Background: Many studies have suggested that hepatitis B virus (HBV) genotypes show not only geographical distribution and race specificity, but also are associated with disease progression and response to interferon treatment. The objective of this study was to develop a nested polymerase chain reaction (nPCR) assay for genotypes A-D and subgenotypes B1, B2, C1 and C2 of hepatitis B virus (HBV) and to investigate the distribution characteristics of HBV genotypes/subgenotype in China. Methods: After redesigning the primers and optimizing the reaction conditions using common Taq polymerase, the sensitivity, specificity and reproducibility of the method were evaluated using plasmids and serum samples. In total, 642 serum samples from patients with chronic HBV infection were applied to investigate the distribution of HBV genotype and subgenotype in China. Results: The genotype and subgenotype could be identified when the HBV DNA load of a sample was ≥10^2.3 IU/mL. For the 639 successfully genotyped samples, the sequencing results of 130 randomly selected samples (20.3%, 130/639) were consistent with those of the nPCR method. The present study showed that HBV genotype B (11.2%, 72/642), C (68.2%, 438/642) and D (7.2%, 46/642) were circulating in China, while genotype C was the dominant strain except for western region where genotype D was the prevalent strain. The main subgenotypes of genotypes B and C were B2 (87.5%, 63/72) and C2 (92.9%, 407/438), respectively. Conclusions: The low-cost nPCR method would be a useful tool for clinical and epidemiological investigation in the regions where genotypes A-D are predominant.

Objective: This study enrolled 119 elderly patients with severe lower respiratory tract infections (LRTIs) and used machine learning (ML) to evaluate the predictive value of unconventional T lymphocytes (uT cells) in sepsis and 90‐day prognosis.Methods: We used random forest (RF) and LASSO analyses to screen model uT cells (identified by RF‐LASSO overlapping). The ML models, including LR, LDA, RandomForest, XGBoost, KNN, QDA, NaiveBayes, and ANN, were developed. These models were evaluated and compared based on accuracy, precision, recall, F1 score, sensitivity, specificity, area under the ROC curve (AUROC), and Brier score.Results: Two T cells were identified as factors of sepsis diagnosis: CD3⁺ and CD4⁺CD25⁺CD127dim. The LDA model demonstrated superior performance, achieving an accuracy of 0.806, AUROC of 0.771, F1 score of 0.720, and a low Brier score of 0.182. Four T cells were identified for predicting the 90‐day prognosis: CD3⁺, CD3⁺CD4⁺, CD4⁺CD28⁺, and CD4⁺CD25⁺CD127dim. For the 90‐day prognosis, the LDA model again performed best, with an accuracy of 0.972, F1 score of 0.952, AUROC of 0.935, and a low Brier score of 0.059.Conclusions: The LDA model is optimal for both diagnosing sepsis and predicting the 90‐day prognosis in elderly patients with severe LRTIs. Key T‐cell markers identified for sepsis include CD3⁺ and CD4⁺CD25⁺CD127dim, while the 90‐day prognosis model includes CD3⁺, CD3⁺CD4⁺, CD4⁺CD28⁺, and CD4⁺CD25⁺CD127dim T cells. These markers should be prioritized for clinical testing. Trial Registration: Not applicable