Epidemiological characteristics of respiratory syncytial virus infection in pediatric patients before, during the COVID-19 pandemic and after easing of COVID-19 restrictive measures in China.

We aimed to assess the epidemiological characteristics of respiratory syncytial virus (RSV) infection in Chinese children at different phases of the coronavirus disease 2019 (COVID-19) pandemic, that is, before, during the pandemic and after easing of restrictive measures. We included 123 623 patients aged 0-18 years with respiratory infection symptoms who were suspected with RSV infection from January 1, 2019 to June 30, 2023 in Hangzhou Children's Hospital. Clinical information and RSV test result were extracted from the laboratory information system. We calculated the positive rate of RSV detection by age groups, gender, seasons, types of patients and phases of COVID-19 pandemic. Nonlinear associations between age and risk of RSV infection in three phases of pandemic were assessed by restricted cubic spline regression models. Among 123 623 patients, 3875 (3.13%) were tested as positive. The highest positive rate was observed in children aged 0-28 days (i.e., 12.28%). RSV infection was most prevalent in winter (6.04%), and followed by autumn (2.52%). Although there is no statistical significance regarding the positive rate at three phases of the pandemic, we observed that the rate was lowest during the pandemic and increased after easing the measures in certain age groups (p < 0.05), which was consisted with results from the nonlinear regression analyses. In addition, regression analyses suggested that the age range of children susceptible to RSV got wider, that is, 0-3.5 years, after easing all restrictive measures compared with that before (i.e., 0-3 years) and during the pandemic (i.e., 0-1 year). Based on our findings, we called for attention from health professionals and caregivers on the new epidemiological characteristics of RSV infection in the post-pandemic era after easing the restrictive measures.

Nonalcoholic fatty liver disease (NAFLD) detection and deep learning in a Chinese community-based population.

OBJECTIVES: We aimed to develop and validate a deep learning system (DLS) by using an auxiliary section that extracts and outputs specific ultrasound diagnostic features to improve the explainable, clinical relevant utility of using DLS for detecting NAFLD. METHODS: In a community-based study of 4144 participants with abdominal ultrasound scan in Hangzhou, China, we sampled 928 (617 [66.5%] females, mean age: 56 years ± 13 [standard deviation]) participants (2 images per participant) to develop and validate DLS, a two-section neural network (2S-NNet). Radiologists' consensus diagnosis classified hepatic steatosis as none steatosis, mild, moderate, and severe. We also explored the NAFLD detection performance of six one-section neural network models and five fatty liver indices on our data set. We further evaluated the influence of participants' characteristics on the correctness of 2S-NNet by logistic regression. RESULTS: Area under the curve (AUROC) of 2S-NNet for hepatic steatosis was 0.90 for ≥ mild, 0.85 for ≥ moderate, and 0.93 for severe steatosis, and was 0.90 for NAFLD presence, 0.84 for moderate to severe NAFLD, and 0.93 for severe NAFLD. The AUROC of NAFLD severity was 0.88 for 2S-NNet, and 0.79-0.86 for one-section models. The AUROC of NAFLD presence was 0.90 for 2S-NNet, and 0.54-0.82 for fatty liver indices. Age, sex, body mass index, diabetes, fibrosis-4 index, android fat ratio, and skeletal muscle via dual-energy X-ray absorptiometry had no significant impact on the correctness of 2S-NNet (p > 0.05). CONCLUSIONS: By using two-section design, 2S-NNet had improved the performance for detecting NAFLD with more explainable, clinical relevant utility than using one-section design. KEY POINTS: • Based on the consensus review derived from radiologists, our DLS (2S-NNet) had an AUROC of 0.88 by using two-section design and yielded better performance for detecting NAFLD than using one-section design with more explainable, clinical relevant utility. • The 2S-NNet outperformed five fatty liver indices with the highest AUROCs (0.84-0.93 vs. 0.54-0.82) for different NAFLD severity screening, indicating screening utility of deep learning-based radiology may perform better than blood biomarker panels in epidemiology. • The correctness of 2S-NNet was not significantly influenced by individual's characteristics, including age, sex, body mass index, diabetes, fibrosis-4 index, android fat ratio, and skeletal muscle via dual-energy X-ray absorptiometry.

Metabolomic Profiles of Plasma Retinol-Associated Dyslipidemia in Men and Women.

Background and Aims: Studies of both animals and humans show that a high intake of vitamin A is associated with a lower risk of dyslipidemia. However, an association of plasma retinol levels with dyslipidemia is unclear. Therefore, the aim of this study is to investigate an association between plasma retinol and dyslipidemia and to identify related metabolites and pathways in the general population. Methods: We included 250 participants aged 20-80 years from the Wellness Living Laboratory (WELL) China cohort. Associations between plasma retinol levels and dyslipidemia were analyzed using adjusted logistic models. Related metabolites were identified using ANCOVA, adjusted for the false discovery rate (FDR) and used for pathway analyses. Because there are sex differences in plasma retinol levels, all analyses were conducted separately by sex. Results: Plasma retinol was significantly higher in men than in women. A positive association between plasma retinol and dyslipidemia was found in both sexes. In men, the 2nd and 3rd tertiles showed significantly higher proportions of dyslipidemia than the 1st tertile (1st tertile vs. 2nd tertile: p = 0.026; 1st tertile vs. 3rd tertile: p = 0.003). In women, the 3rd tertile showed a significantly higher proportion of dyslipidemia than the 1st and 2nd tertile (3rd tertile vs. 1st tertile: p = 0.002, 3rd tertile vs. 2nd tertile: p = 0.002). Overall, 75 and 30 metabolites were significantly associated with retinol levels in men and women, respectively. According to these metabolites, lipid metabolic pathways, including glycerophospholipid, arachidonic acid, linoleic acid, alpha-linolenic acid, and glycosylphosphatidylinositol (GPI), as well as steroid hormone biosynthesis pathways were found to overlap across the sexes. These pathways showed that elevated retinol levels might be associated with hormone metabolism and inflammation status. Conclusions: We found a positive association between plasma retinol levels and dyslipidemia. Related metabolomic profiles and interrupted pathways showed that such an increase might be associated with steroid hormone synthesis and inflammation. In addition, large, population-based longitudinal studies and intervention studies are needed to confirm the role of retinol in lipid metabolism and the prevention of cardiovascular disease (CVD).