COVID-19

Integrated genomic profiling identifies predictive biomarkers for neoadjuvant therapy response in Chinese breast cancer patients.

Neoadjuvant therapy (NAT) has emerged as a standard treatment strategy for locally advanced breast cancer (BC), yet robust biomarkers for response prediction remain elusive. Here, we established a real-world NAT cohort of 1161 Chinese BC patients, including 1145 cases with matched clinicopathological data and targeted sequencing, to systematically evaluate genomic features associated with NAT outc...

Construction and optimization of machine learning models based on remote sensing inversion of NH3-N and TP pollution in the Qiantang River Basin, China.

Rapid industrialization, urbanization, and intensive agriculture have worsened river basin water pollution globally, including in China. Traditional water pollution monitoring, though widely used, is time-consuming, limited in coverage, and lacking in spatiotemporal continuity, hindering dynamic water quality parameters (WQPs) analysis. As a key water source in the Yangtze River Delta, the Qiantan...

Explainable machine learning integrating metabolic and inflammatory signatures for personalized prognosis in resected intrahepatic cholangiocarcinoma.

BACKGROUND: The precise prognostic stratification of intrahepatic cholangiocarcinoma (iCCA) remains challenging. We aimed to develop and validate inte...

Chemometric and learning-based multivariate models for quantifying a challenging quaternary mixture of bupropion, dextromethorphan, and their related impurities by UV-Vis spectrophotometry.

This study presents a robust, green, sustainable and time-efficient approach for the simultaneous determination of Bupropion HCl (BUP) and Dextrometho...

[Expert consensus on optimizing the multidisciplinary clinical pathways and management models for pulmonary function testing].

The promotion and application of pulmonary function tests (PFTs) in China have achieved preliminary success;however, numerous deficiencies persist in ...

Identifying mitochondria-related signatures for tuberculosis diagnosis through machine learning on single-cell transcriptomics data and experimental verification.

Tuberculosis (TB) remains a major cause of infectious disease mortality. Early diagnosis is crucial for curbing transmission and initiating timely tre...

Accelerated long-read variant calling with Clair3 for whole-genome sequencing.

SUMMARY: The rapid growth of genomic data and increasing adoption of long-read sequencing technologies have rendered variant calling one of the most c...

The emergence of superficial dermatophytosis due to Trichophyton indotineae and Trichophyton mentagrophytes genotypes VII and II* in New York: a need for comprehensive testing approaches.

We report a rapid increase in dermatophyte infections in New York City and surrounding counties from 2022 to 2024. Reported cases include 135 of Trich...

AI-Designed Peptides as Tools for Biochemistry.

Peptides occupy a unique niche as biochemical tools: they are small, modular reagents capable of perturbing protein function with a precision that is ...

Enhanced Quantitative Phosphocreatine MR Imaging of Skeletal Muscle Using a Global-Local Two-Branch Deep Learning Model.

PURPOSE: Phosphocreatine (PCr) is an essential marker of muscle metabolism, and accurate quantification of its (fs) and its exchange rate (ksw) is ess...

Smart diagnostics: Leveraging artificial intelligence for accurate at-home COVID-19 testing.

Reverse transcriptase polymerase chain reaction (RT-PCR), the reference standard for COVID-19 diagnosis, is time-consuming, resource-intensive, and co...

Regulatory risk loci link disrupted androgen response to the pathophysiology of polycystic ovary syndrome.

A major challenge in deciphering the complex genetic landscape of polycystic ovary syndrome (PCOS) lies in the limited understanding of how susceptibi...

Improved tumor-only variant calling and mutation burden estimation with VarNet-T.

Somatic variant calling algorithms typically detect mutations in cancer genomes by comparing sequence data from a tumor sample against a matched norma...

Fine-grained and multi-pattern anti-nuclear antibody recognition: A new dataset and framework.

Indirect immunofluorescence (IIF) staining imaging on human epithelial cell (HEp-2) is a standard technique for anti-nuclear antibodies (ANA) screenin...

Machine learning-based programmed cell death gene signature for prognosis and drug sensitivity in breast cancer.

Breast cancer (BRCA) heterogeneity necessitates robust prognostic biomarkers. Programmed cell death (PCD) serves a key role in tumor progression and t...

Linking rare variants to cell-type function in profound autism with brain transcriptomics and foundation models.

Genetic association studies have identified numerous genes harboring protein-disrupting variants in individuals with profound autism, but identifying ...

"Unpatients" in haemostasis and thrombosis. A yardstick of current paths in healthy carriers of a genetic susceptibility.

"Unpatients" are health-conscious individuals -carrying a genetic susceptibility whose impact is unclear with respect to the risk of a clinical event-...

Digital SERS Nanoprobe Immunoassay With Single-Particle Sensing Resolution for Detection of Pancreatic Cancer Biomarkers From Clinical Blood Samples.

Surface-enhanced Raman scattering (SERS) nanoprobe-based immunoassay is an emerging liquid biopsy modality for the detection of blood biomarkers. Howe...

Machine-Learning Source Apportionment of Particulate Pollution Aids Urban Emission Regulations.

Reducing urban particulate pollution is essential for improving air quality and reducing health risks. Achieving this objective requires accurate iden...

Radiopathomic Graph Deep Learning for Multiscale Spatial-Contextual Modeling of Intratumoral Heterogeneity to Predict Breast Cancer Response to Neoadjuvant Therapy.

Purpose To develop an explainable radio-pathomic graph deep-learning (RPGDL) system for multiscale spatial-contextual modeling of intratumoral heterog...