Acute respiratory distress syndrome (ARDS) causes uncontrolled pulmonary inflammation, resulting in high morbidity and mortality in severe cases. Given the antioxidative effect of molecular hydrogen, some recent studies suggest the potential use of molecular hydrogen as a biomedicine for the treatment of ARDS. In this study, we aimed to explore the protective effects of magnesium hydride (MgH₂) on two types of ARDS models and its underlying mechanism in a lipopolysaccharide (LPS)‐induced ARDS model of the A549 cell line. The results showed that LPS successfully induced oxidative stress, inflammatory reaction, apoptosis, and barrier breakdown in alveolar epithelial cells (AEC). MgH₂ can exert an anti‐inflammatory effect by down‐regulating the expressions of inflammatory cytokines (IL‐1β, IL‐6, and TNF‐α). In addition, MgH₂ decreased oxidative stress by eliminating intracellular ROS, inhibited apoptosis by regulating the expressions of cytochrome c, Bax, and Bcl‐2, and suppressed barrier breakdown by up‐regulating the expression of ZO‐1 and occludin. Mechanistically, the expressions of p‐AKT, p‐mTOR, p‐P65, NLRP3, and cleaved‐caspase‐1 were decreased after MgH₂ treatment, indicating that AKT/mTOR and NF‐κB/NLRP3/IL‐1β pathways participated in the protective effects of MgH₂. Furthermore, the in vivo study also demonstrated that MgH₂‐treated mice had a better survival rate and weaker pathological damage. All these findings demonstrated that MgH₂ could exert an ARDS‐protective effect by regulating the AKT/mTOR and NF‐κB/NLRP3/IL‐1β pathways to suppress LPS‐induced inflammatory reaction, oxidative stress injury, apoptosis, and barrier breakdown, which may provide a potential strategy for the prevention and treatment of ARDS.

Background: Inflammation plays a key role in the initiation and progression of atrial fibrillation (AF). Lymphocyte‐to‐monocyte ratio (LMR) has been proved to be a reliable predictor of many inflammation‐associated diseases, but little data are available on the relationship between LMR and AF. We aimed to evaluate the predictive value of LMR in predicting all‐cause mortality among AF patients.Methods: Data of patients diagnosed with AF were retrieved from the Medical Information Mart for Intensive Care‐III (MIMIC‐III) database. X‐tile analysis was used to calculate the optimal cutoff value for LMR. The Cox regression model was used to assess the association of LMR and 28‐day, 90‐day, and 1‐year mortality. Additionally, a propensity score matching (PSM) method was performed to minimize the impact of potential confounders.Results: A total of 3567 patients hospitalized with AF were enrolled in this study. The X‐tile software indicated that the optimal cutoff value of LMR was 2.67. A total of 1127 pairs were generated, and all the covariates were well balanced after PSM. The Cox proportional‐hazards model showed that patients with the low LMR (≤2.67) had a higher 1‐year all‐cause mortality than those with the high LMR (>2.67) in the study cohort before PSM (HR = 1.640, 95% CI: 1.437–1.872, p < 0.001) and after PSM (HR = 1.279, 95% CI: 1.094–1.495, p = 0.002). The multivariable Cox regression analysis for 28‐day and 90‐day mortality yielded similar results.Conclusions: The lower LMR (≤2.67) was associated with a higher risk of 28‐day, 90‐day, and 1‐year all‐cause mortality, which might serve as an independent predictor in AF patients.

Background: The prognostic roles of three lymph node classifications, number of positive lymph nodes (NPLN), log odds of positive lymph nodes (LODDS), and lymph node ratio (LNR) in lung adenocarcinoma are unclear. We aim to find the classification with the strongest predictive power and combine it with the American Joint Committee on Cancer (AJCC) 8th TNM stage to establish an optimal prognostic nomogram. Methods: 25,005 patients with T1-4N0–2M0 lung adenocarcinoma after surgery between 2004 to 2016 from the Surveillance, Epidemiology, and End Results database were included. The study cohort was divided into training cohort (13,551 patients) and external validation cohort (11,454 patients) according to different geographic region. Univariate and multivariate Cox regression analyses were performed on the training cohort to evaluate the predictive performance of NPLN (Model 1), LODDS (Model 2), LNR (Model 3) or LODDS+LNR (Model 4) respectively for cancer-specific survival and overall survival. Likelihood-ratio χ² test, Akaike Information Criterion, Harrell concordance index, integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were used to evaluate the predictive performance of the models. Nomograms were established according to the optimal models. They’re put into internal validation using bootstrapping technique and external validation using calibration curves. Nomograms were compared with AJCC 8th TNM stage using decision curve analysis. Results: NPLN, LODDS and LNR were independent prognostic factors for cancer-specific survival and overall survival. LODDS+LNR (Model 4) demonstrated the highest Likelihood-ratio χ² test, highest Harrell concordance index, and lowest Akaike Information Criterion, and IDI and NRI values suggested Model 4 had better prediction accuracy than other models. Internal and external validations showed that the nomograms combining TNM stage with LODDS+LNR were convincingly precise. Decision curve analysis suggested the nomograms performed better than AJCC 8th TNM stage in clinical practicability. Conclusions: We constructed online nomograms for cancer-specific survival and overall survival of lung adenocarcinoma patients after surgery, which may facilitate doctors to provide highly individualized therapy.

Background: Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer. Fatty acid metabolism takes part in malignancy progression. However, the roles fatty acid metabolism plays in LUAD are still unclear.Methods: The transcriptomic and clinical data of LUAD patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were extracted. ssGSEA, WGCNA, univariable Cox regression, and LASSO Cox regression analyses were performed to identify the fatty acid metabolism-related genes which influenced the overall survival (OS) and build a fatty acid-related risk score (FARS) model. A nomogram was established based on the FARS and other clinicopathological features, and ROC and calibration plots were used to validate the prediction accuracy. The tumor microenvironment (TME) of patients with high and low FARS was compared.Results: A total of 38 genes were identified to be independently related to the survival outcome and put into a FARS model. High FARS patients exhibited significantly worse OS. The nomogram included the FARS and pathological stage, and the AUC of the nomogram predicting 1-, 2-, 3-, 4-, and 5-year OS was 0.789, 0.807, 0.798, 0.809, and 0.753, respectively. Calibration plots also indicated good accuracy. Moreover, the samples of the high FARS had higher expression of PDL1.Conclusion: We constructed a FARS model which could accurately predict the survival outcome of the LUAD patients. The genes of the FARS are related to the tumor microenvironment and patients with high FARS can potentially benefit more from anti-PD1/PDL1 immunotherapy. In addition, the mechanisms of the genes in the FARS affecting prognosis are worthy of further research to develop new gene-targeted drugs.

Background: Acute lung injury (ALI)/acute respiratory distress syndrome (ARDS) is a clinical syndrome associated with mitochondria and lacks effective preventive and therapeutic measures. This bibliometric study aims to gain insight into the scientific findings regarding mitochondria in ALI/ARDS. Methods: We retrieved the Science Citation Index Expanded (SCIE) of the Web of Science Core Collection (WoSCC) for mitochondria in ALI/ARDS publications from 2012–2021. VOSviewer, CiteSpace (5.8. R3) and Bibliometrix (3.1.4) R package were used for further analysis and visualization. Result: A total of 756 English-language articles and reviews were identified. The annual number of publications presented a rapidly developing trend. China was the most productive and cited country, and the USA had the greatest impact. In the keyword co-occurring network, the terms “acute lung injury”, “oxidative stress”, “inflammation”, “mitochondria” and “apoptosis” occurred most frequently. The co-citation network revealed that #1 mesenchymal stromal cell and #3 endothelial cell had the most bursts of citations. In addition, research hotspots have shifted from “potential therapeutic treatments” and “mitochondrial DNA (mtDNA)” to “endothelial cell” and “mesenchymal stromal cell (MSC)”. Conclusion: This bibliometric analysis reveals the research directions and frontier hotspots of mitochondria in ALI/ARDS, which has shown a rapid growth trend in annual publication numbers. mtDNA, mitophagy, and apoptosis have been the most active research areas, while studies on mitochondrial transfer in stem cells have become a hot topic in recent years.

Background: This research aimed to investigate the predictive performance of log odds of positive lymph nodes (LODDS) for the long-term prognosis of patients with node-positive lung neuroendocrine tumors (LNETs).Methods: We collected 506 eligible patients with resected N1/N2 classification LNETs from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015. The study cohort was split into derivation cohort (n=300) and external validation cohort (n=206) based on different geographic regions. Nomograms were constructed based on the derivation cohort and validated using the external validation cohort to predict the 1-, 3-, and 5-year cancer-specific survival (CSS) and overall survival (OS) of patients with LNETs. The accuracy and clinical practicability of nomograms were tested by Harrell’s concordance index (C-index), integrated discrimination improvement (IDI), net reclassification improvement (NRI), calibration plots, and decision curve analyses.Results: The Cox proportional-hazards model showed the high LODDS group (-0.79≤LODDS) had significantly higher mortality compared to those in the low LODDS group (LODDS<-0.79) for both CSS and OS. In addition, age at diagnosis, sex, histotype, type of surgery, radiotherapy, and chemotherapy were also chosen as predictors in Cox regression analyses using stepwise Akaike information criterion method and included in the nomograms. The values of C-index, NRI, and IDI proved that the established nomograms were better than the conventional eighth edition of the TNM staging system. The calibration plots for predictions of the 1-, 3-, and 5-year CSS/OS were in excellent agreement. Decision curve analyses showed that the nomograms had value in terms of clinical application.Conclusions: We created visualized nomograms for CSS and OS of LNET patients, facilitating clinicians to bring individually tailored risk assessment and therapy.