Accumulation patterns of tobacco root allelopathicals across different cropping durations and their correlation with continuous cropping challenges.

Introduction: Continuous cropping challenges have gradually emerged as pivotal factors limiting the sustainable development of agricultural production. Allelopathicals are considered to be the primary obstacles. However, there is limited information on allelopathic accumulation across various continuous cropping years and its correlation with the associated challenges.

Application of machine learning in prediction of bone cement leakage during single-level thoracolumbar percutaneous vertebroplasty.

Background: In the elderly, osteoporotic vertebral compression fractures (OVCFs) of the thoracolumbar vertebra are common, and percutaneous vertebroplasty (PVP) is a common surgical method after fracture. Machine learning (ML) was used in this study to assist clinicians in preventing bone cement leakage during PVP surgery.

Methods: The clinical data of 374 patients with thoracolumbar OVCFs who underwent single-level PVP at The First People's Hospital of Chenzhou were chosen.

Difference between the blood samples of patients with bone and joint tuberculosis and patients with tuberculosis studied using machine learning.

Background: Tuberculosis (TB) is a chronic infectious disease. Bone and joint TB is a common type of extrapulmonary TB and often occurs secondary to TB infection. In this study, we aimed to find the difference in the blood examination results of patients with bone and joint TB and patients with TB by using machine learning (ML) and establish a diagnostic model to help clinicians better diagnose the disease and allow patients to receive timely treatment.

A Predictive Clinical-Radiomics Nomogram for Differentiating Tuberculous Spondylitis from Pyogenic Spondylitis Using CT and Clinical Risk Factors.

Objective: The study aimed to develop and validate a nomogram model with clinical risk factors and radiomic features for differentiating tuberculous spondylitis (TS) from pyogenic spondylitis (PS).

Methods: A total of 254 patients with TS (n = 141) or PS (n = 113) were randomly divided into training (n = 180) and validation (n = 74) groups. In addition, 43 patients (TS = 22 and PS = 21) were collected to construct a test cohort.

Development and Validation of a Machine Learning-Based Nomogram for Prediction of Ankylosing Spondylitis.

Introduction: Ankylosing spondylitis (AS) is a chronic progressive inflammatory disease of the spine and its affiliated tissues. AS mainly affects the axial bone, sacroiliac joint, hip joint, spinal facet, and adjacent ligaments. We used machine learning (ML) methods to construct diagnostic models based on blood routine examination, liver function test, and kidney function test of patients with AS.

Effects of a selective cyclooxygenase-2 inhibitor (celecoxib) on fracture healing in rats.

Background: Several studies suggested that celecoxib interferes with bone healing while others contradict these findings. This study was conducted to investigate the effects of celecoxib on bone healing in rats femur mold with a dose based on body surface area conversion.

Materials And Methods: 72 adult female Sprague Dawley rats were randomly divided into three groups after the internal fixation operation of nondisplaced transverse mid diaphyseal fractures of the right femurs.