Development and validation of a risk prediction model and prediction tools for post-thrombotic syndrome in patients with lower limb deep vein thrombosis.

PURPOSE: Our research aims to compare the predictive performance of decision tree algorithms (DT) and logistic regression analysis (LR) in constructing models, and develop a Post-Thrombotic Syndrome (PTS) risk stratification tool. METHODS: We retrospectively collected and analyzed relevant case information of 618 patients diagnosed with DVT from January 2012 to December 2021 in three different tertiary hospitals in Jiangxi Province as the modeling group. Additionally, we used the case information of 212 patients diagnosed with DVT from January 2022 to January 2023 in two tertiary hospitals in Hubei Province and Guangdong Province as the validation group. We extracted electronic medical record information including general patient data, medical history, laboratory test indicators, and treatment data for analysis. We established DT and LR models and compared their predictive performance using receiver operating characteristic (ROC) curves and confusion matrices. Internal and external validations were conducted. Additionally, we utilized LR to generate nomogram charts, calibration curves, and decision curves analysis (DCA) to assess its predictive accuracy. RESULTS: Both DT and LR models indicate that Year, Residence, Cancer, Varicose Vein Operation History, DM, and Chronic VTE are risk factors for PTS occurrence. In internal validation, DT outperforms LR (0.962 vs 0.925, z = 3.379, P < 0.001). However, in external validation, there is no significant difference in the area under the ROC curve between the two models (0.963 vs 0.949, z = 0.412, P = 0.680). The validation results of calibration curves and DCA demonstrate that LR exhibits good predictive accuracy and clinical effectiveness. A web-based calculator software of nomogram (https://sunxiaoxuan.shinyapps.io/dynnomapp/) was utilized to visualize the logistic regression model. CONCLUSIONS: The combination of decision tree and logistic regression models, along with the web-based calculator software of nomogram, can assist healthcare professionals in accurately assessing the risk of PTS occurrence in individual patients with lower limb DVT.

Resveratrol protects BV2 mouse microglial cells against LPS-induced inflammatory injury by altering the miR-146a-5p/TRAF6/NF-κB axis.

Objective: To investigate the role of miR-146a-5p in the effects of resveratrol (RSV) on inflammatory response in BV2 mouse microglial cells. Materials and methods: BV2 cells were pretreated by RSV and stimulated with lipopolysaccharide (LPS). Cell Viability was checked using a MTT assay. Real-Time PCR was performed to detect the levels of pro-inflammatory cytokines (tumor necrosisfactor-α－TNF-α, interleukin-1ß－IL-1ß and interleukin-6 － IL-6) and miR-146a-5p expression. Western blot was used to analyze the protein expression of TNF receptor associated factor 6 (TRAF6) and phospho-nuclear factor kappa B (pNF-κB). Gain-of-function and loss-of-function analysis of miR-146a-5p was performed using transfection of miR-146a-5p mimic and miR-146a-5p inhibitor, respectively. Results: Pretreatment with RSV significantly and dose dependently inhibited LPS-induced production of TNF-α, IL-1ß and IL-6 in BV2 cells. MiR-146a-5p was significantly upregulated after LPS treatment, and further increased in RSV and LPS-co-treated cells. MiR-146a-5p overexpression via miR-146a-5p mimic transfection downregulated the mRNA level of TNF-α, IL-1ß and IL-6, as well as abrogated the protein expression of TRAF6 and pNF-κB in BV2 cells exposed to LPS. More importantly, the reducion of TNF-α, IL-1ß and IL-6 level by RSV were reversed by miR-146a-5p silence via miR-146a-5p inhibitor transfection. Furthermore, silencing miR-146a-5p attenuated the inhibitory effect of RSV on the TRAF6/NF-κB pathway which was activated after induction with LPS. Conclusions: RSV can suppress LPS-induced inflammatory injury via modulating the miR-146a-5p/TRAF6/NF-κB axis in BV2 mouse microglial cells.