Establishment and application of the "electronic follow-up platform for endometriosis" during the COVID-19 pandemic.

The management of endometriosis has been complicated by the COVID-19 pandemic. We aimed to introduce the establishment and application of a new follow-up method during the COVID-19 pandemic-the electronic follow-up (e-follow-up) platform for endometriosis-and to test the applicability of the platform-based follow-up management model and patient satisfaction. We used the platform for information entry and post-operative follow-up of 152 patients with endometriosis from January 2021 to August 2022, and compared patients' Zung's Self-Rating Depression Scale (SDS), Self-Rating Anxiety Scale (SAS), and Visual Analogue Score (VAS) (range: 0 - 10, indicating: no pain-extreme pain) scores preoperatively and after 6-month of follow-up, together with recording patients' follow-up satisfaction and number of recurrence of lesions. Eventually, the SDS, SAS, and VAS scores were significantly lower than those at pre-surgery (p < .001), and the follow-up satisfaction rate reached 100%, with 91.41% expressing great satisfaction. The cumulative number of recurrences was 2 out of 138. Follow-up using this platform reduce the risk of COVID-19 transmission, enabled more efficient access to healthcare resources for patients with endometriosis, improved the efficiency of follow-up management, met the mental health needs of the patients.

Neutrophil-to-lymphocyte ratio as a potential biomarker in predicting influenza susceptibility.

Background: Human population exposed to influenza viruses exhibited wide variation in susceptibility. The ratio of neutrophils to lymphocytes (NLR) has been examined to be a marker of systemic inflammation. We sought to investigate the relationship between influenza susceptibility and the NLR taken before influenza virus infection. Methods: We investigated blood samples from five independent influenza challenge cohorts prior to influenza inoculation at the cellular level by using digital cytometry. We used multi-cohort gene expression analysis to compare the NLR between the symptomatic infected (SI) and asymptomatic uninfected (AU) subjects. We then used a network analysis approach to identify host factors associated with NLR and influenza susceptibility. Results: The baseline NLR was significantly higher in the SI group in both discovery and validation cohorts. The NLR achieved an AUC of 0.724 on the H3N2 data, and 0.736 on the H1N1 data in predicting influenza susceptibility. We identified four key modules that were not only significantly correlated with the baseline NLR, but also differentially expressed between the SI and AU groups. Genes within these four modules were enriched in pathways involved in B cell-mediated immune responses, cellular metabolism, cell cycle, and signal transduction, respectively. Conclusions: This study identified the NLR as a potential biomarker for predicting disease susceptibility to symptomatic influenza. An elevated NLR was detected in susceptible hosts, who may have defects in B cell-mediated immunity or impaired function in cellular metabolism, cell cycle or signal transduction. Our work can serve as a comparative model to provide insights into the COVID-19 susceptibility.