Ac2-26 Reduced Lung Injury After Cardiopulmonary Bypass via the AKT1/GSK3ß/eNOS Pathway.

INTRODUCTION: Cardiopulmonary bypass (CPB) leads to severe inflammation and lung injury. Our previous study showed that Ac2-26 (an active n-terminal peptide of Annexin A1) can reduce acute lung injury. The aim of this study was to evaluate the effect of Ac2-26 on lung injury in CPB rats. METHODS: Forty rats were randomly divided into the sham, CPB, Ac, Ac/serine/threonine kinase 1 (AKT1), and Ac/ glycogen synthase kinase (GSK)-3ß groups. The rats in the sham group only received anesthesia, intubation, and cannulation. The rats in the other 4 groups received the standard CPB procedure. The rats in the CPB, Ac, Ac/AKT1, and Ac/GSK3ß groups were immediately injected with saline, Ac2-26 (1 mg/kg), Ac2-26 combined with short hairpin RNA (AKT1), or Ac2-26 combined with a GSK3ß inhibitor after CPB. At 12 h after the end of CPB, the PaO2/ fraction of inspired oxygen ratio, wet/dry weight ratio and protein content in the bronchoalveolar lavage fluid (BALF) were recorded. The numbers of macrophages and neutrophils in the BALF and blood were determined. Cytokine levels in the blood and BALF were investigated. Lung tissue histology and apoptosis were estimated. The expression of nuclear factor kappa- B, AKT1, GSK3ß, endothelial nitric oxide synthase and apoptosis-related proteins was analyzed. The survival of all the rats was recorded. RESULTS: Compared with the rats in the sham group, all the parameters examined worsened in the rats that received CPB. Compared with those in the CPB group, Ac2-26 significantly improved pulmonary capillary permeability, reduced cytokine levels, and decreased histological scores and apoptosis. The protective effect of Ac2-26 on lung injury was significantly reversed by AKT1 short hairpin RNA or a GSK3ß inhibitor. CONCLUSIONS: Ac2-26 significantly reduced lung injury and inflammation after CPB. The protective effect of Ac2-26 mainly depended on the AKT1/GSK3ß/endothelial nitric oxide synthase pathway.

Ray of dawn: Anti-PD-1 immunotherapy enhances the chimeric antigen receptor T-cell therapy in Lymphoma patients.

BACKGROUND: Chimeric antigen receptor T (CAR-T) cell therapy, a new adoptive cell therapy, has been widely used to treat lymphoma patients. Immune checkpoint blockade may improve the cytotoxicity of CAR-T cells by reducing the failure of CAR-T cells and improving antitumor activity. It has shown promising efficacy. METHOD: We searched PubMed, the Cochrane Library, Embase and Web of Science from January 2012 to August 2022 to find data reporting the results of CAR-T cells therapy combined with PD-1 in tumor patients. An updated search was conducted in October 2023. The partial response rate (PR), complete response rate (CR), objective response rate (ORR), mortality rate, and incidence of adverse reactions were calculated. RESULTS: We analyzed 57 lymphoma patients from 5 clinical trials. The pooled partial, complete and overall response rates were 21% (95% CI 0.06-0.39, I2 = 0.37%), 27% (95% CI 0.03-0.60, I2 = 60.43%) and 65% (95% CI 0.23-0.98, I2 = 76.31%), respectively. The pooled incidence of cytokine release syndrome, neutropenia, fever, and fatigue was estimated to be 57% (95% CI 0.08-0.99, I2 = 85.20%), 47% (95% CI 0.14-0.81, I2 = 74.17%), 59% (95% CI 0.27-0.89, I2 = 60.23%), and 50% (95% CI 0.13-0.87, I2 = 73.89%), respectively. CONCLUSION: CAR-T-cell therapy combined with anti-PD-1 immunotherapy in the treatment of lymphoma patients has efficacy, and the most common adverse effect is fever. REGISTRATION: The protocol was registered in prospero, with the registration number CRD42022342647.

The accuracy of artificial intelligence in predicting COVID-19 patient mortality: a systematic review and meta-analysis.

BACKGROUND: The purpose of this paper was to systematically evaluate the application value of artificial intelligence in predicting mortality among COVID-19 patients. METHODS: The PubMed, Embase, Web of Science, CNKI, Wanfang, China Biomedical Literature, and VIP databases were systematically searched from inception to October 2022 to identify studies that evaluated the predictive effects of artificial intelligence on mortality among COVID-19 patients. The retrieved literature was screened according to the inclusion and exclusion criteria. The quality of the included studies was assessed using the QUADAS-2 tools. Statistical analysis of the included studies was performed using Review Manager 5.3, Stata 16.0, and Meta-DiSc 1.4 statistical software. This meta-analysis was registered in PROSPERO (CRD42022315158). FINDINGS: Of 2193 studies, 23 studies involving a total of 25 AI models met the inclusion criteria. Among them, 18 studies explicitly mentioned training and test sets, and 5 studies did not explicitly mention grouping. In the training set, the pooled sensitivity was 0.93 [0.87, 0.96], the pooled specificity was 0.94 [0.87, 0.97], and the area under the ROC curve was 0.98 [0.96, 0.99]. In the validation set, the pooled sensitivity was 0.84 [0.78, 0.88], the pooled specificity was 0.89 [0.85, 0.92], and the area under the ROC curve was 0.93 [1.00, 0.00]. In the subgroup analysis, the areas under the summary receiver operating characteristic (SROC) curves of the artificial intelligence models KNN, SVM, ANN, RF and XGBoost were 0.98, 0.98, 0.94, 0.92, and 0.91, respectively. The Deeks funnel plot indicated that there was no significant publication bias in this study (P > 0.05). INTERPRETATION: Artificial intelligence models have high accuracy in predicting mortality among COVID-19 patients and have high prognostic value. Among them, the KNN, SVM, ANN, RF, XGBoost, and other models have the highest levels of accuracy.

Preoperative intestinal microbiome and metabolome in elderly patients with delayed neurocognitive recovery.

BACKGROUND: Delayed neurocognitive recovery (dNCR) is a common complication of the central nervous system in elderly patients. Currently, it is not clear whether the occurrence of dNCR is associated with the intestinal microbiota and its related metabolites. This study investigated the preoperative intestinal microflora and faecal metabolites of dNCR patients. METHODS: Twenty-two elderly urological patients were divided into a dNCR group (D group) and a non-dNCR group (ND group) according to the postoperative Mini-Mental State Examination (MMSE) score on the first and third day after surgery. A postoperative MMSE score ≤ 2 points compared with the preoperative score was considered evidence of dNCR. We used a comprehensive method that combined 16S rRNA gene sequencing and untargeted metabolomics to study the preoperative intestinal microflora and faecal metabolites of the two groups, and conducted correlation analysis between them. RESULTS: Compared with the D group, the microbial community in the ND group was more abundant. At the family level, the ND group was significantly enriched in Lachnospiraceae, Peptostreptococcaceae and Muribaculaceae. At the genus level, the faecal microbiota of the ND group was differentially enriched in Agathobacter, Dorea, Fusicatenibacter, Coprococcus_2 and Romboutsia while that of the D group was differentially enriched in Anaerofilum. Untargeted metabolomics revealed significant differences in eight different metabolites between the two groups, including ribose, ethanol, leucine, maltose, pentadecanoic acid, malonic acid 1,3,4-dihydroxybenzoic acid and 3-hydroxypalmitic acid. In addition, differential metabolites were associated with the abundance of specific bacteria. CONCLUSIONS: The occurrence of dNCR may be associated with the intestinal flora and its related metabolite composition of patients before surgery.