Pathology

Opportunities for deep learning techniques to advance the histological analysis of preclinical models of osteoarthritis beyond ordinal rank systems.

Tools for assessing disease progression are needed to identify and confirm new mechanisms driving osteoarthritis (OA) progression and guide therapeutic development. This review focuses on the advantages and feasibility of leveraging deep learning techniques for quantitative analysis of rodent knee histology in preclinical OA models. Previous studies using radiographs and micro-CT have used deep-le...

Benign-by-design chemistry: Reinventing ligand-based drug design at the edge of AI.

Ligand-based drug design (LBDD) has long driven therapeutic innovation; however, its traditional potency-centered paradigm often oversimplifies biological complexity. Advances in artificial intelligence (AI) now enable multiobjective strategies that integrate polypharmacology, safety and environmental sustainability. Despite this progress, a unified framework that systematically incorporates these...

Prostate Cancer Detection on Micro-Ultrasound Raw Data Using a Deep Learning Neural Network.

BACKGROUND: Micro-ultrasound (micro-US) is a clinically available novel high-resolution imaging technology for guiding prostate biopsies. However, cli...

Risk classification of oral potentially malignant disorders (OPMDs) using explainable machine learning through a community-based cross-sectional study in rural India.

BACKGROUND: Oral cancer is a major public health problem among the population of India. In rural setting whereby there is exposure to high-risk factor...

Beyond Weight Loss: Obesogenic Memory as Biological Hysteresis in Adipose Tissue Revealed by AI Semantic Mapping With an Exportable Core Corpus.

BACKGROUND: Obesity is commonly viewed as a reversible condition primarily driven by excess body weight. Increasing evidence, however, suggests that a...

Deep learning model for noninvasive prediction of Ki-67 expression and prognostic stratification in breast cancer: a multicenter retrospective study.

OBJECTIVE: Ki-67 correlates with prognosis for patients with breast cancer. However, the evaluation of Ki-67expression relies on pathological analysis...

Deep learning approach for sex determination using medial clavicle histomorphology.

Sex determination from skeletal remains presents significant challenges, particularly when bones are damaged or incomplete. In such cases, histomorpho...

Deep learning-powered quantification of endocrine cells and CD3+ T cells in the natural history of type 1 diabetes.

AIMS/HYPOTHESIS: Histopathological analysis in type 1 diabetes presents challenges in achieving precise characterisation with cellular quantification ...

Artificial intelligence algorithm for real-time detection and counting of Trypanosoma cruzi parasites using smartphone microscopy.

Chagas disease affects 6-7 million people worldwide and causes approximately 12,000 deaths annually. Diagnostic methods vary by disease stage, with se...

Autoencoder/RandomForest-TabPFN for Cross-Cancer Metabolomics: Prostate and Breast Cancer Diagnosis Using Paper Spray and Ion Mobility-Mass Spectrometry Techniques.

Accurate and rapid disease diagnosis, particularly in prostate cancer (PC) and breast cancer (BC), is critical for early intervention and improved pat...

Application of hiPSCs for tissue modeling and therapy: are we on track?

Stem cells are key for development of disease modeling and therapies. While promising, however, current application of cutting-edge hiPSC technologies...

Artificial intelligence in cancer diagnostics: a primer for clinicians and scientists.

Artificial Intelligence (AI) is rapidly transforming cancer care by enabling healthcare teams to make more accurate diagnoses, predict responses to th...

Pancreatic cancer.

Pancreatic ductal adenocarcinoma remains one of the deadliest malignancies, characterized by late diagnosis, aggressive biology and limited therapeuti...

Physics-informed neural networks for predicting laser-tissue interaction in maxillofacial reconstruction surgery.

This study employs Physics-Informed Neural Networks (PINNs) to simulate the thermal dynamics of biological tissue under laser irradiation by embedding...

Integrated multi-omics and single-cell analyses identify metabolic heterogeneity and therapeutic vulnerabilities in medullary thyroid cancer.

BACKGROUND: Medullary thyroid cancer (MTC) is a heterogeneous and aggressive malignancy with limited therapeutic options. Metabolic reprogramming, a h...

Machine learning the order-disorder Jahn-Teller transition in LaMnO3.

We investigate the Jahn-Teller structural phase transition in LaMnO3 at TJT ≃ 750 K using molecular dynamics simulations based on machine-learning for...

Identification of a mitochondrial biomarker signature linking neuroinflammation to neuronal dysfunction in spinal cord injury.

To address the lack of reliable biomarkers for mitochondrial dysfunction that drives secondary injury in spinal cord injury (SCI), this study aimed to...

Comparison of automated robotic arm-assisted PET/computed tomography-guided metabolic bone marrow biopsy with conventional bone marrow trephine biopsy for evaluating bone marrow infiltration in newly diagnosed lymphoma.

OBJECTIVES: Bone marrow involvement (BMI) upstages lymphoma and influences prognosis and treatment. Conventional bone marrow trephine biopsy (BMTB) ma...

Multi-omics identification of aging-related diagnostic genes and therapeutic targets in renal fibrosis.

BACKGROUND: Renal fibrosis is a pathological hallmark of progressive chronic kidney disease, partly driven by aging-related molecular alterations. How...

Multi-omics data mining combined with experimental validation reveals ferroptosis- and autophagy-associated hub genes as diagnostic candidates and immune modulators in atherosclerosis.

BACKGROUND: Atherosclerosis (AS) is a chronic inflammatory vascular disease that can lead to severe cardiovascular events. Ferroptosis and autophagy h...