Background::Inflammatory dynamics profoundly influence outcomes in critically ill patients; however, the prognostic significance of early inflammatory trajectories remains unclear. This study aimed to characterize the distinct trajectories of inflammatory mediators and evaluate their association with 28-day hospital mortality in patients in the intensive care unit (ICU).Methods::A retrospective cohort of 511 critically ill patients in the ICU (July 2018-June 2024) was analyzed using group-based multitrajectory modeling for longitudinal measurements of C-reactive protein, procalcitonin (PCT), interleukin (IL)-2, IL-4, IL-6, and IL-10 during the first five ICU days (log-transformed as Y=ln[mediator + 1] for standardization). Multivariable Cox regression, inverse probability weighting, and treatment interaction analyses were performed to assess mortality risk and therapeutic responses across trajectories. Results::Three inflammatory trajectories were identified. The mortality rate was 9.5% in the first trajectory (Traj. 1), characterized by persistent mild inflammation (18.6%). In the second trajectory (Traj. 2), a shift toward moderate inflammation was observed with subsequent resolution (55.6%), leading to a mortality rate of 12.3%. Compared with Traj. 1, this trajectory did not demonstrate a substantial increase in death risk (adjusted hazard ratio [HR]=1.14, 95% confidence interval [CI]: 0.54-2.42). The third trajectory (Traj. 3) exhibited sustained hyperinflammation (25.8%), resulting in a mortality rate of 28.0%, which was significantly associated with an elevated risk of death compared with Traj. 1 (adjusted HR=3.41, 95% CI: 1.57 to 7.41). Inverse probability weighting analysis confirmed that compared to Traj. 1, the absolute mortality increase for Traj. 2 was 4.3% ( P=0.234), whereas that for Traj. 3 was 10.5% ( P=0.031). Notably, corticosteroid use reduced mortality, specifically in Traj. 3 patients ( P=0.031), whereas vasopressors and ulinastatin showed no trajectory-specific benefits. Conclusions::Early inflammatory trajectories robustly predict ICU mortality, with sustained hyperinflammation (Traj. 3) conferring the highest risk. Dynamic biomarker profiling may facilitate the development of targeted immunomodulatory strategies in high-risk subgroups.

Sepsis is a life-threatening syndrome, with high morbidity and mortality. Timely treatments and precise interventions are crucial for improving sepsis patient outcomes. Reinforcement learning (RL) models have made promising advances in associating sepsis treatments. However, existing models face obstacles when applied to heterogeneous sepsis patients with an imbalanced distribution of disease severity, struggling to maintain optimal performance. To provide optimal therapeutic intervention strategies for sepsis with different disease severity, we proposed a novel treatment recommendation model named Medical ontology knowledge and disease diagnosis Position aware Transfer learning - Double Dueling Deep Q Network (MPT-D3QN). Through the attention mechanism, medical ontology knowledge and disease diagnosis positions were introduced to achieve a more accurate representation of patient states. Subsequently, an offline deep reinforcement learning algorithm was used to recommend therapeutic intervention strategies for a specific patient population. The transfer learning framework was responsible for the information transfer across patient groups in different domains. We trained and tested MPT-D3QN model on the Multi-Parameter Intelligent Monitoring in Intensive Care III (MIMIC-III) dataset. Compared with the reported strategies in present clinical practice, the MPT-D3QN model could obtain a higher expected return value (12.98 vs. 10.61) and reduce the estimated mortality from 13.20% to 6.15% in all test datasets. Moreover, the experimental test on an external dataset eICU Collaborative Research Database (eICU) further demonstrated the robust generalization capability of the model. Compared with the state-of-the-art models, the proposed model not only guaranteed higher expected returns but also generated optimal and interpretable treatment strategies for sepsis with different disease severity.

BACKGROUND:Weaning from mechanical ventilation remains a critical challenge in intensive care units. Machine learning has shown potential in supporting clinical decisions during this process. OBJECTIVE:Sepsis frequently leads to ALI/ARDS, requiring mechanical ventilation. However, with evolving definitions of weaning, many existing predictive models have become outdated. This study aimed to develop a predictive model based on the standardized WIND framework to accurately predict successful weaning in septic patients under current clinical practices. METHODS:Data from the MIMIC-IV database were analyzed. Univariate analysis identified risk factors for extubation outcomes, and feature selection was performed using LASSO regression with 10-fold cross-validation and recursive feature elimination (RFE). Predictive models, including XGB, RF, and GBM, were evaluated based on AUC and F1 score. SHAP values were used to assess feature importance. RESULTS:A total of 3774 patients were included. Univariate analysis showed that the failed weaning group had longer ICU stays, higher ventilator settings, and elevated levels of blood urea nitrogen, blood glucose, creatinine, SOFA scores, lactate, and platelet count (P < 0.05). Feature selection reduced 46 variables to 12 key predictors. The XGB model performed best, with AUC values of 0.849, 0.838, and 0.825 for the training, internal, and external cohorts, respectively. SHAP analysis identified mean airway pressure, ICU length of stay, and lactate as the most influential predictors. CONCLUSION:We developed an interpretable, accurate XGB-based model to predict weaning outcomes in septic patients.

BACKGROUND:Renal perfusion pressure plays a crucial role in the pathophysiology of acute kidney injury (AKI). While multiple methods are available for calculating renal perfusion pressure, the optimal calculation approach and its true correlation with AKI remain uncertain. This study aims to investigate the nonlinear relationship between various perfusion pressure indices and AKI, clarifying the connection between perfusion pressure, AKI onset, and recovery. METHODS:Three renal perfusion pressure indices were calculated: MAP-CVP, MAP-Plateau pressure, and MAP-CVP-Plateau pressure. Restricted cubic spline (RCS) analysis was used to examine the association between these perfusion indices and AKI incidence. The relationship between MAP-CVP-Plateau pressure and both AKI occurrence and recovery rate was further assessed through linear spline function and categorical analysis. RESULTS:A total of 8,848 ICU patients were included in the study, with an overall AKI incidence of 40%. RCS analysis revealed nonlinear relationships between the three perfusion indices and AKI incidence, each demonstrating different thresholds. ROC analysis indicated that MAP-CVP-Plateau pressure (cutoff value of 55) had the highest predictive value and was thus selected as the primary perfusion index. In the linear spline analysis, a high MAP-CVP-Plateau pressure was significantly associated with a reduced AKI risk when MAP-CVP-Plateau pressure was < 55 (OR 0.95, 95% CI 0.94-0.96, p < 0.01), while this association reversed when MAP-CVP-Plateau pressure exceeded 55 (OR 1.02, 95% CI 1.01-1.03, p < 0.01). For AKI recovery, a high MAP-CVP-Plateau pressure was significantly associated with a higher recovery rate when MAP-CVP-Plateau pressure was < 55 (OR 1.02, 95% CI 1.01-1.04, p < 0.01). However, when MAP-CVP-Plateau pressure was > 55, an elevated MAP-CVP-Plateau pressure was associated with a lower AKI recovery rate (OR 0.96, 95% CI 0.94-0.98, p < 0.01). The categorical analysis results for AKI incidence and recovery were consistent with the nonlinear relationship identified in the RCS analysis. CONCLUSIONS:This study underscores the critical role of perfusion pressure, particularly MAP-CVP-Plateau pressure, in AKI pathophysiology. Both low and high MAP-CVP-Plateau pressure levels were associated with increased AKI incidence and decreased recovery rates in critically ill patients.

The impact of concurrent sepsis on the prognosis in patients with non-traumatic hemorrhagic brain injury (HBI) remains unclear, and the appropriate hemoglobin (HGB) level in HBI patients with sepsis has not been investigated. This study aimed to investigate the impact of sepsis in HBI and the prognosis of patients with different HGB trajectories with/without sepsis. The association between sepsis and prognosis (including neurologic outcome and 28-day mortality) in patients with non-trauma HBI was investigated, and multivariate logistic model, propensity score matching (PSM), and inverse-probability-weighted regression adjustment (IPWRA) were used to reach a causal relationship. Group-based trajectory analysis was adopted to explore the associations between HGB trajectories and outcomes. A total of 3,040 patients were included. Compared with the HBI-without-sepsis group, the HBI-with-sepsis group had higher 28-day mortality and worse neurological outcomes. After adjusting for confounders, the association between sepsis and mortality remains significant in multivariate logistic model (OR 2.31, 95

BACKGROUND:Endogenous metabolite itaconate and its derivative Dimethyl itaconate (DMI) exhibit significant anti-inflammatory effects. Dimethyl Pent-2-Enedioate (DMP), an isomer of DMI, may possess similar properties. This study investigates the anti-inflammatory effects of DMP in LPS-induced macrophages and explores its potential regulatory mechanisms. METHODS:Inflammatory marker levels were assessed at both the mRNA and protein levels using ELISA and qRT-PCR. The activation status of macrophages was evaluated by flow cytometry, quantifying the number of CD40-positive cells. RNA sequencing was conducted to investigate the transcriptomic changes following DMP treatment. Subsequent GO and KEGG enrichment analyses were performed to identify potential mechanisms underlying DMP's effects. Western blot analysis was employed to assess the expression of p-p65, while immunofluorescence analysis was used to examine p65 nuclear translocation, providing insight into the regulatory effects of DMP on the NF-κB signaling pathway. RESULTS:DMP inhibited the expression of inflammatory markers TNF-α, IL-6, and MCP-1 at both mRNA and protein levels. Flow cytometry analysis revealed a decrease in CD40-positive cells. RNA sequencing identified DEGs enriched in inflammation-related pathways. Western blotting and immunofluorescence confirmed that DMP reduced p-p65 expression and inhibited p65 nuclear translocation, suggesting a potential regulatory effect on the NF-κB signaling pathway. CONCLUSION:DMP significantly inhibits LPS-induced inflammation in macrophages, with its underlying mechanisms being complex. Our data demonstrate that DMP exerts its anti-inflammatory effects at least in part through the downregulation of the NF-κB pathway, offering potential applications in the prevention and treatment of inflammation-related diseases.

Mechanical power has been identified as a predictor of prognosis in ARDS; however, previous studies based on cross-sectional data may fail to capture the dynamic pathophysiological changes and progression of pulmonary conditions. This study aimed to investigate the association between mechanical power trajectories and 28-day mortality using longitudinal data. Data were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV, version 2.2) database. Mechanical power was divided into quartiles to compare distribution characteristics and temporal trends across groups. Group-based trajectory modeling (GBTM) identified distinct mechanical power trajectories. Multivariate logistic regression analyzed the association between trajectory groups and 28-day mortality. A total of 1,439 eligible patients were included. Stratification by mechanical power quartiles showed that higher mechanical power was associated with increases in arterial oxygen partial pressure, carbon dioxide, serum creatinine, blood urea nitrogen, potassium, hemoglobin, white blood cell count, respiratory rate, minute ventilation, tidal volume, plateau pressure, positive end-expiratory pressure, FiO₂, and peak airway pressure (all P for trend < 0.001). GBTM identified three trajectories classified as low, medium, and high mechanical power groups. Multivariate logistic regression revealed that compared to the low-power group, the high-power group had a significantly higher risk of 28-day mortality after full adjustment (P = 0.017; OR 1.33; 95

OBJECTIVE:To identify the independent factors of unplanned interruption during continuous renal replacement therapy (CRRT) and construct a risk prediction model, and to verify the clinical application effectiveness of the model.METHODS:A retrospective study was conducted on critically ill adult patients who received CRRT treatment in the intensive care unit (ICU) of Zhejiang Hospital from January 2021 to August 2022 for model construction. According to whether unplanned weaning occurred, the patients were divided into two groups. The potential influencing factors of unplanned CRRT weaning in the two groups were compared. The independent influencing factors of unplanned CRRT weaning were screened by binary Logistic regression and a risk prediction model was constructed. The goodness of fit of the model was verified by a Hosmer-Lemeshow test and its predictive validity was evaluated by receiver operator characteristic curve (ROC curve). Then embed the risk prediction model into the hospital's ICU multifunctional electronic medical record system for severe illness, critically ill patients with CRRT admitted to the ICU of Zhejiang Hospital from November 2022 to October 2023 were prospectively analyzed to verify the model's clinical application effect.RESULTS:(1) Model construction and internal validation: a total of 331 critically ill patients with CRRT were included to be retrospectively analyzed. Among them, there were 238 patients in planned interruption group and 93 patients in unplanned interruption group. Compared with the planned interruption group, the unplanned interruption group was shown as a lower proportion of males (80.6% vs. 91.6%) and a higher proportion of chronic diseases (60.2% vs. 41.6%), poor blood purification catheter function (31.2% vs. 6.3%), as a higher platelet count (PLT) before CRRT initiation [×109/L: 137 (101, 187) vs. 109 (74, 160)], lower level of blood flow rate [mL/min: 120 (120, 150) vs. 150 (140, 180)], higher proportion of using pre-dilution (37.6% vs. 23.5%), higher filtration fraction [23.0% (17.5%, 32.9%) vs. 19.1% (15.7%, 22.6%)], and frequency of blood pump stops [times: 19 (14, 21) vs. 9 (6, 13)], the differences of the above 8 factors between the two groups were statistically significant (all P < 0.05). Binary Logistic regression analysis showed that chronic diseases [odds ratio (OR) = 3.063, 95% confidence interval (95%CI) was 1.200-7.819], blood purification catheter function (OR = 4.429, 95%CI was 1.270-15.451), blood flow rate (OR = 0.928, 95%CI was 0.900-0.957), and frequency of blood pump stops (OR = 1.339, 95%CI was 1.231-1.457) were the independent factors for the unplanned interruption of CRRT (all P < 0.05). These 4 factors were used to construct a risk prediction model, and ROC curve analysis showed that the area under the curve (AUC) predicted by the model was 0.952 (95%CI was 0.930-0.973, P = 0.003 0), with a sensitivity of 88.2%, a specificity of 89.9%, and a maximum value of 1.781 for the Youden index. (2) External validation: prospective inclusion of 110 patients, including 63 planned interruption group and 47 unplanned interruption group. ROC curve analysis showed that the AUC of the risk prediction model was 0.919 (95%CI was 0.870-0.969, P = 0.004 3), with a sensitivity of 91.5%, a specificity of 79.4%, and a maximum value of the Youden index of 1.709.CONCLUSIONS:The risk prediction model for unplanned interruption during CRRT has a high predictive efficiency, allowing for rapid and real-time identification of the high risk patients, thus providing references for preventative nursing.

Background: Sepsis is a syndrome involving multi-organ dysfunction, and the mortality in sepsis patients correlates with the number of lesioned organs. Precise prognosis models play a pivotal role in enabling healthcare practitioners to administer timely and accurate interventions for sepsis, thereby augmenting patient outcomes. Nevertheless, the majority of available models consider the overall physiological attributes of patients, overlooking the asynchronous spatiotemporal interactions among multiple organ systems. These constraints hinder a full application of such models, particularly when dealing with limited clinical data. To surmount these challenges, a comprehensive model, denoted as recurrent Graph Attention Network-multi Gated Recurrent Unit (rGAT-mGRU), was proposed. Taking into account the intricate spatiotemporal interactions among multiple organ systems, the model predicted in-hospital mortality of sepsis using data collected within the 48-hour period post-diagnosis. Material and methods: Multiple parallel GRU sub-models were formulated to investigate the temporal physiological variations of single organ systems. Meanwhile, a GAT structure featuring a memory unit was constructed to capture spatiotemporal connections among multi-organ systems. Additionally, an attention-injection mechanism was employed to govern the data flowing within the network pertaining to multi-organ systems. The proposed model underwent training and testing using a dataset of 10,181 sepsis cases extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database. To evaluate the model's superiority, it was compared with the existing common baseline models. Furthermore, ablation experiments were designed to elucidate the rationale and robustness of the proposed model. Results: Compared with the baseline models for predicting mortality of sepsis, the rGAT-mGRU model demonstrated the largest area under the receiver operating characteristic curve (AUROC) of 0.8777 +/- 0.0039 and the maximum area under the precision-recall curve (AUPRC) of 0.5818 +/- 0.0071, with sensitivity of 0.8358 +/- 0.0302 and specificity of 0.7727 +/- 0.0229, respectively. The proposed model was capable of delineating the varying contribution of the involved organ systems at distinct moments, as specifically illustrated by the attention weights. Furthermore, it exhibited consistent performance even in the face of limited clinical data. Conclusion: The rGAT-mGRU model has the potential to indicate sepsis prognosis by extracting the dynamic spatiotemporal interplay information inherent in multi-organ systems during critical diseases, thereby providing clinicians with auxiliary decision-making support.

目的 探讨影响ICU脓毒症患者肠内喂养不耐受(EFI)的风险因素,并构建临床预测模型.方法 收集浙江医院科研数据库2019年6月至2022年6月符合Sepsis-3标准并启动肠内营养治疗的患者,根据有无发生EFI分为不耐受组和耐受组.利用R软件建立logistic回归模型,绘制列线图和ROC曲线、校准曲线、决策曲线,对预测模型的诊断效能进行评估和验证.结果 共纳入140例患者,其中不耐受组69例,耐受组71例,EFI发生率为49.29％.急性生理学和慢性健康状况评价(APACHE)Ⅱ 评分(OR=1.092,95％CI:1.006～1.185)、持续性肾脏替代治疗(CRRT)(OR=3.584,95％CI:1.043～12.311)、腹内压(OR=8.253,95％CI:2.400～28.370)、改良版危重症营养风险(mNutric)评分(OR=2.916,95％CI:1.842～4.616)、低热卡能量(OR=0.212,95％CI:0.069～0.644)是脓毒症患者发生EFI的风险因素.由此构建的风险预测模型评估脓毒症患者发生EFI的AUC为0.906,最大约登指数0.192,特异度0.833,灵敏度0.875;校准曲线显示该模型预测结果和预期结果具有较好的一致性.结论 APACHE Ⅱ评分、CRRT、腹内压、mNutric评分和低热卡能量等因素与脓毒症患者发生EFI有关.此研究建立的风险预测模型可有效预测脓毒症患者发生EFI的风险,有助于临床医护对患者进行科学、个体化的肠内营养治疗.

Noninvasive ventilation (NIV) has been recognized as a first-line treatment for respiratory failure in patients with chronic obstructive pulmonary disease (COPD) and hypercapnia respiratory failure, which can reduce mortality and burden of intubation. However, during the long-term NIV process, failure to respond to NIV may cause overtreatment or delayed intubation, which is associated with increased mortality or costs. Optimal strategies for switching regime in the course of NIV treatment remain to be explored.For the goal of reducing 28-day mortality of the patients undergoing NIV, Double Dueling Deep Q Network (D3QN) of offline-reinforcement learning algorithm was adopted to develop an optimal regime model for making treatment decisions of discontinuing ventilation, continuing NIV, or intubation. The model was trained and tested using the data from Multi-Parameter Intelligent Monitoring in Intensive Care III (MIMIC-III) and evaluated by the practical strategies. Furthermore, the applicability of the model in majority disease subgroups (Catalogued by International Classification of Diseases, ICD) was investigated. Compared with physician's strategies, the proposed model achieved a higher expected return score (4.25 vs. 2.68) and its recommended treatments reduced the expected mortality from 27.82% to 25.44% in all NIV cases. In particular, for these patients finally received intubation in practice, if the model also supported the regime, it would warn of switching to intubation 13.36 hours earlier than clinicians (8.64 vs. 22 hours after the NIV treatment), granting a 21.7% reduction in estimated mortality. In addition, the model was applicable across various disease groups with distinguished achievement in dealing with respiratory disorders. The proposed model is promising to dynamically provide personalized optimal NIV switching regime for patients undergoing NIV with the potential of improving treatment outcomes.

Objective:To investigate the value of heparin binding protein (HBP) level in early diagnosis of disease severity and prognosis prediction in elderly sepsis patients.Methods:A multi-center prospective study was used. Patients admitted to the intensive care unit (ICU) of 18 tertiary A hospitals across the country during July 2014 and March 2015 were enrolled in the study. Once patients were included in the study, they received fluid resuscitation and active treatment immediately according to the guidelines as needed. Venous blood was collected on the day of enrollment and 24 hours after enrollment, and the levels of plasma HBP, procalcitonin (PCT), and C-reactive protein (CRP) were detected. According to the sepsis 2.0 diagnostic criteria, the patients were divided into sepsis group, severe sepsis group, septic shock group and non-infectious systemic inflammatory response syndrome (SIRS) group. The gender, age, underlying diseases, acute physiology and chronic health scoreⅡ (APACHEⅡ) score, 28-day mortality, and the levels of HBP, PCT, CRP were compared among the groups. The correlation between HBP, PCT, CRP levels and APACHEⅡ score were analyzed using Spearman non parametric correlation coefficient, and the predictive value of HBP for the prognosis of patients was evaluated using survival function.Results:A total of 295 patients were included in the study, including 124 patients with sepsis, 58 patients with severe sepsis, 73 patients with septic shock, and 40 patients with SIRS. There were differences in APACHEⅡ scores, baseline HBP, PCT and CRP levels among the groups (F=3.841; H=14.470, 44.298, 14.778; P < 0.01), and only HBP was correlated with APACHEⅡscore on the day after enrollment (r=-0.196, P < 0.05). The 28-day mortality rates in the SIRS group, sepsis group, severe sepsis group, and septic shock group were 2.50%, 2.42%, 18.97%, and 20.55%, respectively, and the survival rate of patients with HBP≥15 μg/L was higher (χ2=0.020, P < 0.05).Conclusion:The level of HBP is correlated with the severity of disease in elderly sepsis patients. Baseline HBP can be used to predict the prognosis, and patients with baseline HBP >15 ng/mL have an increased risk of death.

Background A training program for intensive care unit (ICU) physicians entitled “Chinese Critical Care Certified Course” (5 C) started in China in 2009, intending to improve the quality of intensive care provision. This study aimed to explore the associations between the 5 C certification of physicians and the quality of intensive care provision in China. Methods This nationwide analysis collected data regarding 5 C-certified physicians between 2009 and 2019. Fifteen ICU quality control indicators (three structural, four procedural, and eight outcome-based) were collected from the Chinese National Report on the Services, Quality, and Safety in Medical Care System. Provinces were stratified into three groups based on the cumulative number of 5 C certified physicians per million population. Results A total of 20,985 (80.41%) physicians from 3,425 public hospitals in 30 Chinese provinces were 5 C certified. The deep vein thrombosis (DVT) prophylaxis rate in the high 5 C physician-number provinces was significantly higher than in the intermediate 5 C physician-number provinces (67.6% vs. 55.1%, p = 0.043), while ventilator-associated pneumonia (VAP) rate in the low 5 C physician-number provinces was significantly higher than in the high 5 C physician-number provinces (14.9% vs. 8.9%, p = 0.031). Conclusions The higher number of 5 C-certified physicians per million population seemed to be associated with higher DVT prophylaxis rates and lower VAP rates in China, suggesting that the 5 C program might have a beneficial impact on the quality of intensive care provision.

The current study identified three septic shock phenotypes according to the pulse index continuous cardiac output parameters: Phenotype-1: normal cardiac output and vascular resistance, high blood volume; Phenotype-2: low cardiac output, normal blood volume, and high vascular resistance. Phenotype-3: normal vascular resistance and blood volume, high cardiac output. The mortality was significantly high in phenotype-1, but comparable between phenotype-2 and phenotype-3 (13/17 (76.4) vs. 13/35 (37.1) vs. 16/46 (34.7), p <0.001). Compared to phenotype 1 and 3, phenotype 2 had a higher AKI incidence and higher creatinine level at discharge.

OBJECTIVE:To investigate the value of high-flow oxygen therapy after weaning in successful extubation of critically ill patients with mechanical ventilation.METHODS:A retrospective study was conducted. The weaned patients who were older than 18 years old and underwent mechanical ventilation for the first time due to cerebrovascular accidents, surgical operations, cardiovascular diseases, and pneumonia admitted to the department of critical care medicine of Zhejiang Hospital from January 2018 to June 2020 were enrolled. Among the patients, 40 cases received high-flow oxygen therapy after weaning, and 37 cases received Venturi combined with the humidifier. The patient's gender, age, primary disease, severity score, duration of mechanical ventilation before weaning, heart rate (HR), blood pressure, pulse oxygen saturation (SpO2) at 0, 6, 12, 18, and 24 hours after weaning, and pH value, arterial partial pressure of oxygen (PaO2), arterial partial pressure of carbon dioxide (PaCO2) at 6, 12, 18, and 24 hours after weaning, the rate of performing mechanical ventilation after weaning, extubation time after weaning, and the rate of reintubation after extubation for 72 hours were collected.RESULTS:There was no significant difference in baseline data such as gender, age, primary disease, severity score, and duration of mechanical ventilation before weaning between the two groups. After weaning, the vital signs of the two groups were stable, and there was no significant difference in HR, systolic blood pressure (SBP), diastolic blood pressure (DBP) or SpO2 at each time point between the two groups. After weaning, the pH of arterial blood gas analysis in the two groups and the fluctuations of PaO2 and PaCO2 in the high-flow group were not obvious. In the Venturi group, PaO2 gradually decreased after weaning, PaCO2 increased significantly at 12 hours, and slowly decreased after 12 hours. The PaO2 from 6 hours and PaCO2 from 12 hours in the high-flow group were significantly lower than those in the Venturi group, and continued to 24 hours [PaO2 (mmHg, 1 mmHg ≈ 0.133 kPa): 112.34±38.25 vs. 156.76±68.44 at 6 hours, 110.92±38.66 vs. 150.64±59.07 at 12 hours, 111.12±36.77 vs. 141.30±39.05 at 18 hours, 110.82±39.37 vs. 139.65±41.50 at 24 hours; PaCO2 (mmHg): 41.30±7.51 vs. 47.42±7.54 at 12 hours, 40.97±6.98 vs. 45.83±8.63 at 18 hours, 40.10±7.06 vs. 46.14±9.15 at 24 hours, all P < 0.01]. The rate of performed mechanical ventilation after weaning and the rate of reintubation after extubation for 72 hours in the high-flow group were significantly lower than those in the Venturi group [17.5% (7/40) vs. 40.5% (15/37), 6.2% (2/32) vs. 31.8% (7/22), both P < 0.05], and the extubation time after weaning was significantly shorter than that in the Venturi group (hours: 22.43±11.72 vs. 28.07±10.42, P < 0.05).CONCLUSIONS:Using high-flow oxygen therapy to the extubation process of critically ill mechanical ventilation patients can reduce the incidence of carbon dioxide retention and the rate of performed mechanical ventilation after weaning, shorten the extubation time after weaning, and reduce the rate of reintubation after extubation for 72 hours.

Background Older adult patients mainly suffer from multiple comorbidities and are at a higher risk of deep venous thrombosis (DVT) during their stay in the intensive care unit (ICU) than younger adult patients. This study aimed to analyze the risk factors for DVT in critically ill older adult patients. Methods This was a subgroup analysis of a prospective, multicenter, observational study of patients who were admitted to the ICU of 54 hospitals in Zhejiang Province from September 2019 to January 2020 (ChiCTR1900024956). Patients aged > 60 years old on ICU admission were included. The primary outcome was DVT during the ICU stay. The secondary outcomes were the 28- and 60-day survival rates, duration of stay in ICU, length of hospitalization, pulmonary embolism, incidence of bleeding events, and 60-day coagulopathy. Results A total of 650 patients were finally included. DVT occurred in 44 (2.3%) patients. The multivariable logistic regression analysis showed that age (≥75 vs 60-74 years old, odds ratio (OR) = 2.091, 95% confidence interval (CI): 1.308-2.846, P = 0.001), the use of analgesic/sedative/muscarinic drugs (OR = 2.451, 95%CI: 1.814-7.385, P = 0.011), D-dimer level (OR = 1.937, 95%CI: 1.511-3.063, P = 0.006), high Caprini risk score (OR = 2.862, 95%CI: 1.321-2.318, P = 0.039), basic prophylaxis (OR = 0.111, 95%CI: 0.029-0.430, P = 0.001), and physical prophylaxis (OR = 0.322, 95%CI: 0.109-0.954, P = 0.041) were independently associated with DVT. There were no significant differences in 28- and 60-day survival rates, duration of stay in ICU, total length of hospitalization, 60-day pulmonary embolism, and coagulation dysfunction between the two groups, while the DVT group had a higher incidence of bleeding events (2.6% vs. 8.9%, P < 0.001). Conclusion In critically ill older adult patients, basic prophylaxis and physical prophylaxis were found as independent protective factors for DVT. Age (≥75 years old), the use of analgesic/sedative/muscarinic drugs, D-dimer level, and high Caprini risk score were noted as independent risk factors for DVT. Trial registration Chinese Clinical Trial Registry (ChiCTR1900024956).URL: http://www.chictr.org.cn/listbycreater.aspx .

Background : Lipopolysaccharide (LPS) desensitization, which is characterized by hyporesponsiveness and a form of immunosuppression, is important in the negative regulation of responses to LPS and inflammatory disease such as sepsis. However, effect of IL-33 in the desensitization to LPS remains unclear. Methods : We used RNA-sequencing technology to analyze changes in mRNA in bone-marrow-derived macrophages (BMDMs) stimulated with LPS. Changes in expression and secretion of inflammatory cytokines were detected by qPCR and ELISA, respectively. Mechanisms were further studied through p65 phosphorylation detection. Results : IL-33 expression was significantly increased in LPS-treated macrophages, indicating its involvement in LPS-induced inflammation. Exogenous IL-33 increased the inflammatory response and ameliorated LPS desensitization by increasing the secretion of proinflammatory cytokines. It also activated p65 phosphorylation in resistant cells. Conclusion : IL-33 can enhance the inflammatory response induced by LPS and ameliorate LPS desensitization possibly by activating the NF- κ B pathway in mouse macrophages.

Recognizing emotion from Electroencephalography (EEG) is a promising and valuable research issue in the field of affective brain-computer interfaces (aBCI). To improve the accuracy of emotion recognition, an emotional feature extraction method is proposed based on the temporal information in the EEG signal. This study adopts microstate analysis as a spatio-temporal analysis for EEG signals. Microstates are defined as a series of momentary quasi-stable scalp electric potential topographies. Brain electrical activity could be modeled as being composed of a time sequence of microstates. Microstate sequences provide an ideal macroscopic window for observing the temporal dynamics of spontaneous brain activity. To further analyze the fine structure of the microstate sequence, we propose a feature extraction method based on k-mer. K-mer is a k-length substring of a given sequence. It has been widely used in computational genomics and sequence analysis. We extract features that are based on the D2∗ statistic of k-mer. In addition, we also extract four parameters (duration, occurrence, time coverage, GEV) of each microstate class as features at the coarse level. We conducted experiments on the DEAP dataset to evaluate the performance of the proposed features. The experimental results demonstrate that the fusion of features in fine and coarse levels can effectively improve classification accuracy.

Lung diseases such as acute respiratory distress syndrome affect the patient's lung compliance, which in turn affects the ability of gas exchange. Changes in alveolar diameter relate to local lung compliance. How alveolar diameter affects gas exchange, particularly oxygen concentrations in alveolar capillaries, is a topic of concern for researchers, and can be studied using mathematical models. The level of small-scale mathematical models of the pulmonary circulatory system was the alveolar capillaries, but existing models do not consider the gas-exchange function and fail to reflect the influence of alveolar diameter. Therefore, we proposed a pulmonary acinar capillary model with gas exchange function, and most importantly, introduced alveolar diameter into the model, to analyze the effect of alveolar diameter on the gas exchange function of the pulmonary acini. The model was tested by three respiratory function simulation experiments. According to the simulation results of changing diameter, we found that the alveolar diameter mainly affects the alveolar gas exchange function of lung acinar inlets and the middle section compared with the peripheral section.

目的 探讨血淀粉酶水平在预测心肺复苏术后患者预后中的价值.?方法 收集2018年8月至2021年3月入住浙江医院经心肺复苏成功并转入ICU的患者33例,监测其心肺复苏成功后0 h、24 h、48 h、7 d的血淀粉酶水平及相应时间点的血乳酸水平,同时收集其相关临床资料如年龄、性别、基础疾病、急性生理与慢性健康(APACHEⅡ)评分,急性肾损伤(AKI)的发生,7 d后格拉斯哥昏迷(GCS)评分及28 d的病死率,根据淀粉酶水平分为高淀粉酶组15例及低淀粉酶组18例,根据其28 d预后分为生存组14例及死亡组19例,分析血淀粉酶水平与相关临床指标及预后的关系.?结果 高淀粉酶组相比低淀粉酶组其APACHEⅡ评分、AKI的发生率及28 d病死率更高,7 d后GCS评分更低,差异均具有统计学意义(U/χ2=70.500、6.906、5.661、76.000,均P<0.05).死亡组相比生存组在24 h、48 h及7 d时的血淀粉酶水平更高,差异具有统计学意义(U=33.000、17.000、6.000,均P<0.05).死亡组在心肺复苏24 h后相比0 h淀粉酶水平显著升高,差异具有统计学意义(U=25.000,P<0.05).ROC曲线分析提示心肺复苏后24 h及48 h的血淀粉酶水平可较好预测28 d预后,曲线下面积为0.853(0.711～0.994)、0.879(0.701～1.000),敏感度为81.3％、80.0％,特异度为85.7％、92.9％.?结论 血淀粉酶预测心肺复苏术后的患者预后的效力较好,可作为这类患者在临床上预测预后指标的重要补充.