Developing a Research Network of Early Warning Systems for Infectious Diseases Transmission Between China and Australia.

This article offers a thorough review of current early warning systems (EWS) and advocates for establishing a unified research network for EWS in infectious diseases between China and Australia. We propose that future research should focus on improving infectious disease surveillance by integrating data from both countries to enhance predictive models and intervention strategies. The article highlights the need for standardized data formats and terminologies, improved surveillance capabilities, and the development of robust spatiotemporal predictive models. It concludes by examining the potential benefits and challenges of this collaborative approach and its implications for global infectious disease surveillance. This is particularly relevant to the ongoing project, early warning systems for Infectious Diseases between China and Australia (NetEWAC), which aims to use seasonal influenza as a case study to analyze influenza trends, peak activities, and potential inter-hemispheric transmission patterns. The project seeks to integrate data from both hemispheres to improve outbreak predictions and develop a spatiotemporal predictive modeling system for seasonal influenza transmission based on socio-environmental factors.

Pathogen characteristics of respiratory infections in the season after the COVID-19 pandemic between August and December 2023: evidence from direct-to-consumer testing-based surveillance in Guangzhou and Beijing, China.

OBJECTIVES: In the fall-winter of 2023, China experienced its first epidemic season of respiratory diseases since the COVID-19 pandemic. Gathering timely data about pathogenetic characteristics of respiratory infections is crucial to complement current respiratory surveillance mechanisms in China. Data from direct-to-consumer (DTC) multi-respiratory pathogen (MRP) testing could serve as a novel source of multi-pathogen data for community-based surveillance. METHODS: A pioneering initiative was launched to detect multiple respiratory pathogens in Beijing and Guangzhou, China. Direct-to-consumer multi-respiratory pathogen tests were used to provide proactive surveillance ahead of medical services. RESULTS: A total of 28,018 participants were enrolled between 22 August and 10 December 2023. Positive findings for at least one respiratory pathogen were observed in 26,202 (93.5%) participants. Influenza virus A (IFV-A), respiratory syncytial virus (RSV) and human adenovirus (HAdV) are the three leading viral pathogens detected with proportions of 18.0%, 10.6% and 8.8%. Viral-bacterial pathogens were co-detected in 9,736 (34.7%) of participants, which reduced to 22.2% for bacterial-bacterial co-detection, and 22.0% for bacterial mono-detection. The epidemiological ecology of respiratory pathogens within both viral clusters and specific pathogens varied among cities. The peak of RSV epidemics in Guangzhou occurred in the fall of 2023, earlier than in Beijing. CONCLUSIONS: The innovative programme offered enhanced surveillance capabilities beyond traditional methods, enabling prompt feedback about test results and mitigating the risk of cross-infection caused by waits in healthcare facilities.

Mapping multimorbidity progression among 190 diseases.

BACKGROUND: Current clustering of multimorbidity based on the frequency of common disease combinations is inadequate. We estimated the causal relationships among prevalent diseases and mapped out the clusters of multimorbidity progression among them. METHODS: In this cohort study, we examined the progression of multimorbidity among 190 diseases among over 500,000 UK Biobank participants over 12.7 years of follow-up. Using a machine learning method for causal inference, we analyzed patterns of how diseases influenced and were influenced by others in females and males. We used clustering analysis and visualization algorithms to identify multimorbidity progress constellations. RESULTS: We show the top influential and influenced diseases largely overlap between sexes in chronic diseases, with sex-specific ones tending to be acute diseases. Patterns of diseases that influence and are influenced by other diseases also emerged (clustering significance Pau > 0.87), with the top influential diseases affecting many clusters and the top influenced diseases concentrating on a few, suggesting that complex mechanisms are at play for the diseases that increase the development of other diseases while share underlying causes exist among the diseases whose development are increased by others. Bi-directional multimorbidity progress presents substantial clustering tendencies both within and across International Classification Disease chapters, compared to uni-directional ones, which can inform future studies for developing cross-specialty strategies for multimorbidity. Finally, we identify 10 multimorbidity progress constellations for females and 9 for males (clustering stability, adjusted Rand index >0.75), showing interesting differences between sexes. CONCLUSION: Our findings could inform the future development of targeted interventions and provide an essential foundation for future studies seeking to improve the prevention and management of multimorbidity.

Mapping out clusters of diseases is crucial to addressing the rising challenge of co-occurrence of multiple diseases, known as multimorbidity. However, the current way of grouping diseases based on their associations isn't enough to understand how they develop over time. We've come up with a new approach to map out how groups of diseases progress together based on the strength of their causal relationships. By looking at how each disease affects the development of others, we can get a better understanding of how they form clusters. Our research goes beyond just showing which diseases occur together, and it's a step toward improving how we prevent and manage multiple health conditions in the future.

Exploring the Lagged Correlation Between Baidu Index and Influenza-Like Illness - China, 2014-2019.

Introduction: This study investigated the lagged correlation between Baidu Index for influenza-related keywords and influenza-like illness percentage (ILI%) across regions in China. The aim is to establish a scientific foundation for utilizing Baidu Index as an early warning tool for influenza-like illness epidemics. Methods: In this study, data on ILI% and Baidu Index were collected from 30 provincial-level administrative divisions (PLADs) spanning April 2014 to March 2019. The Baidu Index was categorized into Overall Index, Ordinary Index, Prevention Index, Symptom Index, and Treatment Index based on search query themes. The lagged correlation between the Baidu Index and ILI% was examined through the cross-correlation function (CCF) method. Results: Correlating the Baidu Overall Index of 30 PLADs with ILI% revealed CCF values ranging from 0.46 to 0.86, with a median lag of 0.5 days. Subcategory analysis indicated that the Prevention Index and Symptom Index exhibited quicker responses to ILI%, with median lags of -9 and -0.5 days, respectively, compared to 0 and 3 days for the Ordinary and Treatment Indexes. The median lag days between the Baidu Index and the ILI% were earlier in the northern PLADs compared to the southern PLADs. Discussion: The Prevention and Symptom Indexes show promising predictive capabilities for influenza-like illness epidemics.

Comparison Between Threshold Method and Artificial Intelligence Approaches for Early Warning of Respiratory Infectious Diseases - Weifang City, Shandong Province, China, 2020-2023.

Introduction: Respiratory infectious diseases, such as influenza and coronavirus disease 2019 (COVID-19), present significant global public health challenges. The emergence of artificial intelligence (AI) and big data offers opportunities to improve traditional disease surveillance and early warning systems. Methods: The study analyzed data from January 2020 to May 2023, comprising influenza-like illness (ILI) statistics, Baidu index, and clinical data from Weifang. Three methodologies were evaluated: the adaptive dynamic threshold method (ADTM) for dynamic threshold adjustments, the machine learning supervised method (MLSM), and the machine learning unsupervised method (MLUM) utilizing anomaly detection. The comparison focused on sensitivity, specificity, timeliness, and warning consistency. Results: ADTM issued 37 warnings with a sensitivity of 71% and a specificity of 85%. MLSM generated 35 warnings, with a sensitivity of 82% and a specificity of 87%. MLUM produced 63 warnings with a sensitivity of 100% and specificity of 80%. The initial warnings from ADTM and MLUM preceded those from MLSM by five days. The Kappa coefficient indicated moderate agreement between the methods, with values ranging from 0.52 to 0.62 (P<0.05). Discussion: The study explores the comparison between traditional methods and two machine learning approaches for early warning systems. It emphasizes the validation of machine learning's reliability and underscores the unique advantages of each method. Furthermore, it stresses the significance of integrating machine learning models with various data sources to enhance public health preparedness and response, alongside acknowledging limitations and the need for broader validation.

H2Se-evolving bio-heterojunctions promote cutaneous regeneration in infected wounds by inhibiting excessive cellular senescence.

Pathogenic infection leads to excessive senescent cell accumulation and stagnation of wound healing. To address these issues, we devise and develop a hydrogen selenide (H2Se)-evolving bio-heterojunction (bio-HJ) composed of graphene oxide (GO) and FeSe2 to deracinate bacterial infection, suppress cellular senescence and remedy recalcitrant infected wounds. Excited by near-infrared (NIR) laser, the bio-HJ exerts desired photothermal and photodynamic effects, resulting in rapid disinfection. The crafted bio-HJ could also evolve gaseous H2Se to inhibit cellular senescence and dampen inflammation. Mechanism studies reveal the anti-senescence effects of H2Se-evolving bio-HJ are mediated by selenium pathway and glutathione peroxidase 1 (GPX1). More critically, in vivo experiments authenticate that the H2Se-evolving bio-HJ could inhibit cellular senescence and potentiate wound regeneration in rats. As envisioned, our work not only furnishes the novel gasotransmitter-delivering bio-HJ for chronic infected wounds, but also gets insight into the development of anti-senescence biomaterials.

A H2S-Evolving Alternately-Catalytic Enzyme Bio-Heterojunction with Antibacterial and Macrophage-Reprogramming Activity for All-Stage Infectious Wound Regeneration.

The disorder of the macrophage phenotype and the hostile by-product of lactate evoked by pathogenic infection in hypoxic deep wound inevitably lead to the stagnant skin regeneration. In this study, hydrogen sulfide (H2S)-evolving alternately catalytic bio-heterojunction enzyme (AC-BioHJzyme) consisting of CuFe2S3 and lactate oxidase (LOD) named as CuFe2S3@LOD is developed. AC-BioHJzyme exhibits circular enzyme-mimetic antibacterial (EMA) activity and macrophage re-rousing capability, which can be activated by near-infrared-II (NIR-II) light. In this system, LOD exhausts lactate derived from bacterial anaerobic respiration and generated hydrogen peroxide (H2O2), which provides an abundant stock for the peroxidase-mimetic activity to convert the produced H2O2 into germicidal •OH. The GPx-mimetic activity endows AC-BioHJzyme with a glutathione consumption property to block the antioxidant systems in bacterial metabolism, while the O2 provided by the CAT-mimetic activity can generate 1O2 under the NIR-II irradiation. Synchronously, the H2S gas liberated from CuFe2S3@LOD under the infectious micromilieu allows the reduction of Fe(III)/Cu(II) to Fe(II)/Cu(І), resulting in sustained circular EMA activity. In vitro and in vivo assays indicate that the CuFe2S3@LOD AC-BioHJzyme significantly facilitates the infectious cutaneous regeneration by killing bacteria, facilitating epithelialization/collagen deposition, promoting angiogenesis, and reprogramming macrophages. This study provides a countermeasure for deep infectious wound healing via circular enzyme-mimetic antibiosis and macrophage re-rousing.

Prolonged lymphopenia and prognoses among inpatients with different respiratory virus infections: A retrospective cohort study.

Background: Lymphopenia is common in respiratory viral infection. However, no studies elucidated the impact of prolonged lymphopenia on worse outcome in the way of quantitative risk. Methods: Adult patients with laboratory-confirmed respiratory virus infection (influenza, SARS-CoV-2, and other viruses) between January 1st, 2016, and February 1st, 2023 were enrolled in this retrospective cohort study. Serial data of laboratory examination during hospitalization were acquired. The primary outcome was in-hospital all-cause death, and all information was obtained from the electronic medical records system. Legendre orthogonal polynomials (LOP), restricted cubic splines, and multivariable logistic regression were performed. Results: Finally, 2388 inpatients were involved in this study, including 436 patients with influenza, 1397 with SARS-CoV-2, and 319 with other respiratory virus infections. After being adjusted for age, corticosteroids, chronic kidney disease, chronic respiratory disease, cardiovascular disease, lymphopenia on admission and length of hospital stay, prolonged lymphopenia was significantly associated with death in influenza (OR 7.20, 95 % CI 2.27-22.77, p = 0. 0008 for lasting for 3-7 days; OR 17.80, 95 % CI 5.21-60.82, p < 0.0001 for lasting for more than 7 days) and SARS-CoV-2 (OR 3.07, 95 % CI 1.89-5.01, p < 0.0001 for lasting for 3-7 days; OR 6.28, 95 % CI 3.53-11.18, p < 0.0001 for lasting for more than 7 days), compared with a transient lymphopenia of 1-2 days, while no significant association was found in other respiratory viruses. Prolonged lymphopenia was also associated with multi-organ damage in influenza and SARS-CoV-2 infections. Conclusions: Prolonged lymphopenia was significantly associated with worse clinical prognoses in influenza and SARS-CoV-2 infections, but not in other respiratory virus infections.

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.

An effectiveness study of vaccination and quarantine combination strategies for containing mpox transmission on simulated college campuses.

The ongoing transmission of mpox in specific countries and regions necessitates urgent action. It is essential to implement targeted containment strategies that concentrate on high-risk populations and critical locations, such as college campuses, to effectively curb the spread of mpox. This study is dedicated to evaluating the performance of various vaccination and quarantine strategies in curbing the spread of mpox and estimating the outbreak risk. To accomplish this, we constructed a stochastic, agent-based, discrete-time susceptible-latent-infectious-recovered (SLIR) model, to examine mpox transmission on a simulated college campus. Our findings reveal that relying solely on PEP is insufficient in containing mpox effectively. To bolster the population immunity and protect the vulnerable, pre-exposure vaccination among high-risk populations prior to an outbreak is imperative. Our study demonstrates that a pre-exposure vaccination rate of 50% in high-risk populations can led to a remarkable 74.2% reduction of infections. This translated to a mere 1.0% cumulative infection incidence in the overall population. In cases where the desired vaccination coverage is not attainable, enhancing case detection and isolation measures can serve as an effective emergency response to contain mpox outbreaks. For pre-exposure vaccination coverage of 20% or lower, a 40% isolation ratio is necessary to keep the cumulative number of infections in check. However, when the coverage exceeds 30%, a reduced isolation ratio of 20% becomes sufficient to manage the outbreak effectively. These insights underscore the importance of strategic pre-exposure vaccination in conjunction with robust surveillance and isolation protocols to safeguard public health and prevent the escalation of mpox outbreaks.

A Large-Scale Online Survey on Clinical Severity and Associated Risk Factors for SARS-CoV-2 Omicron Infection - China, April-May 2023.

What is already known about this topic?: Individuals who initially contract severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lack significant mixed immunity. Therefore, it is crucial to monitor the clinical characteristics and associated factors of these individuals in order to inform policy-making. What is added by this report?: The common symptoms reported were fever, cough, and sore throat. Reinfections and receiving four vaccination doses within a 6-month period were found to be associated with a shorter duration of virus shedding, decreased hospitalization rate, and reduced risk of pneumonia. Individuals aged 60 years and older, as well as those with underlying medical conditions, had a higher risk of developing pneumonia. What are the implication for public health practices?: Online surveys conducted through social media platforms have the potential to complement disease surveillance and data collection efforts. In terms of vaccination prioritization, it is recommended to prioritize older individuals and those with underlying diseases.

Employees' seeking preference towards influenza vaccination in organization: A discrete choice experiment in China.

To clarify the preferences of employees seeking influenza vaccination, a discrete choice experiment aims to understand the essential factors that close the gap between intention and behavior. A total of 866 employees with vaccination willingness willing to participated in a discrete choice experiment (DCE) between October 31st and December 6th, 2022 in China including the following attributes: price, vaccination setting, appointment mode, and service time. The data was analyzed using mixed logit models. Employees from smaller enterprises were more likely to get vaccinated collectively. For employees willing to get the influenza vaccine, 95.08 % of their choice was dominated by price. Employees' behavior varied according to their socioeconomic characteristics. Only female employees strongly favored work-site-based vaccination. Price was the primary factor considered by employees for getting the influenza vaccine. DCE would help to develop influenza vaccination intervention targeted at different groups in future studies.

Predicting influenza-like illness trends based on sentinel surveillance data in China from 2011 to 2019: A modelling and comparative study1.

Background: Influenza is an acute respiratory infectious disease with a significant global disease burden. Additionally, the coronavirus disease 2019 pandemic and its related non-pharmaceutical interventions (NPIs) have introduced uncertainty to the spread of influenza. However, comparative studies on the performance of innovative models and approaches used for influenza prediction are limited. Therefore, this study aimed to predict the trend of influenza-like illness (ILI) in settings with diverse climate characteristics in China based on sentinel surveillance data using three approaches and evaluate and compare their predictive performance. Methods: The generalized additive model (GAM), deep learning hybrid model based on Gate Recurrent Unit (GRU), and autoregressive moving average-generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) model were established to predict the trends of ILI 1-, 2-, 3-, and 4-week-ahead in Beijing, Tianjin, Shanxi, Hubei, Chongqing, Guangdong, Hainan, and the Hong Kong Special Administrative Region in China, based on sentinel surveillance data from 2011 to 2019. Three relevant metrics, namely, Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and R squared, were calculated to evaluate and compare the goodness of fit and robustness of the three models. Results: Considering the MAPE, RMSE, and R squared values, the ARMA-GARCH model performed best, while the GRU-based deep learning hybrid model exhibited moderate performance and GAM made predictions with the least accuracy in the eight settings in China. Additionally, the models' predictive performance declined as the weeks ahead increased. Furthermore, blocked cross-validation indicated that all models were robust to changes in data and had low risks of overfitting. Conclusions: Our study suggested that the ARMA-GARCH model exhibited the best accuracy in predicting ILI trends in China compared to the GAM and GRU-based deep learning hybrid model. Therefore, in the future, the ARMA-GARCH model may be used to predict ILI trends in public health practice across diverse climatic zones, thereby contributing to influenza control and prevention efforts.

Enhancing infectious diseases early warning: A deep learning approach for influenza surveillance in China.

Objective: This study aimed to develop a universally applicable, feedback-informed Self-Excitation Attention Residual Network (SEAR) model. This model dynamically adapts to evolving disease trends and surveillance system changes, accommodating various scenarios. Thereby enhancing the effectiveness of early warning systems. Methods: Surveillance data on influenza-like illness (ILI) was collected from various regions including Northern China, Southern China, Beijing, and Yunnan. The reproduction number (Rt) was estimated to determine the threshold for issuing warnings. The Self-Excitation Attention Residual Network (SEAR) was devised employing deep learning algorithms and was trained, validated, and tested. The SEAR model's efficacy was assessed based on five metrics: accuracy rate, recall rate, F1 score, confusion matrix, and the receiver operating characteristic curve. Results: With an advance warning set at three days, the SEAR model outperformed five primary models - logistic regression, support vector machine, random forest, Extreme Gradient Boosting, and Long Short-Term Memory model - in all five evaluation metrics. Notably, the model's warning performance declined with an increase in the early warning value and the number of warning days, albeit maintaining a ROC value over 0.7 in all scenarios. Conclusion: The SEAR model demonstrated robust early warning performance for influenza in diverse Chinese regions with high accuracy and specificity. This novel model, augmenting traditional systems, supports widespread application for respiratory disease outbreak monitoring. Future evaluations could incorporate alternative indicators, with the model continuously updating through data feedback, thus enhancing its universal applicability. Ongoing optimization, using iterative feedback and expert judgment, heralds a transformative approach to surveillance-based early warning strategies.

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.

Real-world effectiveness and safety of goserelin 10.8-mg depot in Chinese patients with localized or locally advanced prostate cancer.

OBJECTIVE: Real-word data on long-acting luteinizing hormone-releasing hormone (LHRH) agonists in Chinese patients with prostate cancer are limited. This study aimed to determine the real-world effectiveness and safety of the LHRH agonist, goserelin, particularly the long-acting 10.8-mg depot formulation, and the follow-up patterns among Chinese prostate cancer patients. METHODS: This was a multicenter, prospective, observational study in hormone treatment-naïve patients with localized or locally advanced prostate cancer who were prescribed goserelin 10.8-mg depot every 12 weeks or 3.6-mg depot every 4 weeks with or without an anti-androgen. The patients had follow-up evaluations for 26 weeks. The primary outcome was the effectiveness of goserelin in reducing serum testosterone and prostate-specific antigen (PSA) levels. The secondary outcomes included testosterone and PSA levels, attainment of chemical castration (serum testosterone <50 ng/dL), and goserelin safety. The exploratory outcome was the monitoring pattern for serum testosterone and PSA. All analyses were descriptive. RESULTS: Between September 2017 and December 2019, a total of 294 eligible patients received ≥ 1 dose of goserelin; 287 patients (97.6%) were treated with goserelin 10.8-mg depot. At week 24 ± 2, the changes from baseline [standard deviation (95% confidence interval)] in serum testosterone (n = 99) and PSA (n = 131) were -401.0 ng/dL [308.4 ng/dL (-462.5, -339.5 ng/dL)] and -35.4 ng/mL [104.4 ng/mL (-53.5, -17.4 ng/mL)], respectively. Of 112 evaluable patients, 100 (90.2%) achieved a serum testosterone level < 50 ng/dL. Treatment-emergent adverse events (TEAEs) and severe TEAEs occurred in 37.1% and 10.2% of patients, respectively. The mean testing frequency (standard deviation) was 1.6 (1.5) for testosterone and 2.2 (1.6) for PSA. CONCLUSIONS: Goserelin 10.8-mg depot effectively achieved and maintained castration and was well-tolerated in Chinese patients with localized and locally advanced prostate cancer.

Healthcare-seeking behaviours of patients with acute respiratory infection: a cross-sectional survey in a rural area of southwest China.

OBJECTIVES: This study aimed to assess the healthcare-seeking behaviour and related factors of people with acute respiratory symptoms in the rural areas of central and western China to estimate the disease burden of influenza more accurately. DESIGN: Cross-sectional survey. SETTINGS: Fifty-two communities/villages in the Wanzhou District, Chongqing, China, a rural area in southwest China, from May 2022 to July 2022. PARTICIPANTS: The participants were those who had been living in Wanzhou District continuously for more than 6 months and consented to participate. OUTCOME MEASURES: A semistructured questionnaire was used to determine the healthcare-seeking behaviour of participants, and the dichotomous response of 'yes' or 'no' was used to assess whether participants had acute respiratory symptoms and their healthcare-seeking behaviour. RESULTS: Only 50.92% (360 of 707) of the patients with acute respiratory infection visited medical and health institutions for treatment, whereas 49.08% (347 of 707) avoided treatment or opted for self-medication. The primary reason for not seeing a doctor was that patients felt their condition was not serious and visiting a medical facility for treatment was unnecessary. Short distance (87.54%) and reasonable charges (49.48%) were ranked as the most important reasons for choosing treatment at primary medical and health facilities (80.27%). The primary reasons for which patients visited secondary and tertiary hospitals (7.78% and 8.61%, respectively) were that doctors in such facilities were better at diagnosis (57.14%) and at treatment (87.10%). CONCLUSION: The findings provided in this study indicated that regular healthcare-seeking behaviour investigations should be conducted. The disease burden of influenza can be calculated more accurately when healthcare-seeking behaviour investigations are combined with surveillance in the hospitals.

Antibacterial and Angiogenic (2A) Bio-Heterojunctions Facilitate Infectious Ischemic Wound Regeneration via an Endogenous-Exogenous Bistimulatory Strategy.

In infectious ischemic wounds, a lack of blood perfusion significantly worsens microbe-associated infection symptoms and frequently complicates healing. To overcome this daunting issue, antibacterial and angiogenic (2A) bio-heterojunctions (bio-HJs) consisting of CuS/MXene heterojunctions and a vascular endothelial growth factor (VEGF)-mimicking peptide (VMP) are devised and developed to accelerate infectious cutaneous regeneration by boosting angiogenesis via an endogenous-exogenous bistimulatory (EEB) strategy. Assisted by near-infrared irradiation, the bio-HJ platform exhibits versatile synergistic photothermal, photodynamic, and chemodynamic effects for robust antibacterial efficacy. In addition, copper ions liberated from 2A bio-HJs elevate VEGF secretion from fibroblasts, which provokes VEGF receptors (VEGFR) activation through an endogenous pathway, whereas VMP itself promotes an exogenous pathway to facilitate endothelial cell multiplication and tube formation by directly activating the VEGFR signaling pathway. Moreover, employing an in vivo model of infectious ischemic wounds, it is confirmed that the EEB strategy can considerably boost cutaneous regeneration through pathogen elimination, angiogenesis promotion, and collagen deposition. As envisaged, this work leads to the development of a powerful 2A bio-HJ platform that can serve as an effective remedy for bacterial invasion-induced ischemic wounds through the EEB strategy.