Importance: Coronavirus disease 2019 (COVID-19) is a pandemic with no specific drugs and high mortality. The most urgent thing is to find effective treatments. Objective: To determine whether hydroxychloroquine application may be associated with a decreased risk of death in critically ill COVID-19 patients and what is potential mechanism. Design, Setting and Patients: This retrospective study included all 568 critically ill COVID-19 patients who were confirmed by pathogen laboratory tests despite antiviral treatment and had severe acute respiratory distress syndrome, PAO2/FIO2 <300 with need of mechanical ventilation in Tongji Hospital, Wuhan, between February 1 of 2020 to April 8 of 2020. All 568 patients received comparable basic treatments including antiviral drugs and antibiotics, and 48 of them additionally received oral hydroxychloroquine (HCQ) treatment (200 mg twice a day for 7-10 days). Primary endpoint is mortality of patients, and inflammatory cytokines levels were compared between hydroxychloroquine and non-hydroxychloroquine (NHCQ) treatments. MAIN OUTCOMES AND MEASURES: In-hospital death and hospital stay time (day) were obtained, level of inflammatory cytokine (IL-6) was measured and compared between HCQ and NHCQ treatments. RESULTS: The median age of 568 critically ill patients is 68 (57, 76) years old with 37.0% being female. Mortalities are 18.8% (9/48) in HCQ group and 45.8% (238/520) in NHCQ group (p<0.001). The time of hospital stay before patient death is 15 (10-21) days and 8 (4 - 14) days for the HCQ and NHCQ groups, respectively (p<0.05). The level of inflammatory cytokine IL-6 was significantly lowered from 22.2 (8.3-118.9) pg/mL at the beginning of the treatment to 5.2 (3.0-23.4) pg/ml (p<0.05) at the end of the treatment in the HCQ group but there is no change in the NHCQ group. CONCLUSIONS AND RELEVANCE: Hydroxychloroquine treatment is significantly associated with a decreased mortality in critically ill patients with COVID-19 through attenuation of inflammatory cytokine storm. Therefore, hydroxychloroquine should be prescribed for treatment of critically ill COVID-19 patients to save lives. Key Points: Question: Could hydroxychloroquine administration be beneficial in the treatment of critically ill patients with COVID-19? Findings: In this retrospective study, a total of 568 critically ill patients with COVID-19 all received basic therapy and additionally 48 of them received hydroxychloroquine for 7–10 days (200 mg twice per day). Hydroxychloroquine treatment is significantly associated with a decreased mortality in critically ill COVID-19 patients and attenuated inflammatory cytokine IL-6 level. Meaning: Our data suggest that hydroxychloroquine could be used to treat critically ill patients with COVID-19 which may save a lot of lives.

(1) Background: The molecular mechanism of oxidative stress-related genes (OSRGs) in myocardial ischemia–reperfusion injury (MIRI) has not been fully elucidated. (2) Methods: Differential expression analysis, enrichment analysis, and PPI analysis were performed on the MIRI-related datasets GSE160516 and GSE61592 to find key pathways and hub genes. OSRGs were obtained from the Molecular Signatures Database (MSigDB). The expression pattern and time changes of them were studied on the basis of their raw expression data. Corresponding online databases were used to predict miRNAs, transcription factors (TFs), and therapeutic drugs targeting common differentially expressed OSRGs. These identified OSRGs were further verified in the external dataset GSE4105 and H9C2 cell hypoxia–reoxygenation (HR) model. (3) Results: A total of 134 DEGs of MIRI were identified which were enriched in the pathways of “immune response”, “inflammatory response”, “neutrophil chemotaxis”, “phagosome”, and “platelet activation”. Six hub genes and 12 common differentially expressed OSRGs were identified. A total of 168 miRNAs, 41 TFs, and 21 therapeutic drugs were predicted targeting these OSRGs. Lastly, the expression trends of Aif1, Apoe, Arg1, Col1a1, Gpx7, and Hmox1 were confirmed in the external dataset and HR model. (4) Conclusions: Aif1, Apoe, Arg1, Col1a1, Gpx7, and Hmox1 may be involved in the oxidative stress mechanism of MIRI, and the intervention of these genes may be a potential therapeutic strategy.

Background: Acute myocardial infarction (AMI) is one of the most serious cardiovascular diseases. Apoptosis is a type of programmed cell death that causes DNA degradation and chromatin condensation. The role of apoptosis in AMI progression remains unclear. Methods: Three AMI-related microarray datasets (GSE48060, GSE66360 and GSE97320) were obtained from the Gene Expression Omnibus database and combined for further analysis. Differential expression analysis and enrichment analysis were performed on the combined dataset to identify differentially expressed genes (DEGs). Apoptosis-related genes (ARGs) were screened through the intersection of genes associated with apoptosis in previous studies and DEGs. The expression pattern of ARGs was studied on the basis of their raw expression data. Three machine learning algorithms, Least Absolute Shrinkage and Selection Operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE), and Random Forest (RF) were utilized to screen crucial genes in these ARGs. Immune infiltration was estimated by single sample gene set enrichment analysis (ssGSEA). Corresponding online databases were used to predict miRNAs, transcription factors (TFs) and therapeutic agents of crucial genes. A nomogram clinical prediction model of the crucial genes was constructed and evaluated. The Mendelian randomization analysis was employed to investigate whether there is a causal relationship between apoptosis and AMI. Finally, an AMI mouse model was established, and apoptosis in the hearts of AMI mice was assessed via TUNEL staining. qRT-PCR was employed to validate these crucial genes in the hearts of AMI mice. The external dataset GSE59867 was used for further validating the crucial genes. Results: Fifteen ARGs (GADD45A, DDIT3, FEZ1, PMAIP1, IER3, IFNGR1, CDKN1A, GNA15, IL1B, EREG, BCL10, JUN, EGR3, GADD45B, and CD14) were identified. Six crucial genes (CDKN1A, BCL10, PMAIP1, IL1B, GNA15, and CD14) were screened from ARGs by machine learning. A total of 102 miRNAs, 13 TFs and 23 therapeutic drugs were predicted targeting these crucial genes. The clinical prediction model of the crucial genes has shown good predictive capability. The Mendelian randomization analysis demonstrated that apoptosis is a risk factor for AMI. Lastly, the expression of CDKN1A, CD14 and IL1B was verified in the AMI mouse model and external dataset. Conclusions: In this study, ARGs were screened by machine learning algorithms, and verified by qRT-PCR in the AMI mouse model. Finally, we demonstrated that CDKN1A, CD14 and IL1B were the crucial genes involved in apoptosis in AMI. These genes may provide new target for the recognition and intervention of apoptosis in AMI.

Aims: Protein glycosylation regulated by glycosyltransferases is an important type of post-translational modification. The role of the glycosyltransferase genes (GTGs) in heart failure (HF) remains unclear and requires further investigation. Methods: Differential expression analysis was performed on the transverse aortic constriction (TAC)-related dataset GSE36074 to screen out the differentially expressed GTGs. Enrichment and protein–protein interaction analyses explored their functional mechanisms and interconnections. Pearson correlation analysis revealed the relationship between GTGs and pathological cardiac remodelling. The upstream miRNAs of GTGs were predicted using corresponding online databases, and the downstream target genes were identified by weighted correlation network analysis (WGCNA). Computer virtual screening and molecular docking predicted potential therapeutic drugs. The identified GTGs were validated in vivo, in vitro and in the human HF-related dataset GSE57338. Results: Twenty-one differentially expressed GTGs were identified, and these genes were significantly up-regulated in the TAC model except for C1galt1, Extl2 and Pigh. Pearson correlation analysis revealed that 11 GTGs were significantly associated with pathological cardiac remodelling. Fifty-six miRNAs and 31 drugs were predicted to target these GTGs. WGCNA indicated that these GTGs were associated with lipid metabolism-related genes and pathways. Up-regulation of B3gnt9, C1galt1, Gcnt1, Gxylt2 and Mgat5b was observed in the TAC model. GXYLT2 is up-regulated and has high disease-predictive value in patients with dilated cardiomyopathy and ischaemic cardiomyopathy. Knockdown of GXYLT2 in human AC16 cardiomyocytes significantly attenuated angiotensin II (AngII)-induced hypertrophy. Conclusions: Dysregulation of GTG expression may affect TAC-induced HF through metabolic pathways, and GXYLT2 may be a new potential therapeutic target for HF.