Automatic segmentation of white matter lesions on multi-parametric MRI: convolutional neural network versus vision transformer.

Background And Purpose: White matter hyperintensities in brain MRI are key indicators of various neurological conditions, and their accurate segmentation is essential for assessing disease progression. This study aims to evaluate the performance of a 3D convolutional neural network and a 3D Transformer-based model for white matter hyperintensities segmentation, focusing on their efficacy with limited datasets and similar computational resources.

Materials And Methods: We implemented a convolution-based model (3D ResNet-50 U-Net with spatial and channel squeeze & excitation) and a Transformer-based model (3D Swin Transformer with a convolutional stem).

A phosphorylation-regulated NPF transporter determines salt tolerance by mediating chloride uptake in soybean plants.

Chloride (Cl) ions cause major damage to crops in saline soils. Understanding the key factors that influence Cl uptake and translocation will aid the breeding of more salt-tolerant crops. Here, using genome-wide association study and transcriptomic analysis, we identified a NITRATE TRANSPORTER 1 (NRT1)/PEPTIDE TRANSPORTER family (NPF) protein, GmNPF7.

Common biomarkers of idiopathic pulmonary fibrosis and systemic sclerosis based on WGCNA and machine learning.

Interstitial lung disease (ILD) is known to be a major complication of systemic sclerosis (SSc) and a leading cause of death in SSc patients. As the most common type of ILD, the pathogenesis of idiopathic pulmonary fibrosis (IPF) has not been fully elucidated. In this study, weighted correlation network analysis (WGCNA), protein‒protein interaction, Kaplan-Meier curve, univariate Cox analysis and machine learning methods were used on datasets from the Gene Expression Omnibus database.

Impact of lymph node metastasis on immune microenvironment and prognosis in colorectal cancer liver metastasis: insights from multiomics profiling.

Background: This study aimed to investigate the prognostic impact of lymph node metastasis (LNM) on patients with colorectal cancer liver metastasis (CRLM) and elucidate the underlying immune mechanisms using multiomics profiling.

Methods: We enrolled patients with CRLM from the US Surveillance, Epidemiology, and End Results (SEER) cohort and a multicenter Chinese cohort, integrating bulk RNA sequencing, single-cell RNA sequencing and proteomics data. The cancer-specific survival (CSS) and immune profiles of the tumor-draining lymph nodes (TDLNs), primary tumors and liver metastasis were compared between patients with and without LNM.