A machine learning-based model analysis for serum markers of liver fibrosis in chronic hepatitis B patients.

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.

Genome Analysis of ST1 Bartonella henselae, a Zoonotic Pathogen Causing Endocarditis in an Elderly Patient in China.

Infective endocarditis (IE) is a rare disease but with high associated mortality. Currently, the mainstays of diagnosis are still echocardiography and blood cultures. Here, we reported a case of infective endocarditis with negative blood cultures, and blood and aortic valve tissue metagenomic next-generation sequencing (mNGS) results suggested Bartonella henselae. In addition, we obtained the whole genomic sequence of B. henselae ZJBH strain. To our knowledge, this is the first report of B. henselae genomic analysis isolated from clinic in China. Furthermore, we described the whole genome sequencing (WGS) data incorporating all B. henselae from diverse sources worldwide and shed light on underlying risk of B. henselae transmitted between cats and humans.

Clinical Distribution Characteristics and Identification for Significant Liver Inflammation of Patients in Chronic Hepatitis B with Indeterminate Phase.

Aim: In clinical practice, a considerable proportion of patients with chronic hepatitis B (CHB) who do not conform to any immune status are considered to be in the "indeterminate phase". In this study, we aim to study the clinical distribution characteristics and identification of significant liver inflammation in patients in indeterminate phase. Methods: This study retrospectively analyze clinical data of 1226 patients with CHB at two medical centers in Zhejiang province. According to American Association for the Study of Liver Diseases (AASLD) 2018 hepatitis B guidance, CHB can be divided into four phases: immune-tolerant phase, HBeAg-positive immune active phase, inactive phase, and HBeAg-negative immune active phase. Liver inflammation grade was evaluated using the Scheuer scoring system, and significant liver inflammation was defined as G ≥ 2. Results: The distribution of different immune status was as follows: 259 (21.1%) patients in immune-tolerant phase, 365 (29.8%) patients in HBeAg-positive immune active phase, 128 (10.4%) patients in inactive phase, and 33 (2.7%) patients in HBeAg-negative immune active phase. However, 441 (36.0%) patients did not meet any of the above immune phases, which were defined as indeterminate phase. Significant liver inflammation (54.1%) was common in CHB patients with indeterminate phase. Prothrombin time (PT), platelet count (PLT), alanine aminotransferase (ALT), and hepatitis B virus (HBV)-DNA were associated with significant inflammation. Conclusions: The results of this study showed that about 36.0% of patients were divided into indeterminate phase. The proportion of patients with significant inflammation in indeterminate phase and liver inflammation becomes more severe with aggravation of fibrosis stage. PT, PLT, ALT, and HBV-DNA may have a significant correlation with severe inflammation and prognosis of CHB.

Antibacterial Activity of Eravacycline Against Carbapenem-Resistant Gram-Negative Isolates in China: An in vitro Study.

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.

Clinical evaluation of polymerase chain reaction coupled with quantum dot fluorescence analysis in the identification of bacteria and yeasts in patients with suspected bloodstream infections.

Bloodstream infections are serious and complex infectious diseases that often require a rapid diagnosis. Polymerase chain reaction coupled with quantum dot fluorescence analysis (PCR-QDFA) is a novel diagnostic technique. This study aimed to evaluate the diagnostic performance of PCR-QDFA for pathogen detection in patients with suspected bloodstream infections (BSIs). It evaluates 29 kinds of common pathogens (24 bacteria and 5 yeasts) from blood culture bottles. The results of PCR-QDFA identification and traditional microbial laboratory identification were compared, and the latter was used as the 'gold standard' to analyse the diagnostic performance of the PCR-QDFA. In total, 517 blood culture bottles were included in this study. The PCR-QDFA identified microorganisms in 368/422 (87.2%) samples with monomicrobial growth. For the pathogens on the PCR-QDFA list, the assay showed a higher sensitivity of 97.4% (368/378). When polymicrobial growth was analysed, the PCR-QDFA successfully detected 19/25 (76%) microorganisms on the PCR-QDFA list. In addition, 82/82 negative blood culture bottles also showed no pathogens by PCR-QDFA with a specificity of 100%. In conclusion, the PCR-QDFA assay could identify a majority of the common pathogens encountered in clinical practice, showing excellent diagnostic performance for pathogen detection in patients with suspected BSIs.

Emergence of Plasmids Co-Harboring Carbapenem Resistance Genes and tmexCD2-toprJ2 in Sequence Type 11 Carbapenem Resistant Klebsiella pneumoniae Strains.

Objectives: To characterize two plasmids co-harboring carbapenem resistance genes and tmexCD2-toprJ2 in carbapenem-resistant Klebsiella pneumoniae (CRKP) strains. Methods: Two clinical CRKP strains were isolated and characterized by antimicrobial susceptibility testing, conjugation assays, whole-genome sequencing, and bioinformatics analysis. Results: The two CRKP strains NB4 and NB5 were both resistant to imipenem, meropenem and tigecycline. Whole-genome sequencing revealed that two CRKP strains belonged to the ST11 type and carried multiple resistance genes. The tmexCD2-toprJ2 clusters in both strains were located on the IncFIB(Mar)-like/HI1B-like group of hybrid plasmids, which co-harbored the metallo-ß-lactamase gene blaNDM-1. In addition, the co-existence of blaNDM-1 and blaKPC-2 and the presence of tmexCD2-toprJ2 in CRKP strain NB5 was observed. Conclusions: In this study, tmexCD2-toprJ2 gene clusters were identified in two NDM-1-producing CRKP ST11 strains. These gene clusters will likely spread into clinical high-risk CRKP clones and exacerbate the antimicrobial resistance crisis. In addition, we detected the co-occurrence of blaNDM-1, blaKPC-2 and tmexCD2-toprJ2 in a single strain, which will undoubtedly accelerate the formation of a "superdrug resistant" bacteria. Hence, effective control measures should be implemented to prevent the further dissemination of such organisms in clinical settings.