Molecular architecture of the altered cortical complexity in autism.

Autism spectrum disorder (ASD) is characterized by difficulties in social interaction, communication challenges, and repetitive behaviors. Despite extensive research, the molecular mechanisms underlying these neurodevelopmental abnormalities remain elusive. We integrated microscale brain gene expression data with macroscale MRI data from 1829 participants, including individuals with ASD and typically developing controls, from the autism brain imaging data exchange I and II.

[Discussion on the genetic mechanisms and efficacy of growth hormone therapy in children with idiopathic short stature].

Idiopathic short stature (ISS) is a term that encompasses a group of short stature disorders with unknown etiology. The genetic factors associated with ISS are complex, and the known genetic mechanisms include alterations in hormones, hormone receptors, or related pathways, defects in fundamental cellular processes (such as intracellular signaling pathways and transcriptional regulation), issues with extracellular matrix or paracrine signaling, as well as genetic variations in the genes encoding these proteins. Recombinant human growth hormone (rhGH) therapy is currently an effective clinical method for improving height in children with ISS.

Investigating the causal links between inflammatory cytokines and scoliosis through bidirectional Mendelian randomization analysis.

Background: Scoliosis, characterized by a lateral curvature of the spine, affects millions globally. The role of inflammatory cytokines in the pathogenesis of scoliosis is increasingly acknowledged, yet their causal relationships remain poorly defined.

Aims: This study aims to explore the genetic-level causal relationships between inflammatory cytokines and scoliosis utilizing bidirectional Mendelian randomization (MR) analysis.

Machine learning-enabled prediction of prolonged length of stay in hospital after surgery for tuberculosis spondylitis patients with unbalanced data: a novel approach using explainable artificial intelligence (XAI).

Background: Tuberculosis spondylitis (TS), commonly known as Pott's disease, is a severe type of skeletal tuberculosis that typically requires surgical treatment. However, this treatment option has led to an increase in healthcare costs due to prolonged hospital stays (PLOS). Therefore, identifying risk factors associated with extended PLOS is necessary.