Shenfu injection alleviate gut ischemia/reperfusion injury after severe hemorrhagic shock through improving intestinal microcirculation in rats.

Background: Shenfu (SF) injection, a traditional Chinese medication, would improve microcirculation in cardiogenic shock and infectious shock. This study was aimed to explore the therapeutic potential of the SF injection in gut ischemia-reperfusion (I/R) injury after severe hemorrhagic shock (SHS) and resuscitation. Furthermore, we also investigated the optimal adm？ inistration timing. Methods: Twenty-four male SD rats were randomly divided into four groups: Sham group (sham, n = 6), Control group (n = 6), SF injection group (SF, n = 6), and Delayed Shenfu injection administration group (SF-delay, n = 6). In SHS and resuscitation model, rats were induced by blood draw to a mean arterial pressure (MAP) of 40 ± 5 mmHg within 1 h and then maintained for 40 min; HR, MAP 'were recorded, microcirculation index [De Backer score, perfused small vessel density (PSVD), total vessel density (TVD), microcirculation flow index score (MFI), flow heterogeneity index (HI)] were analyzed. The blood gas index was detected, interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), diamine oxidase (DAO), malondialdehyde (MDA) were measured by ELISA; ZO-1, and claudin-1 were measured by Western blotting. In addition, hematoxylin-eosin (HE) and periodic acid schiff (PAS) staining pathological sections of the intestinal mucosal tissues were also performed. Results: SF injection increased the MAP, relieved the metabolic acidosis degree associated with the hypoperfusion, and improved the intestinal microcirculatory density and perfusion quality after I/R injury. The expression of DAO, MDA in intestinal tissue, and plasma IL-6, TNF-α significantly decreased in the SF injection group compared to the control group. The concentration of ZO-1 and claudin-1 is also higher in the SF injection group. In addition, the HE and PAS staining results also showed that SF injection could decrease mucosal damage and maintain the structure. In the SF-delay group, the degree of intestinal tissue damage was intermediate between that of the control group and SF injection group. Conclusions: SF injection protect the intestine from I/R injury induced by SHS and resuscitation, the mechanism of which might be through improving intestinal microcirculation, reducing the excessive release of inflammatory factors and increasing intestinal mucosal permeability. Furthermore, the protection effect is more pronounced if administration during the initial resuscitation phase.

LNMVSNet: A Low-Noise Multi-View Stereo Depth Inference Method for 3D Reconstruction.

With the widespread adoption of modern RGB cameras, an abundance of RGB images is available everywhere. Therefore, multi-view stereo (MVS) 3D reconstruction has been extensively applied across various fields because of its cost-effectiveness and accessibility, which involves multi-view depth estimation and stereo matching algorithms. However, MVS tasks face noise challenges because of natural multiplicative noise and negative gain in algorithms, which reduce the quality and accuracy of the generated models and depth maps. Traditional MVS methods often struggle with noise, relying on assumptions that do not always hold true under real-world conditions, while deep learning-based MVS approaches tend to suffer from high noise sensitivity. To overcome these challenges, we introduce LNMVSNet, a deep learning network designed to enhance local feature attention and fuse features across different scales, aiming for low-noise, high-precision MVS 3D reconstruction. Through extensive evaluation of multiple benchmark datasets, LNMVSNet has demonstrated its superior performance, showcasing its ability to improve reconstruction accuracy and completeness, especially in the recovery of fine details and clear feature delineation. This advancement brings hope for the widespread application of MVS, ranging from precise industrial part inspection to the creation of immersive virtual environments.

Development of an Intelligent Reactive Oxygen Species-Responsive Dual-Drug Delivery Nanoplatform for Enhanced Precise Therapy of Acute Lung Injury.

Introduction: Acute lung injury (ALI) and its most severe form acute respiratory distress syndrome (ARDS) are commonly occurring devastating conditions that seriously threaten the respiratory system in critically ill patients. The current treatments improve oxygenation in patients with ALI/ARDS in the short term, but do not relieve the clinical mortality of patients with ARDS. Purpose: To develop the novel drug delivery systems that can enhance the therapeutic efficacy of ALI/ARDS and impede adverse effects of drugs. Methods: Based on the key pathophysiological process of ARDS that is the disruption of the pulmonary endothelial barrier, bilirubin (Br) and atorvastatin (As) were encapsulated into an intelligent reactive oxygen species (ROS)-responsive nanocarrier DSPE-TK-PEG (DPTP) to form nanoparticles (BA@DPTP) in which the thioketal bonds could be triggered by high ROS levels in the ALI tissues. Results: BA@DPTP could accumulate in inflammatory pulmonary sites through passive targeting strategy and intelligently release Br and As only in the inflammatory tissue via ROS-responsive bond, thereby enhancing the drugs effectiveness and markedly reducing side effects. BA@DPTP effectively inhibited NF-κB signaling and NLRP3/caspase-1/GSDMD-dependent pyroptosis in mouse pulmonary microvascular endothelial cells. BA@DPTP not only protected mice with lipopolysaccharide-induced ALI and retained the integrity of the pulmonary structure, but also reduced ALI-related mortality. Conclusion: This study combined existing drugs with nano-targeting strategies to develop a novel drug-targeting platform for the efficient treatment of ALI/ARDS.

Interpretable machine learning model for early prediction of 28-day mortality in ICU patients with sepsis-induced coagulopathy: development and validation.

OBJECTIVE: Sepsis-induced coagulopathy (SIC) is extremely common in individuals with sepsis, significantly associated with poor outcomes. This study attempted to develop an interpretable and generalizable machine learning (ML) model for early predicting the risk of 28-day death in patients with SIC. METHODS: In this retrospective cohort study, we extracted SIC patients from the Medical Information Mart for Intensive Care III (MIMIC-III), MIMIC-IV, and eICU-CRD database according to Toshiaki Iba's scale. And the overlapping in the MIMIC-IV was excluded for this study. Afterward, only the MIMIC-III cohort was randomly divided into the training set, and the internal validation set according to the ratio of 7:3, while the MIMIC-IV and eICU-CRD databases were considered the external validation sets. The predictive factors for 28-day mortality of SIC patients were determined using recursive feature elimination combined with tenfold cross-validation (RFECV). Then, we constructed models using ML algorithms. Multiple metrics were used for evaluation of performance of the models, including the area under the receiver operating characteristic curve (AUROC), area under the precision recall curve (AUPRC), accuracy, sensitivity, specificity, negative predictive value, positive predictive value, recall, and F1 score. Finally, Shapley Additive Explanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME) were employed to provide a reasonable interpretation for the prediction results. RESULTS: A total of 3280, 2798, and 1668 SIC patients were screened from MIMIC-III, MIMIC-IV, and eICU-CRD databases, respectively. Seventeen features were selected to construct ML prediction models. XGBoost had the best performance in predicting the 28-day mortality of SIC patients, with AUC of 0.828, 0.913 and 0.923, the AUPRC of 0.807, 0.796 and 0.921, the accuracy of 0.785, 0.885 and 0.891, the F1 scores were 0.63, 0.69 and 0.70 in MIMIC-III (internal validation set), MIMIC-IV, and eICU-CRD databases. The importance ranking and SHAP analyses showed that initial SOFA score, red blood cell distribution width (RDW), and age were the top three critical features in the XGBoost model. CONCLUSIONS: We developed an optimal and explainable ML model to predict the risk of 28-day death of SIC patients 28-day death risk. Compared with conventional scoring systems, the XGBoost model performed better. The model established will have the potential to improve the level of clinical practice for SIC patients.

Bi-DexHands: Towards Human-Level Bimanual Dexterous Manipulation.

Achieving human-level dexterity in robotics remains a critical open problem. Even simple dexterous manipulation tasks pose significant difficulties due to the high number of degrees of freedom and the need for cooperation among heterogeneous agents (e.g., finger joints). While some researchers have utilized reinforcement learning (RL) to control a single hand in manipulating objects, tasks that require coordinated bimanual cooperation are still under-explored due to the fewer suitable environments, which can result in difficulties and sub-optimal performance. To address these challenges, we introduce Bi-DexHands, a simulator with two dexterous hands featuring 20 bimanual manipulation tasks and thousands of target objects, designed to match various levels of human motor skills based on cognitive science research. We developed Bi-DexHands in Issac Gym, enabling highly efficient RL training at over 30,000 frames per second using a single NVIDIA RTX 3090. Based on Bi-DexHands, we present a comprehensive evaluation of popular RL algorithms in different settings, including single-agent/multi-agent RL, offline RL, multi-task RL, and meta RL. Our findings show that on-policy algorithms, such as PPO, can master simple manipulation tasks that correspond to those of 48-month-old babies, such as catching a flying object or opening a bottle. Furthermore, multi-agent RL can improve the ability to perform manipulations that require skilled bimanual cooperation, such as lifting a pot or stacking blocks. Despite achieving success in individual tasks, current RL algorithms struggle to learn multiple manipulation skills in most multi-task and few-shot learning scenarios. This highlights the need for further research and development within the RL community.

The impact of aspirin exposure prior to intensive care unit admission on the outcomes for patients with sepsis-associated acute respiratory failure.

Objectives: This present study aimed to infer the association between aspirin exposure prior to ICU admission and the clinical outcomes of patients with Sepsis-associated acute respiratory failure (S-ARF). Methods: We obtained data from the Medical Information Mart for Intensive Care IV 2.0. Patients were divided into pre-ICU aspirin exposure group and Non-aspirin exposure group based on whether they took aspirin before ICU admission. The primary outcome is 28-day mortality. Augmented inverse propensity weighted was used to explore the average treatment effect (ATE) of the pre-ICU aspirin exposure. A generalized additive mixed model was used to analyze the longitudinal data of neutrophil to lymphocyte ratio (NLR), red cell distribution width (RDW), oxygenation index (P/F), dynamic lung compliance (Cdyn), mechanical power (MP), and mechanical power normalized to predicted body weight (WMP) in the two groups. A multiple mediation model was constructed to explore the possible mediators between pre-ICU aspirin exposure and outcomes of patients with S-ARF. Results: A total of 2090 S-ARF patients were included in this study. Pre-ICU aspirin exposure decreased 28-day mortality (ATE, -0.1945, 95% confidence interval [CI], -0.2786 to -0.1103, p < 0.001), 60-day mortality (ATE, -0.1781, 95% Cl, -0.2647 to -0.0915, p < 0.001), and hospital mortality (ATE, -0.1502, 95%CI, -0.2340 to -0.0664, p < 0.001). In subgroup analysis, the ATE for 28-day mortality, 60-day mortality, and hospital mortality were not statistically significant in the coronary care unit group, high-dose group (over 100 mg/d), and no invasive mechanical ventilation (IMV) group. After excluding these non-beneficiaries, Cdyn and P/F ratio of the pre-ICU aspirin exposure group increased by 0.31mL/cmH2O (SE, 0.21, p = 0.016), and 0.43 mmHg (SE, 0.24, p = 0.041) every hour compared to that of non-aspirin exposure group after initialing IMV. The time-weighted average of NLR, Cdyn, WMP played a mediating role of 8.6%, 24.7%, and 13% of the total effects of pre-ICU aspirin exposure and 28-day mortality, respectively. Conclusion: Pre-ICU aspirin exposure was associated with decreased 28-day mortality, 60-day mortality, and hospital mortality in S-ARF patients except those admitted to CCU, and those took a high-dose aspirin or did not receive IMV. The protective effect of aspirin may be mediated by a low dynamic level of NLR and a high dynamic level of Cdyn and WMP. The findings should be interpreted cautiously, given the sample size and potential for residual confounding.

Acetaldehyde dehydrogenase 2 activation attenuates sepsis-induced brain injury through NLRP3 inflammasome regulation.

OBJECTIVE: Acetaldehyde dehydrogenase 2 (ALDH2) plays an important part in neuroprotection; however, its effect on sepsis-induced brain injury is nuclear. Our aim is to investigate the potential effect and mechanism of ALDH2 in this condition. METHODS: We established an animal model using cecal ligation and perforation (CLP). Twenty-four rats were divided into sham group (n = 6), CLP group (n = 6), CLP + Alda-1 group (n = 6) and CLP + Cyanamide (CYA) group (n = 6). Vital signs were monitored, and arterial blood gas analysis, hippocampal histological staining and ALDH2 activity analysis were conducted. Western blot analysis and enzyme-linked immunosorbent assays were also carried out. Lipopolysaccharide (LPS)-treated HT22 cells were employed as an in vitro model of sepsis-induced brain injury, with and without pretreatment with Alda-1 or CYA, to further examine the potential mechanisms. Real-time quantitative polymerase chain reaction and western blot were used to determine the levels of pyrin domain-containing 3 (NLRP3) inflammasome. RESULTS: We found hippocampal cell injury in the CLP group (p < 0.05), with decreased ALDH2 activity (p < 0.05) and suspected overexpression of NLRP3/caspase-1 axis (p < 0.05). In the group pretreated with Alda-1, there were increased ALDH2 activity (p < 0.05), decreased hippocampal cell damage (p < 0.05), and reduced protein levels of NLRP3, apoptosis-associated speck like protein containing a caspase recruitment domain (ASC), cleaved caspase-1 and Gasdermin D (GSDMD) (p < 0.05). The levels of interleukin 18 (IL-18) and interleukin 1ß (IL-1ß) were also reduced (p < 0.05). In the group pretreated with CYA, ALDH2 activity was further declined, the cell injury grade increased, and the elevated levels of pyroptosis-related proteins aggravated (p < 0.05). LPS treatment decreased the cell viability and ALDH2 activity of the HT22 cells (p < 0.05), along with increased mRNA levels of the NLRP3 inflammasome, as well as IL-1ß and IL-18 (p < 0.05). Western blot further revealed elevated levels of NLRP3, ASC, cleaved caspase-1 and GSDMD (p < 0.05). In the LPS+Alda-1 group, there were increased cell viability (p < 0.05), elevated ALDH2 activity (p < 0.05), and reduced levels of NLRP3 inflammasome and pyroptosis-related proteins (p < 0.05). In the CYA+LPS group, cell viability and ALDH2 activity were further declined (p < 0.05), while levels of NLRP3 /caspase-1 axis were increased (p < 0.05). CONCLUSIONS: The activation of ALDH2 can attenuate sepsis-induced brain injury, hypothetically through regulation of the NLRP3/caspase-1 signaling pathway. Therefore, ALDH2 could potentially be considered as a new therapeutic target for the treatment of sepsis-induced brain injury.

Effect of Fluid Resuscitation Strategies for Obese Patients with Sepsis and Septic Shock: A Systematic Review.

Purpose: As the Surviving Sepsis Campaign (2021) recommended, patients with sepsis should be given a liquid infusion of 30 ml/kg (ideal body weight). However, the strategy may result in insufficient resuscitation for obese patients with sepsis. Therefore, we conducted a systematic evaluation of the effectiveness of the initial resuscitation strategy in obese sepsis patients. Materials and methods: A computer search of PubMed, Embase, Cochrane library, and other databases collected cohort studies from the beginning of the survey to December 2021 to include articles evaluating initial resuscitation strategies for sepsis-obese patients. Results: Of the six studies included, five used ideal body weight infusion strategies, and three used actual body weight infusion strategies. Differences in fluid volume were observed between the two strategies, but no significant difference was observed in the mortality of obese sepsis patients. In addition, there may be an infusion strategy other than the above two infusion methods, and the safety and efficacy of the new infusion strategy are unclear. The obesity paradox has been observed in most infusion strategies. Conclusion: The association between obesity and infusion strategy has rarely been investigated in patients with sepsis and septic shock, and the existing results are conflicting. The risk of bias in all included studies was moderate or high. Before providing broad recommendations on the optimal first resuscitation approach to lower the chance of mortality, further clinical trials, and prospective research need to be done.

Influence of the Initial Neutrophils to Lymphocytes and Platelets Ratio on the Incidence and Severity of Sepsis-Associated Acute Kidney Injury: A Double Robust Estimation Based on a Large Public Database.

Background: Acute kidney injury (AKI) is a frequent consequence of sepsis and has been linked to poor prognosis. In critically ill patients, the ratio of neutrophils to lymphocytes and platelets (N/LP) has been confirmed as an inflammation-related marker connected with the development of renal dysfunction. However, the effect of the N/LP ratio on the initiation and development of AKI in patients with sepsis remained unclear. The purpose of this study was to determine if the N/LP ratio on intensive care unit (ICU) admission was associated with the occurrence of sepsis-associated AKI (S-AKI) and severe AKI. Methods: Adult septic patients from the Medical Information Mart for Intensive Care-IV database were screened and classified into three categories (low, middle, or high) based on their N/LP ratio quartiles. The Cox proportional hazard and competing risk models were used to determine the risk of S-AKI in various N/LP groups, whilst the logistic regression model and restricted cubic splines (RCS) analysis were employed to investigate the link between N/LP ratios and the occurrence of severe AKI. Finally, we did a doubly robust estimation, a subgroup analysis, and a sensitivity analysis to determine the findings' robustness. Results: We categorized 485, 968, and 485 septic patients into three groups based on their N/LP ratios: low, intermediate, and high. According the Cox proportional hazard model, the hazard rate (95% CI) for those in the middle and high N/LP groups on the incidence of S-AKI were 1.30(1.07, 1.58) and 1.27(1.02, 1.59), respectively, as compared to those in the low N/LP group. And the Fine-Gray proportional subdistribution hazards model indicated that mortality was not a substantial competing risk for S-AKI. Additionally, multivariate logistic regression revealed that the risk of severe AKI increased 1.83 fold in the high group compared to the low group. The RCS result also suggested that the probability of severe AKI rose significantly when N/LP > 9.5. The consistency of these findings was confirmed using doubly robust estimation. However, subgroup and sensitivity analyses revealed that the association between N/LP and the incidence of S-AKI, severe AKI varied considerably between different populations and diagnostic criteria. Conclusion: A raised initial N/LP level may induce the development of S-AKI and severe AKI within 7 days after ICU admission in septic patients. These influences were enhanced in elder, male, septic shock, and those with poor health condition. Furthermore, high NLP was more strongly connected to the risk of S-AKI and severe AKI in sepsis patients on the urine output-based AKI criteria than on the serum creatinine-based criteria.

Association of Blood Glucose Level and Glycemic Variability With Mortality in Sepsis Patients During ICU Hospitalization.

Background: There was considerable debate regarding the effect of mean blood glucose (MBG) and glycemic variability (GV) on the mortality of septic patients. This retrospective cohort study aimed to assess the association between MBG and GV with ICU mortality of sepsis patients and to explore the optimal MBG range. Methods: Sepsis patients were enrolled from the Medical Information Mart for Intensive Care IV database (MIMIC-IV). MBG and glycemic coefficient of variation (GluCV) were, respectively, calculated to represent the overall glycemic status and GV during ICU stay. The associations between MBG, GluCV, and ICU mortality of the septic patients were assessed by using multivariate logistic regression in different subgroups and the severity of sepsis. Restricted cubic splines evaluated the optimal MBG target. Results: A total of 7,104 adult sepsis patients were included. The multivariate logistic regression results showed that increased MBG and GluCV were significantly correlated with ICU mortality. The adjusted odds ratios were 1.14 (95% CI 1.09-1.20) and 1.05 (95% CI 1.00-1.12). However, there was no association between hyperglycemia and ICU mortality among diabetes, liver disease, immunosuppression, and hypoglycemia patients. And the impact of high GluCV on ICU mortality was not observed in those with diabetes, immunosuppression, liver disease, and non-septic shock. The ICU mortality risk of severe hyperglycemia (≧200 mg/dl) and high GluCV (>31.429%), respectively, elevated 2.30, 3.15, 3.06, and 2.37, 2.79, 3.14-folds in mild (SOFA ≦ 3), middle (SOFA 3-7), and severe group (SOFA ≧ 7). The MBG level was associated with the lowest risk of ICU mortality and hypoglycemia between 120 and 140 mg/dl in the subgroup without diabetes. For the diabetic subset, the incidence of hypoglycemia was significantly reduced when the MBG was 140-190 mg/dl, but a glycemic control target effectively reducing ICU mortality was not observed. Conclusion: MBG and GluCV during the ICU stay were associated with all-cause ICU mortality in sepsis patients; however, their harms are not apparent in some particular subgroups. The impact of hyperglycemia and high GV on death increased with the severity of sepsis. The risk of ICU mortality and hypoglycemia in those with no pre-existing diabetes was lower when maintaining the MBG in the range of 120-140 mg/dl.

A Nomogram With Six Variables Is Useful to Predict the Risk of Acquiring Carbapenem-Resistant Microorganism Infection in ICU Patients.

Background: Carbapenem-resistant microorganism (CRO) transmission in the medical setting confers a global threat to public health. However, there is no established risk prediction model for infection due to CRO in ICU patients. This study aimed to develop a nomogram to predict the risk of acquiring CRO infection in patients with the first ICU admission and to determine the length of ICU stay (ICU-LOS) and 28-day survival. Methods: Patient data were retrieved from the Medical Information Mart for Intensive Care (MIMIC-IV) database based on predetermined inclusion and exclusion criteria. A CRO was defined as a bacterium isolated from any humoral microbial culture that showed insensitivity or resistance to carbapenems. The characteristics of CRO and non-CRO patients in the first ICU admission were compared. Propensity score matching was applied to balance the differences between the CRO and non-CRO cohorts. Kaplan-Meier curves were constructed to determine the 28-day survival rate and ICU-LOS. Furthermore, after randomization of the CRO cohort into the training and validation sets, a predictive nomogram was constructed based on LASSO regression and Logistic regression analysis, and its performance was verified by internal validation. Results: Overall, 4531 patients who had first ICU admission as recorded in MIMIC-IV were enrolled, 183 (4.04%) of whom were diagnosed with CRO infection. Moreover, CRO infection was independently associated with 28-day survival and ICU-LOS in ICU patients. Parameters eligible for inclusion in this nomogram were male sex, hemoglobin-min, temperature-max, use of a peripherally inserted central catheter line, dialysis treatment, and use of carbapenems. This nomogram showed a better performance as indicated by the area under the receiver operating characteristic curve values of 0.776 (95% confidence interval [CI] 0.667-0.750) and 0.723 (95% CI 0.556-0.855) in the training and validation sets, respectively, in terms of predicting the risk of acquiring CRO infection. Conclusions: CRO infection was independently associated with ICU-LOS and 28-day survival in patients with first ICU admission. The nomogram showed the best prediction of the risk of acquiring CRO infection in ICU patients. Based on the nomogram-based scoring, we can management the risk factors and guide individualized prevention and control of CRO.

MuellerNet: a hybrid 3D-2D CNN for cell classification with Mueller matrix images.

Different from conventional microimaging techniques, polarization imaging can generate multiple polarization images in a single perspective by changing the polarization angle. However, how to efficiently fuse the information in these multiple polarization images by a convolutional neural network (CNN) is still a challenging problem. In this paper, we propose a hybrid 3D-2D convolutional neural network called MuellerNet, to classify biological cells with Mueller matrix images (MMIs). The MuellerNet includes a normal stream and a polarimetric stream, in which the first Mueller matrix image is taken as the input of normal stream, and the rest MMIs are stacked to form the input of a polarimetric stream. The normal stream is mainly constructed with a backbone network and, in the polarimetric stream, the attention mechanism is used to adaptively assign weights to different convolutional maps. To improve the network's discrimination, a loss function is introduced to simultaneously optimize parameters of the two streams. Two Mueller matrix image datasets are built, which include four types of breast cancer cells and three types of algal cells, respectively. Experiments are conducted on these two datasets with many well-known and recent networks. Results show that the proposed network efficiently improves the classification accuracy and helps to find discriminative features in MMIs.

Efficacy and safety of abdominal paracentesis drainage on patients with acute pancreatitis: a systematic review and meta-analysis.

OBJECTIVES: To conduct a systematic review and meta-analysis of the efficacy and safety of abdominal paracentesis drainage (APD) in patients with acute pancreatitis (AP) when compared with conventional 'step-up' strategy based on percutaneous catheter drainage (PCD). DESIGN: Systematic review and meta-analysis. METHODS: PubMed, EMBASE, Cochrane Library, MEDLINE (OVID), China National Knowledge Infrastructure and Wanfang Database were electronically searched to collect cohort studies and randomised controlled trials (RCTs) from inception to 25 July 2020. Studies related to comparing APD with conventional 'step-up' strategy based on PCD were included. OUTCOMES: The primary outcome was all-cause mortality. The secondary outcomes were the rate of organ dysfunction, infectious complications, hospitalisation expenses and length of hospital stay. RESULTS: Five cohort studies and three RCTs were included in the analysis. Compared with the conventional 'step-up' method, pooled results suggested APD significantly decreased all-cause mortality during hospitalisation (cohort studies: OR 0.48, 95% CI 0.26 to 0.89 and p=0.02), length of hospital stay (cohort studies: standard mean difference (SMD) -0.31, 95% CI -0.53 to -0.10 and p=0.005; RCTs: SMD -0.45, 95% CI -0.64 to -0.26 and p<0.001) and hospitalisation expenses (cohort studies: SMD -2.49, 95% CI -4.46 to -0.51 and p<0.001; RCTs: SMD -0.67, 95% CI -0.89 to -0.44 and p<0.001). There was no evidence to prove that APD was associated with a higher incidence of infectious complications. However, the incidence of organ dysfunction between cohort studies and RCTs subgroup slightly differed (cohort studies: OR 0.66, 95% CI 0.34 to 1.28 and p=0.22; RCTs: OR 0.58, 95% CI 0.35 to 0.98 and p=0.04). CONCLUSIONS: The findings suggest that early application of APD in patients with AP is associated with reduced all-cause mortality, expenses during hospitalisation and the length of stay compared with the 'step-up' strategy without significantly increasing the risk of infectious complications. These results must be interpreted with caution because of the limited number of included studies as well as a larger dependence on observational trials. PROSPERO REGISTRATION NUMBER: CRD42020168537.

Development of a Nomogram to Predict 28-Day Mortality of Patients With Sepsis-Induced Coagulopathy: An Analysis of the MIMIC-III Database.

Background: Sepsis-induced coagulopathy (SIC) is a common cause for inducing poor prognosis of critically ill patients in intensive care unit (ICU). However, currently there are no tools specifically designed for assessing short-term mortality in SIC patients. This study aimed to develop a practical nomogram to predict the risk of 28-day mortality in SIC patients. Methods: In this retrospective cohort study, we extracted patients from the Medical Information Mart for Intensive Care III (MIMIC-III) database. Sepsis was defined based on Sepsis 3.0 criteria and SIC based on Toshiaki Iba's criteria. Kaplan-Meier curves were plotted to compare the short survival time between SIC and non-SIC patients. Afterward, only SIC cohort was randomly divided into training or validation set. We employed univariate logistic regression and stepwise multivariate analysis to select predictive features. The proposed nomogram was developed based on multivariate logistic regression model, and the discrimination and calibration were verified by internal validation. We then compared model discrimination with other traditional severity scores and machine learning models. Results: 9432 sepsis patients in MIMIC III were enrolled, in which 3280 (34.8%) patients were diagnosed as SIC during the first ICU admission. SIC was independently associated with the 7- and 28-day mortality of ICU patients. K-M curve indicated a significant difference in 7-day (Log-Rank: P < 0.001 and P = 0.017) and 28-day survival (Log-Rank: P < 0.001 and P < 0.001) between SIC and non-SIC groups whether the propensity score match (PSM) was balanced or not. For nomogram development, a total of thirteen variables of 3,280 SIC patients were enrolled. When predicted the risk of 28-day mortality, the nomogram performed a good discrimination in training and validation sets (AUROC: 0.78 and 0.81). The AUROC values were 0.80, 0.81, 0.71, 0.70, 0.74, and 0.60 for random forest, support vector machine, sequential organ failure assessment (SOFA) score, logistic organ dysfunction score (LODS), simplified acute physiology II score (SAPS II) and SIC score, respectively, in validation set. And the nomogram calibration slope was 0.91, the Brier value was 0.15. As presented by the decision curve analyses, the nomogram always obtained more net benefit when compared with other severity scores. Conclusions: SIC is independently related to the short-term mortality of ICU patients. The nomogram achieved an optimal prediction of 28-day mortality in SIC patient, which can lead to a better prognostics assessment. However, the discriminative ability of the nomogram requires validation in external cohorts to further improve generalizability.

Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning.

BACKGROUND: Despite recent significant progress in the development of automatic sleep staging methods, building a good model still remains a big challenge for sleep studies with a small cohort due to the data-variability and data-inefficiency issues. This work presents a deep transfer learning approach to overcome these issues and enable transferring knowledge from a large dataset to a small cohort for automatic sleep staging. METHODS: We start from a generic end-to-end deep learning framework for sequence-to-sequence sleep staging and derive two networks as the means for transfer learning. The networks are first trained in the source domain (i.e. the large database). The pretrained networks are then finetuned in the target domain (i.e. the small cohort) to complete knowledge transfer. We employ the Montreal Archive of Sleep Studies (MASS) database consisting of 200 subjects as the source domain and study deep transfer learning on three different target domains: the Sleep Cassette subset and the Sleep Telemetry subset of the Sleep-EDF Expanded database, and the Surrey-cEEGrid database. The target domains are purposely adopted to cover different degrees of data mismatch to the source domains. RESULTS: Our experimental results show significant performance improvement on automatic sleep staging on the target domains achieved with the proposed deep transfer learning approach. CONCLUSIONS: These results suggest the efficacy of the proposed approach in addressing the above-mentioned data-variability and data-inefficiency issues. SIGNIFICANCE: As a consequence, it would enable one to improve the quality of automatic sleep staging models when the amount of data is relatively small.11The source code and the pretrained models are published at https://github.com/pquochuy/sleep_transfer_learning.

A Simple Weaning Model Based on Interpretable Machine Learning Algorithm for Patients With Sepsis: A Research of MIMIC-IV and eICU Databases.

BACKGROUND: Invasive mechanical ventilation plays an important role in the prognosis of patients with sepsis. However, there are, currently, no tools specifically designed to assess weaning from invasive mechanical ventilation in patients with sepsis. The aim of our study was to develop a practical model to predict weaning in patients with sepsis. METHODS: We extracted patient information from the Medical Information Mart for Intensive Care Database-IV (MIMIC-IV) and the eICU Collaborative Research Database (eICU-CRD). Kaplan-Meier curves were plotted to compare the 28-day mortality between patients who successfully weaned and those who failed to wean. Subsequently, MIMIC-IV was divided into a training set and an internal verification set, and the eICU-CRD was designated as the external verification set. We selected the best model to simplify the internal and external validation sets based on the performance of the model. RESULTS: A total of 5020 and 7081 sepsis patients with invasive mechanical ventilation in MIMIC-IV and eICU-CRD were included, respectively. After matching, weaning was independently associated with 28-day mortality and length of ICU stay (p < 0.001 and p = 0.002, respectively). After comparison, 35 clinical variables were extracted to build weaning models. XGBoost performed the best discrimination among the models in the internal and external validation sets (AUROC: 0.80 and 0.86, respectively). Finally, a simplified model was developed based on XGBoost, which included only four variables. The simplified model also had good predictive performance (AUROC:0.75 and 0.78 in internal and external validation sets, respectively) and was developed into a web-based tool for further review. CONCLUSIONS: Weaning success is independently related to short-term mortality in patients with sepsis. The simplified model based on the XGBoost algorithm provides good predictive performance and great clinical applicablity for weaning, and a web-based tool was developed for better clinical application.

[Translation and interpretation of Sudden cardiac arrest survivorship: a scientific statement from the American Heart Association].

Sudden cardiac arrest survivors are a special group of patients, often with unique and complex needs that are not adequately addressed by current treatments. In May 2020, the American Heart Association (AHA) developed a scientific statement on sudden cardiac arrest survivorship based on relevant research developments, which emphasized the importance of a unified patient-centered assessment and resources. This article attempts to interpret the key content of the scientific statement, hoping to provide reference and help for domestic medical staff to understand and apply it.

[Progress of intensive care unit delirium research from 2010 to 2020: analysis based on knowledge visualization].

OBJECTIVE: To explored the progress of intensive care unit (ICU) delirium between 2010 and 2020 based on knowledge visualization analysis. METHODS: The literatures related to ICU delirium included in Web of Sciences (WOS) and China National Knowledge Infrastructure (CNKI) databases from 2010 to 2020 were collected. A bibliometric analysis was performed. The growth trend was showed by Excel 2019 software. The information about country, institution and author were extracted by VOSviewer 1.6.15 for generating cooperative network, to find the main research power and each cooperative relation. At the same time, Citespace 5.0.R1 was used to analyze those high frequency keywords and bursting keywords and build the map of co-citation reference, in order to explore the evolution of research in the field of ICU delirium and the hotspots about this field in recent 10 years. RESULTS: A total of 1 102 Chinese journal articles and 2 422 English "Articles" or "Reviews" from 2010 to 2020 were collected preliminarily, and the number of published literatures increased steadily. In the respect of quality, the impact factors of most articles were concentrated between 2 and 3, and the literatures with impact factor over 5 accounted for 27.9% (337/1 209). According to the knowledge visualization analysis, the United States published most of the related articles (total 1 152) in this field, while the England and Canada ranked second and third respectively, totaling 220 and 204. In terms of the distribution of research institutions, the Vanderbilt University School of Medicine was not only far ahead in the number of publication (n = 149), but more importantly, top three high-impact authors located in this institution. The amount of domestic publications was lower than developed countries, however, the burst index, which reflected the sudden increase, ranked first (7.09), suggesting that the interest and investment of Chinese researchers was increasing recently. The most productive institution in China was Capital Medical University School of Nursing with totaling 23 articles. Wu Ying, who published most Chinese papers (n = 14), belongs to this institution. However, it was a pity that there was no large scientific community be constructed in China, and the cooperation between institutions was deficient. By generating the co-occuring keyword mapping, the research hotpots mainly focused on the prevention, treatment and prevention of delirium in mechanically ventilated patients, the effect of dexmedetomidine and exploring the risk factor of ICU delirium. Finally, the results of co-citation reference analysis showed that Cluster 4 (risk assessment) was still in the process of development, in hence it was the frontier in this domain. CONCLUSIONS: There was a big gap between China and leading countries in the field of ICU delirium research. The main research power was located in the United States, and the trending of future studies mainly focus on delirium-related risk assessment.

[Translation and interpretation of the Telecommunicator cardiopulmonary resuscitation: a policy statement from the American Heart Association].

Out-of-hospital cardiac arrest (OHCA) is a global medical challenge. Early case recognition and initiating the chain of survival is associated with good prognosis of these patients. On the basis of former research, American Heart Association (AHA) published a policy statement related to telecommunicator cardiopulmonary resuscitation (T-CPR) in March 2020, and introduced its specific procedures, standards and precautions. To assist Chinese doctors in better understanding of the T-CPR, and give a reference for the emergency curing of OHCA, the guideline was translated and interpreted in this paper.

Efficacy and Safety of Recombinant Human Thrombopoietin on Sepsis Patients With Thrombocytopenia: A Systematic Review and Meta-Analysis.

BACKGROUND: The efficacy and safety of the administration of recombinant human thrombopoietin (rhTPO) in sepsis patients with thrombocytopenia were still inconclusive. OBJECTIVES: To investigate whether rhTPO is a benefit for sepsis patients with thrombocytopenia. METHODS: PubMed, Cochrane library, Embase, China National Knowledge Infrastructure, and Wanfang Database were electronically searched to the randomized controlled trials (RCTs) from inception to March 4, 2020. The primary outcome was the level of platelet (PLT) on the 7th day of treatment, and secondary outcomes were 28-d mortality, the level of coagulation indicators, hepatic and renal function indicators, blood transfusion, and length of intensive care unit (ICU) stay. RESULTS: Ten RCTs involving 681 patients were included. For compared with conventional antibiotic therapy, rhTPO could significantly increase platelet counts (PCs) [standardized mean difference (SMD), 2.61; 95% confidence interval (CI), 1.28-3.94; P < 0.001], decreased 28-d mortality [relative risk (RR), 0.66; 95%CI, 0.46-0.97; P=0.03], transfusion volume of blood products and length of ICU stay. Additionally, for compared with conventional antibiotic therapy combined with intravenous immunoglobulin, the pooled results shown that rhTPO also associated with an improvement of PCs on 7th of treatment (SMD, 0.86; 95%CI, 0.54-1.17; P < 0.001), and a reduced transfusion volume of blood products. However, there were no differences in 28-d mortality and the length of ICU stay. CONCLUSIONS: Current evidence shown that rhTPO could increase PCs on 7th day of treatment and reduce the transfusion volume of blood products in sepsis-related thrombocytopenia during hospitalization. The conclusions are needed to be verified indeed by more multicenter RCTs due to the limitation of the included studies.