ABSTRACT
Objective:To assess the ability of the acute physiology and chronic health evaluation Ⅱ (APACHEⅡ) and trauma-injury severity score (TRISS) in predicting mortality in intensive care unit (ICU) trauma patients.Methods:Databases of PubMed, Cochrane Library, SinoMed, CNKI were retrieved from January 1980 to December 2020. The ability of the APACHE Ⅱ and the TRISS to predict mortality in the ICU trauma patients was compared in the retrieval literatures. The relevant literatures were screened by two researchers independently. The data of the included literatures were extracted, and the quality of the included literatures was evaluated. MetaDiSc 1.4 software was used to test the heterogeneity among studies. Meta-analysis was performed on diagnostic accuracy indicators and the summary receiver operator characteristics curve (SROC curve) was fitted. The area under SROC curve (AUC) of the two scores was compared. Deek test was used to analyze literature publication bias.Results:Six studies were selected with 4 054 patients involved with medium and high quality. Meta-analysis results showed that APACHE Ⅱ and TRISS had low sensitivity [the pooled sensitivity and 95% confidence interval (95% CI) was 0.48 (0.41-0.55) and 0.51 (0.41-0.62)], high specificity [the pooled specificity and 95% CI was 0.96 (0.93-0.97) and 0.98 (0.95-0.99)], the pooled diagnostic odds ratio ( DOR) and 95% CI was 20 (14-28) and 46 (18-120), and overall good performance in terms of AUC [the AUC and 95% CI was 0.79 (0.75-0.82) and 0.80 (0.76-0.83)] in predicting the prognosis of ICU trauma patients. There was no statistical difference in AUC between the two scores ( Z = 1.542, P > 0.05). Deek funnel plot showed little publication bias. Conclusion:Both APACHE Ⅱ and TRISS scores could accurately predict mortality in ICU trauma patients.
ABSTRACT
Recent studies suggested that TLR4 signaling pathways played an important role in the development of LPS-induced acute lung injury (ALI). Sesamin, a sesame lignan exacted from sesame seeds, has been shown to exhibit significant anti-inflammatory activity. The purpose of this study was to investigate the anti-inflammatory effects of sesamin on LPS-induced ALI in mice. Mice ALI model was induced by intratracheal instillation of LPS. Sesamin was given 1 h after LPS challenge. Our results showed that sesamin inhibited LPS-induced lung pathological change, edema, and myeloperoxidase (MPO) activity. Sesamin suppressed LPS-induced inflammatory cytokines TNF-α, IL-6, and IL-1ß production. Furthermore, sesamin inhibited LPS-induced TLR4 expression and NF-κB activation. In conclusion, the results of this study indicated that sesamin protected against LPS-induced ALI by inhibition of TLR4 signaling pathways.
Subject(s)
Acute Lung Injury/drug therapy , Anti-Inflammatory Agents/therapeutic use , Dioxoles/therapeutic use , Lignans/therapeutic use , Toll-Like Receptor 4/metabolism , Animals , Edema/drug therapy , Edema/prevention & control , Enzyme Activation/drug effects , Interleukin-1beta/biosynthesis , Interleukin-6/biosynthesis , Lipopolysaccharides , Lung/pathology , Male , Mice , Mice, Inbred BALB C , NF-kappa B/metabolism , Peroxidase/metabolism , Signal Transduction/drug effects , Tumor Necrosis Factor-alpha/biosynthesisABSTRACT
<p><b>OBJECTIVE</b>To review the research methods of mass casualty incident (MCI) systematically and introduce the concept and characteristics of complexity science and artificial system, computational experiments and parallel execution (ACP) method.</p><p><b>DATA SOURCES</b>We searched PubMed, Web of Knowledge, China Wanfang and China Biology Medicine (CBM) databases for relevant studies. Searches were performed without year or language restrictions and used the combinations of the following key words: "mass casualty incident", "MCI", "research method", "complexity science", "ACP", "approach", "science", "model", "system" and "response".</p><p><b>STUDY SELECTION</b>Articles were searched using the above keywords and only those involving the research methods of mass casualty incident (MCI) were enrolled.</p><p><b>RESULTS</b>Research methods of MCI have increased markedly over the past few decades. For now, dominating research methods of MCI are theory-based approach, empirical approach, evidence-based science, mathematical modeling and computer simulation, simulation experiment, experimental methods, scenario approach and complexity science.</p><p><b>CONCLUSIONS</b>This article provides an overview of the development of research methodology for MCI. The progresses of routine research approaches and complexity science are briefly presented in this paper. Furthermore, the authors conclude that the reductionism underlying the exact science is not suitable for MCI complex systems. And the only feasible alternative is complexity science. Finally, this summary is followed by a review that ACP method combining artificial systems, computational experiments and parallel execution provides a new idea to address researches for complex MCI.</p>
