Machine learning-based models for advanced fibrosis and cirrhosis diagnosis in chronic hepatitis B patients with hepatic steatosis.

BACKGROUND AND AIMS: The global rise of chronic hepatitis B (CHB) superimposed on hepatic steatosis (HS) warrants non-invasive, precise tools for assessing fibrosis progression. This study leveraged machine learning (ML) to develop diagnostic models for advanced fibrosis and cirrhosis in this patient population. METHODS: Treatment-naive CHB patients with concurrent HS who underwent liver biopsy in ten medical centers were enrolled as a training cohort and an independent external validation cohort (NCT05766449). Six ML models were implemented to predict advanced fibrosis and cirrhosis. The final models, derived from Shapley Additive exPlanations, were compared to Fibrosis-4 Index (FIB-4), Nonalcoholic fatty liver disease Fibrosis Score (NFS), and Aspartate transaminase to platelet ratio index (APRI) using the area under receiver operating characteristic curve (AUROC), and decision curve analysis (DCA). RESULTS: Of 1,198 eligible patients, the random forest (RF) model achieved AUROCs of 0.778 [95% confidence interval (CI) 0.749-0.807] for diagnosing advanced fibrosis (RF-AF model) and 0.777 (95%CI 0.748-0.806) for diagnosing cirrhosis (RF-C model) in the training cohort, and maintained high AUROCs in the validation cohort. In the training cohort, the RF-AF model obtained an AUROC of 0.825 (95% CI 0.787-0.862) in patients with HBV DNA ≥105 IU/ml, and RF-C model had an AUROC of 0.828 (95% CI 0.774-0.883) in female patients. The two models outperformed FIB-4, NFS, and APRI in the training cohort, and also performed well in the validation cohort. CONCLUSION: The RF models provide reliable, non-invasive tools for identifying advanced fibrosis and cirrhosis in CHB patients with concurrent HS, offering a significant advancement in the co-management of the two diseases.

Development and evaluation of a C-ELISA for rapid detection of antibody to AIV- H7.

Since 2013, the H7 subtype avian influenza virus (AIV-H7) has seriously endangered human life and health, and has had a serious impact on the poultry industry in China. A competitive enzyme-linked immunosorbent assay (C-ELISA) which detects the antibody for AIV-H7 was developed, basing on a monoclonal antibody (mAb) against the neutralizing epitopes on hemagglutinin (HA)gene. Twelve hybridoma cell lines were screened by cell fusion. Hemagglutination inhibition (HI) assay and indirect ELISA were used to identify the competitive effect of the mAbs. High-affinity mAb 1H11 was selected as a competitive antibody. The reaction conditions for the C-ELISA were optimized for AIV-H7 antibody detection. The cross-reactivity of the C-ELISA was determined by AIV-(H1H15), NDV, IBV and IBDV positive serum. A total of 1294 field samples (chicken (462), duck (318), goose (219), quail (203) and pigeon (92) were simultaneously detected by C-ELISA and HI assay. The C-ELISA was found to have a high specificity of 93.23% and a sensitivity of 96.24%. These results reveal a positive coincidence between C-ELISA and HI assay at a coincidence rate of 97.52%. In addition, It confirmed that this method can be used for the diagnosis of AIV-H7 antibodies from chicken, ducks, goose, quail and pigeons.

Dielectrophoretic choking phenomenon of a deformable particle in a converging-diverging microchannel.

The translational motion of small particles in an electrokinetic fluid flow through a constriction can be enhanced by an increase of the applied electric potential. Beyond a critical potential, however, the negative dielectrophoresis (DEP) can overpower other forces to prevent particles that are even smaller than the constriction from passing through the constriction. This DEP choking phenomenon was studied previously for rigid particles. Here, the DEP choking phenomenon is revisited for deformable particles, which are ubiquitous in many biomedical applications. Particle deformability is measured by the particle shear modulus, and the choking conditions are reported through a parametric study that includes the channel geometry, external electric potential, and particle zeta potential. The study was carried out using a numerical model based on an arbitrary Lagrangian-Eulerican (ALE) finite-element method.

Development of a machine learning-based model to predict hepatic inflammation in chronic hepatitis B patients with concurrent hepatic steatosis: a cohort study.

Background: With increasingly prevalent coexistence of chronic hepatitis B (CHB) and hepatic steatosis (HS), simple, non-invasive diagnostic methods to accurately assess the severity of hepatic inflammation are needed. We aimed to build a machine learning (ML) based model to detect hepatic inflammation in patients with CHB and concurrent HS. Methods: We conducted a multicenter, retrospective cohort study in China. Treatment-naive CHB patients with biopsy-proven HS between April 2004 and September 2022 were included. The optimal features for model development were selected by SHapley Additive explanations, and an ML algorithm with the best accuracy to diagnose moderate to severe hepatic inflammation (Scheuer's system ≥ G3) was determined and assessed by decision curve analysis (DCA) and calibration curve. This study is registered with ClinicalTrials.gov (NCT05766449). Findings: From a pool of 1,787 treatment-naive patients with CHB and HS across eleven hospitals, 689 patients from nine of these hospitals were chosen for the development of the diagnostic model. The remaining two hospitals contributed to two independent external validation cohorts, comprising 509 patients in validation cohort 1 and 589 in validation cohort 2. Eleven features regarding inflammation, hepatic and metabolic functions were identified. The gradient boosting classifier (GBC) model showed the best performance in predicting moderate to severe hepatic inflammation, with an area under the receiver operating characteristic curve (AUROC) of 0.86 (95% CI 0.83-0.88) in the training cohort, and 0.89 (95% CI 0.86-0.92), 0.76 (95% CI 0.73-0.80) in the first and second external validation cohorts, respectively. A publicly accessible web tool was generated for the model. Interpretation: Using simple parameters, the GBC model predicted hepatic inflammation in CHB patients with concurrent HS. It holds promise for guiding clinical management and improving patient outcomes. Funding: This research was supported by the National Natural Science Foundation of China (No. 82170609, 81970545), Natural Science Foundation of Shandong Province (Major Project) (No. ZR2020KH006), Natural Science Foundation of Jiangsu Province (No.BK20231118), Tianjin Key Medical Discipline (Specialty), Construction Project, TJYXZDXK-059B, Tianjin Health Science and Technology Project key discipline special, TJWJ2022XK034, and Research project of Chinese traditional medicine and Chinese traditional medicine combined with Western medicine of Tianjin municipal health and Family Planning Commission (2021022).

A K-nearest Neighbor Model to Predict Early Recurrence of Hepatocellular Carcinoma After Resection.

Background and Aims: Patients with hepatocellular carcinoma (HCC) surgically resected are at risk of recurrence; however, the risk factors of recurrence remain poorly understood. This study intended to establish a novel machine learning model based on clinical data for predicting early recurrence of HCC after resection. Methods: A total of 220 HCC patients who underwent resection were enrolled. Classification machine learning models were developed to predict HCC recurrence. The standard deviation, recall, and precision of the model were used to assess the model's accuracy and identify efficiency of the model. Results: Recurrent HCC developed in 89 (40.45%) patients at a median time of 14 months from primary resection. In principal component analysis, tumor size, tumor grade differentiation, portal vein tumor thrombus, alpha-fetoprotein, protein induced by vitamin K absence or antagonist-II (PIVKA-II), aspartate aminotransferase, platelet count, white blood cell count, and HBsAg were positive prognostic factors of HCC recurrence and were included in the preoperative model. After comparing different machine learning methods, including logistic regression, decision tree, naïve Bayes, deep neural networks, and k-nearest neighbor (K-NN), we choose the K-NN model as the optimal prediction model. The accuracy, recall, precision of the K-NN model were 70.6%, 51.9%, 70.1%, respectively. The standard deviation was 0.020. Conclusions: The K-NN classification algorithm model performed better than the other classification models. Estimation of the recurrence rate of early HCC can help to allocate treatment, eventually achieving safe oncological outcomes.

Development of a chemiluminescence immunoassay to accurately detect African swine fever virus antibodies in serum.

The outbreak of African swine fever (ASF) has caused significant economic losses to animal husbandry worldwide. Currently, there is no effective vaccine or treatment available to control the disease, and therefore, efficient disease control is dependent on early detection and diagnosis of ASF virus (ASFV). In this study, a chemiluminescent immunoassay (CLIA) was developed using the ASFV protein p54 as a serum diagnostic antigen and an anti-p54 monoclonal antibody. After optimizing the working parameters of the CLIA, the sensitivity of the established CLIA was 1:128, ASFV-specific serum antibody was identified, and there was no cross-reaction with other swine virus antibodies. After testing 49 clinical serum samples, the consistency rate between the CLIA and the World Organization for Animal Health (OIE) recommended commercial kit was 100 %. Thus, this CLIA had a high degree of specificity, sensitivity, and reliability, and could be used as a rapid detection method for epidemiological investigations of ASFV infection.