Proteomic analysis reveals critical molecular mechanisms involved in the macrophage anti-spinal tuberculosis process

Prediction model for spinal cord injury in spinal tuberculosis patients using multiple machine learning algorithms: a multicentric study

Spinal cord injury (SCI) is a prevalent and serious complication among patients with spinal tuberculosis (STB) that can lead to motor and sensory impairment and potentially paraplegia. This research aims to identify factors associated with SCI in STB patients and to develop a clinically significant predictive model. Clinical data from STB patients at a single hospital were collected and divided into training and validation sets. Univariate analysis was employed to screen clinical indicators in the training set. Multiple machine learning (ML) algorithms were utilized to establish predictive models. Model performance was evaluated and compared using receiver operating characteristic (ROC) curves, area under the curve (AUC), calibration curve analysis, decision curve analysis (DCA), and precision-recall (PR) curves. The optimal model was determined, and a prospective cohort from two other hospitals served as a testing set to assess its accuracy. Model interpretation and variable importance ranking were conducted using the DALEX R package. The model was deployed on the web by using the Shiny app. Ten clinical characteristics were utilized for the model. The random forest (RF) model emerged as the optimal choice based on the AUC, PRs, calibration curve analysis, and DCA, achieving a test set AUC of 0.816. Additionally, MONO was identified as the primary predictor of SCI in STB patients through variable importance ranking. The RF predictive model provides an efficient and swift approach for predicting SCI in STB patients.

Ruxolitinib inhibits cytokine production by human lung macrophages without impairing phagocytic ability

Background: The Janus kinase (JAK) 1/2 inhibitor ruxolitinib has been approved in an indication of myelofibrosis and is a candidate for the treatment of a number of inflammatory or autoimmune diseases. We assessed the effects of ruxolitinib on lipopolysaccharide (LPS)- and poly (I:C)-induced cytokine production by human lung macrophages (LMs) and on the LMs’ phagocytic activity.

Methods: Human LMs were isolated from patients operated on for lung carcinoma. The LMs were cultured with ruxolitinib (0.5 × 10−7 M to 10–5 M) or budesonide (10–11 to 10–8 M) and then stimulated with LPS (10 ng·ml−1) or poly (I:C) (10 μg·ml−1) for 24 h. Cytokines released by the LMs into the supernatants were measured using ELISAs. The phagocytosis of labelled bioparticles was assessed using flow cytometry.

Results: Ruxolitinib inhibited both the LPS- and poly (I:C)-stimulated production of tumor necrosis factor alpha, interleukin (IL)-6, IL-10, chemokines CCL2, and CXCL10 in a concentration-dependent manner. Ruxolitinib also inhibited the poly (I:C)- induced (but not the LPS-induced) production of IL-1ß. Budesonide inhibited cytokine production more strongly than ruxolitinib but failed to mitigate the production of CXCL10. The LMs’ phagocytic activity was not impaired by the highest tested concentration (10–5 M) of ruxolitinib.

Conclusion: Clinically relevant concentrations of ruxolitinib inhibited the LPS- and poly (I:C)-stimulated production of cytokines by human LMs but did not impair their phagocytic activity. Overall, ruxolitinib’s anti-inflammatory activities are less intense than (but somewhat different from) those of budesonide—particularly with regard to the production of the corticosteroid-resistant chemokine CXCL-10. Our results indicate that treatment with a JAK inhibitor might be a valuable anti-inflammatory strategy in chronic obstructive pulmonary disease, Th1-high asthma, and both viral and non-viral acute respiratory distress syndromes (including coronavirus disease 2019).