Evaluation of disease severity and prediction of severe cases in children hospitalized with influenza A (H1N1) infection during the post-COVID-19 era: a multicenter retrospective study.

BACKGROUND: The rebound of influenza A (H1N1) infection in post-COVID-19 era recently attracted enormous attention due the rapidly increased number of pediatric hospitalizations and the changed characteristics compared to classical H1N1 infection in pre-COVID-19 era. This study aimed to evaluate the clinical characteristics and severity of children hospitalized with H1N1 infection during post-COVID-19 period, and to construct a novel prediction model for severe H1N1 infection. METHODS: A total of 757 pediatric H1N1 inpatients from nine tertiary public hospitals in Yunnan and Shanghai, China, were retrospectively included, of which 431 patients diagnosed between February 2023 and July 2023 were divided into post-COVID-19 group, while the remaining 326 patients diagnosed between November 2018 and April 2019 were divided into pre-COVID-19 group. A 1:1 propensity-score matching (PSM) was adopted to balance demographic differences between pre- and post-COVID-19 groups, and then compared the severity across these two groups based on clinical and laboratory indicators. Additionally, a subgroup analysis in the original post-COVID-19 group (without PSM) was performed to investigate the independent risk factors for severe H1N1 infection in post-COIVD-19 era. Specifically, Least Absolute Shrinkage and Selection Operator (LASSO) regression was applied to select candidate predictors, and logistic regression was used to further identify independent risk factors, thus establishing a prediction model. Receiver operating characteristic (ROC) curve and calibration curve were utilized to assess discriminative capability and accuracy of the model, while decision curve analysis (DCA) was used to determine the clinical usefulness of the model. RESULTS: After PSM, the post-COVID-19 group showed longer fever duration, higher fever peak, more frequent cough and seizures, as well as higher levels of C-reactive protein (CRP), interleukin 6 (IL-6), IL-10, creatine kinase-MB (CK-MB) and fibrinogen, higher mechanical ventilation rate, longer length of hospital stay (LOS), as well as higher proportion of severe H1N1 infection (all P < 0.05), compared to the pre-COVID-19 group. Moreover, age, BMI, fever duration, leucocyte count, lymphocyte proportion, proportion of CD3+ T cells, tumor necrosis factor α (TNF-α), and IL-10 were confirmed to be independently associated with severe H1N1 infection in post-COVID-19 era. A prediction model integrating these above eight variables was established, and this model had good discrimination, accuracy, and clinical practicability. CONCLUSIONS: Pediatric H1N1 infection during post-COVID-19 era showed a higher overall disease severity than the classical H1N1 infection in pre-COVID-19 period. Meanwhile, cough and seizures were more prominent in children with H1N1 infection during post-COVID-19 era. Clinicians should be aware of these changes in such patients in clinical work. Furthermore, a simple and practical prediction model was constructed and internally validated here, which showed a good performance for predicting severe H1N1 infection in post-COVID-19 era.

[Analysis of microRNA in drug-resistant breast cancer cell line MCF-7/ADR].

OBJECTIVE: To analyze the difference in microRNAs expression between MCF-7 and MCF-7/ADR cells and explore the association between microRNA and drug resistance of breast cancer. METHODS: The drug resistance of MCF-7/ADR cells was evaluated using MTT assay and flow cytometry. Microarray technique and RT-PCR were used to analyze the differential expressions of the microRNA between MCF-7 and MCF-7/ADR cells. RESULTS: The drug resistance index of MCF-7/ADR cells relative to the parental MCF-7 cells was 33.2. The percentages of the side population in MCF-7/ADR and MCF-7 cells were (9.50-/+0.9)% and (0.85-/+0.2)%, respectively. Microarray analysis of MCF-7 to MCF-7/ADR cells identified 36 differentially expressed genes, including 16 up-regulated and 20 down-regulated genes in MCF-7/ADR cells. RT-PCR identified 14 microRNAs that were differentially expressed between MCF-7 and MCF-7/ADR cells, including 7 up-regulated and 7 down-regulated ones in MCF-7/ADR cells. Of these differentially expressed microRNAs, mir-221, mir222, mir-130a, and mir-155 showed significantly increased expression, and mir200a, mir-200b, mir-200c, and mir-421 showed significantly lowered expression in MCF-7/ADR cells as indicated by the results of microarray analysis and RT-PCR. CONCLUSION: MCF-7/ADR cells show a different microRNA expression profile from its parental MCF-7 cells, suggesting the involvement of microRNAs in tumor cell drug resistance. This finding provides a experimental basis for further study of mechanism underlying the drug resistance of breast cancer.