Self-sustained biomimetic bioelectronic accelerated metabolic reprogramming of bone regeneration.

Bionic bioelectronics has promising applications in bone defect repair, with current research primarily focusing on the development of electroactive biomaterials and self-powered systems, which can mimic the electrophysiological microenvironment of natural bone tissue, accelerating bone healing by promoting osteoblast proliferation and differentiation through electrical stimulation. However, the biological mechanisms of bionic electrical stimulation in bone defect repair remain incompletely understood. Here, the study developed a self-sustained biomimetic bioelectronic system comprising a triboelectric/piezoelectric hybrid nanogenerator (TP-hNG) and a multifunctional gold-coated polymer internal fixation plate (GP-IFP), which utilizes the natural biomechanical properties of rat heartbeat and respiratory movements to generate bionic electric signals (Bio-SIG) that are closely related to physiological neurofeedback signals.

EMT-related gene classifications predict the prognosis, immune infiltration, and therapeutic response of osteosarcoma.

Background: Osteosarcoma (OS), a bone tumor with high ability of invasion and metastasis, has seriously affected the health of children and adolescents. Many studies have suggested a connection between OS and the epithelial-mesenchymal transition (EMT). We aimed to integrate EMT-Related genes (EMT-RGs) to predict the prognosis, immune infiltration, and therapeutic response of patients with OS.

Construction and validation of a machine learning model for the diagnosis of juvenile idiopathic arthritis based on fecal microbiota.

Purpose: Human gut microbiota has been shown to be significantly associated with various inflammatory diseases. Therefore, this study aimed to develop an excellent auxiliary tool for the diagnosis of juvenile idiopathic arthritis (JIA) based on fecal microbial biomarkers.

Method: The fecal metagenomic sequencing data associated with JIA were extracted from NCBI, and the sequencing data were transformed into the relative abundance of microorganisms by professional data cleaning (KneadData, Trimmomatic and Bowtie2) and comparison software (Kraken2 and Bracken).

Metabolism and senescence in the immune microenvironment of osteosarcoma: focus on new therapeutic strategies.

Osteosarcoma is a highly aggressive and metastatic malignant tumor. It has the highest incidence of all malignant bone tumors and is one of the most common solid tumors in children and adolescents. Osteosarcoma tissues are often richly infiltrated with inflammatory cells, including tumor-associated macrophages, lymphocytes, and dendritic cells, forming a complex immune microenvironment.

A practical dynamic nomogram model for predicting bone metastasis in patients with thyroid cancer.

Purpose: The aim of this study was to established a dynamic nomogram for assessing the risk of bone metastasis in patients with thyroid cancer (TC) and assist physicians to make accurate clinical decisions.

Methods: The clinical data of patients with TC admitted to the First Affiliated hospital of Nanchang University from January 2006 to November 2016 were included in this study. Demographic and clinicopathological parameters of all patients at primary diagnosis were analyzed.

Using Preoperative and Intraoperative Factors to Predict the Risk of Surgical Site Infections After Lumbar Spinal Surgery: A Machine Learning-Based Study.

Objective: To develop a model based on machine learning to predict surgical site infection (SSI) risk in patients after lumbar spinal surgery (LSS).

Methods: Patients who developed postoperative SSI after LSS in the First Affiliated Hospital of Nanchang University between December 2010 and December 2019 were retrospectively reviewed. Preoperative and intraoperative variables, including age, diabetes mellitus, hypertension, body mass index, previous spinal surgery history, surgical duration, number of fused segments, blood loss, and surgical procedure were analyzed.