Objective: Eravacycline is a novel, fully synthetic fluorocycline antibiotic being developed for the treatment of serious infections, with a broad-spectrum antimicrobial activity, including against carbapenem-resistant gram-negative bacteria (CRGNB). However, the in vitro activity of eravacycline against CRGNB has not been well known in China. In this study, we analysed the antibacterial activity of eravacycline against CRGNB isolates in order to provide a theoretical basis for the clinical treatment. Methods: A total of 346 isolates of CRGNB were collected from two different tertiary care hospitals in Zhejiang, China. Carbapenem resistance genes of all isolates were detected by polymerase chain reaction. And we analysed the in vitro activity of eravacycline against CRGNB by antimicrobial susceptibility tests. In addition, the time-kill curves were generated to evaluate the antibacterial effect of tigecycline and eravacycline. Results: Four different types of carbapenem-resistant isolates were collected, including 50 Escherichia coli isolates, 160 Klebsiella pneumoniae isolates, 42 Enterobacter cloacae complex isolates, and 94 Acinetobacter baumannii isolates. The carbapenem resistance genes were identified in 346 isolates, including bla_KPC-2 (48.0%), bla_OXA-23 (27.2%), bla_NDM-1 (23.1%), and bla_NDM-16 (0.3%). The antimicrobial susceptibility testing results showed that the minimum inhibitory concentration (MIC) values of 346 isolates were within the sensitivity range (≤ 0.0625~16 mg/L) and that the MIC₅₀ or MIC₉₀ of eravacycline was generally approximately 2-fold lower than tigecycline. In addition, the time-kill curves showed that the bactericidal effect of eravacycline was stronger than that of tigecycline against four different types of isolates. Conclusion: Our research indicated that eravacycline had a good antibacterial effect on CRGNB, which could provide a theoretical basis for the clinical treatment of drug-resistant bacterial infections in the future.

Early assessment and accurate staging of liver fibrosis may be of great help for clinical diagnosis and treatment in patients with chronic hepatitis B (CHB). We aimed to identify serum markers and construct a machine learning (ML) model to reliably predict the stage of fibrosis in CHB patients. The clinical data of 618 CHB patients between February 2017 and September 2021 from Zhejiang Provincial People's Hospital were retrospectively analyzed, and these data as a training cohort to build the model. Six ML models were constructed based on logistic regression, support vector machine, Bayes, K-nearest neighbor, decision tree (DT) and random forest by using the maximum relevance minimum redundancy (mRMR) and gradient boosting decision tree (GBDT) dimensionality reduction selected features on the training cohort. Then, the resampling method was used to select the optimal ML model. In addition, a total of 571 patients from another hospital were used as an external validation cohort to verify the performance of the model. The DT model constructed based on five serological biomarkers included HBV-DNA, platelet, thrombin time, international normalized ratio and albumin, with the area under curve (AUC) values of the DT model for assessment of liver fibrosis stages (F0-1, F2, F3 and F4) in the training cohort were 0.898, 0.891, 0.907 and 0.944, respectively. The AUC values of the DT model for assessment of liver fibrosis stages (F0-1, F2, F3 and F4) in the external validation cohort were 0.906, 0.876, 0.931 and 0.933, respectively. The simulated risk classification based on the cutoff value showed that the classification performance of the DT model in distinguishing hepatic fibrosis stages can be accurately matched with pathological diagnosis results. ML model of five serum markers allows for accurate diagnosis of hepatic fibrosis stages, and beneficial for the clinical monitoring and treatment of CHB patients.

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.

Background and Aims: An imbalance in lipid metabolism is the main cause of NAFLD. While the pathogenesis of lipid accumulation mediated by extrahepatic regulators has been extensively studied, the intrahepatic regulators modulating lipid homeostasis remain unclear. Previous studies have shown that systemic administration of IL-22 protects against NAFLD; however, the role of IL-22/IL22RA1 signaling in modulating hepatic lipid metabolism remains uncertain. Approach and Results: This study shows that hepatic IL22RA1 is vital in hepatic lipid regulation. IL22RA1 is downregulated in palmitic acid-treated mouse primary hepatocytes, as well as in the livers of NAFLD model mice and patients. Hepatocyte-specific Il22ra1 knockout mice display diet-induced hepatic steatosis, insulin resistance, impaired glucose tolerance, increased inflammation, and fibrosis compared with flox/flox mice. This is attributed to increased lipogenesis mediated by the accumulation of hepatic oxysterols, particularly 3 beta-hydroxy-5-cholestenoic acid (3β HCA). Mechanistically, hepatic IL22RA1 deficiency facilitates 3β HCA deposition through the activating transcription factor 3/oxysterol 7 alpha-hydroxylase axis. Notably, 3β HCA facilitates lipogenesis in mouse primary hepatocytes and human liver organoids by activating liver X receptor-alpha signaling, but IL-22 treatment attenuates this effect. Additionally, restoring oxysterol 7 alpha-hydroxylase or silencing hepatic activating transcription factor 3 reduces both hepatic 3β HCA and lipid contents in hepatocyte-specific Il22ra1 knockout mice. Conclusions: These findings indicate that IL22RA1 plays a crucial role in maintaining hepatic lipid homeostasis in an activating transcription factor 3/oxysterol 7 alpha-hydroxylase-dependent manner and establish a link between 3β HCA and hepatic lipid homeostasis.

Background and Aim: Liver biopsy remains the gold standard for evaluating liver histology. However, it has certain limitations, and many patients refuse it. Non-invasive methods of liver evaluation are thus attracting considerable interest. In this study, we sought predictors of liver inflammation in chronic hepatitis B (CHB) patients with alanine aminotransferase (ALT) levels ≤ 2-fold the upper limit of normal (ULN); these may guide decisions on whether to commence antiviral therapy.Methods: We retrospectively analyzed 720 patients with CHB who underwent liver biopsy and whose ALT levels were ≤2 ULN. The patients were randomly divided into a training and validation set. We used univariate and multivariate regression analyses of data from the training set to construct a model that predicted significant (grade ≥2) liver inflammation, and validated the model employing the validation set.Results: Aspartate aminotransferase (AST) level, prothrombin time (PT), glutamyl transpeptidase (GGT) level, and anti-hepatitis B virus core antibody (anti-HBC) level were independent predictors of significant liver inflammation in CHB patients with ALT levels ≤ 2 ULN. A model featuring these four parameters afforded areas under the ROC curve of 0.767 and 0.714 for the training and validation sets. The model was more predictive than were the individual factors.Conclusion: AST, GGT, anti-HBC, and PT reflect significant liver inflammation among CHB patients with ALT levels ≤ 2 ULN. Their combination indicates whether antiviral therapy is required.