Leukemia

An interpretable deep learning biomarker for prognostication and prediction of adjuvant chemotherapy benefit in gastric cancer.

Prognostic stratification in gastric cancer (GC) currently relies on the tumour-node-metastasis (TNM) staging system, which incompletely captures tumour heterogeneity. Routine haematoxylin and eosin (H&E)-stained whole-slide images (WSIs) contain additional prognostic information that is not routinely quantified. We developed an interpretable deep learning framework using a weakly supervised Trans...

Machine learning-based prognostic signature integrating mitochondrial function and programmed cell death patterns in pancreatic adenocarcinoma.

The interplay between mitochondria and programmed cell deaths (PCD) is associated with tumor pathogenesis. However, the specific roles of genes related to mitochondrial function and PCD patterns in pancreatic adenocarcinoma (PAAD) remain unclear. Though integrated multi-omics and bioinformatic analyses, we identified PCD activity-associated mitochondrial function genes (PCDMGs), and constructed a ...

Single-cell and machine learning-integrated bulk RNA-seq analysis reveals TKT as an oxidative stress-associated diagnostic biomarker in acute myocardial infarction.

BACKGROUND: Acute myocardial infarction (AMI) is a leading cause of death worldwide, with oxidative stress (OS) playing a central role in its pathogen...

Integrating single-cell multi-omics and machine learning to reveal triaptosis heterogeneity in clear cell renal cell carcinoma.

Triaptosis, an emerging form of cell death, remains poorly characterized in terms of its heterogeneity within clear cell renal cell carcinoma (ccRCC)....

Association between the endothelial activation and stress index and 28-day all-cause mortality in critically ill patients with chronic obstructive pulmonary disease: a retrospective cohort study and predictive model establishment based on machine learning.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) remains a major global health burden and is currently the third leading cause of death worldw...

Histology-defined activated cancer-associated fibroblasts are associated with poor survival in diffuse-type gastric adenocarcinoma.

Activated cancer-associated fibroblasts (aCAFs), characterized by distinct histological features including fibroblast proliferation and extensive desm...

[Artificial intelligence algorithm for bone age estimation].

BACKGROUND: Bone age (BA) estimation with automated methods can eliminate interindividual variation. OBJECTIVE: To evaluate BA by creating an artifici...

Adaptive multimodal learning for driver cognitive state monitoring using transformer-based fusion with personalized meta-learning and federated optimization.

Road accidents caused by driver fatigue and cognitive overload remain a significant public safety concern. According to recent traffic safety data, dr...

Design and in silico evaluation of an mRNA vaccine against HTLV-1 using AI-driven reverse vaccinology approaches.

Human T-lymphotropic virus type 1 (HTLV-1) is the first discovered human oncogenic retrovirus that can cause adult T-cell leukemia/lymphoma, HTLV-1-as...

The density of tumour infiltrating lymphocytes in oesophago-gastric cancer varies with disease stage, geographical region and treatment: a post hoc analysis of nine phase III clinical trials.

BACKGROUND: Tumour infiltrating lymphocytes (TILs) are a key component of the tumour microenvironment. To establish a clinically relevant TILs cut-off...

Machine learning for the prediction of three-year survival in locally advanced breast cancer patients receiving neoadjuvant chemotherapy using quantitative ultrasound imaging.

Locally Advanced Breast Cancer (LABC) is a serious type of cancer with a poor prognosis despite advances in cancer treatment. As the disease is often ...

Protocol for cerebrospinal fluid analysis using enrichment-enhanced surface-enhanced Raman spectroscopy and transformer-enabled spectral classification.

The rapid detection and precise classification of cerebrospinal fluid in acute leukemia patients constitute a crucial clinical imperative. Here, we pr...

Development and validation of a risk stratification model for sarcopenia in patients with chronic lung disease: a cross-sectional study based on CHARLS data.

OBJECTIVE: The aim of this study was to develop a machine learning-based stratification model to identify high-risk individuals for sarcopenia among p...

Multimodal deep learning model for multiclass classification of renal tumors.

Accurate classification of renal masses before treatment is crucial for therapeutic decision-making and patient outcome. This study developed and vali...

Development and evaluation of an ontology for non-invasive respiratory support in acute care.

Managing patients with respiratory failure increasingly involves non-invasive respiratory support (NIRS) strategies to support respiration, often prev...

Genetic variants and polygenic risk scores associated with paroxysmal atrial fibrillation in the Japanese population.

Early-stage diagnosis of paroxysmal atrial fibrillation (PAF) is challenging owing to its asymptomatic nature. However, the genetic factors underlying...

Identification of potential biomarkers for osteoporosis and chronic kidney disease through bioinformatics and machine learning algorithm.

OBJECTIVE: The purpose of this study is to identify hub genes associated with both osteoporosis (OP) and chronic kidney disease (CKD) through bioinfor...

Development and external validation of an interpretable multimodal deep learning model for 5-year mortality in high-risk stage ii colorectal cancer.

PURPOSE: High-risk stage II colorectal cancer (CRC) shows heterogeneous outcomes despite adjuvant chemotherapy. We developed and validated an interpre...

A prognostic model based on nucleotide metabolism genes in osteosarcoma.

Osteosarcoma typically arises during adolescence, posing a significant challenge. Despite comprehensive treatment strategies encompassing surgery, rad...

Identification of MMP14 and FOS as key regulators of vascular smooth muscle cell senescence in diabetic kidney disease: A combined bioinformatics and experimental study.

OBJECTIVE: Diabetic kidney disease (DKD) is a leading microvascular complication of diabetes in which vascular smooth muscle cell (VSMC) senescence pl...