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A predictive model for pathological complete response in neoadjuvant-treated breast cancer: integrating pretreatment clinicopathologic and contrast-enhanced ultrasound characteristics via interpretable machine learning.

BACKGROUND: The ability to predict pathological complete response (pCR) prior to neoadjuvant therapy (NAT) would inform and facilitate tailored therapeutic regimens for patients with breast cancer. This study aimed to develop and internally validate an interpretable machine-learning model for pretreatment pCR prediction using routinely available clinicopathologic variables, hematologic indices, an...

Routine biomarkers map response probability in HER2-positive breast cancer treated with neoadjuvant chemotherapy and dual HER2 blockade.

BACKGROUND: Pathologic complete response (pCR) after neoadjuvant chemotherapy and dual HER2 blockade is associated with improved outcomes in HER2-positive breast cancer, but treatment response remains heterogeneous. We aimed to develop and externally validate an interpretable model based on routine clinicopathological biomarkers to predict pCR and identify clinically meaningful response states. ME...

Data-driven methods allow prediction of utility of DNA rework.

In forensic DNA casework, complex trace samples may benefit from rework procedures involving additional PCR replicates and lowered analytical threshol...

Forging New Pathways in Oncology: Strategic Insights from the 17th Annual Frontiers in Cancer Science Conference.

The 17th Annual Frontiers in Cancer Science (FCS) conference (2025) highlighted the convergence of multiomics, computational biology, and ancestry-spe...

Deep learning-enabled microfluidic digital PCR platform for efficient seven-color quantification.

Digital PCR (dPCR), as a high-sensitivity technology for absolute nucleic acid quantification, holds significant value in biomedical research and envi...

Admission-Based Machine-Learning Models for Predicting Mechanical Ventilation and Mortality in Fibrotic Interstitial Lung Disease: A Multicenter Cohort Study with External Validation.

BACKGROUND: Admission-based risk stratification tools are limited for hospitalized patients with fibrotic interstitial lung disease (F-ILD). AIMS: To ...

Diagnostic challenges in re-emerging rickettsioses: why current tools fall short.

SUMMARYRickettsial diseases, encompassing scrub typhus, spotted fever group rickettsioses, and typhus group rickettsioses, represent a significant and...

From manual scoring to machine learning: recent developments in image-based arbuscular mycorrhizal fungi root quantification.

Selecting an appropriate method to quantify arbuscular mycorrhizal fungi (AMF) colonization in plants can be challenging, particularly for non-experts...

Early Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer using a Longitudinal US-based Stack-model.

RATIONALE AND OBJECTIVES: To evaluate the diagnostic performance of a longitudinal ultrasound (US)-based stack-model for early prediction of pathologi...

Lung Recruitment Maneuvers During Invasive Mechanical Ventilation: Single-Center Retrospective PICU Cohort Study, 2016-2023.

OBJECTIVES: Recruitment maneuvers (RMs) during invasive mechanical ventilation (IMV) for acute hypoxemic respiratory failure are not characterized in ...

Enhancing the positive predictive value of early-stage ovarian cancer detection using a two-step machine learning framework.

BACKGROUND: Early detection of epithelial ovarian cancer (EOC) remains a major clinical challenge. Although serum tumor markers are widely used for de...

From heterogeneity to mechanism: a systems-level analysis pinpoints the S100A8/A9 axis in colorectal cancer.

BACKGROUND: The functional roles of the S100 protein family in colorectal cancer (CRC) are enigmatic, marked by widespread dysregulation but limited p...

Identification of PRKCB, NLRC4, and TNFSF10 as Key Regulators of the Lipid Metabolism-Autophagy Network in Atherosclerosis.

BACKGROUND: The occurrence and development of atherosclerosis (AS) are closely related to disorders of lipid metabolism and dysfunction of autophagy. ...

ADC Target Profiling in NSCLC: Generalizable AI Separates TROP-2 and cMET Phenotypes.

PURPOSE: Antibody-drug conjugates (ADC) targeting trophoblast cell surface antigen 2 (TROP-2) and cMET are entering clinical trials in non-small cell ...

Integrative machine learning and CRISPR/Cas9 analysis reveals the role of APOE in lipid metabolism-associated kidney fibrosis.

Kidney fibrosis, characterized by excessive extracellular matrix deposition and progressive renal tissue remodeling, frequently culminates in End-stag...

Post-stroke rehabilitation in inflammatory rheumatic diseases: outcome measures, digital tools, and patient education perspectives.

Inflammatory rheumatic diseases (IRDs) represent a significant risk factor for cerebrovascular events, independent of traditional cardiovascular risk ...

Physics-constrained dual-domain network for CBCT reconstruction from orthogonal X-rays in gynecologic radiotherapy.

BACKGROUND: Daily cone-beam computed tomography (CBCT) is widely used for image-guided radiotherapy (IGRT) in gynecologic (GYN) cancer to verify patie...

DeepMethylation: A deep learning framework for tissue-specific DNA methylation prediction and functional variant annotation.

DNA methylation is a key epigenetic modification that regulates gene expression and plays a vital role in cell differentiation, development, and tumor...

An empirical study of defect clustering in deep neural networks and its implications for testing.

DNN decision logic is vulnerable to adversarial attacks, making robustness testing essential for safety-critical intelligent systems. Existing coverag...

Deep learning classification of nasal anatomical variants on computed tomography for endoscopic sinus surgery.

Endoscopic sinus surgery (ESS) carries risks such as vision loss and intracranial injury due to the proximity of critical structures and unrecognised ...