Background: Inflammation is critical in the etiology and progression of acute respiratory distress syndrome (ARDS). This study aims to rigorously assess the predictive capacity of systemic immune-inflammation index (SII) in determining the outcomes of patients with ARDS. Methods: Patient data were extracted from version 2.2 of the Medical Information Mart for Intensive Care IV (MIMIC-IV). The Receiver Operating Characteristic (ROC) curve was deployed to determine the optimal cutoff value for the SII, facilitating the stratification of participants into distinct cohorts based on SII levels. The relationship between SII and survival outcomes was rigorously evaluated using Cox proportional hazards models. The association between SII and patient survival was rigorously examined using Cox proportional-hazard models. The impact of varying SII levels on mortality was quantitatively assessed through these models, with the results articulated as hazard ratios (HRs) and 95% confidence intervals (CIs). Three distinct models were formulated for this analysis: Model 1 employed univariate Cox regression to relate SII with mortality; Model 2 introduced adjustments for age and sex; and Model 3 extended these adjustments to include age, sex, race, SAPS II, APSIII, Hemoglobin, Albumin, Pneumonia, SpO2, and SBP. Results: Post-application of the inclusion criteria, a cohort of 976 eligible patients was delineated for detailed examination. Univariate analysis focusing on 30-day mortality within the SII >= 1694, the hazard ratio (HR) was 1.42 (95% confidence interval (CI): 1.11, 1.81). However, after adjusting for confounding factors such as age, sex, race, Simplified Acute Physiology Score II (SAPS II), Acute Physiology Score (APS) III, Hemoglobin, Albumin, Pneumonia, SpO2, and Systolic Blood Pressure (SBP), an SII value of >= 1694 was identified as an independent and significant risk factor for mortality in patients with ARDS, with an HR of 1.38 (95% CI: 1.08-1.77, P = 0.0016). This trend was consistent for 90-day and one-year mortality rates. Conclusions: SII surfaced as an autonomous determinant of mortality in ARDS patients, affirming its status as an accessible and dependable prognostic indicator for individuals newly diagnosed with this critical condition. Additional research is imperative to further elucidate the prognostic implications of SII in the therapeutic management of patients with ARDS.

Background Acute respiratory failure (ARF) is a life-threatening complication in elderly patients. We developed a nomogram model to explore the risk factors of prognosis and the short-term mortality in elderly patients with ARF. Methods A total of 759 patients from MIMIC-III database were categorized into the training set and 673 patients from our hospital were categorized into the validation set. Demographical, laboratory variables, SOFA score and APS-III score were collected within the first 24 h after the ICU admission. A 30-day follow-up was performed for all patients. Results Multivariate logistic regression analysis showed that the heart rate, respiratoryrate, systolic pressure, SPO2, albumin and 24 h urine output were independent prognostic factors for 30-day mortality in ARF patients. A nomogram was established based on above independent prognostic factors. This nomogram had a C-index of 0.741 (95% CI [0.7058–0.7766]), and the C-index was 0.687 (95% CI [0.6458–0.7272]) in the validation set. The calibration curves both in training and validation set were close to the ideal model. The SOFA had a C-index of 0.653 and the APS-III had a C-index of 0.707 in predicting 30-day mortality. Conclusion Our nomogram performed better than APS-III and SOFA scores and should be useful as decision support on the prediction of mortality risk in elderly patients with ARF.

Sepsis and septic shock are the main cause of mortality in intensive care units. The prevention and treatment of sepsis remains a significant challenge worldwide. The endothelial cell barrier plays a critical role in the development of sepsis. Aminophylline, a non-selective phosphodiesterase inhibitor, has been demonstrated to reduce endothelial cell permeability. However, little is known regarding the role of aminophylline in regulating vascular permeability during sepsis, as well as the potential underlying mechanisms. In the present study, the Slit2/Robo4 signaling pathway, the downstream protein, vascular endothelial (VE)-cadherin and endothelial cell permeability were investigated in a lipopolysaccharide (LPS)-induced inflammation model. It was indicated that, in human umbilical vein endothelial cells (HUVECs), LPS downregulated Slit2, Robo4 and VE-cadherin protein expression levels and, as expected, increased endothelial cell permeability in vitro during inflammation. After administration of aminophylline, the protein expression levels of Slit2, Robo4 and VE-cadherin were upregulated and endothelial cell permeability was significantly improved. These results suggested that the permeability of endothelial cells could be mediated by VE-cadherin via the Slit2/Robo4 signaling pathway. Aminophylline reduced endothelial permeability in a LPS-induced inflammation model. Therefore, aminophylline may represent a promising candidate for modulating vascular permeability induced by inflammation or sepsis.

Background: To explore the risk factors of prognosis in elderly patients with acute respiratory failure (ARF), and to develop a nomogram model to predict the short-term mortality risk of ARF. Methods: A total of 1432 patients were included in this study from MIMIC-III database. 759 patients were categorized into the training set and 673 patients were categorized into the validation set. Demographical, laboratory variables, SOFA score and APS-III score were collected within the first 24 h after the ICU admission. The univariate and multivariate logistic regression were used to identify risk factors from the training data set. A nomogram model was developed to predict the mortality risk of ARF patients within 30 days according to the risk factors. Results: Multivariate logistic regression analysis showed that the heart rate, respiratory rate, systolic pressure, SPO 2 , albumin and 24 h urine output were independent prognostic factors for 30-day mortality in ARF patients. A nomogram was established based on above independent prognostic factors. This nomogram had C-index of 0.741 (95% CI: 0.7058–0.7766), and the C-index was 0.687 (95%CI: 0.6458-0.7272) in the validation set. The calibration curves both in training and validation set were close to the ideal model. The SOFA had a C-index of 0.653 and the APS-III had a C-index of 0.707 in predicting 30-day mortality. The predictive performance of our nomogram is better than the SOFA score and APS-III score. Conclusions: Our nomogram performed better than APS-III and SOFA scores and should be useful as decision support on the prediction of mortality risk in elderly patients with ARF.

Objective To?screen?the?specific?differential?metabolites?in?the?blood?of?sepsis?patients?by?using?metabonomics?and?reliefF?feature?evaluation?method,and?provide?theoretical?basis?for?the?study?of?sepsis?mechanism.?To?construct?a?sepsis?diagnosis?model?by?using?machine?learning?technology,and?provide?an?intelligent?decision?method?for?sepsis?diagnosis.?Methods 16?patients?with?sepsis?were?collected?from?the?emergency?department?of?the?Second?Affiliated?Hospital?of?Wenzhou?Medical?University?from?January?2014?to?January?2015.?At?the?same?time,16?healthy?persons?were?selected?as?control?group.?Venous?blood?was?collected?from?sepsis?group?and?healthy?control?group?respectively.?Serum?was?collected?after?centrifugation.?Blood?metabolic?differences?between?sepsis?group?and?healthy?control?group?were?detected?by?GC-MS?metabonomics,and?reliefF?characteristics?were?used?to?evaluate.?Estimation?methods?were?used?to?screen?specific?differential?metabolites?in?sepsis?blood,and?SVM?classification?algorithm?in?machine?learning?was?used?to?construct?an?intelligent?diagnosis?model.?Results Thirteen?metabolic?differentials?were?screened?out, among?which?glycerol,tetradecanoic?acid,beta-D-glucuronide?and?glycine?were?significantly?different?between?the?two?groups(P<0.05).?The?sensitivity,specificity?and?recognition?rate?of?the?SVM?sepsis?intelligent?diagnosis?model?were?100%,75%?and?87.5%?respectively.?Conclusion Based?on?metabonomics?technology,reliefF?feature?evaluation?method?was?used?to?successfully?screen?differential?metabolites?with?sepsis?specificity,and?a?sepsis?diagnosis?model?based?on?SVM?was?constructed.?It?provides?an?intelligent?decision-making?method?for?sepsis?diagnosis?and?a?reference?for?exploring?other?disease?diagnosis?mathematical?models.