MLKL regulates Cx43 ubiquitinational degradation and mediates neuronal necroptosis in ipsilateral thalamus after focal cortical infarction.

Necroptosis is known to play an important role in the pathophysiology of cerebral ischemia; however, its role in the occurrence of secondary thalamic injury after focal cerebral infarction and the mechanism about how mixed lineage kinase domain-like (MLKL) executes necroptosis in this pathophysiology are still unclear. In this study, Sprague-Dawley rats were subjected to distal branch of middle cerebral artery occlusion (dMCAO). The expression of MLKL, connexin 43 (Cx43) and Von Hippel-Lindau (VHL) in vitro and in vivo were assessed by Western blot. Bioinformatic methods were used to predict the potential binding sites where MLKL interacted with Cx43, and the ubiquitination degradation of Cx43 regulated by VHL. The interactions among MLKL, Cx43, VHL, and Ubiquitin were assessed by immunoprecipitation. Dye uptake assay were used to examine the Cx43 hemichannels. Intracellular Ca2+ concentration was measured using Fluo-4 AM. Overexpression and site-directed mutagenesis studies were used to study the mechanisms by which MLKL regulates Cx43 ubiquitinational degradation to mediate neuronal necroptosis. We found that MLKL and Cx43 were upregulated in the ventral posterolateral nucleus (VPN) of the ipsilateral thalamus after dMCAO. In the in vitro experiments MLKL and Cx43 were upregulated after TSZ-mediated necroptosis in SH-SY5Y cells. The interaction between MLKL and Cx43 inhibited the K48-linked ubiquitination of Cx43 in necroptotic SH-SY5Y cells. VHL is an E3 ubiquitin ligase for Cx43, and MLKL competes with VHL for binding to Cx43. Interaction of MLKL Ser454 with Cx43 can trigger the opening of Cx43 hemichannels, causing increased intracellular Ca2+, and cell necroptosis. This innovative study at animal models, cellular, and molecular levels is anticipated to clarify the roles of MLKL and Cx43 in thalamic damage after focal cortical infarction. Our findings may help identify novel targets for neurological recovery after cortical infarction.

Machine learning algorithms for integrating clinical features to predict intracranial hemorrhage in patients with acute leukemia.

BACKGROUND: Intracranial hemorrhage (ICH) in acute leukemia (AL) patients leads to high morbidity and mortality, treatment approaches for ICH are generally ineffective. Thus, early identification of which subjects are at high risk of ICH is of key importance. Currently, machine learning can achieve well predictive capability through constructing algorithms that simultaneously exploit the information coming from clinical features. METHODS: After rigid data preprocessing, 42 different clinical features from 948 AL patients were used to train different machine learning algorithms. We used the feature selection algorithms to select the top 10 features from 42 clinical features. To test the performance of the machine learning algorithms, we calculated area under the curve (AUC) values from receiver operating characteristic (ROC) curves along with 95% confidence intervals (CIs) by cross-validation. RESULTS: With the 42 features, RF exhibited the best predictive power. After feature selection, the top 10 features were international normalized ratio (INR), prothrombin time (PT), creatinine (Cr), indirect bilirubin (IBIL), albumin (ALB), monocyte (MONO), platelet (PLT), lactic dehydrogenase (LDH), fibrinogen (FIB) and prealbumin (PA). Among the top 10 features, INR, PT, Cr, IBIL and ALB had high predictive performance with an AUC higher than 0.8 respectively. CONCLUSIONS: The RF algorithm exhibited a higher cross-validated performance compared with the classical algorithms, and the selected important risk features should help in individualizing aggressive treatment in AL patients to prevent ICH. Efforts that will be made to test and optimize in independent samples will warrant the application of such algorithm and predictors in the future.

Venous Thromboembolism in ICU Patients with Intracerebral Hemorrhage: Risk Factors and the Prognosis After Anticoagulation Therapy.

PURPOSE: Venous thromboembolism (VTE) is a common complication of intracerebral hemorrhage (ICH) patients in intensive care unit (ICU), but anticoagulation therapy of ICH patients with VTE remains controversial. We aim to explore the risk factors and prognosis of anticoagulation therapy in ICH patients with VTE. PATIENTS AND METHODS: Medical records of ICH patients were collected from the Medical Information Mart for Intensive Care III (MIMIC-III version 1.4) database. The risk factors and prognosis of anticoagulation therapy in ICH patients with VTE were assessed by multivariable logistic regression analysis and Kaplan-Meier survival analysis, respectively. RESULTS: A total of 848 ICH patients were included in our study, of whom 69 ICH patients with VTE were screened, including 58 patients with deep vein thrombosis (DVT), 12 patients with pulmonary embolism (PE), and 1 patient with DVT and PE. In the multivariable logistic regression analysis, malignancy (odds ratio (OR): 4.262, 95% confidence interval (CI): 2.263-8.027, P=0.000), pulmonary circulation disease (OR: 28.717, 95% CI: 9.566-86.208, P=0.000), coagulopathy (OR: 2.453, 95% CI: 1.098-5.483, P=0.029), age > 60 years old (OR: 2.138, 95% CI: 1.087-4.207, P=0.028) and hospitalization time > 16 days (OR: 2.548, 95% CI: 1.381-4.701, P=0.003) were independent risk factors for VTE in ICH patients. Kaplan-Meier survival analysis and log-rank test found that, compared to non-anticoagulation group, anticoagulation group had higher cumulative survival rates during hospitalization, 28-day, 3-month, 1-year, and 4-year after admission, respectively. CONCLUSION: Malignancy, pulmonary circulation disease, coagulopathy, age >60 years old and hospitalization time >16 days were independent risk factors for VTE in ICH patients, and anticoagulation therapy for VTE in ICH patients may be safe and effective. These findings need to be verified by more high-quality and well-designed randomized controlled trials.