Unveiling the future of COVID-19 patient care: groundbreaking prediction models for severe outcomes or mortality in hospitalized cases.

Background: Previous studies have identified COVID-19 risk factors, such as age and chronic health conditions, linked to severe outcomes and mortality. However, accurately predicting severe illness in COVID-19 patients remains challenging, lacking precise methods. Objective: This study aimed to leverage clinical real-world data and multiple machine-learning algorithms to formulate innovative predictive models for assessing the risk of severe outcomes or mortality in hospitalized patients with COVID-19. Methods: Data were obtained from the Taipei Medical University Clinical Research Database (TMUCRD) including electronic health records from three Taiwanese hospitals in Taiwan. This study included patients admitted to the hospitals who received an initial diagnosis of COVID-19 between January 1, 2021, and May 31, 2022. The primary outcome was defined as the composite of severe infection, including ventilator use, intubation, ICU admission, and mortality. Secondary outcomes consisted of individual indicators. The dataset encompassed demographic data, health status, COVID-19 specifics, comorbidities, medications, and laboratory results. Two modes (full mode and simplified mode) are used; the former includes all features, and the latter only includes the 30 most important features selected based on the algorithm used by the best model in full mode. Seven machine learning was employed algorithms the performance of the models was evaluated using metrics such as the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, and specificity. Results: The study encompassed 22,192 eligible in-patients diagnosed with COVID-19. In the full mode, the model using the light gradient boosting machine algorithm achieved the highest AUROC value (0.939), with an accuracy of 85.5%, a sensitivity of 0.897, and a specificity of 0.853. Age, vaccination status, neutrophil count, sodium levels, and platelet count were significant features. In the simplified mode, the extreme gradient boosting algorithm yielded an AUROC of 0.935, an accuracy of 89.9%, a sensitivity of 0.843, and a specificity of 0.902. Conclusion: This study illustrates the feasibility of constructing precise predictive models for severe outcomes or mortality in COVID-19 patients by leveraging significant predictors and advanced machine learning. These findings can aid healthcare practitioners in proactively predicting and monitoring severe outcomes or mortality among hospitalized COVID-19 patients, improving treatment and resource allocation.

Heme oxygenase-1 mediates the inhibitory actions of brazilin in RAW264.7 macrophages stimulated with lipopolysaccharide.

Brazilin, the main constituent of Caesalpinia sappan L., is a natural red pigment that has been reported to possess anti-inflammatory properties. This study aimed to identify a novel anti-inflammatory mechanism of brazilin. We found that brazilin did not cause cytotoxicity below 300 microM, and activated heme oxygenase-1 (HO-1) protein synthesis in a concentration-dependent manner at 10-300 microM in RAW264.7 macrophages without affecting mRNA transcription of HO-1. Additionally, brazilin increased bilirubin production and HO-1 activity in RAW264.7 macrophages. In lipopolysaccharide (LPS)-stimulated macrophages, brazilin suppressed the release of nitric oxide (NO), prostaglandin E(2) (PGE(2)), interleukin (IL)-1beta and tumor necrosis factor-alpha (TNF-alpha), and reduced the expression of inducible nitric oxide synthase (iNOS). A specific inhibitor of HO-1, Zn(II) protoporphyrin IX, blocked the suppression of NO production, cytokines release and iNOS expression by brazilin. These results suggest that brazilin possesses anti-inflammatory actions in macrophages and works through a novel mechanism involving the action of HO-1.

Suppression of lipopolysaccharide-induced of inducible nitric oxide synthase and cyclooxygenase-2 by Sanguis Draconis, a dragon's blood resin, in RAW 264.7 cells.

Sanguis Draconis (SD) is a kind of dragon's blood resin that is obtained from Daemomorops draco (Palmae). It is used in traditional medicine and has shown anti-inflammatory activity in some diseases. In this study, we examined the effects of Sanguis Dranonis ethanol extract (SDEE) on LPS-induced inflammation using RAW 264.7 cells. Our data indicated that SDEE inhibits LPS-stimulated NO, PGE2, IL-1 beta and TNF-alpha release, and iNOS and COX-2 expression. Furthermore, SDEE suppressed the LPS-induced p65 expression of NF-kappa B, which was associated with the inhibition of I kappa B-alpha degradation. We also found that the expression of HO-1 was significantly increased in RAW 264.7 cells by SDEE. These results suggest among possibilities of anti-inflammation that SDEE inhibits the production of NO and PGE2 by the down-regulation of iNOS and COX-2 gene expression via the suppression of NF-kappaB (p65) activation. SDEE can induce HO-1 over-expression in macrophage cells, which indicates that it may possess antioxidant properties. This result means that SEDD its anti-inflammatory effects in macrophages may be through a novel mechanism that involves the action of HO-1. Thus, SD could provide a potential therapeutic approach for inflammation-associated disorders.