Background Vaccination has been proven to be an effective approach against the coronavirus disease 2019 (COVID-19) pandemic. This study aimed to determine the acceptance rate and factors influencing acceptance of COVID-19 vaccination among people living with HIV (PLWH) in Guangxi, China. Methods A cross-sectional survey was carried out in five cities in Guangxi, China from May 7 to June 1, 2021. Questionnaires on the acceptance of COVID-19 vaccination and the related factors were conducted among PLWH recruited by simple random sampling. Univariate and multivariate logistic regression analyses were performed to identify factors associated with acceptance of COVID-19 vaccination. Results Of all valid respondents (n = 903), 72.9% (n = 658) were willing to receive COVID-19 vaccination. Fear of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection was the main reason for being willing to receive vaccination (76.0%), while the main reasons for not willing were the concerns about vaccine safety (54.7%) and the vaccination’s effect on antiretroviral therapy (ART) (50.6%). The most important factors influencing acceptance were the perception that vaccination is unsafe for HIV-infected people (aOR = 0.082, 95% CI = 0.024–0.282) and the poor efficacy in preventing SARS-CoV-2 infection in HIV-infected people (aOR = 0.093, 95% CI = 0.030–0.287). Other factors associated with acceptance included Zhuang ethnicity (aOR = 1.653, 95% CI = 1.109–2.465), highest education level of middle school, high school or above (aOR = 1.747, 95% CI = 1.170–2.608; aOR = 2.492, 95% CI = 1.326–4.682), and the vaccination having little effect on ART efficacy (aOR = 2.889, 95% CI = 1.378–6.059). Conclusions Acceptance rate of the COVID-19 vaccination is relatively low among PLWH compared to the general population in China, although some patients refused vaccination due to concerns about vaccine safety and vaccination affecting ART efficacy. More research is needed to investigate the impact of the COVID-19 vaccines on ART efficacy and the effectiveness in preventing SARS-CoV-2 infection among PLWH.
Background: COVID-19 is a public health emergency of international concern. Its incidence rates and mortality are very high; however, so far, an effective drug treatment remains unknown. Based on the role of convalescent plasma therapy in previously identified viral pneumonias, patients with severe COVID-19 have been given this therapy. This systematic review and meta-analysis aimed to summarize the clinical evidence regarding the efficacy and safety of convalescent plasma therapy in the treatment of severe COVID-19. Methods: PubMed, Embase, Ovid, China Knowledge Network, China Biomedical, VIP Chinese Sci-tech Journal, Wanfang Database, and the International Clinical Trials Registry Platform were searched up to 21 June 2020, to identify clinical studies and registered trials on the use of convalescent plasma in the treatment of critically ill patients with COVID-19. Stata 13.0 was used to perform Meta-analysis. All records were screened as per the protocol eligibility criteria. Results: Nineteen clinical reports regarding convalescent plasma in the treatment of severe COVID-19 were included. Through systematic analysis, convalescent plasma was found to yield some efficacy on severe COVID-19 and had almost no obvious adverse reactions. Conclusion: Convalescent plasma therapy seems to yield some efficacy among patients with severe COVID-19 and almost no obvious adverse reactions were found. However, at present, the clinical evidence is insufficient, and there is an urgent need for support from high-quality clinical trial data.
Objective Talaromycosis is a serious regional disease endemic in Southeast Asia. In China, Talaromyces marneffei (T. marneffei) infections is mainly concentrated in the southern region, especially in Guangxi, and cause considerable in-hospital mortality in HIV-infected individuals. Currently, the factors that influence in-hospital death of HIV/AIDS patients with T. marneffei infection are not completely clear. Existing machine learning techniques can be used to develop a predictive model to identify relevant prognostic factors to predict death and appears to be essential to reducing in-hospital mortality. Methods We prospectively enrolled HIV/AIDS patients with talaromycosis in the Fourth People’s Hospital of Nanning, Guangxi, from January 2012 to June 2019. Clinical features were selected and used to train four different machine learning models (logistic regression, XGBoost, KNN, and SVM) to predict the treatment outcome of hospitalized patients, and 30% internal validation was used to evaluate the performance of models. Machine learning model performance was assessed according to a range of learning metrics, including area under the receiver operating characteristic curve (AUC). The SHapley Additive exPlanations (SHAP) tool was used to explain the model. Results A total of 1927 HIV/AIDS patients with T. marneffei infection were included. The average in-hospital mortality rate was 13.3% (256/1927) from 2012 to 2019. The most common complications/coinfections were pneumonia (68.9%), followed by oral candida (47.5%), and tuberculosis (40.6%). Deceased patients showed higher CD4/CD8 ratios, aspartate aminotransferase (AST) levels, creatinine levels, urea levels, uric acid (UA) levels, lactate dehydrogenase (LDH) levels, total bilirubin levels, creatine kinase levels, white blood-cell counts (WBC) counts, neutrophil counts, procaicltonin levels and C-reactive protein (CRP) levels and lower CD3+ T-cell count, CD8+ T-cell count, and lymphocyte counts, platelet (PLT), high-density lipoprotein cholesterol (HDL), hemoglobin (Hb) levels than those of surviving patients. The predictive XGBoost model exhibited 0.71 sensitivity, 0.99 specificity, and 0.97 AUC in the training dataset, and our outcome prediction model provided robust discrimination in the testing dataset, showing an AUC of 0.90 with 0.69 sensitivity and 0.96 specificity. The other three models were ruled out due to poor performance. Septic shock and respiratory failure were the most important predictive features, followed by uric acid, urea, platelets, and the AST/ALT ratios. Conclusion The XGBoost machine learning model is a good predictor in the hospitalization outcome of HIV/AIDS patients with T. marneffei infection. The model may have potential application in mortality prediction and high-risk factor identification in the talaromycosis population.
The cardioprotective drugs used for treatment against ischemia/reperfusion (MI/R) injury have been well evaluated and are considered inadequate. The Chinese herbal medicine formula, Xinji pill (XJP) has been used traditionally for the prevention and treatment of ischemic heart diseases for decades. In the present study, the cardioprotective effects of XJP against MI/R injury were assessed in vivo and its possible mechanism was examined. Male Sprague‑Dawley rats were selected for establishing an MI/R model, which was induced by ischemia for 30 min followed by 24 h reperfusion. Drugs and saline were administered intragastrically from day 14 prior to MI/R. Blood samples were collected for biochemical detection. The rats were then sacrificed and cardiac muscle tissues were harvested. The mRNA expression levels of antioxidant genes were measured by reverse transcription‑quantitative polymerase chain reaction and the protein levels were measured by western blotting. Pretreatment with XJP for 14 days protected the heart against I/R‑induced myocardial function disorder, protected against heart injury, as demonstrated by normalized serum levels of lactate dehydrogenase and creatine kinase, and suppressed oxidative stress. XJP markedly upregulated the expression of antioxidant genes, including superoxide dismutase, catalase, glutathione reductase and glutathione peroxidase, and promoted the protein expression of heme oxygenase‑1 and NFE2‑related factor 2 (Nrf2) in the heart tissues. Furthermore, Akt kinase was confirmed to be upstream of Nrf2 in the XJP treatment. LY294002, a specific inhibitor of Akt, significantly eliminated the cardioprotective effects of XJP. In conclusion, these results demonstrated that XJP exhibited notable cardioprotective properties, in which the Akt/Nrf2 signaling pathway may be involved.
Background. Mahai capsules (MHC) have been deemed to be an effective herb combination for treatment of cardiovascular diseases (CVD) development and improvement of the life quality of CVD patients. To systematically explore the mechanisms of MHC in CVD, a network pharmacology approach mainly comprising target prediction, network construction, biological process and pathway analysis, and related diseases was adopted in this study. Methods. We collected the bioactive compounds and potential targets of MHC through the TCMSP servers. Candidate targets related to CVD were collected from Therapeutic Targets Database and PharmGkb database and analyzed using ClueGO plugin in Cytoscape. KEGG pathway was enriched and analyzed through the EnrichR platform, and protein-protein interaction networks were calculated by STRING platform. The compound-target, target-disease, and compound-target-disease networks were constructed using Cytoscape. Results. A total of 303 targets of the 57 active ingredients in MHC were obtained. The network analysis showed that PTGS2, PTGS1, HSP90, Scn1a, estrogen receptor, calmodulin, and thrombin were identified as key targets of MHC in the treatment of CVD. The functional enrichment analysis indicated that MHC probably produced the therapeutic effects against CVD by synergistically regulating many biological pathways, such as PI3K-Akt, TNF, HIF-1, FoxO, apoptosis, calcium, T-cell receptor, VEGF, and NF-kappa B signaling pathway. Conclusions. In summary, the analysis of the complete profile of the pharmacological properties, as well as the elucidation of targets, networks, and pathways, can further illuminate that the underlying mechanisms of MHC in CVD might be strongly associated with its synergic regulation of inflammation, apoptosis, and immune function, and provide new clues for its future development of therapeutic strategies and basic research.
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