Background and Aims:The results of basic research implicate the vascular endothelial growth factor (VEGF) family as a potential target of hepatopulmonary syndrome (HPS). However, the negative results of anti-angiogenetic therapy in clinical studies have highlighted the need for markers for HPS. Therefore, we aimed to determine whether VEGF family members and their receptors can be potential biomarkers for HPS through clinical and experimental studies.Methods:Clinically, patients with chronic liver disease from two medical centers were enrolled and examined for HPS. Patients were divided into HPS, intrapulmonary vascular dilation [positive contrast-enhanced echocardiography (CEE) and normal oxygenation] and CEE-negative groups. Baseline information and perioperative clinical data were compared between HPS and non-HPS patients. Serum levels of VEGF family members and their receptors were measured. In parallel, HPS rats were established by common bile duct ligation. Liver, lung and serum samples were collected for the evaluation of pathophysiologic changes, as well as the expression levels of the above factors.Results:In HPS rats, all VEGF family members and their receptors underwent significant changes; however, only soluble VEGFR1 (sFlt-1) and the sFlt-1/ placental growth factor (PLGF) ratio were changed in almost the same manner as those in HPS patients. Furthermore, through feature selection and internal and external validation, sFlt-1 and the sFlt-1/PLGF ratio were identified as the most important variables to distinguish HPS from non-HPS patients.Conclusions:Our results from animal and human studies indicate that sFlt-1 and the sFlt-1/PLGF ratio in serum are potential markers for HPS.

Background: In addition to intrahepatic angiogenesis, patients with cholestasis cirrhosis develop extrahepatic vasculature disorders and functional disturbances of multiple organ systems. Without effective intervention, these vascular disorders will eventually turn into multiple organs vascular syndromes, including the brain, lung and other organ systems. However, studies on the pathogenesis of vascular alterations among extrahepatic organ disturbances are still carried out separately, which hampered the successful translation of preclinical studies to the human setting and required further mechanistic insight into these complications. This study aims to investigate the relationship between extrahepatic angiogenesis and multiple organ impairment, and whether the vascular endothelial growth factor (VEGF) family members and their receptors are involved in this process. Methods: Pathological changes of the multiple organs were determined by histopathological and immunohistochemical staining in the established common bile duct ligation (CBDL) rats, and angiogenesis was estimated by microvessel density (MVD). Levels of the VEGF family members and their receptors in the serum and organ tissues were also measured by using enzyme-linked immunosorbent assays. Results: The MVD and VEGF family members and their receptors were significantly increased in CBDL rats with multiple organ injury, especially in the liver, lung and cerebral cortex. Meanwhile, we noticed moderate elevation of soluble receptor of the vascular endothelial growth factor-1 (sFlt-1) in the liver, lung, and cerebral cortex, whereas the levels of placental growth factor (PLGF) increased significantly. Conclusions: Extrahepatic angiogenesis may represent a common pathophysiological basis for multiple organ dysfunction and the sFlt-1/PLGF ratio could offer an avenue for further studies to target extrahepatic angiogenesis in cholestatic cirrhosis.

Enhanced recovery after surgery (ERAS) can accelerate patient recovery. However, little research has been done on optimizing the ERAS-related measures and how the measures interact with each other. The Bayesian network (BN) is a graphical model that describes the dependencies between variables and is also a model for uncertainty reasoning. In this study, we aimed to develop a method for optimizing anesthetic decisions in ERAS and then investigate the relationship between anesthetic decisions and outcomes. First, assuming that the indicators used were independent, the effects of combinations of single indicators were analyzed based on BN. Additionally, the impact indicators for outcomes were selected with statistical tests. Then, based on the previously selected indicators, the Bayesian network was constructed using the proposed structure learning method based on Strongly Connected Components (SCC) Local Structure determination by Hill Climbing Twice (LSHCT) and adjusted according to the expert’s knowledge. Finally, the relationship is analyzed. The proposed method is validated by the real clinical data of patients with benign gynecological tumors from 3 hospitals in China. Postoperative length of stay (LOS) and total cost (TC) were chosen as the outcomes. Experimental results show that the ERAS protocol has some pivotal indicators influencing LOS and TC. Identifying the relationship between these indicators can help anesthesiologists optimize the ERAS protocol and make individualized decisions.

Supplemental Digital Content is available in the text. Background This study attempted to investigate the impact of hepatopulmonary syndrome (HPS) on postoperative outcomes in hepatitis B virus-induced hepatocellular carcinoma (HBV-HCC) patients. Methods HBV-HCC patients undergoing primary curative hepatectomy for HCC in our hospital were diagnosed with HPS by contrast-enhanced echocardiography (CEE) and arterial blood gas analysis. Patients were divided into HPS, intrapulmonary vascular dilation (IPVD) (patients with positive CEE results and normal oxygenation) and control (patients with negative CEE results) groups. Baseline information, perioperative clinical data and postoperative pulmonary complications (PPCs) were compared among all groups. Cytokines in patient serums from each group (n = 8) were also assessed. Results Eighty-seven patients undergoing hepatectomy from October 2019 to January 2020 were analyzed. The average time in the postanaesthesia care unit (112.10 ± 38.57 min) and oxygen absorption after extubation [34.0 (14.5–54.5) min] in the HPS group was longer than in IPVD [81.81 ± 26.18 min and 16.0 (12.3–24.0) min] and control [93.70 ± 34.06 min and 20.5 (13.8–37.0) min] groups. There were no significant differences in oxygen absorption time after extubation between HPS and control groups. The incidence of PPCs, especially bi-lateral pleural effusions in the HPS group (61.9%), was higher than in IPVD (12.5%) and control (30.0%) groups. Increased serum levels of the growth-regulated oncogene, monocyte chemoattractant protein, soluble CD40 ligand and interleukin 8 might be related to delayed recovery in HPS patients. Conclusions HPS patients with HBV-HCC suffer delayed postoperative recovery and are at higher risk for PPCs, especially bi-lateral pleural effusions, which might be associated with changes in certain cytokines.

Abstract Background Dynamic prediction of patients’ mortality risk in ICU with time series data is limited due to the high dimensionality, uncertainty with sampling intervals, and other issues. New deep learning method, temporal convolution network (TCN), makes it possible to deal with complex clinical time series data in ICU. We aimed to develop and validate it to predict mortality risk using time series data from MIMIC III dataset. Methods Finally, 21139 records of ICU stays were analyzed and in total 17 physiological variables from the MIMIC III dataset were used to predict mortality risk. Then we compared the model performances of attention-based TCN with traditional artificial intelligence (AI) method. Results The Area Under Receiver Operating Characteristic (AUCROC) and Area Under Precision-Recall curve (AUC-PR) of attention-based TCN for predicting the mortality risk 48 h after ICU admission were 0.837(0.824–0.850) and 0.454. The sensitivity and specificity of attention-based TCN were 67.1% and 82.6%, compared to the traditional AI method yield low sensitivity (< 50%). Conclusions Attention-based TCN model achieved better performance in prediction of mortality risk with time series data than traditional AI methods and conventional score-based models. Attention-based TCN mortality risk model has the potential for helping decision-making in critical patients.

BACKGROUND:Dynamic and precise estimation of blood loss (EBL) is quite important for perioperative management. To date, the Triton System, based on feature extraction technology (FET), has been applied to estimate intra-operative haemoglobin (Hb) loss but is unable to directly assess the amount of blood loss. We aimed to develop a method for the dynamic and precise EBL and estimate Hb loss (EHL) based on artificial intelligence (AI).METHODS:We collected surgical patients' non-recycled blood to generate blood-soaked sponges at a set gradient of volume. After image acquisition and preprocessing, FET and densely connected convolutional networks (DenseNet) were applied for EBL and EHL. The accuracy was evaluated using R2, the mean absolute error (MAE), the mean square error (MSE), and the Bland-Altman analysis.RESULTS:For EBL, the R2, MAE and MSE for the method based on DenseNet were 0.966 (95% CI: 0.962-0.971), 0.186 (95% CI: 0.167-0.207) and 0.096 (95% CI: 0.084-0.109), respectively. For EHL, the R2, MAE and MSE for the method based on DenseNet were 0.941 (95% CI: 0.934-0.948), 0.325 (95% CI: 0.293-0.355) and 0.284 (95% CI: 0.251-0.317), respectively. The accuracies of EBL and EHL based on DenseNet were more satisfactory than that of FET. Bland-Altman analysis revealed a bias of 0.02 ml with narrow limits of agreement (LOA) (-0.47 to 0.52 mL) and of 0.05 g with narrow LOA (-0.87 to 0.97 g) between the methods based on DenseNet and actual blood loss and Hb loss.CONCLUSIONS:We developed a simpler and more accurate AI-based method for EBL and EHL, which may be more fit for surgeries primarily using sponges and with a small to medium amount of blood loss.

The incidence of subarachnoid hemorrhage (SAH) and hazard ratio of death increase with age. Overactivation of microglia contributes to brain damage. This study aimed to investigate the effects of A3 adenosine receptors (A3R) activation on neurofunction and microglial phenotype polarization in the context of SAH in aged rats. The A3R agonist (CI-IB-MECA) and antagonist (MRS1523) were used in the SAH model. Microglia were cultured to mimic SAH in the presence or absence of CI-IB-MECA and/or siRNA for A3R. The neurofunction and status of the microglial phenotype were evaluated. The P38 inhibitor SB202190 and the STAT6 inhibitor AS1517499 were used to explore the signaling pathway. The results showed that SAH induced microglia to polarize to the M(LPS) phenotype both in vivo and in vitro. CI-IB-MECA distinctly skewed microglia towards the M(IL-4) phenotype and ameliorated neurological dysfunction, along with the downregulation of inflammatory cytokines. Knockdown of A3R or inhibition of P38 and/or STAT6 weakened the effects of CI-IB-MECA on microglial phenotypic shifting. Collectively, our findings suggest that activation of A3R exerted anti-inflammatory and neuroprotective effects by regulating microglial phenotype polarization through P38/STAT6 pathway and indicated that A3R agonists may be a promising therapeutic options for the treatment of brain injury after SAH.