Machine Learning for Predicting Primary Graft Dysfunction After Lung Transplantation: An Interpretable Model Study.

Background: Primary graft dysfunction (PGD) develops within 72 h after lung transplantation (Lung Tx) and greatly influences patients' prognosis. This study aimed to establish an accurate machine learning (ML) model for predicting grade 3 PGD (PGD3) after Lung Tx.

Methods: This retrospective study incorporated 802 patients receiving Lung Tx between July 2018 and October 2023 (640 in the derivation cohort and 162 in the external validation cohort), and 640 patients were randomly assigned to training and internal validation cohorts in a 7:3 ratio.

Effects of sleep quality on the risk of various long COVID symptoms among older adults following infection: an observational study.

Background: The long-term sequelae of coronavirus disease 2019 (COVID-19) and its recovery have becoming significant public health concerns. Therefore, this study aimed to enhance the limited evidence regarding the relationship between sleep quality on long COVID among the older population aged 60 years or old.

Methods: Our study included 4,781 COVID-19 patients enrolled from April to May 2023, based on the Peking University Health Cohort.

Early identification of potentially reversible cancer cachexia using explainable machine learning driven by body weight dynamics: a multicenter cohort study.

Background: Cachexia is associated with multiple adverse outcomes in cancer. However, clinical decision-making for oncology patients at the cachexia stage presents significant challenges.

Objective: This study aims to develop a machine learning (ML) model to identify potentially reversible cancer cachexia (PRCC).

Targeting WDR5/ATAD2 signaling by the CK2/Ikaros axis demonstrates therapeutic efficacy in T-ALL.

T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive hematological malignancy with a poor prognosis and limited options for targeted therapies. Identifying new molecular targets to develop novel therapeutic strategies is the pressing immediate issue in T-ALL. Here, we observed high expression of WD Repeat-Containing Protein 5 (WDR5) in T-ALL; with in vitro and in vivo models we demonstrated the oncogenic role of WDR5 in T-ALL by activating cell cycle signaling through its new downstream effector, ATPase family AAA domain-containing 2 (ATAD2).

The Effects of Moderate to High Static Magnetic Fields on Pancreatic Damage.

Background: Pancreatic damage is a common digestive system disease with no specific drugs. Static magnetic field (SMF), the key component of magnetic resonance imaging (MRI), has demonstrated prominent effects in various disease models.

Purpose: To study the effects of 0.