Despite that sleep disturbance and poor neurocognitive performance are common complaints among coronavirus disease 2019 (COVID-19) survivors, few studies have focused on the effect of post-COVID-19 sleep disturbance (PCSD) on cognitive function. This study aimed to identify the impact of PCSD on neurocognitive function and explore the associated risk factors for the worsening of this condition. This cross-sectional study was conducted via the web-based assessment in Chinese mainland. Neurocognitive function was evaluated by the modified online Integrated Cognitive Assessment (ICA) and the Number Ordering Test (NOT). Propensity score matching (PSM) was utilized to match the confounding factors between individuals with and without PCSD. Univariate analyses were performed to evaluate the effect of PCSD on neurocognitive function. The risk factors associated with worsened neurocognitive performance in PCSD individuals were explored using binary logistic regression. A total of 8692 individuals with COVID-19 diagnosis were selected for this study. Nearly half (48.80%) of the COVID-19 survivors reported sleep disturbance. After matching by PSM, a total of 3977 pairs (7954 individuals in total) were obtained. Univariate analyses revealed that PCSD was related to worse ICA and NOT performance (P<0.05). Underlying disease, upper respiratory infection, loss of smell or taste, severe pneumonia, and self-reported cognitive complaints were associated with worsened neurocognitive performance among PCSD individuals (P<0.05). Furthermore, aging, ethnicity (minority), and lower education level were found to be independent risk factors for worsened neurocognitive performance in PCSD individuals (P<0.05). PCSD was related to impaired neurocognitive performance. Therefore, appropriate prevention and intervention measures should be taken to minimize or prevent PCSD and eliminate its potential adverse effect on neurocognitive function.
Humans ; COVID-19/epidemiology* ; Male ; Female ; Sleep Wake Disorders/epidemiology* ; Propensity Score ; Middle Aged ; Cross-Sectional Studies ; Adult ; SARS-CoV-2 ; Aged ; Risk Factors ; China/epidemiology* ; Cognition ; Cognitive Dysfunction/etiology* ; Neuropsychological Tests

Extensive epigenetic reprogramming involves in memory CD8+ T-cell differentiation. The elaborate epigenetic rewiring underlying the heterogeneous functional states of CD8+ T cells remains hidden. Here, we profile single-cell chromatin accessibility and map enhancer-promoter interactomes to characterize the differentiation trajectory of memory CD8+ T cells. We reveal that under distinct epigenetic regulations, the early activated CD8+ T cells divergently originated for short-lived effector and memory precursor effector cells. We also uncover a defined epigenetic rewiring leading to the conversion from effector memory to central memory cells during memory formation. Additionally, we illustrate chromatin regulatory mechanisms underlying long-lasting versus transient transcription regulation during memory differentiation. Finally, we confirm the essential roles of Sox4 and Nrf2 in developing memory precursor effector and effector memory cells, respectively, and validate cell state-specific enhancers in regulating Il7r using CRISPR-Cas9. Our data pave the way for understanding the mechanism underlying epigenetic memory formation in CD8+ T-cell differentiation.
CD8-Positive T-Lymphocytes/metabolism* ; Cell Differentiation ; Chromatin/immunology* ; Animals ; Mice ; Immunologic Memory ; Epigenesis, Genetic ; SOXC Transcription Factors/immunology* ; NF-E2-Related Factor 2/immunology* ; Mice, Inbred C57BL ; Gene Regulatory Networks ; Enhancer Elements, Genetic

Objective To analyze the epidemiological characteristics and temporal spatial clustering of hand, foot, and mouth disease in Hubei Province from 2010 to 2023, and to provide evidence for formulating prevention and control measures. Methods Descriptive epidemiological method was used to analyze the surveillance data of hand, foot, and mouth disease of Hubei Province from 2010 to 2023, and spatial clustering analysis was conducted at the district/county level using ArcGIS 10.5 software. Results A total of 1 007 600 cases of hand, foot, and mouth disease were reported from 2010 to 2023, and the average annual incidence rate was 123.60/100 000. Overall , it exhibited a cyclical pattern of high incidence every other year. Except for a few years, two peaks of incidence were observed each year, , with the first peak occurring between April to July and the second occurring in October to December, and the popular season was concentrated from April to July. Children aged < 5 years were primarily affected, with a high incidence in male patients (1.53:1). The incidence of hand, foot, and mouth disease showed a positive spatial autocorrelation（Moran's I was between 0.15 to 0.76, P<0.05）at the district/county level. The hot spots were concentrated in the northwest and southeast of Hubei Province, and the cool spots were concentrated in the east of central Hubei Province. Conclusion The incidence of hand, foot, and mouth disease has obvious seasonality and spatial clustering in Hubei Province. The key prevention and control areas are concentrated in the northwest and southeast of Hubei. Enhanced prevention and control measures should be targeted on children under 5 years old and key areas to effectively reduce the occurrence of cases.

Objective To explore the spatial-temporal characteristics of influenza epidemic in Hubei from 2009 to 2020, and make short-term prediction to provide reference for influenza prevention and control strategies. Methods Time series seasonal decomposition model and geographic spatial analysis method were used to analyze spatial-temporal evolution characteristics of influenza prevalence in Hubei during 2009-2020. LSTM neural network model was used to predict the monthly influenza incidence from 2020 to 2023. Results Influenza was mainly prevalent in the end of winter and the beginning of spring (December to March) were periods of high influenza incidence. In recent years, the influenza pandemic has shown an increasing trend. Influenza epidemic was characterized by significant spatial differentiation, with ^“A-shaped point-axis structure^” surrounding counties were more severe . The epidemic center of gravity experienced a spatial evolution process from west to east and from north to south. LSTM neural network model predicted that although the influenza incidence rate from January 2020 to December 2023 is lower than that in 2019, it is still at a high level, and shows a peak epidemic in winter and spring. Conclusion Influenza epidemic in Hubei is characterized by a high epidemic period in late winter and early spring, and the southeast of Hubei is the key epidemic area. It is suggested that publicity and prevention and control should be strengthened according to people, time and place, and key populations and areas should be encouraged to receive influenza vaccines in advance.

Objective To determine the serum IgM and IgG antibody levels post-COVID-19 vaccination， and provide scientific evidence for COVID-19 antibody response after vaccination. Methods A total of 980 healthy persons were included in Kunming Third People’s Hospital from July through August， 2021， which had been vaccinated with COVID-19 vaccines and then tested for anti-SARS-CoV-2 IgM and IgG antibodies. Results After the COVID-19 vaccination， 469 persons （positive rate， 47.86%） were positive for anti-IgG antibody. Of them， 75 were males with （positive rate， 39.06%）， and the average IgG level was 0.618 （0.180， 2.526） AU·mL^-1［M（Q₁，Q₃）］； 394 were females （50.00%）， and the IgG level was 0.999 （0.305， 3.334） AU·mL^-1. In addition， 53 persons （5.41%） were anti-IgM antibody positive. Of them， 14 were males （positive rate， 7.29%）， and the average IgM level was 0.057 （0.026， 0.195） AU·mL^-1； 39 were females （4.95%）， and the IgM level was 0.047 （0.027， 0.110） AU·mL^-1. The positive rate of anti-IgG antibody was highest in those aged ≤30 years， which was 51.02% in male （n=25） and 55.88% in female （n=133）. The anti-IgG response differed significantly by gender （χ²=7.401， D=0.135 1， P<0.05）， whereas the anti-IgM response was not significantly different （χ²=1.656， P>0.05）. Among the age groups， anti-IgG antibody level was higher in those aged ≤30 and 51-70 years， with 158 （55.05%） and 122 （52.36%） persons， respectively； the average antibody level was 1.209 （0.426， 4.386） AU·mL^-1 and 1.074 （0.191， 7.670） AU·mL^-1， respectively. The differences in the positive rates of IgG and IgM antibodies and the levels of IgG antibodies among different age groups were statistically significant （P<0.05）. Using Kruskal-Wallis test and Spearman correlation analysis， it showed a high correlation between the IgG and IgM antibodies （r=0.836 4， H=64.82， 20.09， P<0.05）. Conclusion The Anti-SARS-CoV-2 IgG antibody remains high six months post-COVID-19 vaccination， while anti-IgM antibody is low. The IgM and IgG response are higher in the young and elderly. The response differs by gender and age， demonstrating a correlation.

Objective To explore spatial clustering of mumps in Hubei Province during 2010-2020, and to provide evidence for mumps prevention and control. Methods The surveillance data of mumps during 2010-2020 in Hubei Province was obtained from the national infectious diseases reporting information system. Trend surface analysis and spatial autocorrelation analysis of mumps incidences at county/district levels were performed using ArcGIS10.5 software. Results Mumps incidence rates in Hubei Province during 2010-2018 ranged from 8.70 per 100 000 to 44.99 per 100 000. The trend surface analysis showed that mumps incidences gradually decreased from west to east, and was low in the middle and high at the north-south direction. Global spatial autocorrelation showed that there were positive spatial correlations in every year except 2012 and 2014 (Morans I> 0, P <0.05). Local autocorrelation analysis showed that the hotspots of mumps incidences varied every year from 2010 to 2020. Conclusions According to the spatial analysis, mumps incidences had obvious spatial clustering in Hubei Province. The hotspots were mainly concentrated in the northwestern region of Hubei, but the hot spots also extended to the urban areas of eastern, central and northern Hubei. It is necessary to take appropriate prevention and control measures in the high-incidence areas.

Objective To analyze the epidemiological characteristics and incidence trend of gonorrhea in Hubei Province, and to provide reference for scientific formulation of prevention and control measures. Methods Based on the surveillance data of gonorrhea from 2010 to 2021, three-way distribution and ARIMA model were used for data analysis and incidence prediction. Results From 2010 to 2021, the reported incidence rate fluctuated between 3.01/100 000-7.07/100 000, and the average annual reported incidence rate was 4.62/100 000. The reported incidence rate showed the characteristics of ^“first fall and then rise, and then fall and rise again^”, and the peak incidence period was from June to December. The male to female ratio of reported cases was 5.78:1, and the number of reported cases in the age group of 20-39 years old accounted for 62.43% of the total number of cases. The reported cases were mainly housework and unemployed, farmers, and unknown occupation. The severity of the regional incidence was divided into 5 categories by the Q-type clustering, and the most serious category included Shennongjia Forest District, Huangshi City, and Wuhan City. The ARIMA model predicted the incidence rate to be in good agreement with the actual incidence rate, with a predicted number of 3 343 cases in 2022. Conclusion At present, gonorrhea in Hubei Province is still at a high prevalence level. There are obvious differences in gender, age, occupation, and regional distribution. The ARIMA model is suitable for predicting the incidence of gonorrhea, and it is predicted that the incidence will increase slightly in 2022.

Objective To establish an ARIMA model and a seasonal index model to predict the trend of mumps, compare the advantages and disadvantages of the two models, and to provide a scientific basis for the prevention and control of mumps. Methods ARIMA model and seasonal index model were established based on the monthly incidence of mumps in Hubei Province from 2008 to 2019. Results The average annual incidence rate from 2008 to 2019 was 28.89 / 100,000. April-July was the month of high incidence. The established ARIMA model and seasonal index model were （1-1.070B+0.441B²-0.291B³）*（1-B¹²）*X_t=（1-0.611B¹²）*Ɛ_t and X_t=（2.802-0.006t）*S_t. The average relative errors of the ARIMA model and the seasonal index model were 11.49% and 20.86%, respectively. Conclusion The ARIMA model and the seasonal index model both have good applicability in predicting the onset time characteristics and trend of mumps. However, while the ARIMA model demonstrated more advantages in fitting the annual change trend, the seasonal index model is better in fitting the monthly change trend. The two models can be used in combination to predict the trend of mumps.

Objective To analyze the epidemiological characteristics of measles, predict the incidence trend of the disease, and provide reference for the development of measles prevention and elimination strategies. Methods Descriptive analysis, seasonal index method, clustering analysis and ARIMA model were used for data analysis and trend prediction. Results The average annual incidence of measles in 2005-2018 was 3.01/100,000. It was at a low level after 2009, and rebounded in 2018. The seasonal index of March-June was greater than 1, which was the month of high incidence. The incidence of men was higher than that of women. The 0-3 years old and 10-30 years old groups had a high incidence, and the population was mainly scattered children, students, and nursery children. The areas with high incidence were mainly northwestern and southeastern Hubei. ARIMA (0,1,0) (0,1,1) was the optimal model. The prediction analysis showed that the incidence rate in 2019 will be 1.26/100 000. Conclusion After the implementation of booster immunization, the measles epidemic in Hubei Province was at a low level, but it fluctuated greatly in recent years and is currently showing a rising trend. The incidence was high in spring. Cases were ^"biphasic shift^" in groups 0-3 years old and 10-30 years old. Occupational distribution was consistent with the characteristics of high incidence in the age group. The mountain areas with poor sanitary conditions and economic backwardness, and regions with large population bases and high floating populations had a high incidence. The ARIMA model had good applicability in predicting the trend of measles incidence, which shows measles will continue to rise in 2019.

Objective Summarize the characteristics of infectious disease reporting management, reflect on the exposed problems, and make reference suggestions under the situation of COVID-19. Methods In-depth analysis through literature research and practical demonstration. Results The functions of each department, organic integration, unified management, communication from top to bottom, upgrade and perfection, and information sharing can give full play to the early warning role of infectious disease surveillance. Conclusion The infectious disease reporting management system should be perfected and upgraded from top to bottom and from the inside out.