AIMC Topic: Humans

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Automated machine learning profiling with MAP-HR for quantifying homologous recombination foci in patient samples.

NAR cancer
Accurate visualization and quantification of homologous recombination (HR)-associated foci in readily available patient samples are critical for identifying patients with HR deficiency (HRD) when they present for care to guide polyADP ribose polymera...

Toward the Development of a Novel Newborn Screening Modality: In-Depth Nontargeted Proteome Analysis of Dried Blood Spots with a Robotic Pipeline Using Low-Cost Iron Powders.

Analytical chemistry
We developed a simple protein extraction method for dried blood spots (DBS) that potentially meets the throughput required for newborn screening (NBS) and optimizes nontargeted proteomic analysis in combination with liquid chromatography coupled mass...

Recent Development of Methods and Techniques in the Detection of Mycotoxins in Agricultural Products.

Journal of agricultural and food chemistry
Mycotoxins are produced by fungi and possess cytotoxic properties that cause extensive cellular damage. Mycotoxins pose a significant threat to the harvesting and storage of crops as well as potential carcinogenic, teratogenic, and mutagenic risks to...

Circulating cell-free RNA signatures for the characterization and diagnosis of myalgic encephalomyelitis/chronic fatigue syndrome.

Proceedings of the National Academy of Sciences of the United States of America
People living with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) experience heterogeneous and debilitating symptoms that lack sufficient biological explanation, compounded by the absence of accurate, noninvasive diagnostic tools. To add...

Building simplified cancer subtyping and prediction models with glycan gene signatures.

Cell reports methods
We identified a gene panel comprising 71 glycosyltransferases (GTs) that alter glycan patterns on cancer cells as they become more virulent. When these cancer-pattern GTs (CPGTs) were run through an algorithm trained on The Cancer Genome Atlas, they ...

A review of machine learning applications in heart health.

Biomedical engineering online
The application of machine learning in healthcare continues to gain attention as researchers attempt to prove its potential for the enhancement of diagnosis and prognosis accuracy. Although many applications of machine learning have been well studied...

Risk factors for tuberculosis treatment outcomes: a statistical learning-based exploration using the SINAN database with incomplete observations.

BMC medical informatics and decision making
BACKGROUND: Understanding early predictors of treatment outcomes allows better outcome prediction and resource allocation for efficient tuberculosis (TB) management.

Machine learning models for the prediction of preclinical coal workers' pneumoconiosis: integrating CT radiomics and occupational health surveillance records.

Journal of translational medicine
OBJECTIVES: This study aims to integrate CT imaging with occupational health surveillance data to construct a multimodal model for preclinical CWP identification and individualized risk evaluation.

An explainable predictive machine learning model for axillary lymph node metastasis in breast cancer based on multimodal data: A retrospective single-center study.

Journal of translational medicine
OBJECTIVE: To develop explainable machine learning models that integrate multimodal imaging and pathological biomarkers to predict axillary lymph node metastasis (ALNM) in breast cancer patients and assess their clinical utility.

Early detection of vascular catheter-associated infections employing supervised machine learning - a case study in Lleida region.

BMC medical informatics and decision making
Healthcare-associated infections (HAIs), particularly Vascular Catheter-Associated Infections (VCAIs), are a significant concern, accounting for over 7% of all infections and are often linked to medical devices. Early detection of VCAIs before invasi...