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Background: The Albumin-Bilirubin (ALBI) score, recommended for assessing the prognosis of hepatocellular carcinoma patients, has garnered attention. The efficacy of ALBI score in forecasting the risk of death in sepsis patients remains limited. We designed two cohort studies to assess the association between ALBI score and in-hospital mortality in patients with sepsis. Methods: A retrospective analysis was conducted utilizing data from the Second Affiliated Hospital of Guangzhou Medical University and the Medical Information Mart for Intensive Care IV(MIMIC-IV). Patients diagnosed with sepsis were included in the analysis. The primary outcome was the in-hospital mortality. Multivariate Cox regression analysis was conducted to assess the independent association between the ALBI score and mortality, with adjustment for potential confounders. Subgroup analysis was conducted to assess the robustness of the findings. Results: The Guangzhou Sepsis Cohort (GZSC) of the Second Affiliated Hospital of Guangzhou Medical University comprised 2969 participants, while the MIMIC-IV database included 8841 participants. The ALBI score were categorized into -1.39. After adjusting for confounders, a linear relationship was observed between ALBI score and mortality. Patients with a high ALBI grade were associated with higher in-hospital mortality in both the GZSC (HR: 1.52, 95%CI: 1.24–1.87, p

Indoor microorganisms impact asthma and allergic rhinitis (AR), but the associated microbial taxa often vary extensively due to climate and geographical variations. To provide more consistent environmental assessments, new perspectives on microbial exposure for asthma and AR are needed. Home dust from 97 cases (32 asthma alone, 37 AR alone, 28 comorbidity) and 52 age- and gender-matched controls in Shanghai, China, were analyzed using high-throughput shotgun metagenomic sequencing and liquid chromatography-mass spectrometry. Homes of healthy children were enriched with environmental microbes, including Paracoccus, Pseudomonas, and Psychrobacter, and metabolites like keto acids, indoles, pyridines, and flavonoids (astragalin, hesperidin) (False Discovery Rate

The systemic benefits of anti-inflammatory pharmacotherapy vary across cardiovascular diseases in clinical practice. We aimed to evaluate the application of artificial intelligence to acute type A aortic dissection (ATAAD) patients to determine the optimal target population who would benefit from urinary trypsin inhibitor use (ulinastatin). Patient characteristics at admission in the Chinese multicenter 5A study database (2016–2022) were used to develop an inflammatory risk model to predict multiple organ dysfunction syndrome (MODS). The population (5,126 patients from 15 hospitals) was divided into a 60% sample for model derivation, with the remaining 40% used for model validation. Next, we trained an extreme gradient-boosting algorithm (XGBoost) to develop a parsimonious patient-level inflammatory risk model for predicting MODS. Finally, a top-six-feature tool consisting of estimated glomerular filtration rate, leukocyte count, platelet count, De Ritis ratio, hemoglobin, and albumin was built and showed adequate predictive performance regarding its discrimination, calibration, and clinical utility in derivation and validation cohorts. By individual risk probability and treatment effect, our analysis identified individuals with differential benefit from ulinastatin use (risk ratio [RR] for MODS of RR 0.802 [95% confidence interval (CI) 0.656, 0.981] for the predicted risk of 23.5%–41.6%; RR 1.196 [0.698–2.049] for the predicted risk of 41.6%). By using artificial intelligence to define an individual’s benefit based on the risk probability and treatment effect prediction, we found that individual differences in risk probability likely have important effects on ulinastatin treatment and outcome, which highlights the need for individualizing the selection of optimal anti-inflammatory treatment goals for ATAAD patients.

The increasing of distributed energy and flexible load incentivize market participants to participate in a more active market. A coordinated peer to peer (P2P) trading model with an aggregated alliance and reserve purchasing is proposed in this paper. Under such a coordinated trading model, the market participants form an alliance, where the agents perform aggregated alliance and purchase reserve from a mobile energy storage supplier. To reduce the risk of deviation penalty in P2P trading, the agents seek to maximize the welfare of the entire alliance in the P2P process, while considering product differentiation and deviation risks. The proposed trading model design comprises: (i) a coordinated P2P market design with the aggregated alliance and reserve purchasing, (ii) a two-stage P2P market-clearing model to maximize trading utility while reducing the risk of deviation. To solve this two-stage multivariable coupling problem in a distributed way, we propose a primal–dual based ADMM method. Through the case study, compared with the traditional P2P trading model, the proposed market model can reduce the deviation penalty and improve the comprehensive welfare, while the convergence can be effectively guaranteed.

This study focuses on altering the diffusion combustion characteristics of the self-reflux burner by optimizing the burner nozzle structure. By adjusting the jet angle of each nozzle, enhancing the diffusion effect of fuel and air in the combustion chamber, a low-oxygen combustion atmosphere is formed in the high-temperature zone of the combustion chamber, avoiding local high temperature caused by fuel accumulation and combustion, reducing the combustion rate of nitrogen, and then the thermal NOx emission is reduced. Parallel, inwardly inclined, and outwardly inclined jet angles are studied in simulation, and five working conditions (-10°, -5°, 0°, 5°, and 10°) are calculated, compared, and analyzed. The cooperative coupling mechanism between multiple fields (velocity, concentration, and temperature) is compared and analyzed. We use the diffusion effect to reduce the oxygen concentration in the central axis and achieve the goal of reducing NOx by reducing the maximum combustion temperature. The simulation results show that the maximum and average temperatures of the combustion chamber are reduced by 28.9 K and 15.38 K, respectively, when the jet angle is outwardly tilted by 5°, which reduces the local high temperature; and NOx emissions are in turn reduced by 104.72 ppm.