Due to a lack of studies on immune-related pathogenesis and a clinical diagnostic model, the diagnosis of Spinal Tuberculosis (STB) remains uncertain. Our study aimed to investigate the possible pathogenesis of STB and to develop a clinical diagnostic model for STB based on immune cell infiltration. Label-free quantification protein analysis of five pairs of specimens was used to determine the protein expression of the intervertebral disc in STB and non-STB. GO enrichment analysis, and KEGG pathway analysis were used to investigate the pathogenesis of STB. The Hub proteins were then eliminated. Four datasets were downloaded from the GEO database to analyze immune cell infiltration, and the results were validated using blood routine test data from 8535TB and 7337 non-TB patients. Following that, clinical data from 164 STB and 162 non-STB patients were collected. The Random-Forest algorithm was used to screen out clinical predictors of STB and build a diagnostic model. The differential expression of MMP9 and STAT1 in STB and controls was confirmed using immunohistochemistry. MMP9 and STAT1 were STB Hub proteins that were linked to disc destruction in STB. MMP9 and STAT1 were found to be associated with Monocytes, Neutrophils, and Lymphocytes in immune cell infiltration studies. Data from 15,872 blood routine tests revealed that the Monocytes ratio and Neutrophils ratio was significantly higher in TB patients than in non-TB patients (p < 0.001), while the Lymphocytes ratio was significantly lower in TB patients than in non-TB patients (p < 0.001). MMP9 and STAT1 expression were downregulated following the anti-TB therapy. For STB, a clinical diagnostic model was built using six clinical predictors: MR, NR, LR, ESR, BMI, and PLT. The model was evaluated using a ROC curve, which yielded an AUC of 0.816. MMP9 and STAT1, immune-related hub proteins, were correlated with immune cell infiltration in STB patients. MR, NR, LR ESR, BMI, and PLT were clinical predictors of STB. Thus, the immune cell Infiltration-related clinical diagnostic model can predict STB effectively.

Accurate classification of patients with ankylosing spondylitis (AS) is the premise of precision medicine so as to perform different medical interventions for different patient types. AS pathology is closely related to the changes in the immune microenvironment. In this study, we used unsupervised machine learning (UML) to classify patients with AS based on clinical characteristics. We then constructed a novel subtype predictive model for AS based on the clinical classification, after which we investigated the difference in the immune microenvironment to unravel the AS pathogenesis. Overall, 196 patients with AS were enrolled. UML was used to cluster AS patients by similar clinical characteristics. Functional ability, disease status, and grading of radiologic features were assessed to verify the accuracy and heterogeneity of UML clustering. Least Absolute Shrinkage and Selection Operator (LASSO) regression and Random Forest algorithm were used to screen and identify predictive factors for the novel subtype of AS. Logistic regression was also performed to construct a predictive model of this novel subtype. Datasets were downloaded from the Gene Expression Omnibus database to assess immune cell infiltration, and the results were validated using data of routine blood tests from 3671 AS patients and 5720 non-AS patients. The differential expression of Fat Mass and Obesity-Associated Protein (FTO), an m⁶A regulator, between AS patients and healthy control subjects was confirmed using immunohistochemistry. UML clustering identified two clusters. The clinical characteristics of the two clusters were significantly heterogeneous. For the novel subtype of AS identified in UML clustering, a predictive model was built using three predictive factors, namely, C-reactive protein (CRP), absolute value of neutrophils (NEU), and absolute value of monocytes (MONO). The area under the curve of the predictive model was 0.983. Heterogeneity in the neutrophil and monocyte counts in AS was verified through immune cell infiltration analysis. Data from routine blood tests revealed that NEU and MONO were significantly higher in AS patients than in non-AS patients (p < 0.001). FTO expression was negatively correlated with both NEU and MONO. Immunohistochemistry analysis confirmed the downregulated expression of FTO. UML provides an explicable and remarkable classification of a heterogeneous cohort of AS patients. A novel subtype of AS was identified in UML clustering. CRP, NEU, and MONO were the independent predictive factors for the novel subtype of AS. FTO expression was correlated with immune cell infiltration in AS patients.

Introduction: To explore the epidemiological characteristics of ankylosing spondylitis (AS) in Guangxi province of China through a large sample survey of more than 50 million aborigines population . Material and methods: A systematic search was conducted using the International...

Preoperative planning of the surgical approach for cervical spondylotic myelopathy (CSM) is central to precision medicine. This study used supervised machine learning (SML) to build a predictive model for surgical approach selection in CSM and applied unsupervised machine learning (UML) to explore clinical heterogeneity. In this retrospective study, a development cohort of 884 surgically treated patients with CSM was used for model development after exclusion of patients with incomplete perioperative data. Variables showing significant between-group differences were entered into four supervised machine-learning algorithms, including support vector machine (SVM), extreme gradient boosting (XGBoost), random forest (RF), and generalized linear model (GLM), for feature screening and model development. In addition, an exploratory unsupervised clustering analysis was performed in patients undergoing anterior surgery on the basis of perioperative profile variables to characterize clinical heterogeneity. An independent external validation cohort of 200 patients was further used to evaluate the discrimination, calibration, and clinical utility of the final model. The final prediction model for surgical approach incorporated four key variables: American Spinal Injury Association impairment scale (ASIA), eosinophils (EOS), total protein (TP), and albumin (Alb), with an area under the curve (AUC) of 0.811 in the development cohort. Model scores were significantly correlated with perioperative indicators. UML identified two exploratory subgroups among patients receiving anterior surgery, indicating distinct perioperative profiles. In the external validation cohort, the model achieved an AUC of 0.726 (95% CI 0.615-0.828), with acceptable calibration and potential clinical net benefit on decision curve analysis. ASIA, EOS, TP, and Alb were identified as variables associated with surgical approach selection in patients with CSM. The internally derived model showed promising discriminative ability and retained moderate performance in an independent external validation cohort. In addition, exploratory unsupervised clustering suggested the presence of distinct perioperative profiles among patients undergoing anterior surgery. These findings provide preliminary support for the potential transportability of the model, although further prospective multicenter validation remains necessary.