Multi-Omics and AI to Support Evidence-Based Molecular Tumor Boards.

The expanding availability of multi-omics profiling and advances in artificial intelli-gence (AI) and machine learning (ML) are changing precision onc...

Artificial intelligence-assisted detection and optical differentiation of colorectal lesions in Lynch syndrome surveillance (CADLY2): a multicentre, open-label, randomised controlled superiority trial.

BACKGROUND: Artificial intelligence (AI)-based computer-aided detection (CADe) systems improve adenoma detection in average-risk colorectal cancer scr...

The PAR dataset: Prostate biopsy whole slide images from an underrepresented Middle Eastern population.

Artificial intelligence (AI) is increasingly used in digital pathology. Publicly available histopathology datasets remain scarce, and those that do ex...

A Noise-Aware Robustness Evaluation Framework for Breast Cancer Classification in Ultrasound Imaging.

Accurate detection of breast cancer is essential, as it remains a leading cause of cancer-related mortality worldwide. Ultrasound is adopted due to it...

From stars to molecules: AI guided device-agnostic super-resolution imaging.

Super-resolution imaging has revolutionized the study of systems ranging from molecular structures to distant galaxies. However, existing super-resolu...

A multimodal optical microscopy dataset for characterizing cytoskeletal organization.

Optical microscopy combined with machine learning for image analysis is seeing increasing use for studying cell morphology and the organization of sub...