AI Medical Compendium

Interpretable machine learning prediction of biochar characteristics based on laser-Raman spectroscopy.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Jan 17, 2026

The precise detection of biochar characteristics serves as a critical determinant in both production process optimization and targeted application selection. In this study, interpretable machine learning prediction models based on Raman spectroscopy,... read more

Integrating hybrid modeling and high throughput screening: A modular process development platform for flowthrough chromatography.

Journal of chromatography. A Jan 17, 2026

As the biopharmaceutical industry continues to advance towards intensified manufacturing and increasingly complex therapeutic pipelines, there is a growing demand for more efficient processes and process development strategies. Flowthrough and fronta... read more

Transfer learning from 2D natural images to 4D fMRI brain images via geometric mapping.

Medical image analysis Jan 17, 2026

Functional magnetic resonance imaging (fMRI) allows real-time observation of brain activity through blood oxygen level-dependent (BOLD) signals and is extensively used in studies related to sex classification, age estimation, behavioral measurements ... read more

CATERPillar: a flexible framework for generating white matter numerical substrates with incorporated glial cells.

Medical image analysis Jan 17, 2026

Monte Carlo diffusion simulations in numerical substrates are valuable for exploring the sensitivity and specificity of the diffusion MRI (dMRI) signal to realistic cell microstructure features. A crucial component of such simulations is the use of n... read more

FRM-PTQ: Feature relationship matching enhanced low-bit post-training quantization for large language models.

Neural networks : the official journal of the International Neural Network Society Jan 17, 2026

Post-Training Quantization (PTQ) has emerged as an effective approach to reduce memory and computational demands during LLMs inference. However, existing PTQ methods are highly sensitive to ultra-low-bit quantization with significant performance loss... read more

A comprehensive biomechanical phenotyping framework for diabetic foot ulcer risk stratification using multi-modal gait analysis and machine learning.

Clinical biomechanics (Bristol, Avon) Jan 17, 2026

BACKGROUND: Current diabetic foot ulcer risk assessment methods lack precision in identifying high-risk biomechanical phenotypes. This study aimed to develop a comprehensive biomechanical profiling framework integrating multi-modal gait analysis with... read more

Development and validation of an interpretable machine learning model for non-invasive screening of precancerous gastric lesions using symptom and lifestyle data: a multicentre cohort study.

EClinicalMedicine Jan 17, 2026

BACKGROUND: Precancerous gastric lesions (PLGC) are a critical stage in gastric cancer progression, where timely intervention can substantially reduce mortality. However, current screening strategies are predominantly endoscopic, which are invasive, ... read more

Inter-rater reliability of a classification systems for distal radius fractures using radiology text and x-rays: what really matters?

European journal of radiology Jan 17, 2026

PURPOSE: This study used different metrics to assess the reliability of radiology text and images in Distal Radial Fractures (DRF) classifications using classifiers with varying levels of experience. METHODS: A random sample of 534 patients (16 + yea... read more

Integrating GC-MS, high-throughput sequencing, and machine learning to elucidate flavor development and microbial succession in Italian-style dry-cured ham.

Food research international (Ottawa, Ont.) Jan 17, 2026

This study investigated effects of ripening on physicochemical, aroma profiles, and microbial succession in Italian-style dry-cured ham (ISDCH). Results showed that moisture content and pH decreased significantly during ripening, while the proteolyti... read more

Intelligent recognition of the fermentation stage of baijiu based on multi-dimensional data fusion and interpretable machine learning.