Automatic machine learning accurately predicts the efficacy of immunotherapy for patients with inoperable advanced non-small cell lung cancer using a computed tomography-based radiomics model.

Purpose: Patients with advanced non-small cell lung cancer (NSCLC) have varying responses to immunotherapy, but there are no reliable, accepted biomarkers to accurately predict its therapeutic efficacy. The present study aimed to construct individualized models through automatic machine learning (autoML) to predict the efficacy of immunotherapy in patients with inoperable advanced NSCLC.

Methods: A total of 63 eligible participants were included and randomized into training and validation groups.

Development and Validation of a Novel Model to Discriminate Idiosyncratic Drug-Induced Liver Injury and Autoimmune Hepatitis.

Background And Aim: Discriminating between idiosyncratic drug-induced liver injury (DILI) and autoimmune hepatitis (AIH) is critical yet challenging. We aim to develop and validate a machine learning (ML)-based model to aid in this differentiation.

Methods: This multicenter cohort study utilised a development set from Beijing Friendship Hospital, with retrospective and prospective validation sets from 10 tertiary hospitals across various regions of China spanning January 2009 to May 2023.

Retraction: Aerobic exercise reverses the NF-κB/NLRP3 inflammasome/5-HT pathway by upregulating irisin to alleviate post-stroke depression.

[This retracts the article DOI: 10.21037/atm-22-5443.].

Fungal Pathogen Diversity and Fungicide Resistance Assessment in Fusarium Crown Rot of Wheat in the Huanghuai Region of China.

Fusarium crown rot (FCR) poses a major threat to wheat production in the Huanghuai wheat region of China. This study aims to enhance understanding of pathogen populations causing FCR, focusing on their pathogenicity, trichothecene genotypes, and fungicide resistance. During the 2022-2023 growing seasons, we collected 1820 fungal isolates from 233 locations in this region.

Constructing a Robust Interphase with 1,1'-Oxalyldiimidazole (ODI) Additive to Enhance the Temperature and Rate Performance of LiNiCoMnO/Graphite Batteries.

This work develops 1,1'-oxalyldiimidazole (ODI) as a functional electrolyte additive. This film-forming additive improves the wide range of temperature and rate performances of LiNiCoMnO/graphite (NCM811) batteries. After 1200 cycles at room temperature (25 °C), the discharge capacity retention rate is 51.