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. Methods: Tobacco was subjected to varying planting durations: 1 year (CR), 5 years (CC5), 10 years (CC10), and 15 years (CC15). Results: Our findings unveiled discernible disparities in tobacco growth patterns across diverse continuous cropping periods. Notably, the most pronounced challenges were observed in the CC5 category, characterized by yield reduction, tobacco black shank outbreaks, and a decline in beneficial flora. Conversely, CC15 exhibited a substantial reduction in challenges as the continuous cropping persisted with no significant differences when compared to CR. Within the tobacco rhizosphere, we identified 14 distinct allelopathic compounds, with 10 of these compounds displaying noteworthy variations among the four treatments. Redundancy analysis (RDA) revealed that eight allelopathic compounds exhibited autotoxic effects on tobacco growth, with MA, heptadecanoic acid, and VA ranking as the most potent inhibitors. Interaction network highlighted the pivotal roles of VA and EA in promoting pathogen proliferation and impeding the enrichment of 13 beneficial bacterial genera. Furthermore, a structural equation model elucidated that MA and EA primarily exert direct toxic effects on tobacco, whereas VA fosters pathogen proliferation, inhibits the enrichment of beneficial bacteria, and synergistically exacerbates the challenges associated with continuous cropping alongside EA. Discussion: These findings suggested discernible disparities in tobacco growth patterns across the various continuous cropping periods. The most pronounced challenges were observed in CC5, whereas CC15 exhibited a substantial reduction in challenges as continuous cropping persisted. VA may play a pivotal role in this phenomenon by interacting with pathogens, beneficial bacterial genera, and EA.

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. It included 150 patients with bone cement leakage and 224 patients without it. We screened the feature variables using four ML methods and used the intersection to generate the prediction model. In addition, predictive models were used in the validation cohort. RESULTS: The ML method was used to select five factors to create a Nomogram diagnostic model. The nomogram model's AUC was 0.646667, and its C value was 0.647. The calibration curves revealed a consistent relationship between nomogram predictions and actual probabilities. In 91 randomized samples, the AUC of this nomogram model was 0.7555116. CONCLUSION: In this study, we invented a prediction model for bone cement leakage in single-segment PVP surgery, which can help doctors in performing better surgery with reduced risk.

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. t-test analysis, de-redundancy analysis, and minimum absolute shrinkage and selection operator (lasso) algorithm were utilized on the training set to obtain the optimal radiomics features from computed tomography (CT) for constructing the radiomics model and determine the radiomics score (Rad-score). Eight clinical risk predictors were identified to develop the clinical model. Combined with clinical risk predictors and Rad-scores, a nomogram model was constructed using multivariate logistic regression analysis. Results: A total of 1781 features were extracted, and 12 optimal radiomic features were utilized to construct the radiomic model and determine the Rad-score. The combined clinical radiomics model revealed good discrimination performance in both the training cohort and the validation cohort (AUC = 0.891 and 0.830) and was superior to the clinical (AUC = 0.807 and 0.785) and radiomics (AUC = 0.796 and 0.811) models. The calibration curve and DCA also depicted that the nomogram had better clinical efficacy. The discriminative performance of the model is well validated in the test cohort (AUC=0.877). Conclusion: The clinical radiomic nomogram could serve as a promising predictive tool for differentiating TS from PS, which could be helpful for clinical decision-making.

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. This method will help clinicians enhance diagnostic efficiency and allow patients to receive systematic treatment as soon as possible. METHODS: We consecutively screened 348 patients with AS through complete blood routine examination, liver function test, and kidney function test at the First Affiliated Hospital of Guangxi Medical University according to the modified New York criteria (diagnostic criteria for AS). By using random sampling, the patients were randomly divided into training and validation cohorts. The training cohort included 258 patients with AS and 247 patients without AS, and the validation cohort included 90 patients with AS and 113 patients without AS. We used three ML methods (LASSO, random forest, and support vector machine recursive feature elimination) to screen feature variables and then took the intersection to obtain the prediction model. In addition, we used the prediction model on the validation cohort. RESULTS: Seven factors-erythrocyte sedimentation rate (ESR), red blood cell count (RBC), mean platelet volume (MPV), albumin (ALB), aspartate aminotransferase (AST), and creatinine (Cr)-were selected to construct a nomogram diagnostic model through ML. In the training cohort, the C value and area under the curve (AUC) value of this nomogram was 0.878 and 0.8779462, respectively. The C value and AUC value of the nomogram in the validation cohort was 0.823 and 0.8232055, respectively. Calibration curves in the training and validation cohorts showed satisfactory agreement between nomogram predictions and actual probabilities. The decision curve analysis showed that the nonadherence nomogram was clinically useful when intervention was decided at the nonadherence possibility threshold of 1%. CONCLUSION: Our ML model can satisfactorily predict patients with AS. This nomogram can help orthopedic surgeons devise more personalized and rational clinical strategies.

AS is a chronic progressive inflammatory disease of the spine and its affiliated tissues. AS starts gradually, and its early symptoms are mild. Some hospitals lack HLA-B27 and related imaging instruments to assist in the diagnosis of AS. There are relatively few studies on liver function and kidney function of patients with AS. We used ML methods to construct diagnostic models. Our model can satisfactorily predict patients with AS. This diagnostic model can help orthopedic surgeons devise more personalized and rational clinical strategies.

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. Methods: A total of 1,667 patients were finally enrolled in the study. Patients were randomly assigned to the training and validation cohorts. The training cohort included 1,268 patients: 158 patients with bone and joint TB and 1,110 patients with TB. The validation cohort included 399 patients: 48 patients with bone and joint TB and 351 patients with TB. We used three ML methods, namely logistic regression, LASSO regression, and random forest, to screen the differential variables, obtained the most representative variables by intersection to construct the prediction model, and verified the performance of the proposed prediction model in the validation group. Results: The results revealed a great difference in the blood examination results of patients with bone and joint TB and those with TB. Infectious markers such as hs-CRP, ESR, WBC, and NEUT were increased in patients with bone and joint TB. Patients with bone and joint TB were found to have higher liver function burden and poorer nutritional status. The factors screened using ML were PDW, LYM, AST/ALT, BUN, and Na, and the nomogram diagnostic model was constructed using these five factors. In the training cohort, the area under the curve (AUC) value of the model was 0.71182, and the C value was 0.712. In the validation cohort, the AUC value of the model was 0.6435779, and the C value was 0.644. Conclusion: We used ML methods to screen out the blood-specific factors-PDW, LYM, AST/ALT, BUN, and Na+-of bone and joint TB and constructed a diagnostic model to help clinicians better diagnose the disease in the future.

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. Each group was treated with 1% methylcellulose, celecoxib (21 mg/kg/d) for 1 week, or celecoxib (21 mg/kg/d) for 4 weeks after surgeries respectively. Bone healing scores and callus formation were evaluated by radiographs at 3, 4, 6 weeks after surgeries. Half of these rats were sacrificed for histological analysis at 4 weeks after surgery. The remaining fractured femurs were evaluated by biomechanical tests at 6 weeks after surgery. RESULTS: The mean radiographic scores for fracture healing of both short and long term groups were lower than that of the control group and the differences among the three groups were statistically significant (P < 0.05) at 3, 4, 6 weeks after surgery. The mean bone trabecula density of both groups was smaller than that of the control group and the differences were also statistically significant (P < 0.05) at 4 week. The maximum load, total energy and stiffness in both the short term and long term groups were significantly decreased compared with those in the control group (P < 0.05) at 6 week. CONCLUSION: Both short term and long term sustained use of celecoxib in rat models has significantly inhibitory effects on rat fracture healing.