Tuberculous meningitis (TBM) is the most common form of central nervous system tuberculosis with a very poor prognosis. We aimed at assessing risk factors related to the prognosis of patients with TBM. Forty-five inpatients with TBM in our institution from January 2013 to December 2015 were enrolled retrospectively. The good or poor prognosis in the patients was defined, based on Glasgow Outcome Scale System at discharge. Patients with a GOS score less than 5 were defined as “poor prognosis.” Univariate and multivariate logistic regression analyses were performed to assess the predictors for TBM outcome. Among 45 TBM patients, 35 (77.8%) and 10 (22.2%) were in good, poor prognoses, respectively. Old age, disturbance of consciousness, moderate to severe electroencephalogram abnormality, hydrocephalus, remarkable increase of protein (≥ 236 mg/dL) and white blood cell counts (≥ 243 /μL) in cerebral spinal fluid were associated with poor prognosis. Multivariate analysis indicated that old age (odds ratio (OR) = 18.395, P = .036) and hydrocephalus (OR = 32.995, P = .049) were independent factors for a poor outcome of TBM. In conclusion, old age and hydrocephalus are the predictors for poor prognosis of TBM. Patients with these risk factors should be treated promptly with a special care paid to improve their outcomes.

With the dramatically fast spread of COVID-9, real-time reverse transcription polymerase chain reaction (RT-PCR) test has become the gold standard method for confirmation of COVID-19 infection. However, RT-PCR tests are complicated in operation andIt usually takes 5-6 hours or even longer to get the result. Additionally, due to the low virus loads in early COVID-19 patients, RT-PCR tests display false negative results in a number of cases. Analyzing complex medical datasets based on machine learning provides health care workers excellent opportunities for developing a simple and efficient COVID-19 diagnostic system. This paper aims at extracting risk factors from clinical data of early COVID-19 infected patients and utilizing four types of traditional machine learning approaches including logistic regression(LR), support vector machine(SVM), decision tree(DT), random forest(RF) and a deep learning-based method for diagnosis of early COVID-19. The results show that the LR predictive model presents a higher specificity rate of 0.95, an area under the receiver operating curve (AUC) of 0.971 and an improved sensitivity rate of 0.82, which makes it optimal for the screening of early COVID-19 infection. We also perform the verification for generality of the best model (LR predictive model) among Zhejiang population, and analyze the contribution of the factors to the predictive models. Our manuscript describes and highlights the ability of machine learning methods for improving the accuracy and timeliness of early COVID-19 infection diagnosis. The higher AUC of our LR-base predictive model makes it a more conducive method for assisting COVID-19 diagnosis. The optimal model has been encapsulated as a mobile application (APP) and implemented in some hospitals in Zhejiang Province.

Neuregulin (NRG) family is involved in energy metabolism, among which NRG1 is a neuregulin proved to play a protective role in MAFLD cells. But the presice echanism has not been fully illustrated. This study aimed to investigate the role of NRG1 via the ERK/SIRT1 signaling in the pathogenesis of MAFLD. C57BL/6 mice were fed with high-fat diet for 8 weeks, and then injected with NRG1 (0.3 mg/kg/d) and PD98059 (0.3 mg/kg/d) via tail vein for 5 weeks. HepG2 cells induced by oleic acid and palmitic acid were treated with 20ng/mL NRG1 and 10µmol/L PD98059. The changes of histopathological, biochemical indexes, inflammatory factors, lipid metabolism, apoptosis and autophagy parameters were measured. The expressions of NRG1 in MAFLD cell and animal models were significantly lower than that in the control group. After the intervention of ERK inhibitor PD98059, the expression of NRG1 decreased significantly in vivo, but no significant change was observed in vitro. Moreover, NRG1 ameliorated hepatic steatosis, enhanced cell viability, reduced cell apoptosis, and attenuated liver injury both in vitro and in vivo. After NRG1 intervention, the expressions of ERBB2, ERBB3, p-ERK1/2, SIRT1 and p-FOXO1 as well as the LC3II/I ratio in MAFLD cells and liver tissues of MAFLD mice were significantly increased, while the expression of SREBP1c was decreased. The aforementioned therapeutic effect of NRG1 was lost after the intervention of PD98059. NRG1 might play a protective role in the pathogenesis of MAFLD by activating the downstream ERK1/2 through ErbB2-ErbB3, which promotes the expression of SIRT1 and autophagy markers. This study might indicate a new therapeutic strategy for MAFLD.