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Severe infection is a critical health threat to humans, and antibiotic treatment is one of the main therapeutic approaches. Nevertheless, the efficacy of various antibiotic injection regimens in severe infection patients remains uncertain. This study aimed to comprehensively evaluate the impact of various antibiotic injection strategies on patients with severe infection through a meta-analysis. Relevant research literature was collected by searching databases such as PubMed, Embase, and Cochrane Library. The retrieved literature was screened according to inclusion and exclusion criteria. Relevant data, including study design, sample size, and antibiotic regimens, were extracted from the included studies. The Cochrane Collaboration's Risk of Bias tool was employed to assess the risk of bias in each study. Statistical analysis was performed based on the results of the included studies. A total of 15 articles were included, covering various types of severe infection patients, including pulmonary and abdominal infections. The analysis provided insights into mortality rates, treatment efficacy, adverse reactions (ARs), Acute Physiology and Chronic Health Evaluation (APACHE) scores, among other outcomes. The results indicated that combination therapy was superior to monotherapy in terms of mortality rate, treatment efficacy, and APACHE scores, while the incidence of ARs was lower in the monotherapy group compared to the combination therapy group (p < 0.05). Combination therapy showed better treatment efficacy compared to monotherapy, although it was associated with a higher incidence of ARs.
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Antibacterianos , Infecciones , Humanos , Antibacterianos/uso terapéutico , Infecciones/tratamiento farmacológicoRESUMEN
Background: Dilated cardiomyopathy (DCM) is a progressive heart condition characterized by ventricular dilatation and impaired myocardial contractility with a high mortality rate. The molecular characterization of DCM has not been determined yet. Therefore, it is crucial to discover potential biomarkers and therapeutic options for DCM. Methods: The hub genes for the DCM were screened using Weighted Gene Co-expression Network Analysis (WGCNA) and three different algorithms in Cytoscape. These genes were then validated in a mouse model of doxorubicin (DOX)-induced DCM. Based on the validated hub genes, a prediction model and a neural network model were constructed and validated in a separate dataset. Finally, we assessed the diagnostic efficiency of hub genes and their relationship with immune cells. Results: A total of eight hub genes were identified. Using RT-qPCR, we validated that the expression levels of five key genes (ASPN, MFAP4, PODN, HTRA1, and FAP) were considerably higher in DCM mice compared to normal mice, and this was consistent with the microarray results. Additionally, the risk prediction and neural network models constructed from these genes showed good accuracy and sensitivity in both the combined and validation datasets. These genes also demonstrated better diagnostic power, with AUC greater than 0.7 in both the combined and validation datasets. Immune cell infiltration analysis revealed differences in the abundance of most immune cells between DCM and normal samples. Conclusion: The current findings indicate an underlying association between DCM and these key genes, which could serve as potential biomarkers for diagnosing and treating DCM.
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BACKGROUND: RNA modifications, especially N6-methyladenosine, N1-methyladenosine and 5-methylcytosine, play an important role in the progression of cardiovascular disease. However, its regulatory function in dilated cardiomyopathy (DCM) remains to be undefined. METHODS: In the study, key RNA modification regulators (RMRs) were screened by three machine learning models. Subsequently, a risk prediction model for DCM was developed and validated based on these important genes, and the diagnostic efficiency of these genes was assessed. Meanwhile, the relevance of these genes to clinical traits was explored. In both animal models and human subjects, the gene with the strongest connection was confirmed. The expression patterns of important genes were investigated using single-cell analysis. RESULTS: A total of 4 key RMRs were identified. The risk prediction models were constructed basing on these genes which showed a good accuracy and sensitivity in both the training and test set. Correlation analysis showed that insulin-like growth factor binding protein 2 (IGFBP2) had the highest correlation with left ventricular ejection fraction (LVEF) (R = -0.49, P = 0.00039). Further validation expression level of IGFBP2 indicated that this gene was significantly upregulated in DCM animal models and patients, and correlation analysis validation showed a significant negative correlation between IGFBP2 and LVEF (R = -0.87; P = 6*10-5). Single-cell analysis revealed that this gene was mainly expressed in endothelial cells. CONCLUSION: In conclusion, IGFBP2 is an important biomarker of left ventricular dysfunction in DCM. Future clinical applications could possibly use it as a possible therapeutic target.