Transplantation

Efferocytosis-associated transcriptomic patterns characterize prognosis and immune landscape in osteosarcoma.

BACKGROUND: Osteosarcoma (OS) is the most common primary malignant bone tumor in adolescents, characterized by high heterogeneity and poor prognosis. Efferocytosis, the clearance of apoptotic cells, has been implicated in tumor progression and immune evasion, but its role in OS remains unclear. METHODS: We integrated TARGET-OS as a training cohort with three GEO datasets for validation. Efferocyto...

Exploration Novel Therapeutic Targets for Periodontitis via Stress Granules Biomarkers.

BACKGROUND: Periodontitis (PD) is associated with stress granules (SGs), which are involved in cellular stress responses. Identifying biomarkers related to SGs in PD is key to grasping its pathogenesis and devising novel therapeutic approaches. METHODS: Microarray datasets GSE10334 and GSE106090 were downloaded from public databases, and experimental verification was conducted. Differentially expr...

Self-driving bioprinting laboratories.

The severe shortage of donor organs and limitations of current disease models highlight the urgent need for transformative strategies in tissue engine...

Machine-learning-guided high-throughput design of asymmetric A-DA'D-A acceptors toward efficient organic solar cells.

Asymmetric structure design in non-fullerene acceptors (NFAs) has driven a significant breakthrough in the power conversion efficiency (PCE) of organi...

A machine learning-based prediction model for poor prognosis in sepsis using lymphocyte count: a national, multicenter prospective cohort.

Sepsis-induced immunosuppression leads to poor prognosis. Circulating lymphocyte count (LC), as an easily accessible clinical marker, closely reflects...

Prehospital transfusion strategies in haemorrhagic shock.

BACKGROUND: Massive transfusion protocols are established in-hospital practices for managing haemorrhagic shock, yet critical bleeding accounts for up...

Numerical modeling and prediction of late estimated glomerular filtration rate in kidney transplant recipients based on machine learning models and the Monte Carlo simulation method.

OBJECTIVES: This paper presents an experimental numerical method for modeling and analyzing stochastic systems. For this purpose, various machine pred...

AI-driven optimization of sustainable solvent-based extraction: A deep learning approach for green sample preparation.

BACKGROUND: This study introduces a data-driven deep learning framework for optimizing the extraction of plant biomolecules, aiming to improve both ef...

STF: A Unified Framework for Joint Pixel-Level Segmentation and Tracking of Tissues in Endoscopic Surgery.

Endoscopic minimally invasive surgery relies on precise tissue video segmentation to avoid complications such as vascular bleeding or nerve injury. Ho...

Transcriptomic profiling and machine learning uncover gene signatures of psoriasis endotypes and disease severity.

BACKGROUND: Despite increased understanding of psoriasis pathogenesis, molecular classification of clinical phenotypes and disease severity is poorly ...

Longitudinal Peripheral Blood Transcriptomics Reveal Novel Signatures During Cardiac Allograft Rejection.

BACKGROUND: Acute cellular rejection (ACR) remains a major cause of morbidity after heart transplantation despite advances in immunosuppression. Whole...

Machine Learning for Individual EV Classification Based on Highly Sensitive Multiplexed Mass Spectrometry Measurements.

Here, we report on combining Random Forest (RF) classification with nanoprojectile secondary ion mass spectrometry (NP-SIMS) to analyze single extrace...

Semantic segmentation deep learning model boosts surgeons' organ recognition in minimally invasive hysterectomy - a prospective multi-center reader performance study using pre-selected video clips.

BACKGROUND: Injury to the ureter and bladder during minimally invasive hysterectomy remains a serious complication, often resulting from insufficient ...

Biofabrication of 3D bioprinted and organ-on-chip blood-brain barrier models using hCMEC/D3 for intranasal delivery of central nervous system therapeutics.

The BBB remains a major obstacle to effective treatment of CNS disorders by limiting the entry of most therapeutics into the brain. The hCMEC/D3 is wi...

BIPE: Artificial Intelligence-Driven Peptide Bitterness Intensity Prediction Engine.

Bitterness, alongside sour, sweet, umami, and salty tastes, constitutes one of the five basic tastes and serves as a key dimension in shaping food fla...

Prediction of myeloid malignant cells in Fanconi anemia using machine learning.

Fanconi anemia (FA) is an inherited bone marrow failure syndrome with cancer predisposition. Most FA patients develop aplastic anemia during childhood...

Leveraging machine learning to predict de novo skin malignancy following lung transplantation.

AIMS: This study aimed to predict post-transplant malignancy risks at multiple levels among lung transplant recipients using machine learning (ML) and...

Artificial intelligence (AI)-based multi-organ contour quality assurance with uncertainty estimation for online adaptive radiotherapy (oART).

Accurate delineation of treatment targets and organs at risk (OARs) is essential to the success of radiotherapy (RT). Although artificial intelligence...

Machine Learning Integrated Extracellular Vesicle Proteome Analysis For Early Markers Of Bronchopulmonary Dysplasia.

Bronchopulmonary dysplasia (BPD) is a serious and often lethal complication of pre-term birth that typically manifests about one month after pre-term ...

Resolving energy transfer dynamics in Eu²⁺-activated multi-site phosphors via metaheuristic optimization and physics-informed neural networks.

Multi-exponential fitting has long been the standard approach for luminescence decay analysis, not because it is physically meaningful but because it ...