Association between influenza vaccination and one-year all-cause and cardiovascular mortality risk: A self-controlled case series and matched case-control study.

Debates surrounding the efficacy of influenza vaccination for survival benefits persist, and there is a lack of data regarding its duration of protection. A self-controlled case series (SCCS) and a 1:4 matched case-control study were conducted using the National Health Interview Survey (NHIS) and public-use mortality data from 2005 to 2018 in the United States. The SCCS study identified participants who received influenza vaccination within 12 months before the survey and subsequently died within 1 year of postvaccination. The matched case-control study paired participants who died during the influenza season at the time of survey with four survivors. Among 1167 participants in the SCCS study, there was a 46% reduction in all-cause mortality and a 43% reduction in cardiovascular mortality within 29-196 days of postvaccination. The greatest protection was observed during days 29-56 (all-cause mortality: RI: 0.19; 95% CI: 0.12-0.29; cardiovascular mortality: RI: 0.28; 95% CI: 0.14-0.56). Among 626 cases and 2504 controls included in the matched case-control study, influenza vaccination was associated with a reduction in all-cause mortality (OR: 0.74, 95% CI: 0.60-0.92) and cardiovascular mortality (OR: 0.64, 95% CI: 0.44-0.93) during the influenza season. This study highlights the importance of influenza vaccination in reducing the risks of all-cause and cardiovascular mortality, with effects lasting for approximately 6 months.

Towards development of functional climate-driven early warning systems for climate-sensitive infectious diseases: Statistical models and recommendations.

Climate, weather and environmental change have significantly influenced patterns of infectious disease transmission, necessitating the development of early warning systems to anticipate potential impacts and respond in a timely and effective way. Statistical modelling plays a pivotal role in understanding the intricate relationships between climatic factors and infectious disease transmission. For example, time series regression modelling and spatial cluster analysis have been employed to identify risk factors and predict spatial and temporal patterns of infectious diseases. Recently advanced spatio-temporal models and machine learning offer an increasingly robust framework for modelling uncertainty, which is essential in climate-driven disease surveillance due to the dynamic and multifaceted nature of the data. Moreover, Artificial Intelligence (AI) techniques, including deep learning and neural networks, excel in capturing intricate patterns and hidden relationships within climate and environmental data sets. Web-based data has emerged as a powerful complement to other datasets encompassing climate variables and disease occurrences. However, given the complexity and non-linearity of climate-disease interactions, advanced techniques are required to integrate and analyse these diverse data to obtain more accurate predictions of impending outbreaks, epidemics or pandemics. This article presents an overview of an approach to creating climate-driven early warning systems with a focus on statistical model suitability and selection, along with recommendations for utilizing spatio-temporal and machine learning techniques. By addressing the limitations and embracing the recommendations for future research, we could enhance preparedness and response strategies, ultimately contributing to the safeguarding of public health in the face of evolving climate challenges.

Increased population susceptibility to seasonal influenza during the COVID-19 pandemic in China and the United States.

To the best of our knowledge, no previous study has quantitatively estimated the dynamics and cumulative susceptibility to influenza infections after the widespread lifting of COVID-19 public health measures. We constructed an imitated stochastic susceptible-infected-removed model using particle-filtered Markov Chain Monte Carlo sampling to estimate the time-dependent reproduction number of influenza based on influenza surveillance data in southern China, northern China, and the United States during the 2022-2023 season. We compared these estimates to those from 2011 to 2019 seasons without strong social distancing interventions to determine cumulative susceptibility during COVID-19 restrictions. Compared to the 2011-2019 seasons without a strong intervention with social measures, the 2022-2023 influenza season length was 45.0%, 47.1%, and 57.1% shorter in southern China, northern China, and the United States, respectively, corresponding to an 140.1%, 74.8%, and 50.9% increase in scale of influenza infections, and a 60.3%, 72.9%, and 45.1% increase in population susceptibility to influenza. Large and high-intensity influenza epidemics occurred in China and the United States in 2022-2023. Population susceptibility increased in 2019-2022, especially in China. We recommend promoting influenza vaccination, taking personal prevention actions on at-risk populations, and monitoring changes in the dynamic levels of influenza and other respiratory infections to prevent potential outbreaks in the coming influenza season.

One-Year Temporal Changes in Long COVID Prevalence and Characteristics: A Systematic Review and Meta-Analysis.

OBJECTIVES: This study aimed to explore the 1-year temporal change in prevalence, variety, and potential risk factors of long COVID symptoms and to further predict the prognostic trends of long COVID. METHODS: We searched electronic databases for related studies published from January 2020 to February 2022 and conducted 1-group meta-analysis and locally weighted regression to explore the monthly temporal change in the prevalence of each long COVID symptom in 1-year follow-up period. RESULTS: A total of 137 studies were included in meta-analysis, including 134 093 participants. The temporal change of any long COVID symptom showed a steep decrease initially (from 92% at acute phase to 55% at 1-month follow-up), followed by stabilization at approximately 50% during 1-year follow-up. Six months or more after the acute phase, the odds ratio of population characteristic-related factors increased, such as female (from 1.62 to 1.82), whereas the odds ratio value of acute phase-related factors (severe or critical cases and hospitalization) decreased. As for specific symptoms, approximately two-thirds of the symptoms did not significantly reduce during the 1-year follow-up, and the neuropsychiatric symptoms showed a higher long-term prevalence (approximately 25%) and longer persistence than physical symptoms. CONCLUSIONS: The temporal changes in the prevalence and characteristics speculate that long COVID may persist longer than expected. In particular, we should pay more attention to neuropsychiatric symptoms and other symptoms for which there is no significant downward trend in prevalence. The influence of acute phase-related factors for long COVID gradually decreases over time, whereas the influence of population characteristic-related factors gradually increases.

Association between ozone and influenza transmissibility in China.

BACKGROUND: Common air pollutants such as ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2), and particulate matter play significant roles as influential factors in influenza-like illness (ILI). However, evidence regarding the impact of O3 on influenza transmissibility in multi-subtropical regions is limited, and our understanding of the effects of O3 on influenza transmissibility in temperate regions remain unknown. METHODS: We studied the transmissibility of influenza in eight provinces across both temperate and subtropical regions in China based on 2013 to 2018 provincial-level surveillance data on influenza-like illness (ILI) incidence and viral activity. We estimated influenza transmissibility by using the instantaneous reproduction number ([Formula: see text]) and examined the relationships between transmissibility and daily O3 concentrations, air temperature, humidity, and school holidays. We developed a multivariable regression model for [Formula: see text] to quantify the contribution of O3 to variations in transmissibility. RESULTS: Our findings revealed a significant association between O3 and influenza transmissibility. In Beijing, Tianjin, Shanghai and Jiangsu, the association exhibited a U-shaped trend. In Liaoning, Gansu, Hunan, and Guangdong, the association was L-shaped. When aggregating data across all eight provinces, a U-shaped association was emerged. O3 was able to accounted for up to 13% of the variance in [Formula: see text]. O3 plus other environmental drivers including mean daily temperature, relative humidity, absolute humidity, and school holidays explained up to 20% of the variance in [Formula: see text]. CONCLUSIONS: O3 was a significant driver of influenza transmissibility, and the association between O3 and influenza transmissibility tended to display a U-shaped pattern.

Deep-Learning Model for Influenza Prediction From Multisource Heterogeneous Data in a Megacity: Model Development and Evaluation.

BACKGROUND: In megacities, there is an urgent need to establish more sensitive forecasting and early warning methods for acute respiratory infectious diseases. Existing prediction and early warning models for influenza and other acute respiratory infectious diseases have limitations and therefore there is room for improvement. OBJECTIVE: The aim of this study was to explore a new and better-performing deep-learning model to predict influenza trends from multisource heterogeneous data in a megacity. METHODS: We collected multisource heterogeneous data from the 26th week of 2012 to the 25th week of 2019, including influenza-like illness (ILI) cases and virological surveillance, data of climate and demography, and search engines data. To avoid collinearity, we selected the best predictor according to the weight and correlation of each factor. We established a new multiattention-long short-term memory (LSTM) deep-learning model (MAL model), which was used to predict the percentage of ILI (ILI%) cases and the product of ILI% and the influenza-positive rate (ILI%×positive%), respectively. We also combined the data in different forms and added several machine-learning and deep-learning models commonly used in the past to predict influenza trends for comparison. The R2 value, explained variance scores, mean absolute error, and mean square error were used to evaluate the quality of the models. RESULTS: The highest correlation coefficients were found for the Baidu search data for ILI% and for air quality for ILI%×positive%. We first used the MAL model to calculate the ILI%, and then combined ILI% with climate, demographic, and Baidu data in different forms. The ILI%+climate+demography+Baidu model had the best prediction effect, with the explained variance score reaching 0.78, R2 reaching 0.76, mean absolute error of 0.08, and mean squared error of 0.01. Similarly, we used the MAL model to calculate the ILI%×positive% and combined this prediction with different data forms. The ILI%×positive%+climate+demography+Baidu model had the best prediction effect, with an explained variance score reaching 0.74, R2 reaching 0.70, mean absolute error of 0.02, and mean squared error of 0.02. Comparisons with random forest, extreme gradient boosting, LSTM, and gated current unit models showed that the MAL model had the best prediction effect. CONCLUSIONS: The newly established MAL model outperformed existing models. Natural factors and search engine query data were more helpful in forecasting ILI patterns in megacities. With more timely and effective prediction of influenza and other respiratory infectious diseases and the epidemic intensity, early and better preparedness can be achieved to reduce the health damage to the population.

Influenza Epidemic Trend Surveillance and Prediction Based on Search Engine Data: Deep Learning Model Study.

BACKGROUND: Influenza outbreaks pose a significant threat to global public health. Traditional surveillance systems and simple algorithms often struggle to predict influenza outbreaks in an accurate and timely manner. Big data and modern technology have offered new modalities for disease surveillance and prediction. Influenza-like illness can serve as a valuable surveillance tool for emerging respiratory infectious diseases like influenza and COVID-19, especially when reported case data may not fully reflect the actual epidemic curve. OBJECTIVE: This study aimed to develop a predictive model for influenza outbreaks by combining Baidu search query data with traditional virological surveillance data. The goal was to improve early detection and preparedness for influenza outbreaks in both northern and southern China, providing evidence for supplementing modern intelligence epidemic surveillance methods. METHODS: We collected virological data from the National Influenza Surveillance Network and Baidu search query data from January 2011 to July 2018, totaling 3,691,865 and 1,563,361 respective samples. Relevant search terms related to influenza were identified and analyzed for their correlation with influenza-positive rates using Pearson correlation analysis. A distributed lag nonlinear model was used to assess the lag correlation of the search terms with influenza activity. Subsequently, a predictive model based on the gated recurrent unit and multiple attention mechanisms was developed to forecast the influenza-positive trend. RESULTS: This study revealed a high correlation between specific Baidu search terms and influenza-positive rates in both northern and southern China, except for 1 term. The search terms were categorized into 4 groups: essential facts on influenza, influenza symptoms, influenza treatment and medicine, and influenza prevention, all of which showed correlation with the influenza-positive rate. The influenza prevention and influenza symptom groups had a lag correlation of 1.4-3.2 and 5.0-8.0 days, respectively. The Baidu search terms could help predict the influenza-positive rate 14-22 days in advance in southern China but interfered with influenza surveillance in northern China. CONCLUSIONS: Complementing traditional disease surveillance systems with information from web-based data sources can aid in detecting warning signs of influenza outbreaks earlier. However, supplementation of modern surveillance with search engine information should be approached cautiously. This approach provides valuable insights for digital epidemiology and has the potential for broader application in respiratory infectious disease surveillance. Further research should explore the optimization and customization of search terms for different regions and languages to improve the accuracy of influenza prediction models.

Macro-analysis of climatic factors for COVID-19 pandemic based on Köppen-Geiger climate classification.

This study integrated dynamic models and statistical methods to design a novel macroanalysis approach to judge the climate impacts. First, the incidence difference across Köppen-Geiger climate regions was used to determine the four risk areas. Then, the effective influence of climate factors was proved according to the non-climate factors' non-difference among the risk areas, multi-source non-major component data assisting the proof. It is found that cold steppe arid climates and wet temperate climates are more likely to transmit SARS-CoV-2 among human beings. Although the results verified that the global optimum temperature was around 10 °C, and the average humidity was 71%, there was evident heterogeneity among different climate risk areas. The first-grade and fourth-grade risk regions in the Northern Hemisphere and fourth-grade risk regions in the Southern Hemisphere are more sensitive to temperature. However, the third-grade risk region in the Southern Hemisphere is more sensitive to relative humidity. The Southern Hemisphere's third-grade and fourth-grade risk regions are more sensitive to precipitation.

Exploring meteorological impacts based on Köppen-Geiger climate classification after reviewing China's response to COVID-19.

More than 30 months into the novel coronavirus 2019 (COVID-19) pandemic, efforts to bring this prevalence under control have achieved tentative achievements in China. However, the continuing increase in confirmed cases worldwide and the novel variants imply a severe risk of imported viruses. High-intensity non-pharmaceutical interventions (NPIs) are the mainly used measures of China's early response to COVID-19, which enabled effective control in the first wave of the epidemic. However, their efficiency is relatively low across China at the current stage. Therefore, this study focuses on whether measurable meteorological variables be found through global data to learn more about COVID-19 and explore flexible controls. This study first examines the control measures, such as NPIs and vaccination, on COVID-19 transmission across 189 countries, especially in China. Subsequently, we estimate the association between meteorological factors and time-varying reproduction numbers based on the global data by meta-population epidemic model, eliminating the aforementioned anthropogenic factors. According to this study, we find that the basic reproduction number of COVID-19 transmission varied wildly among Köppen-Geiger climate classifications, which is of great significance for the flexible adjustment of China's control protocols. We obtain that in southeast China, Köppen-Geiger climate sub-classifications, Cwb, Cfa, and Cfb, are more likely to spread COVID-19. In August, the RSIM of Cwb climate subclassification is about three times that of Dwc in April, which implies that the intensity of control efforts in different sub-regions may differ three times under the same imported risk. However, BSk and BWk, the most widely distributed in northwest China, have smaller basic reproduction numbers than Cfa, distributed in southeast coastal areas. It indicates that northwest China's control intensity could be appropriately weaker than southeast China under the same prevention objectives.

Risk of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Transmission Among Air Passengers in China.

BACKGROUND: Modern transportation plays a key role in the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and new variants. However, little is known about the exact transmission risk of the virus on airplanes. METHODS: Using the itinerary and epidemiological data of coronavirus disease 2019 (COVID-19) cases and close contacts on domestic airplanes departing from Wuhan city in China before the lockdown on 23 January 2020, we estimated the upper and lower bounds of overall transmission risk of COVID-19 among travelers. RESULTS: In total, 175 index cases were identified among 5797 passengers on 177 airplanes. The upper and lower attack rates (ARs) of a seat were 0.60% (34/5622, 95% confidence interval [CI] .43-.84%) and 0.33% (18/5400, 95% CI .21-.53%), respectively. In the upper- and lower-bound risk estimates, each index case infected 0.19 (SD 0.45) and 0.10 (SD 0.32) cases, respectively. The seats immediately adjacent to the index cases had an AR of 9.2% (95% CI 5.7-14.4%), with a relative risk 27.8 (95% CI 14.4-53.7) compared to other seats in the upper limit estimation. The middle seat had the highest AR (0.7%, 95% CI .4%-1.2%). The upper-bound AR increased from 0.7% (95% CI 0.5%-1.0%) to 1.2% (95% CI .4-3.3%) when the co-travel time increased from 2.0 hours to 3.3 hours. CONCLUSIONS: The ARs among travelers varied by seat distance from the index case and joint travel time, but the variation was not significant between the types of aircraft. The overall risk of SARS-CoV-2 transmission during domestic travel on planes was relatively low. These findings can improve our understanding of COVID-19 spread during travel and inform response efforts in the pandemic.

Seroprevalence and humoral immune durability of anti-SARS-CoV-2 antibodies in Wuhan, China: a longitudinal, population-level, cross-sectional study.

BACKGROUND: Wuhan was the epicentre of the COVID-19 outbreak in China. We aimed to determine the seroprevalence and kinetics of anti-SARS-CoV-2 antibodies at population level in Wuhan to inform the development of vaccination strategies. METHODS: In this longitudinal cross-sectional study, we used a multistage, population-stratified, cluster random sampling method to systematically select 100 communities from the 13 districts of Wuhan. Households were systematically selected from each community and all family members were invited to community health-care centres to participate. Eligible individuals were those who had lived in Wuhan for at least 14 days since Dec 1, 2019. All eligible participants who consented to participate completed a standardised electronic questionnaire of demographic and clinical questions and self-reported any symptoms associated with COVID-19 or previous diagnosis of COVID-19. A venous blood sample was taken for immunological testing on April 14-15, 2020. Blood samples were tested for the presence of pan-immunoglobulins, IgM, IgA, and IgG antibodies against SARS-CoV-2 nucleocapsid protein and neutralising antibodies were assessed. We did two successive follow-ups between June 11 and June 13, and between Oct 9 and Dec 5, 2020, at which blood samples were taken. FINDINGS: Of 4600 households randomly selected, 3599 families (78·2%) with 9702 individuals attended the baseline visit. 9542 individuals from 3556 families had sufficient samples for analyses. 532 (5·6%) of 9542 participants were positive for pan-immunoglobulins against SARS-CoV-2, with a baseline adjusted seroprevalence of 6·92% (95% CI 6·41-7·43) in the population. 437 (82·1%) of 532 participants who were positive for pan-immunoglobulins were asymptomatic. 69 (13·0%) of 532 individuals were positive for IgM antibodies, 84 (15·8%) were positive for IgA antibodies, 532 (100%) were positive for IgG antibodies, and 212 (39·8%) were positive for neutralising antibodies at baseline. The proportion of individuals who were positive for pan-immunoglobulins who had neutralising antibodies in April remained stable for the two follow-up visits (162 [44·6%] of 363 in June, 2020, and 187 [41·2%] of 454 in October-December, 2020). On the basis of data from 335 individuals who attended all three follow-up visits and who were positive for pan-immunoglobulins, neutralising antibody levels did not significantly decrease over the study period (median 1/5·6 [IQR 1/2·0 to 1/14·0] at baseline vs 1/5·6 [1/4·0 to 1/11·2] at first follow-up [p=1·0] and 1/6·3 [1/2·0 to 1/12·6] at second follow-up [p=0·29]). However, neutralising antibody titres were lower in asymptomatic individuals than in confirmed cases and symptomatic individuals. Although titres of IgG decreased over time, the proportion of individuals who had IgG antibodies did not decrease substantially (from 30 [100%] of 30 at baseline to 26 [89·7%] of 29 at second follow-up among confirmed cases, 65 [100%] of 65 at baseline to 58 [92·1%] of 63 at second follow-up among symptomatic individuals, and 437 [100%] of 437 at baseline to 329 [90·9%] of 362 at second follow-up among asymptomatic individuals). INTERPRETATION: 6·92% of a cross-sectional sample of the population of Wuhan developed antibodies against SARS-CoV-2, with 39·8% of this population seroconverting to have neutralising antibodies. Our durability data on humoral responses indicate that mass vaccination is needed to effect herd protection to prevent the resurgence of the epidemic. FUNDING: Chinese Academy of Medical Sciences & Peking Union Medical College, National Natural Science Foundation, and Chinese Ministry of Science and Technology. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.

Bioheterojunction-Engineered Polyetheretherketone Implants With Diabetic Infectious Micromilieu Twin-Engine Powered Disinfection for Boosted Osteogenicity.

Diabetic infectious micromilieu (DIM) leads to a critical failure rate of osseointegration by virtue of two main peculiarities: high levels of topical glucose and inevitable infection. To tackle the daunting issue, a bioheterojunction-engineered orthopedic polyetheretherketone (PEEK) implant consisting of copper sulfide/graphene oxide (CuS/GO) bioheterojunctions (bioHJs) and glucose oxidase (GOx) is conceived and developed for DIM enhanced disinfection and boosted osseointegration. Under hyperglycemic micromilieu, GOx can convert surrounding glucose into hydrogen peroxide (H2 O2 ). Then, upon infectious micromilieu, the bioHJs enable the catalyzation of H2 O2 to highly germicidal hydroxyl radical (·OH). As a result, the engineered implants massacre pathogenic bacteria through DIM twin-engine powered photo-chemodynamic therapy in vitro and in vivo. In addition, the engineered implants considerably facilitate cell viability and osteogenic activity of osteoblasts under a hyperglycemic microenvironment via synergistic induction of copper ions (Cu2+ ) and GO. In vivo studies using bone defect models of diabetic rats at 4 and 8 weeks further authenticate that bioHJ-engineering PEEK implants substantially elevate their osseointegration through biofilm elimination and vascularization, as well as macrophage reprogramming. Altogether, the present study puts forward a tactic that arms orthopedic implants with DIM twin-engine powered antibacterial and formidable osteogenic capacities for diabetic stalled osseointegration.

Humoral responses after inactivated COVID-19 vaccination in individuals with and without prior SARS-CoV-2 infection: A prospective cohort study.

We evaluated and compared humoral immune responses after inactivated coronavirus disease 2019 (COVID-19) vaccination among naïve individuals, asymptomatically infected individuals, and recovered patients with varying severity. In this multicenter, prospective cohort study, blood samples from 666 participants were collected before and after 2 doses of inactivated COVID-19 vaccination. Among 392 severe acute respiratory syndrome coronavirus 2-naïve individuals, the seroconversion rate increased significantly from 51.8% (median antispike protein pan-immunoglobulins [S-Igs] titer: 0.8 U/ml) after the first dose to 96% (median S-Igs titer: 79.5 U/ml) after the second dose. Thirty-two percent of naïve individuals had detectable neutralizing antibodies (NAbs) against the original strain but all of them lost neutralizing activity against the Omicron variant. In 274 individuals with natural infection, humoral immunity was significantly improved after a single vaccine dose, with median S-Igs titers of 596.7, 1176, 1086.5, and 1828 U/ml for asymptomatic infections, mild cases, moderate cases, and severe/critical cases, respectively. NAb titers also improved significantly. However, the second dose did not substantially increase antibody levels. Although a booster dose is needed for those without infection, our findings indicate that recovered patients should receive only a single dose of the vaccine, regardless of the clinical severity, until there is sufficient evidence to confirm the benefits of a second dose.

Seroprevalence and dynamics of anti-SARS-CoV-2 antibodies: a longitudinal study based on patients with underlying diseases in Wuhan.

BACKGROUND: Assessing the humoral immunity of patients with underlying diseases after being infected with SARS-CoV-2 is essential for adopting effective prevention and control strategies. The purpose of this study is to analyze the seroprevalence of people with underlying diseases and the dynamic change features of anti-SARS-CoV-2 antibodies. METHODS: We selected 100 communities in Wuhan using the probability-proportional-to-size sampling method. From these 100 communities, we randomly selected households according to a list provided by the local government. Individuals who have lived in Wuhan for at least 14 days since December 2019 and were ≥ 40 years old were included. From April 9-13, 2020, community staff invited all selected individuals to the community healthcare center in batches by going door-to-door or telephone. All participants completed a standardized electronic questionnaire simultaneously. Finally, 5 ml of venous blood was collected from all participants. Blood samples were tested for the presence of pan-immunoglobulins, IgM, IgA, and IgG antibodies against SARS-CoV-2 nucleocapsid protein and neutralising antibodies were assessed. During the period June 11-13, 2020 and October 9-December 5, 2020, all family members of a positive family and matched negative families were followed up twice. RESULTS: The seroprevalence of anti-SARS-CoV-2 antibodies in people with underlying diseases was 6.30% (95% CI [5.09-7.52]), and that of people without underlying diseases was 6.12% (95% CI [5.33-6.91]). A total of 313 people were positive for total antibodies at baseline, of which 97 had underlying disease. At the first follow-up, a total of 212 people were positive for total antibodies, of which 66 had underlying disease. At the second follow-up, a total of 238 people were positive for total antibodies, of which 68 had underlying disease. A total of 219 participants had three consecutive serum samples with positive total antibodies at baseline. The IgG titers decreased significantly with or without underlying diseases (P < 0.05) within the 9 months at least, while the neutralizing antibody titer remained stable. The titer of asymptomatic patients was lower than that of symptomatic patients (baseline, P = 0.032, second follow-up, P = 0.018) in the underlying diseases group. CONCLUSION: Our research focused on the serological changes of people with and without underlying diseases in a state of single natural infection. Regardless of the underlying diseases, the IgG titer decreased significantly over time, while there was no significant difference in the decline rate of IgG between with and without underlying diseases. Moreover, the neutralizing antibody titer remained relatively stable within the 9 months at least.

Willingness to accept herpes zoster vaccines and the influencing factors in China.

BACKGROUND: Herpes zoster increases the burden on the elderly in an aging society. Although an effective vaccine licensed by China Food and Drug Administration in 2019 was introduced into the market in June 2020, the willingness and influencing factors of herpes zoster vaccines in Chinese adults ≥ 50-years-old during coronavirus disease-2019 pandemic are yet to be elucidated. METHODS: An online questionnaire survey was conducted using a simple random sampling method in October 2021 for viewers of the broadcast program. A binary logistic regression and multiple response analysis were conducted for herpes zoster vaccine and vaccination willingness. Pareto's graphs were plotted to present the multiple-choice questions of influencing factors. RESULTS: A total of 3838 eligible participants were included in this study. Among them, 43.02% intended to be vaccinated, including 10.34% self-reported about receiving at least one shot of shingles vaccine, 30.22% declined, and 26.76% were hesitant. This population comprised a large proportion of middle-aged and older people (≥ 50-years-old) who have not experienced an episode of herpes zoster (54.98%) or are unaware of the virus (33.22%). The strongest determinants of vaccine hesitancy among older people were education background of Master's degree or above compared to senior high or equivalent and below, personal monthly income < 3000 RMB compared to 3000-5999 RMB, and living in a rural area. CONCLUSIONS: The willingness to get shingles vaccines can be improved further. Professional education and credible recommendation might prompt the elderly to improve their willingness and reassure them of the safety and efficacy of the vaccine. Also, accessibility and affordability should also be improved in the future.

Tumor suppressive lncRNA MEG3 binds to EZH2 and enhances CXCL3 methylation in gallbladder cancer.

Gallbladder cancer is a malignant tumor with a high mortality rate. Accumulating evidence supports that lncRNA MEG3 may halt the progression of gallbladder cancer, while the downstream mechanism is rarely studied. Thus, we aim to investigate the molecular basis of the tumor-suppressing role of lncRNA MEG3 in gallbladder cancer. The expression of lncRNA MEG3 and CXCL3 was measured in patient serum and cell lines of gallbladder cancer. The viability, apoptosis, migration, and invasion of gallbladder cancer cells were assessed following ectopic MEG3 expression, as detected by CCK-8, flow cytometry, and Transwell assays. The interaction among lncRNA MEG3, EZH2, and CXCL3 was explored through ChIP, RNA pull-down, and RIP assays. The effects of lncRNA MEG3 and CXCL3 on tumor growth were evaluated by a mouse xenograft model. lncRNA MEG3 was expressed at a low level in gallbladder cancer patient serum and cell lines, while CXCL3 was highly expressed. MEG3 overexpression repressed the malignant behaviors of gallbladder cancer cells and promoted their apoptosis. MEG3 was mainly localized in the nucleus. MEG3 bound to EZH2, and EZH2 catalyzed the H3K27 trimethylation of the CXCL3 promoter region. MEG3 downregulated CXCL3 by activating EZH2-mediated H3K27 trimethylation of CXCL3; MEG3 overexpression attenuated cancer cell malignant behaviors in vitro and suppressed tumor growth in vivo in gallbladder cancer by inhibiting CXCL3 expression. Altogether, our results indicate that lncRNA MEG3 impedes gallbladder cancer development via the EZH2-CXCL3 axis, offering potential biomarkers for gallbladder cancer management.

Impacts of worldwide individual non-pharmaceutical interventions on COVID-19 transmission across waves and space.

Governments worldwide have rapidly deployed non-pharmaceutical interventions (NPIs) to mitigate the COVID-19 pandemic. However, the effect of these individual NPI measures across space and time has yet to be sufficiently assessed, especially with the increase of policy fatigue and the urge for NPI relaxation in the vaccination era. Using the decay ratio in the suppression of COVID-19 infections and multi-source big data, we investigated the changing performance of different NPIs across waves from global and regional levels (in 133 countries) to national and subnational (in the United States of America [USA]) scales before the implementation of mass vaccination. The synergistic effectiveness of all NPIs for reducing COVID-19 infections declined along waves, from 95.4% in the first wave to 56.0% in the third wave recently at the global level and similarly from 83.3% to 58.7% at the USA national level, while it had fluctuating performance across waves on regional and subnational scales. Regardless of geographical scale, gathering restrictions and facial coverings played significant roles in epidemic mitigation before the vaccine rollout. Our findings have important implications for continued tailoring and implementation of NPI strategies, together with vaccination, to mitigate future COVID-19 waves, caused by new variants, and other emerging respiratory infectious diseases.

[Introduction and implications of WHO position paper: vaccines against influenza, May 2022].

On May 13, 2022, World Health Organization(WHO) Position Paper on Influenza Vaccine (2022 edition) was published. This position paper updates information on influenza epidemiology, high risk population, the impact of immunization on disease, influenza vaccines and effectiveness and safety, and propose WHO's position and recommendation that all countries should consider implementing seasonal influenza vaccine immunization programmes to prepare for an influenza pandemic. In addition, it proposes that the influenza surveillance platform can be integrated with the surveillance of other respiratory viruses, such as SARS-CoV-2 and Respiratory Syncytial Virus. This position paper has some implications for the prevention and control of influenza and other respiratory infectious diseases in China: (1) Optimize influenza vaccine policies to facilitate the implementation of immunization services; (2) Influenza prevention and control should from the perspective of Population Medicine focus on the individual and community to integrate with "Promotion, Prevention, Diagnosis, Control, Treatment, Rehabilitation"; (3) Incorporate prevention and control of other respiratory infectious diseases such as influenza, COVID-19, respiratory syncytial virus and adenovirus, and intelligently monitor by integrating multi-channel data to achieve the goal of co-prevention and control of multiple diseases.

Mitochondrial DNA enables AIM2 inflammasome activation and hepatocyte pyroptosis in nonalcoholic fatty liver disease.

Mitochondria damage exacerbates NAFLD through trigerring AIM2 inflammasome activation and hepatocyte pyroptosis. This study provides novel insights into the underlying mechanisms of mitochondrial DNA synthesis in NAFLD and also suggests potential therapeutic targets for the treatment of NAFLD.

Growth Factor-Decorated Ti3 C2 MXene/MoS2 2D Bio-Heterojunctions with Quad-Channel Photonic Disinfection for Effective Regeneration of Bacteria-Invaded Cutaneous Tissue.

Phototherapy has recently emerged as a competent alternative for combating bacterial infection without antibiotic-resistance risk. However, owing to the bacterial endogenous antioxidative glutathione (GSH), the exogenous reactive oxygen species (ROS) generated by phototherapy can hardly behave desired antibacterial effect. To address the daunting issue, a quad-channel synergistic antibacterial nano-platform of Ti3 C2 MXene/MoS2 (MM) 2D bio-heterojunctions (2D bio-HJs) are devised and fabricated, which possess photothermal, photodynamic, peroxidase-like (POD-like), and glutathione oxidase-like properties. Under near-infrared (NIR) laser exposure, the 2D bio-HJs both yield localized heating and raise extracellular ROS level, leading to bacterial inactivation. Synchronously, Mo4+ ions can easily invade into ruptured bacterial membrane, arouse intracellular ROS, and deplete intracellular GSH. Squeezed between the "ROS hurricane" from both internal and external sides, the bacteria are hugely slaughtered. After being further loaded with fibroblast growth factor-21 (FGF21), the 2D bio-HJs exhibit benign cytocompatibility and boost cell migration in vitro. Notably, the in vivo evaluations employing a mouse-infected wound model demonstrate the excellent photonic disinfection towards bacterial infection and accelerated wound healing. Overall, this work provides a powerful nano-platform for the effective regeneration of bacteria-invaded cutaneous tissue using 2D bio-HJs.