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Background In China, the 2022–2023 influenza season began earlier and was characterized by higher levels of influenza activity and co-circulation of various respiratory pathogens compared with seasons before the coronavirus disease 2019 (COVID-19) pandemic. Timely and precise estimates of influenza vaccine effectiveness (IVE) against infections can be used to guide public health measures. Methods A test-negative study was conducted to estimate IVE against laboratory-confirmed influenza using data from the CHinese Electronic health Records Research in Yinzhou (CHERRY) study that prospectively integrated laboratory, vaccination, and health administrative data in Yinzhou, southern China. We included patients who presented influenza-like illness and received nucleic acid tests and/or antigen tests between October 2023 and March 2024. Estimates of IVE were adjusted for age, gender, month of specimen submitted, chronic comorbidities, and hospitalization status. Results A total of 205 028 participants, including 96 298 influenza cases (7.6% vaccinated) and 108 730 influenza-negative controls (13.4% vaccinated), were eligible for this analysis. The estimates of IVE were 49.4% (95% CI, 47.8%–50.9%), 41.9% (95% CI, 39.8%–44.0%), and 59.9% (95% CI, 57.9%–61.9%) against overall influenza, influenza A, and influenza B, respectively. A lower IVE was observed for individuals aged 7–17 years (38.6%), vs 45.8% for 6 months–6 years, 46.7% for 18–64 years, and 46.1% for ≥65 years. Vaccination reduced the risk of infection by 44.4% among patients with chronic comorbidities. IVEs varied by epidemic weeks with the changes in influenza activity levels and the switch of dominant influenza strains. Conclusions Influenza vaccination in the 2023–2024 season was protective against infection for the entire population. [ABSTRACT FROM AUTHOR]

Aims: Existing electronic health records (EHRs) often consist of abundant but irregular longitudinal measurements of risk factors. In this study, we aim to leverage such data to improve the risk prediction of atherosclerotic cardiovascular disease (ASCVD) by applying machine learning (ML) algorithms, which can allow automatic screening of the population., Methods and Results: A total of 215 744 Chinese adults aged between 40 and 79 without a history of cardiovascular disease were included (6081 cases) from an EHR-based longitudinal cohort study. To allow interpretability of the model, the predictors of demographic characteristics, medication treatment, and repeatedly measured records of lipids, glycaemia, obesity, blood pressure, and renal function were used. The primary outcome was ASCVD, defined as non-fatal acute myocardial infarction, coronary heart disease death, or fatal and non-fatal stroke. The eXtreme Gradient boosting (XGBoost) algorithm and Least Absolute Shrinkage and Selection Operator (LASSO) regression models were derived to predict the 5-year ASCVD risk. In the validation set, compared with the refitted Chinese guideline-recommended Cox model (i.e. the China-PAR), the XGBoost model had a significantly higher C -statistic of 0.792, (the differences in the C -statistics: 0.011, 0.006-0.017, P < 0.001), with similar results reported for LASSO regression (the differences in the C -statistics: 0.008, 0.005-0.011, P < 0.001). The XGBoost model demonstrated the best calibration performance (men: D x = 0.598, P = 0.75; women: D x = 1.867, P = 0.08). Moreover, the risk distribution of the ML algorithms differed from that of the conventional model. The net reclassification improvement rates of XGBoost and LASSO over the Cox model were 3.9% (1.4-6.4%) and 2.8% (0.7-4.9%), respectively., Conclusion: Machine learning algorithms with irregular, repeated real-world data could improve cardiovascular risk prediction. They demonstrated significantly better performance for reclassification to identify the high-risk population correctly., Competing Interests: Conflict of interest: P.G. reported receiving research funds from Bayer and Merck. These funding sources had no relation to this study. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Introduction: Electrical burns are caused by the conversion of electrical energy flowing through the body into heat energy, which can cause coagulative necrosis of the skin and deep tissues. Deep tissue damage is often more serious than skin damage. Electrical burns have the characteristics of destructive and progressive damage and present the common symptoms of severe local tissue damage accompanied by a wide range of deep tissue necrosis, resulting in injury of nerves, blood vessels, bones, and internal organs. Autologous skin grafting alone cannot effectively cover deep tissues or repair electrical burn wounds., Case Report: This article describes 2 patients with deep electrical burns in the lower abdomen that showed extensive skin and soft tissue damage, partial necrosis of abdominal muscle tissue, and weak abdominal wall. As a single tissue flap was too small to effectively cover the defect wound, ilioinguinal flap and tensor fascia lata muscle flap were utilized in both cases with good outcomes. These flaps survived completely, and the wounds were effectively repaired. After repair, the shape was satisfactory, and the function of the lower abdomen was normal., Conclusions: Transfer of flaps from a site near the wound for repairing electrical burns is convenient for transfer with minimal surgical trauma and a simpler operating procedure than the free flap.