Pathology

Single-Cell Transcriptomic Analysis of Chemotherapy-Induced Changes in Osteosarcoma With a Pyroptosis-Related Gene-Based Prognostic Model.

Osteosarcoma, the most common primary malignant bone tumour, presents significant treatment challenges due to its complex tumour microenvironment and the development of chemoresistance. This study employs single-cell transcriptomics to investigate chemotherapy-induced changes in osteosarcoma at both the cellular and molecular levels. Single-cell RNA sequencing data were analysed to identify cell s...

NR3C1 Modulates Wnt Signalling to Influence the Invasiveness and Immune Features of Nonfunctioning Invasive Pituitary Adenomas.

Pituitary adenomas (PAs) are common intracranial tumours, and invasiveness in nonfunctioning invasive pituitary adenomas (NIPAs) predicts poor prognosis. The molecular mechanisms driving this phenotype remain unclear. This study explored the role of nuclear receptor subfamily 3 group C member 1 (NR3C1) in NIPA invasiveness and its regulation of Wnt signalling. mRNA expression profiles of 32 PA sam...

MorphoNet: Morphological sub-region-based structure learning for WSI analysis.

Representation learning of Whole slide image (WSI) is fundamental to computational pathology, enabling tasks such as tumor subtyping, survival predict...

Neurosurgical Application of Artificial Intelligence in Pediatric Neuro-Oncology.

Pediatric neuro-oncology is a critical field of neurosurgery, representing the leading cause of disease-related mortality in children. Despite its rar...

SKOOTS: Skeleton-Oriented Object Segmentation for Mitochondria in High-Resolution Cochlear EM Datasets.

Segmenting individual instances of mitochondria from imaging datasets can provide rich quantitative information, but manual segmentation is prohibitiv...

Crashworthiness and technoeconomic assessment of bioinspired GFRP PP tubes using experiments numerical modeling and artificial neural networks.

This study presents a comprehensive experimental and computational investigation of the quasi-static axial compression behavior of glass fiber-reinfor...

A deep joint-learning proteomics model for diagnosis of six conditions associated with dementia.

Co-pathology is a common feature of neurodegenerative diseases that complicates diagnosis, treatment and clinical management. However, sensitive, spec...

A Multitask Deep Learning Approach for Tumor Segmentation and Morphological Quantification in Acral Melanoma Whole-Slide Images.

This study presents an integrated multitask deep learning framework for the automated analysis of acral melanoma from whole-slide images (WSIs). We co...

Single-Cell Sequencing and Transcriptomic Sequencing Exploration of the Mechanisms Governing Lactylation in Mitochondrial Diseases.

Mitochondrial diseases (MDs) consist of a heterogeneous spectrum of disorders resulting from mutations in either nuclear or mitochondrial DNA, disrupt...

Recent advances in corneal specular microscopy image analysis through artificial intelligence.

Although conventional automated analysis of corneal specular microscopy images has historically been limited by reproducibility challenges in the pres...

Inflammatory-nutritional biomarker scores and risk of malignant effusion in a pan-cancer cohort of 68,916 patients: Nonlinear associations and machine learning validation.

PURPOSE: Inflammatory-nutritional biomarker scores derived from routine blood tests have established prognostic value in cancer, yet their association...

High-accuracy instance segmentation of cellular structures in head and neck squamous cell carcinoma histopathology using a cyto R-CNN deep learning framework.

This study develops a deep learning-based model to automate the instance segmentation of nuclei and whole cells in hematoxylin and eosin-stained head ...

Recent advances in machine learning-enhanced extracellular vesicle omics for oncology.

Extracellular vesicles (EVs) are nanoscale, membrane-bound particles that carry nucleic acids, proteins, metabolites, and lipids. Their omics profiles...

Transformer-based super-resolution lung CT images improve visualization of multiple diseases.

OBJECTIVES: To assess the Transformer-based Swin2SR model for super-resolution (SR) enhancement of lung CT images and its clinical potential. METHODS:...

Developing and validating ultrasound-based machine-learning models incorporating radiomics features to predict malignancy in adnexal masses.

OBJECTIVE: The primary aim of this study was to develop and internally validate ultrasound-based radiomics models to discriminate between all types of...

Digital holographic microscopy: use of artificial intelligence to enable rapid blood culture Gram stain digitization and interpretation.

Gram staining is one of the most commonly performed tests in the clinical microbiology laboratory. Results are used to both guide empiric antimicrobia...

Concurrent AI-human interaction in prostate cancer MRI interpretation: More hype than help?

OBJECTIVE: We evaluated a commercial artificial intelligence (AI) system as a concurrent decision-support tool for clinically significant prostate can...

Development of Machine Learning Models for Predicting Prostate Cancer in Biopsy Candidates Using Prostate-Specific Antigen, Magnetic Resonance Imaging, and Hematologic Parameters.

INTRODUCTION: Prostate-specific antigen (PSA) alone is insufficient for the diagnosis of prostate cancer (PCa), particularly within the gray zone rang...

Thermal Decomposition Simulations of Hydroxylamine Pentazolate With Deep Neural Network Potential.

Against the backdrop of insufficient research into the microscopic reaction mechanisms of pentazole anion ( N 5 - $$ {\mathrm{N}}_5^{-} $$ ) salts...

IGSF9 drives malignant transformation and predicts early-stage lung adenocarcinoma in integrated transcriptomic analyses.

PURPOSE: Lung adenocarcinoma (LUAD), the most common subtype of non-small cell lung cancer (NSCLC), often presents with mild or absent symptoms in its...