The role of IL-33 in depression: a systematic review and meta-analysis

Depression has long been considered a disease involving immune hyperactivation. The impact of pro-inflammatory cytokines such as TNF-α, IL-1β, IL-6, and IL-8 on depression has been widely studied. However, the effect of IL-33, another pro-inflammatory cytokine, has been less researched. Currently, research on the correlation between IL-33 and depression risk is inconsistent. In response to these divergent results, we conducted a review and meta-analysis aimed at resolving published research on the correlation between IL-33 and depression risk, and understanding the potential role of IL-33 in the development and treatment of depression. After searching different databases, we analyzed 8 studies. Our meta-analysis showed that IL-33 had a positive correlation with reduced risk of depression. The pooled standard mean differences (SMD) = 0.14, 95% confidence interval (CI): 0.05–0.24. Subgroup analysis results showed that IL-33 and ST2 levels in cerebrospinal fluid and serum is positive correlated with reduced risk of major depressive disorder (MDD) and bipolar disorder (BD). According to the characteristics of the included literature, the results mainly focuses on Caucasian. Furthermore, according to the subgroup analysis of depression-related data sources for disease or treatment, the correlation between IL-33 and depression risk is reflected throughout the entire process of depression development and treatment. Therefore, the change of IL-33 level in serum and cerebrospinal fluid can serve as useful indicators for assessing the risk of depression, and the biomarker provides potential treatment strategies for reducing the burden of the disease

Validation and refinement of a predictive nomogram using artificial intelligence: assessing in-hospital mortality in patients with large hemispheric cerebral infarction

BackgroundLarge Hemispheric Infarction (LHI) poses significant mortality and morbidity risks, necessitating predictive models for in-hospital mortality. Previous studies have explored LHI progression to malignant cerebral edema (MCE) but have not comprehensively addressed in-hospital mortality risk, especially in non-decompressive hemicraniectomy (DHC) patients.MethodsDemographic, clinical, risk factor, and laboratory data were gathered. The population was randomly divided into Development and Validation Groups at a 3:1 ratio, with no statistically significant differences observed. Variable selection utilized the Bonferroni-corrected Boruta technique (p < 0.01). Logistic Regression retained essential variables, leading to the development of a nomogram. ROC and DCA curves were generated, and calibration was conducted based on the Validation Group.ResultsThis study included 314 patients with acute anterior-circulating LHI, with 29.6% in the Death group (n = 93). Significant variables, including Glasgow Coma Score, Collateral Score, NLR, Ventilation, Non-MCA territorial involvement, and Midline Shift, were identified through the Boruta algorithm. The final Logistic Regression model led to a nomogram creation, exhibiting excellent discriminative capacity. Calibration curves in the Validation Group showed a high degree of conformity with actual observations. DCA curve analysis indicated substantial clinical net benefit within the 5 to 85% threshold range.ConclusionWe have utilized NIHSS score, Collateral Score, NLR, mechanical ventilation, non-MCA territorial involvement, and midline shift to develop a highly accurate, user-friendly nomogram for predicting in-hospital mortality in LHI patients. This nomogram serves as valuable reference material for future studies on LHI patient prognosis and mortality prevention, while addressing previous research limitations

Unraveling the impact of nitric oxide, almitrine, and their combination in COVID-19 (at the edge of sepsis) patients: a systematic review

Introduction: During the coronavirus disease 2019 (COVID-19) pandemic, a large number of critically ill and severe COVID-19 patients meet the diagnostic criteria for sepsis and even septic shock. The treatments for COVID-19 patients with sepsis are still very limited. For sepsis, improving ventilation is one of the main treatments. Nitric oxide (NO) and almitrine have been reported to improve oxygenation in patients with “classical” sepsis. Here, we conducted a systematic review and meta-analysis to evaluate the efficacy and safety of NO, almitrine, and the combination of both for COVID-19 (at the edge of sepsis) patients.Method: A systematic search was performed on Embase, PubMed, the Cochrane Library, the Web of Science, Wanfang Data, and China National Knowledge Infrastructure. Randomized clinical trials, cohort studies, cross-sectional studies, case-control studies, case series, and case reports in COVID-19 patients with suspected or confirmed sepsis were performed. Study characteristics, patient demographics, interventions, and outcomes were extracted from eligible articles.Results: A total of 35 studies representing 1,701 patients met eligibility criteria. Inhaled NO did not affect the mortality (OR 0.96, 95% CI 0.33–2.8, I2 = 81%, very low certainty), hospital length of stay (SMD 0.62, 95% CI 0.04–1.17, I2 = 83%, very low certainty), and intubation needs (OR 0.82, 95% CI 0.34–1.93, I2 = 56%, very low certainty) of patients with COVID-19 (at the edge of sepsis). Meanwhile, almitrine did not affect the mortality (OR 0.44, 95% CI 0.17–1.13, low certainty), hospital length of stay (SMD 0.00, 95% CI -0.29–0.29, low certainty), intubation needs (OR 0.94, 95% CI 0.5–1.79, low certainty), and SAEs (OR 1.16, 95% CI 0.63–2.15, low certainty). Compared with pre-administration, the PaO2/FiO2 of patients with NO (SMD-0.87, 95% CI -1.08–0.66, I2 = 0%, very low certainty), almitrine (SMD-0.73, 95% CI-1.06–0.4, I2 = 1%, very low certainty), and the combination of both (SMD-0.94, 95% CI-1.71–0.16, I2 = 47%, very low certainty) increased significantly.Conclusion: Inhaled NO, almitrine, and the combination of the two drugs improved oxygenation significantly, but did not affect the patients’ mortality, hospitalization duration, and intubation needs. Almitrine did not significantly increase the patients’ SAEs. Well-designed high-quality studies are needed for establishing a stronger quality of evidence.Systematic Review Registration:https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=367667, identifier CRD42022367667

Household, community, sub-national and country-level predictors of primary cooking fuel switching in nine countries from the PURE study

Introduction. Switchingfrom polluting (e.g. wood, crop waste, coal)to clean (e.g. gas, electricity) cooking fuels can reduce household air pollution exposures and climate-forcing emissions.While studies have evaluated specific interventions and assessed fuel-switching in repeated cross-sectional surveys, the role of different multilevel factors in household fuel switching, outside of interventions and across diverse community settings, is not well understood. Methods.We examined longitudinal survey data from 24 172 households in 177 rural communities across nine countries within the Prospective Urban and Rural Epidemiology study.We assessed household-level primary cooking fuel switching during a median of 10 years offollow up (∼2005–2015).We used hierarchical logistic regression models to examine the relative importance of household, community, sub-national and national-level factors contributing to primary fuel switching. Results. One-half of study households(12 369)reported changing their primary cookingfuels between baseline andfollow up surveys. Of these, 61% (7582) switchedfrom polluting (wood, dung, agricultural waste, charcoal, coal, kerosene)to clean (gas, electricity)fuels, 26% (3109)switched between different polluting fuels, 10% (1164)switched from clean to polluting fuels and 3% (522)switched between different clean fuels

<i>g</i>-Good-Neighbor Diagnosability of Arrangement Graphs under the PMC Model and MM* Model

Diagnosability of a multiprocessor system is an important research topic. The system and interconnection network has a underlying topology, which usually presented by a graph G = ( V , E ) . In 2012, a measurement for fault tolerance of the graph was proposed by Peng et al. This measurement is called the g-good-neighbor diagnosability that restrains every fault-free node to contain at least g fault-free neighbors. Under the PMC model, to diagnose the system, two adjacent nodes in G are can perform tests on each other. Under the MM model, to diagnose the system, a node sends the same task to two of its neighbors, and then compares their responses. The MM* is a special case of the MM model and each node must test its any pair of adjacent nodes of the system. As a famous topology structure, the ( n , k ) -arrangement graph A n , k , has many good properties. In this paper, we give the g-good-neighbor diagnosability of A n , k under the PMC model and MM* model

The Tightly Super 2-good-neighbor connectivity and 2-good-neighbor Diagnosability of Crossed Cubes

The reliability of an interconnection network is an important issue for multiprocessor systems. We know that connectivity and the diagnosability are two important parameters for measuring the reliability of an interconnection network. In 2012, Peng et al. proposed the g-good-neighbor diagnosability, which has been widely accepted as a new measure of the diagnosability by restricting that every fault-free vertex contains at least gnbspfault-free neighbors. As an important variant of the hypercube, the n-dimensional crossed cube CQnnbsphas many good properties. In this paper, we show that (1) the 2-good-neighbor connectivity of nbspCQnnbspis 4n-8nbspfor nge4, (2) CQnnbspis tightly (4n-8)nbspsuper 2-good-neighbor connected for nge6nbspand (3) the 2-good-neighbor diagnosability of nbspCQnnbspis 4n-5nbspunder the PMC model and MM* model for nge4nbsp