Clinical characteristics of extracorporeal cardiopulmonary resuscitation in China: a multicenter retrospective study.

PURPOSE: Extracorporeal cardiopulmonary resuscitation (ECPR) might markedly increase the survival of selected patients with refractory cardiac arrest. But the application situation and indications remained unclear. MATERIALS AND METHODS: We respectively reviwed all adult patients who underwent ECPR from January 2017 to March 2021. Patient characteristics, initiation and management of ECMO, complications, and outcomes were collected and compared between the survivors and nonsurvivors. LASSO regression was used to screen risk factors. Multivariate logistic regression was performed with several parameters screened by LASSO regression. RESULTS: Data were reported from 42 ECMO centers covering 19 provinces of China. A total of 648 patients were included in the study, including 491 (75.8%) males. There were 11 ECPR centers in 2017, and the number increased to 42 in 2020. The number of patients received ECPR increased from 33 in 2017 to 274 in 2020, and the survival rate increased from 24.2% to 33.6%. Neurological complications, renal replacement therapy, epinephrine dosage after ECMO, recovery of spontaneous circulation before ECMO, lactate clearance and shockable rhythm were risk factors independently associated with outcomes of whole process. Sex, recovery of spontaneous circulation before ECMO, lactate, shockable rhythm and causes of arrest were pre-ECMO risk factors independently affecting outcomes. CONCLUSIONS: From January 2017 to March 2021, the numbers of ECPR centers and cases in mainland China increased gradually over time, as well as the survival rate. Pre-ECMO risk factors, especially recovery of spontaneous circulation before ECMO, shockable rhythm and lactate, are as important as post-ECMO management,. Neurological complications are vital risk factors after ECMO that deserved close attention. TRIAL REGISTRATION: NCT04158479, registered on 2019/11/08. https://clinicaltrials.gov/NCT04158479.

ARDSFlag: an NLP/machine learning algorithm to visualize and detect high-probability ARDS admissions independent of provider recognition and billing codes.

BACKGROUND: Despite the significance and prevalence of acute respiratory distress syndrome (ARDS), its detection remains highly variable and inconsistent. In this work, we aim to develop an algorithm (ARDSFlag) to automate the diagnosis of ARDS based on the Berlin definition. We also aim to develop a visualization tool that helps clinicians efficiently assess ARDS criteria. METHODS: ARDSFlag applies machine learning (ML) and natural language processing (NLP) techniques to evaluate Berlin criteria by incorporating structured and unstructured data in an electronic health record (EHR) system. The study cohort includes 19,534 ICU admissions in the Medical Information Mart for Intensive Care III (MIMIC-III) database. The output is the ARDS diagnosis, onset time, and severity. RESULTS: ARDSFlag includes separate text classifiers trained using large training sets to find evidence of bilateral infiltrates in radiology reports (accuracy of 91.9%±0.5%) and heart failure/fluid overload in radiology reports (accuracy 86.1%±0.5%) and echocardiogram notes (accuracy 98.4%±0.3%). A test set of 300 cases, which was blindly and independently labeled for ARDS by two groups of clinicians, shows that ARDSFlag generates an overall accuracy of 89.0% (specificity = 91.7%, recall = 80.3%, and precision = 75.0%) in detecting ARDS cases. CONCLUSION: To our best knowledge, this is the first study to focus on developing a method to automate the detection of ARDS. Some studies have developed and used other methods to answer other research questions. Expectedly, ARDSFlag generates a significantly higher performance in all accuracy measures compared to those methods.

Esmolol increases the fecal abundance of Lactobacillus in a rat model of sepsis.

BACKGROUND: Disorders of the gut microbiome could be responsible for the progression of multiple organ dysfunction syndrome. In this study, we examined the effect of esmolol on the gut microbiome in a rat model of sepsis induced by cecal ligation and puncture (CLP). METHODS: The animals (n = 32) were randomly divided into 3 groups: Sham group (sham operation + normal saline treatment, n = 8), CLP group (cecal ligation and puncture + normal saline treatment, n = 12), and CLP + ESM group (cecal ligation and puncture + esmolol treatment, n = 12). After 24 h, feces in the colon were collected for 16S rRNA gene sequencing and nitric oxide analysis. In addition, colon was removed for immunohistochemical staining of inducible nitric oxide synthase (iNOS). RESULTS: Four rats in the CLP group and two rats in the CLP + ESM group died. The abundance of Lactobacillus in the CLP + ESM group was higher than CLP group (P = 0.048). In the linear discriminant analysis effect size analysis, Norank f Muribaculaceae, Escherichia-Shigella and Lactobacillus were the predominant bacteria in the Sham group, CLP group and CLP + ESM group, respectively. The iNOS expression in colonocytes stained by brown in the CLP group were much more than Sham group (P = 0.001). Compared to CLP group, the iNOS expression in colonocytes reduced after esmolol treatment (P = 0.013). The concentration of nitric oxide in colon feces was different in Sham group, CLP group and CLP + ESM group (1.31 ± 0.15µmmol/l vs. 1.98 ± 0.27µmmol/l vs. 1.51 ± 0.14µmmol/l, P = 0.001). In addition, the concentration of nitric oxide in CLP group was higher than Sham group (P = 0.001) or CLP + ESM group (P = 0.001). CONCLUSIONS: Esmolol increased the fecal abundance of Lactobacillus in a rat model of sepsis. Moreover, esmolol reduced the iNOS expression of colonocytes and the nitric oxide concentration of colon feces.

Polyamine Anabolism Promotes Chemotherapy-Induced Breast Cancer Stem Cell Enrichment.

Breast cancer patients may initially benefit from cytotoxic chemotherapy but experience treatment resistance and relapse. Chemoresistant breast cancer stem cells (BCSCs) play a pivotal role in cancer recurrence and metastasis, however, identification and eradication of BCSC population in patients are challenging. Here, an mRNA-based BCSC signature is developed using machine learning strategy to evaluate cancer stemness in primary breast cancer patient samples. Using the BCSC signature, a critical role of polyamine anabolism in the regulation of chemotherapy-induced BCSC enrichment, is elucidated. Mechanistically, two key polyamine anabolic enzymes, ODC1 and SRM, are directly activated by transcription factor HIF-1 in response to chemotherapy. Genetic inhibition of HIF-1-controlled polyamine anabolism blocks chemotherapy-induced BCSC enrichment in vitro and in xenograft mice. A novel specific HIF-1 inhibitor britannin is identified through a natural compound library screening, and demonstrate that coadministration of britannin efficiently inhibits chemotherapy-induced HIF-1 transcriptional activity, ODC1 and SRM expression, polyamine levels, and BCSC enrichment in vitro and in xenograft and autochthonous mouse models. The findings demonstrate the key role of polyamine anabolism in BCSC regulation and provide a new strategy for breast cancer treatment.