Suppression of OGN in lung myofibroblasts attenuates pulmonary fibrosis by inhibiting integrin αv-mediated TGF-ß/Smad pathway activation.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) represents a severe and progressive manifestation of idiopathic interstitial pneumonia marked by an uncertain etiology along with an unfavorable prognosis. Osteoglycin (OGN), belonging to the small leucine-rich proteoglycans family, assumes pivotal functions in both tissue formation and damage response. However, the roles and potential mechanisms of OGN in the context of lung fibrosis remain unexplored. METHODS: The assessment of OGN expression levels in fibrotic lungs was conducted across various experimental lung fibrosis mouse models. To elucidate the effects of OGN on the differentiation of lung myofibroblasts, both OGN knockdown and OGN overexpression were employed in vitro. The expression of integrin αv, along with its colocalization with lysosomes and latency-associated peptide (LAP), was monitored in OGN-knockdown lung myofibroblasts. Furthermore, the role of OGN in lung fibrosis was investigated through OGN knockdown utilizing adeno-related virus serotype 6 (AAV6)-mediated delivery. RESULTS: OGN exhibited upregulation in both lungs and myofibroblasts across diverse lung fibrosis mouse models. And laboratory experiments in vitro demonstrated that OGN knockdown inhibited the TGF-ß/Smad signaling pathway in lung myofibroblasts. Conversely, OGN overexpression promoted TGF-ß/Smad pathway in these cells. Mechanistic insights revealed that OGN knockdown facilitated lysosome-mediated degradation of integrin αv while inhibiting its binding to latency-associated peptide (LAP). Remarkably, AAV6-targeted OGN knockdown ameliorated the extent of lung fibrosis in experimental mouse models. CONCLUSION: Our results indicate that inhibiting OGN signaling could serve as a promising therapeutic way for lung fibrosis.

Machine learning-based models for predicting mortality and acute kidney injury in critical pulmonary embolism.

OBJECTIVES: We aimed to use machine learning (ML) algorithms to risk stratify the prognosis of critical pulmonary embolism (PE). MATERIAL AND METHODS: In total, 1229 patients were obtained from MIMIC-IV database. Main outcomes were set as all-cause mortality within 30 days. Logistic regression (LR) and simplified eXtreme gradient boosting (XGBoost) were applied for model constructions. We chose the final models based on their matching degree with data. To simplify the model and increase its usefulness, finally simplified models were built based on the most important 8 variables. Discrimination and calibration were exploited to evaluate the prediction ability. We stratified the risk groups based on risk estimate deciles. RESULTS: The simplified XGB model performed better in model discrimination, which AUC were 0.82 (95% CI: 0.78-0.87) in the validation cohort, compared with the AUC of simplified LR model (0.75 [95% CI: 0.69-0.80]). And XGB performed better than sPESI in the validation cohort. A new risk-classification based on XGB could accurately predict low-risk of mortality, and had high consistency with acknowledged risk scores. CONCLUSIONS: ML models can accurately predict the 30-day mortality of critical PE patients, which could further be used to reduce the burden of ICU stay, decrease the mortality and improve the quality of life for critical PE patients.

Development of SAB model for predicting mortality in intensive care unit after aortic aneurysm surgery.

BACKGROUND: Aortic aneurysm (AA) patients after vascular surgery are at high risk of death, some of them need intensive care. Our aim was to develop a simplified model with baseline data within 24 hours of intensive care unit (ICU) admission to early predict mortality. METHODS: Univariate analysis and least absolute shrinkage and selection operator were used to select important variables, which were then taken into logistic regression to fit the model. Discrimination and validation were used to evaluate the performance of the model. Bootstrap method was conducted to perform internal validation. Finally, decision clinical analysis curve was used to test the clinical usefulness of the model. RESULTS: We obtained baseline data of 482 AA patients from Medical Information Mart for Intensive Care III database, 33 (6.8%) of whom died in ICU. Our final model contained three variables and was called SAB model based on initials of three items [Sepsis, Anion gap, Bicarbonate (SAB)]. Area under the curve of SAB was 0.904 (95% CI: 0.841-0.967) while brier score was 0.043 (95% CI: 0.028-0.057). After internal validation, corrected area under the curve was 0.898 and brier score was 0.045, which showed good prediction ability of SAB model. The model can be assessed on https://vascularmodel.shinyapps.io/AorticAneurysm/. CONCLUSIONS: SAB model derived in this study can be easily used to predict in-ICU mortality of AA patients after surgery precisely.

Association between anion gap and mortality of aortic aneurysm in intensive care unit after open surgery.

BACKGROUND: There has not been a well-accepted prognostic model to predict the mortality of aortic aneurysm patients in intensive care unit after open surgery repair. Otherwise, our previous study found that anion gap was a prognosis factor for aortic aneurysm patients. Therefore, we wanted to investigate the relationship between anion gap and mortality of aortic aneurysm patients in intensive care unit after open surgery repair. METHODS: From Medical Information Mart for Intensive Care III, data of aortic aneurysm patients in intensive care unit after open surgery were enrolled. The primary clinical outcome was defined as death in intensive care unit. Univariate analysis was conducted to compare the baseline data in different groups stratified by clinical outcome or by anion gap level. Restricted cubic spline was drawn to find out the association between anion gap level and mortality. Subgroup analysis was then conducted to show the association in different level and was presented as frost plot. Multivariate regression models were built based on anion gap and were adjusted by admission information, severity score, complication, operation and laboratory indicators. Receiver operating characteristic curves were drawn to compare the prognosis ability of anion gap and simplified acute physiology score II. Decision curve analysis was finally conducted to indicate the net benefit of the models. RESULTS: A total of 405 aortic aneurysm patients were enrolled in this study and the in-intensive-care-unit (in-ICU) mortality was 6.9%. Univariate analysis showed that elevated anion gap was associated with high mortality (P value < 0.001), and restricted cubic spline analysis showed the positive correlation between anion gap and mortality. Receiver operating characteristic curve showed that the mortality predictive ability of anion gap approached that of simplified acute physiology score II and even performed better in predicting in-hospital mortality (P value < 0.05). Moreover, models based on anion gap showed that 1 mEq/L increase of anion gap improved up to 42.3% (95% confidence interval 28.5-59.8%) risk of death. CONCLUSIONS: The level of serum anion gap was an important prognosis factor for aortic aneurysm mortality in intensive care unit after open surgery.