Objective: This study aimed to establish a risk assessment model to predict postoperative severe acute lung injury (ALI) risk in patients with acute type A aortic dissection (ATAAD). Methods: Consecutive patients with ATAAD admitted to our hospital were included in this retrospective assessment and placed in the postoperative severe ALI and nonsevere ALI groups based on the presence or absence of ALI within 72 h postoperatively (oxygen index [OI] ≤ 100 mmHg). Patients were then randomly divided into training and validation groups in a ratio of 8:2. Univariate and multivariate stepwise forward logistic regression analyses were used to statistically assess data and establish the prediction model. The prediction model's effectiveness was evaluated via 10-fold cross-validation of the validation group to facilitate the construction of a nomogram. Results: After the screening, 479 patients were included in the study: 132 (27.6%) in the postoperative severe ALI group and 347 (72.4%) in the postoperative nonsevere ALI group. Based on multivariate logistics regression analyses, the following variables were included in the model: coronary heart disease, cardiopulmonary bypass (CPB) ≥ 257.5 min, left atrium diameter ≥ 35.5 mm, hemoglobin ≤ 139.5 g/L, preCPB OI ≤ 100 mmHg, intensive care unit OI ≤ 100 mmHg, left ventricular posterior wall thickness ≥ 10.5 mm, and neutrophilic granulocyte percentage ≥ 0.824. The area under the receiver operating characteristic (ROC) curve of the modeling group was 0.805 and differences between observed and predicted values were not deemed statistically significant via the Hosmer-Lemeshow test (χ 2 = 6.037, df = 8, p = .643).For the validation group, the area under the ROC curve was 0.778, and observed and predicted value differences were insignificant when assessed using the Hosmer-Lemeshow test (χ 2 = 3.3782, df = 7; p = .848). The average 10-fold crossvalidation score was 0.756. Conclusions:This study established a prediction model and developed a nomogram to determine the risk of postoperative severe ALI after ATAAD. Variables used in the model were easy to obtain clinically and the effectiveness of the model was good.

Objective: This study aimed to establish a risk assessment model to predict postoperative severe acute lung injury (ALI) risk in patients with acute type A aortic dissection (ATAAD). Methods: Consecutive patients with ATAAD admitted to our hospital were included in this retrospective assessment and placed in the postoperative severe ALI and non-severe ALI groups based on the presence or absence of ALI within 72 h postoperatively (oxygen index (OI) ≤100 mmHg). Patients were then randomly divided into training and validation groups in a ratio of 8:2. Logistic regression analyses were used to statistically assess data and establish the prediction model. The prediction model’s effectiveness was evaluated via tenfold cross-validation of the validation group to facilitate construction of a nomogram. Results: After screening, 479 patients were included in the study: 132 (27.5%) in the postoperative severe ALI group and 347 (72.5%) in the postoperative non-severe ALI group. Based on logistics regression analyses, the following variables were included in the model: coronary heart disease (CHD), cardiopulmonary bypass (CPB) ≥257.5 min, left atrium (LA) diameter ≥35.5 mm, hemoglobin ≤139.5 g/L, preCPB OI ≤100 mmHg, intensive care unit (ICU) OI ≤100 mmHg, left ventricular posterior wall thickness (LVPWT) ≥10.5 mm, and neutrophilic granulocyte percentage (NEUT) ≥0.824. The area under the receiver operating characteristic (ROC) curve of the modeling group was 0.805, and differences between observed and predicted values were not deemed statistically significant via the Hosmer–Lemeshow test (χ2=6.037, df=8, P=0.643). For the validation group, the area under the ROC curve was 0.778, and observed and predicted value differences were insignificant when assessed using the Hosmer–Lemeshow test (χ =3.3782, df=7; P=0.848). The average tenfold cross-validation score was 0.756. Conclusions: This study established a prediction model and developed a nomogram to determine the risk of postoperative severe ALI after ATAAD. Variables used in the model were easy to obtain clinically and the effectiveness of the model was good.

BackgroundAcute type A aortic dissection (ATAAD) is a rare, life-threatening condition affecting the aorta. This study explores the relationship between the level of admission D-dimer, which was assessed during the first 2 h from admission, and in-hospital major adverse events (MAE) with ATAAD.MethodsA total of 470 patients with enhanced computed tomography (CT) confirmed diagnosis of ATAAD who underwent operation treatment in Guangdong Provincial People's hospital between September 2017 and June 2021 were enrolled in the present study. The X-tile program was used to determine the optimal D-dimer thresholds for risk. Restricted cubic spline (RSC) was performed to assess the association between D-dimer and endpoint. The perioperative data were compared between the two groups, univariate and multivariate analyses were used to investigate the risk factors of major adverse events (in-hospital mortality, gastrointestinal bleeding, paraplegia, acute kidney failure, reopen the chest, low cardiac output syndrome, cerebrovascular accident, respiratory insufficiency, MODS, gastrointestinal bleeding, and severe infection).ResultsAmong 470 patients, 151 (32.1%) had MAE. In-hospital mortality was 7.44%. The patients with D-dimer >14,500 ng/ml were more likely to present with acute kidney failure, low cardiac output, cerebrovascular accident, multiple organ dysfunction syndromes (MODS), gastrointestinal bleeding, and severe infection. D-dimer level was an independent risk factor for acute kidney failure (OR 2.09, 95% CI: 1.25–3.51, p = 0.005), MODS (OR 6.40, 95% CI: 1.23–33.39, p = 0.028), gastrointestinal bleeding (OR 17.76, 95% CI: 1.99–158.78, p = 0.010) and mortality (OR 3.17, 95% CI: 1.32–7.63, p = 0.010). Multivariate regression analysis of adverse events also suggested that D-dimer >14,500 ng/ml (OR 1.68, 95% CI: 1.09–2.61, p = 0.020) was the independent risk factor of major adverse events.ConclusionsIncreasing D-dimer levels were independently associated with the in-hospital MAE and thus can be used as a useful prognostic biomarker before the surgery.

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BackgroundsThe goal of this study was to assess the impact of neutrophil count, in patients with acute type A aortic dissection (ATAAD).MethodsThis study retrospectively collected data from patients between September 2017 and June 2021. Youden's index was used to determine the optimal cut-off value for the neutrophil count and patients were divided into two subgroups. A restricted cubic spline (RCS) was used to model the relationship between variables and in-hospital mortality. The least absolute shrinkage and selection operator (LASSO) method and multivariate logistic regression analyses were used to investigate the independent prognostic factors for in-hospital mortality in patients with ATAAD.ResultsA total of 467 patients were enrolled in this study. In-hospital mortality was 7.28%. The group with elevated neutrophil counts had significantly higher mortality than the group with decreased neutrophil counts (10.8% vs. 3.2%, P = 0.02). This data shows that elevated neutrophil count was significantly associated with in-hospital mortality (OR 3.07, 95% CI 1.22–7.62, P = 0.02).ConclusionsNeutrophil count is an independent risk factor for in-hospital mortality in patients with ATAAD. It is an effective inflammatory index, which can be individualized for patients.

In recent years, peer-to-peer (P2P) as a new network application model has become more and more popular for its scalability, high fault tolerance and other outstanding advantages. The main feature of P2P technology is to ensure the network resources distributed on terminal computers, including computing resources, bandwidth resources, content resources, etc., are fully utilized to reduce the consumption of central server resources. This paper analyzes the performance differences of nodes in P2P networks from many aspects, such as available bandwidth, online time and storage space. Based on this mechanism and with the idea of mission algorithm, the data distribution efficiency of this algorithm is significantly improved compared with the classical mission algorithm, which can ensure the minimal network load and achieve the efficiency of flood data distribution strategy.

Chronic atrophic gastritis (CAG) is a common chronically digestive disease which is notoriously characterized by atrophy of the epithelium and glands of the gastric mucosa, reduced number, thinning of the gastric mucosa, thickening of the mucosal base, or pyloric glandular hyperplasia and intestinal glandular hyperplasia, or with atypical hyperplasia. Banxia Xiexin decoction (BXD) has been applied for two thousand years and is considered an effective therapy for functional dyspepsia, gastroesophageal reflux disease and colon cancer. In this current study, to probe into the underlying mechanism of BXD on CAG, network pharmacology was conducted to collect druggable ingredients and predicted targets of BXD and the CAG-associated targets were harvested to take intersection with druggable ingredients from BXD predicted targets to obtain potential critical action targets. Subsequently, GO enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were conducted to elucidate the underlying mechanisms and roles from the perspective of overall pathways and cellular functions. Eventually, molecular docking integrated with molecular dynamics simulations was conducted to further investigate the mechanism of action of BXD active ingredients on CAG from drug molecule-target interactions and to provide a theoretical basis for BXD drug development.

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