Infective endocarditis (IE) caused by Streptococcus sinensis is rare and challenging to diagnose due to the irregular use of antibiotics and the limitations of conventional diagnostic methods. Metagenomaic next-generation sequencing (mNGS) has emerged as a valuable diagnostic tool, offering significantly enhanced accuracy in identifying elusive pathogens, particularly in blood culture-negative endocarditis (BCNE). We reported the case of a 33-year-old male who presented with recurrent intermittent fever lasting over six months. Despite multiple diagnostic attempts, including blood cultures, no causative pathogen was identified. Blood mNGS revealed the presence of S. sinensis, a rare pathogen associated with IE. Echocardiography confirmed vegetation on the mitral and aortic valves, leading to a possible diagnosis of IE. The patient underwent successful surgical valve replacement, and subsequent mNGS of excised valve tissue confirmed the presence of S. sinensis. We administered a tailored antibiotic regimen, and the patient achieved a favorable recovery with no significant complications during follow-up. We identified 11 additional cases of S. sinensis-induced IE through a literature review, including patients from diverse geographic regions and age groups. Most cases were associated with prior dental procedures or poor oral health. Common diagnostic methods included blood culture and 16 S rRNA sequencing, while recent cases utilized mNGS. Treatment typically involved penicillin-based antibiotics combined with gentamicin, with surgical intervention in most cases leading to favorable outcomes. This case highlights the diagnostic value of mNGS in identifying pathogens in IE, particularly when traditional methods fail due to prior antibiotic use. While mNGS has significant advantages in pathogen detection, it cannot replace standard microbiological techniques and should be considered a complementary approach. Integrating mNGS into clinical workflows can improve diagnostic accuracy, especially in BCNE cases, and guide effective treatment strategies. Not applicable.

Type 3 hereditary hemochromatosis (HH) is a rare form of HH characterized by genetic mutation in the TFR2 gene. Clinical features reported in patients with type 3 HH include abnormal liver function, liver fibrosis, cirrhosis, diabetes, hypogonadism, cardiomyopathy, and skin pigmentation. Since its original description in 2000, 33 pathogenic TFR2 mutations associated with HH have been described until now. Here, we first reported a Chinese pedigree of TFR2-related hemochromatosis with a novel compound heterozygous mutation c.1288G > A (p.G430R)/c.960T > A (p.Y320X). Interestingly, different phenotypes were reported although the proband and his sister shared the same gene mutation. This inconsistency between genotypes and phenotypes indicates multifactorial etiology contributing to the development of HH. Our report broadens the mutation spectrum of the TFR2 gene associated with HH.

Background: Chronic progression is a major clinical challenge in human brucellosis (HB), affecting nearly one-third of patients and leading to long-term disability. Reliable early prediction tools are lacking, hindering timely risk stratification and individualized management. This study aimed to develop and validate machine learning (ML) models to predict chronic progression using routinely available clinical and laboratory data. Methods: We retrospectively analyzed 555 patients with confirmed brucellosis admitted between 2019 and 2024. Clinical characteristics and laboratory indicators at admission were collected. Feature selection was performed using Boruta and recursive feature elimination. Six supervised ML models (random forest [RF], LightGBM, XGBoost, logistic regression [LR], multilayer perceptron [MLP], and support vector machine [SVM]) were constructed and evaluated by discrimination, calibration, clinical utility, and predictive metrics. Model interpretability was assessed using SHapley Additive exPlanations (SHAP), and a web-based prediction tool was developed. Results: Of 555 patients, 144 (25.9%) progressed to chronic brucellosis. Compared with the recovery group, chronic cases presented more frequently with arthralgia and arthritis and showed distinct biochemical profiles, including lower alanine aminotransferase (ALT), aspartate aminotransferase (AST), triglycerides (TG), and higher high-density lipoprotein cholesterol (HDL-C), albumin (ALB), blood urea nitrogen (BUN), and uric acid (UA). Among the six models, RF consistently demonstrated the most robust performance across metrics, achieving the highest AUC in the test set (0.782, 95% CI: 0.701 - 0.856), superior calibration (Emax = 0.155), and the greatest net clinical benefit in decision curve analysis. SHAP analysis identified TG, HDL-C, UA, eosinophil count, PA, ALT, BUN, and GLB as the most influential predictors, with biologically plausible associations. Conclusion: Using eight routinely available variables, the RF model demonstrated moderate discrimination with well-calibrated probability estimates but limited sensitivity. The tool may assist early risk stratification of chronic brucellosis when combined with clinical judgment; however, its predictive performance should be interpreted cautiously until validated in external, multicenter, and prospective studies.

Objective. To assess the geographic distribution of HCV genotypes, effectiveness, and safety of DAA treatment for HCV-infected patients in North and Northeast China. Methods. The geographic distribution of HCV genotypes was analyzed in 2162 patients recruited from April 2018 to February 2021. Sustained virologic response rates at 12 (SVR12) or 24 (SVR24) weeks posttreatment and safety were analyzed in 405 patients who completed DAA treatment according to patient baseline characteristics and treatment. Results. Four genotypes and six subtypes were identified as follows: 1b (1187, 54.90%), 2a (790, 36.54%), 3a/b (134, 6.20%), 6a/n (44, 2.04%), mixed genotypes (2a-6a or 2a-3a) (7, 0.32%). Overall, 99.01% patients achieved SVR12, while 98.43% achieved SVR24. All patients treated with elbasvir/grazoprevir (EBR/GZR), sofosbuvir/velpatasvir ± ribavirin (SOF/VEL ± RBV), and SOF/ledipasvir (LDV) achieved SVR12 or SVR24; 92.86% SVR12 and 95.83% SVR24 were observed in patients using SOF + RBV. SVR12 was higher in noncirrhosis versus compensated cirrhosis patients (100% vs. 97.09%, ). No severe drug-related adverse event was observed. Conclusions. Genotypes 1b and 2a were dominant subtypes in North and Northeast China. The approved drug regimens EBR/GZR and SOF/LDV for subtype 1b and SOF/VEL for nongenotype 1b are the optimal effective and safety profile.

Background: Non-alcoholic fatty liver disease (NAFLD) is increasingly prevalent among adolescents and poses a significant public health challenge. Due to limitations in imaging and invasive diagnostic methods such as liver biopsy, there is a pressing need for accurate, cost-effective, and non-invasive risk prediction tools. This study aims to develop and compare multiple machine learning (ML) models to predict NAFLD risk in adolescents using routine anthropometric and laboratory data from the National Health and Nutrition Examination Survey (NHANES) 2011–2020 dataset. Methods: Data from 2,132 U.S. adolescents (NHANES 2011–2020) were analyzed. Nine machine learning (ML) models were developed using features selected by Light Gradient Boosting Machine (LightGBM). Performance was assessed by AUC, accuracy, sensitivity, precision, F1-score, and calibration. The Extra Trees (ET) model was further compared with TyG-based logistic regression models. Model interpretability was evaluated using SHapley Additive exPlanations (SHAP), and an interactive online prediction tool was deployed. Results: NAFLD prevalence was 13.0%. The ET model achieved the best overall performance (AUC = 0.784, ACC = 0.773, Kappa = 0.320), outperforming other ML algorithms and TyG-based models, which showed higher sensitivity but poorer precision. SHAP analysis identified waist circumference, triglycerides, insulin, and HDL as key predictors, revealing nonlinear threshold effects. The online tool allows individualized risk estimation based on routine clinical variables. Conclusion: The ET-based ML model provides an accurate and interpretable approach for adolescent NAFLD risk prediction. By surpassing traditional metabolic indicators and offering an accessible web-based calculator, it supports scalable, cost-effective early screening and targeted prevention strategies.

Objective: To explore the safety of inactivated novel coronavirus vaccine inoculation and the fluctuating neutralizing antibody in patients with chronic hepatitis B (CHB). Methods: Retrospective and prospective epidemiological research methods were employed. 153 CHB patients who visited the Department of Infectious Diseases at the First Hospital of Shanxi Medical University from September 2021 to February 2022 were selected as the research subjects. Information on vaccination-related adverse reactions was collected. Colloidal gold immunochromatography was used to identify neutralizing antibodies in the body after 3-6 months of vaccination. Statistical analysis was performed using the χ²-test or Fisher's exact test. Results: The positive rates of neutralizing antibodies after inactivated novel coronavirus vaccine inoculation in 153 patients with CHB were 45.50%, 44.70%, 40.00% and 16.20%, respectively, at 3, 4, 5, and 6 months. The neutralizing antibody concentrations were 10.00 (2.95, 30.01) U/ml, 6.08 (3.41, 24.50) U/ml, 5.90 (3.93, 14.68) U/ml, and 1.25 (0.92, 3.75) U/ml, respectively. The difference was not statistically significant (P>0.05) when the neutralizing antibody positivity rates in hepatitis B virus (HBV) DNA-negative and positive patients and HBeAg-negative and positive patients at different time points were compared. The overall incidence of adverse reactions following vaccination was 18.30%. Pain at the site of inoculation and fatigue were the main presentations, and no serious adverse reactions occurred. Conclusion: CHB patients, when inoculated with an inactivated novel coronavirus vaccine, can produce neutralizing antibodies, which can stay at certain levels for 3, 4, and 5 months. However, the neutralizing antibody level gradually decreases over time, and the decrease is remarkable at 6 months. So, it is recommended to boost vaccinations at an appropriate time. Additionally, the results of the study suggest that HBV replication status has little effect on the production of neutralizing antibodies in CHB patients with relatively stable liver function, which means the inactivated novel coronavirus vaccine has a good safety profile.