Purpose: Metagenomic next-generation sequencing (mNGS) is a novel technique of pathogens detection that plays an increasingly important role in clinical practice. In this study, we explored the application value of mNGS in pulmonary infection combined with pleural effusion applied to samples of pleural effusion fluid. Patients and Methods: We reviewed 80 cases of pulmonary infection with pleural effusion between August 2020 and October 2021. Among them, 40 patients were placed in the mNGS group and underwent both culture and mNGS testing; the patients in the control group were only subjected to culture test. The effectiveness of mNGS was evaluated for microbial composition and diagnostic accuracy in every pleural effusion specimen type. Results: We found that the positive rate of mNGS was 70% (28/40). The comparison between mNGS and culture method resulted that the sensitivity was 100% (95% CI: 29.2– 100%) and the specificity was 64.9% (95% CI: 47.5– 79.8%). The positive predictive value of mNGS was 18.8% (95% CI, 13.0– 26.3%), and the negative predictive value was 100%. The most commonly identified potential pathogens were bacteria, such as Streptococcus, Prevotella, Parvimonas, Porphyromonas and Gemella. The most detected fungal infection was Candida and Pneumocystis. A total of 11 patients were identified as mixed infection by mNGS. Treatment regimen adjustments were made according to mNGS results and the overall length of hospital stay in the mNGS group was shorter compared to that of the control group. Conclusion: In this study, mNGS produced higher positive rates than the culture method in detecting pathogens in the pleural effusion specimens. The technology performed satisfactorily, providing more diagnostic evidence and reducing the length of hospital stay.

Cerebral ischemia-reperfusion injury induces a secondary immune inflammatory reaction that exacerbates brain injury and clinical prognosis. Dendritic cells (DCs) and microglia are both important regulators of neuroinflammation. Studies have confirmed that a large number of cells express the DC surface marker CD11c in the ischemic area, and some of these cells also express microglial markers. However, the specific mechanism of transformation between microglia and DCs and their roles in the process of cerebral ischemia-reperfusion injury are still not clear. In this study, we established a mouse model and flow cytometry was used to detect the expression of mature DC surface molecules in activated microglia. IFN-γ knockout mice were used to determine the regulatory effect of IFN-γ on microglial transformation. We found that CD11c⁺ cells were derived from microglia after ischemia-reperfusion injury, and this group of cells highly expressed MHC-II molecules and other costimulatory molecules, such as CD80 and CD86, which were regulated by IFN-γ and its downstream signaling molecules ERK/c-myc. In summary, our results showed in cerebral ischemia-reperfusion injury, IFN-γ regulates the transformation of microglia to DC-like cells. Microglial-derived DC-like cells possess the ability to present antigens and activate naïve T cells which is regulated by the ERK/c-myc signaling pathway.

Analysis of Risk Factors for Thromboembolic Events in 88 Patients with COVID-19 Pneumonia in Wuhan, China: A Retrospective Descriptive Report - Article abstract #929708

Chronic bronchitis (CB), as a core precursor of Chronic Obstructive Pulmonary Disease (COPD), is crucial for global disease burden prevention and control. Although the association between heavy metal exposure and respiratory damage has been preliminarily demonstrated, traditional linear models are difficult to resolve the nonlinear interactions and dose–response heterogeneity. The aim of this study was to construct the first heavy metal exposure-chronic bronchitis risk prediction model by integrating exposureomics data through machine learning (ML). Weighted logistic regression was used to assess the association of 14 blood and urine heavy metals with CB based on nationally representative samples from the 2005–2015 National Health and Nutrition Examination Survey (NHANES). The Boruta algorithm was further applied to screen the characteristic variables and construct 10 ML models. The best model was selected by four evaluation metrics: accuracy, specificity, sensitivity, and area under the ROC curve (AUC), and the best model was visually interpreted using Shapley's additive interpretation (SHAP). The multifactorial logistic regression model showed that urinary cadmium (OR = 1.53, 95% CI = 1.17–1.98) versus blood cadmium (OR = 1.36, 1.13–1.65) was an independent risk factor for CB. The CatBoost model had the best predictive performance (AUC = 0.805), with smoking as the most significant predictor, followed by blood cadmium concentration and gender. In this research, the first risk prediction diagnostic model for heavy metal-chronic bronchitis was developed, in which CatBoost model had the best performance, and it provides a referenceable prediction model for the screening of high-risk groups.

Purpose: This study aims to establish a risk prediction model for muscular calf vein thrombosis (MCVT) in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD). Methods: The research sample consisted of 248 patients with AECOPD and all of them underwent vascular ultrasounds of both lower limbs in this retrospective study. Univariate analysis and multivariate logistic regression analysis were conducted on factors with significant group differences to screen for the independent risk factors of MCVT. A nomogram to predict the risk of MCVT was constructed and validated with bootstrap resampling. Results: According to the exclusion criteria, 240 patients were included for analysis, divided into the MCVT group (n = 81) and the non-MCVT group (n = 159). Multivariate logistic regression analyses showed that hypertension, elevated MPV, reduced albumin (ALB), elevated D-dimer and bed rest ≥ 3 days were independent risk factors for MCVT in AECOPD. A nomogram model for predicting AECOPD with MCVT was established based on them. The area under the curve (AUC) of receiver operating characteristic (ROC) curve for the prediction model and the simplified Wells score was 0.784 (95% CI: 0.722– 0.847) and 0.659 (95% CI: 0.583– 0.735), respectively. The cut-off value and Youden index of prediction model were 0.248 and 0.454, respectively. At the same time, the sensitivity, specificity, positive predictive value, and negative predictive value of the prediction model were 85.9%, 59.5%, 84.6%, and 77.4%, respectively. The sensitivity and specificity of the simplified Wells score were 67.9% and 56.3%, respectively. Validation by the use of bootstrap resampling revealed optimal discrimination and calibration, and the decision analysis curve (DAC) suggested that this prediction model involved high clinical practicability. Conclusion: We developed a nomogram that can predict the risk of MCVT for AECOPD patients. This model has the potential to assist clinicians in making treatment recommendations and formulating corresponding prevention measures.

Background: Several miRNAs are now known to have clear connections to the pathogenesis of asthma. The present study focused on the potential role of miR-3934 during asthma development. Methods: miR-3934 was detected as a down-regulated miRNA in basophils by sequencing analysis. Next, the expression levels of miR-3934 in peripheral blood mononuclear cells of 50 asthma patients and 50 healthy volunteers were examined by RT-qPCR methods. The basophils were then treated with AGEs and transfected with miR-3934 mimics. The apoptosis levels were examined by flow cytometry assay; and the expression levels of cytokines were detected using the ELISA kits. Finally, the Western blot was performed to examined the expression of key molecules in the TGF-β/Smad signaling pathway. Results: miR-3934 was down-regulated in the basophils of asthmatic patients. The expression of the pro-inflammatory cytokines IL-6, IL-8 and IL-33 was enhanced in basophils from asthmatic patients, and this effect was partially reversed by transfection of miR-3934 mimics. Furthermore, receiver operating characteristics analysis showed that miR-3934 levels can be used to distinguish asthma patients from healthy individuals. miR-3934 partially inhibited advanced glycation end products-induced increases in basophil apoptosis by suppressing expression of RAGE. Conclusion: Our results indicate that miR-3934 acts to mitigate the pathogenesis of asthma by targeting RAGE and suppressing TGF-β/Smad signaling.