Application of Adipose Extracellular Matrix and Reduced Graphene Oxide Nanocomposites for Spinal Cord Injury Repair.

Graphene-based materials (GBMs) hold strong promise to restore the spinal cord microenvironment and promote functional recovery after spinal cord injury (SCI). Nanocomposites consisting of reduced graphene oxide (rGO) and adipose tissue-derived extracellular matrix (adECM) are known to promote neuronal growth in vitro and to evoke a biocompatible response in vivo when implanted on top of the intact spinal cord. In this study, pristine adECM and adECM-rGO nanocomposites are implanted directly after hemisection SCI in rats.

Integrating radiomics with clinical data for enhanced prediction of vertebral fracture risk.

Introduction: Osteoporotic vertebral fractures are a major cause of morbidity, disability, and mortality among the elderly. Traditional methods for fracture risk assessment, such as dual-energy X-ray absorptiometry (DXA), may not fully capture the complex factors contributing to fracture risk. This study aims to enhance vertebral fracture risk prediction by integrating radiomics features extracted from computed tomography (CT) scans with clinical data, utilizing advanced machine learning techniques.

Switching of RNA splicing regulators in immature neuroblasts during adult neurogenesis.

The lateral wall of the mouse subventricular zone harbors neural stem cells (NSC, B cells) which generate proliferating transient-amplifying progenitors (TAP, C cells) that ultimately give rise to neuroblasts (NB, A cells). Molecular profiling at the single-cell level struggles to distinguish these different cell types. Here, we combined transcriptome analyses of FACS-sorted cells and single-cell RNAseq to demonstrate the existence of an abundant, clonogenic and multipotent population of immature neuroblasts (iNB cells) at the transition between TAP and migrating NB (mNB).

Multimodal artificial intelligence-based pathogenomics improves survival prediction in oral squamous cell carcinoma.

In this study, we aimed to develop a novel prognostic algorithm for oral squamous cell carcinoma (OSCC) using a combination of pathogenomics and AI-based techniques. We collected comprehensive clinical, genomic, and pathology data from a cohort of OSCC patients in the TCGA dataset and used machine learning and deep learning algorithms to identify relevant features that are predictive of survival outcomes. Our analyses included 406 OSCC patients.

Artificial intelligence-based analysis of associations between learning curve and clinical outcomes in endoscopic and microsurgical lumbar decompression surgery.

Purpose: A common spine surgery procedure involves decompression of the lumbar spine. The impact of the surgeon's learning curve on relevant clinical outcomes is currently not well examined in the literature. A variety of machine learning algorithms have been investigated in this study to determine how a surgeon's learning curve and other clinical parameters will influence prolonged lengths of stay (LOS), extended operating times (OT), and complications, as well as whether these clinical parameters can be reliably predicted.