Pathology

Quantitative characterization of crack coalescence mechanisms in granite at the mesoscopic scale using deep learning.

This study quantitatively characterized the crack coalescence mechanisms in granite at the mesoscopic scale using deep learning and scanning electron microscopy (SEM). The meso-morphology of the crack surface created by crack coalescence was acquired by SEM. The fractal dimension of the morphology image was computed. Mesoscopic tensile and shear morphologies on the crack surface were quantitativel...

Adversarial sample generation technology combining benign salient areas and improved GAN in white-box and black-box attack scenarios.

A end-to-end malware visual security assessment and defense framework is proposed to address the serious vulnerability faced by malware visual classification models in combating escape attacks. Firstly, in a white box scenario, an end-to-end attack method based on benign salient region guidance (BREAM) is proposed, which achieves high concealment escape while maintaining the executable functionali...

Role of systemic and epicardial adipose tissue in cardiometabolic disease.

Adipose tissue is increasingly recognized as an immunological and metabolic organ composed of multiple specialized depots that exert diverse effects o...

Robust quantification of ICG fluorescence perfusion in neonatal bowel surgery via deep point tracking.

PURPOSE: Indocyanine green (ICG) fluorescence imaging is increasingly used for intraoperative bowel perfusion assessment in neonatal surgery. However,...

Real-time identification of malignant breast tissue during electrosurgical resection.

Achieving complete resection of breast cancer with clear margins remains a significant surgical challenge, particularly for infiltrating subtypes, whe...

Performance and clinical utility of image-based machine learning models for the diagnosis of neglected tropical diseases in low- and middle-income countries: a systematic review.

BACKGROUND: Neglected tropical diseases (NTDs) disproportionately affect populations in low- and middle-income countries (LMICs), where diagnostic cap...

Integrating machine learning and retrospective cohort to explore the effects of AEDs on the prognosis of LGG and identify related molecular targets.

Antiepileptic drugs (AEDs) were frequently employed in glioma patients, especially those with low-grade glioma (LGG), in which epilepsy manifested in ...

Metabolic SERS Profiling under Triple-Coupled Hotspots Aided by Machine Learning for Precise Identification of Gastrointestinal Malignancies.

Serum metabolic analysis based on label-free SERS fingerprints represents a highly promising technique for the detection of tumors. However, the insuf...

Deep Learning-Driven 3D Histopathology Method: A Pipeline for Cellular-Resolution Myocarditis Analysis.

Infiltrative cardiovascular diseases are characterized by complex spatial distributions of abnormal tissue that hinder accurate diagnosis and therapeu...

Functional, molecular, and digital measurements of biological age.

The reality of an aging population demands a deeper understanding of aging as a biological process, rather than as a chronological descriptor. Chronol...

Endometriosis and the mental health burden: a registry-based study of redeemed prescriptions and psychiatric hospital contacts before and after diagnosis of endometriosis.

STUDY QUESTION: Do women with endometriosis have a higher use of antidepressants and anxiolytics, and more frequent psychiatric hospital contacts due ...

Machine learning in the diagnosis of Hirschsprung disease: a systematic review and meta-analysis.

PURPOSE: To evaluate current evidence on machine learning (ML) for the diagnosis of Hirschsprung disease (HSCR) and summarize its diagnostic performan...

Deep learning-based automated detection of oral squamous cell carcinoma in histopathological images: a comparative study of five CNN architectures.

Oral squamous cell carcinoma (OSCC) is the most common malignancy of the oral cavity, and early diagnosis plays a crucial role in improving patient pr...

A radio-pathological fusion model for predicting PD-L1 expression and immunotherapy response in non-small cell lung cancer.

OBJECTIVE: This study aims to construct a multimodal fusion model (FM) based on CT and hematoxylin and eosin (H&E) stained slices to predict the PD-L1...

E2E-OCT: end-to-end joint learning model using optical coherence tomography images for vocal cord leukoplakia diagnosis.

In our previous study, a home-built handheld OCT system was used to collect OCT images in vocal cord leukoplakia. First, 383 valid OCT images were col...

An Aptamer-Engineered Phenotyping System for Extracellular Vesicles Based on Honeycomb-like Melamine-Formaldehyde Microspheres and Carbon Dot Nanozymes.

Molecular phenotyping of extracellular vesicles (EVs) holds promise for noninvasive cancer diagnosis; however, current methodologies encounter limitat...

A myeloid immunosuppressive phenotype defines primary refractoriness to atezolizumab Plus bevacizumab in hepatocellular caarcinoma.

BACKGROUND: Despite improved outcomes with atezolizumab plus bevacizumab (A+B) in hepatocellular carcinoma (HCC), primary refractoriness (PRef), chara...

Multi-omics body-fluid biomarkers in immunoglobulin a nephropathy: Analytical Validation, standardization, and clinical chemistry translation of diagnostic and prognostic Analytes.

IgA nephropathy (IgAN) is the most prevalent primary glomerulonephritis worldwide. Although optimized supportive therapy is administered to these pati...

Multimodal multitask deep learning for grading management system in non-small cell lung cancer.

Accurate histologic subtyping, tumor node metastasis classification (TNM) staging and prognostic assessment are central to clinical management of non-...

Robust artificial intelligence frameworks for lung cancer subtyping and malignancy detection on thoracic CT.

Accurate characterization of thoracic malignancies on computed tomography (CT) remains challenging because histological subtype differentiation and no...