Emerging concern on air pollution and health: Trade-off between air pollution exposure and physical activity.

Air pollution is a major contributor to the global disease burden, especially affecting respiratory and cardiovascular health. However, physical activity is associated with improved lung function, a slower decline in lung function, and lower mortality. The public is more likely to be exposed to air pollution during outdoor physical activity. However, studies on how long-term and short-term exposure to air pollution interacts with physical activity yield inconsistent results, and the thresholds for air pollution and physical activity remain unclear. Thus, more studies are needed to provide sufficient evidence to guide the public to safely engage in outdoor physical activity when exposed to air pollution.

Giant Tunability of Rashba Splitting at Cation-Exchanged Polar Oxide Interfaces by Selective Orbital Hybridization.

The 2D electron gas (2DEG) at oxide interfaces exhibits extraordinary properties, such as 2D superconductivity and ferromagnetism, coupled to strongly correlated electrons in narrow d-bands. In particular, 2DEGs in KTaO3 (KTO) with 5d t2g orbitals exhibit larger atomic spin-orbit coupling and crystal-facet-dependent superconductivity absent for 3d 2DEGs in SrTiO3 (STO). Herein, by tracing the interfacial chemistry, weak anti-localization magneto-transport behavior, and electronic structures of (001), (110), and (111) KTO 2DEGs, unambiguously cation exchange across KTO interfaces is discovered. Therefore, the origin of the 2DEGs at KTO-based interfaces is dramatically different from the electronic reconstruction observed at STO interfaces. More importantly, as the interface polarization grows with the higher order planes in the KTO case, the Rashba spin splitting becomes maximal for the superconducting (111) interfaces approximately twice that of the (001) interface. The larger Rashba spin splitting couples strongly to the asymmetric chiral texture of the orbital angular moment, and results mainly from the enhanced inter-orbital hopping of the t2g bands and more localized wave functions. This finding has profound implications for the search for topological superconductors, as well as the realization of efficient spin-charge interconversion for low-power spin-orbitronics based on (110) and (111) KTO interfaces.

Analyzing entropy features in time-series data for pattern recognition in neurological conditions.

In the field of medical diagnosis and patient monitoring, effective pattern recognition in neurological time-series data is essential. Traditional methods predominantly based on statistical or probabilistic learning and inference often struggle with multivariate, multi-source, state-varying, and noisy data while also posing privacy risks due to excessive information collection and modeling. Furthermore, these methods often overlook critical statistical information, such as the distribution of data points and inherent uncertainties. To address these challenges, we introduce an information theory-based pipeline that leverages specialized features to identify patterns in neurological time-series data while minimizing privacy risks. We incorporate various entropy methods based on the characteristics of different scenarios and entropy. For stochastic state transition applications, we incorporate Shannon's entropy, entropy rates, entropy production, and the von Neumann entropy of Markov chains. When state modeling is impractical, we select and employ approximate entropy, increment entropy, dispersion entropy, phase entropy, and slope entropy. The pipeline's effectiveness and scalability are demonstrated through pattern analysis in a dementia care dataset and also an epileptic and a myocardial infarction dataset. The results indicate that our information theory-based pipeline can achieve average performance improvements across various models on the recall rate, F1 score, and accuracy by up to 13.08 percentage points, while enhancing inference efficiency by reducing the number of model parameters by an average of 3.10 times. Thus, our approach opens a promising avenue for improved, efficient, and critical statistical information-considered pattern recognition in medical time-series data.

ADMM-inspired image reconstruction for terahertz off-axis digital holography.

Image reconstruction in off-axis terahertz digital holography is complicated due to the harsh recording conditions and the non-convexity form of the problem. In this paper, we propose an inverse problem-based reconstruction technique that jointly reconstructs the object field and the amplitude of the reference field. Regularization in the wavelet domain promotes a sparse object solution. A single objective function combining the data-fidelity and regularization terms is optimized with a dedicated algorithm based on an alternating direction method of multipliers framework. Each iteration alternates between two consecutive optimizations using projections operating on each solution and one soft thresholding operator applying to the object solution. The method is preceded by a windowing process to alleviate artifacts due to the mismatch between camera frame truncation and periodic boundary conditions assumed to implement convolution operators. Experiments demonstrate the effectiveness of the proposed method, in particular, improvements of reconstruction quality, compared to two other methods.

Targeting fusion proteins of the interleukin family: A promising new strategy for the treatment of autoinflammatory diseases.

As a means of communication between immune cells and non-immune cells, Interleukins (ILs) has the main functions of stimulating the proliferation and activation of inflammatory immune cells such as dendritic cells and lymphocytes, promote the development of blood cells and so on. However, dysregulation of ILs expression is a major feature of autoinflammatory diseases. The drugs targeting ILs or IL-like biologics have played an important role in the clinical treatment of autoinflammatory diseases. Nevertheless, the widespread use of IL products may result in significant off-target adverse reactions. Thus, there is a clear need to develop next-generation ILs products in the biomedical field. Fusion proteins are proteins created through the joining of two or more genes that originally coded for separate proteins. Over the last 30 years, there has been increasing interest in the use of fusion protein technology for developing anti-inflammatory drugs. In comparison to single-target drugs, fusion proteins, as multiple targets drugs, have the ability to enhance the cytokine therapeutic index, resulting in improved efficacy over classical drugs. The strategy of preparing ILs or their receptors as fusion proteins is increasingly used in the treatment of autoimmune and chronic inflammation. This review focuses on the efficacy of several fusion protein drugs developed with ILs or their receptors in the treatment of autoinflammatory diseases, in order to illustrate the prospects of this new technology as an anti-inflammatory drug development protocol in the future.

Low-dose ethanol consumption inhibits neutrophil extracellular traps formation to alleviate rheumatoid arthritis.

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease. Ethanol consumption has been reported to reduce morbidity in RA patients, but the mechanism behind it remains unclear. Our results showed that Muribaculaceae was predominant in the gut microbiota of mice after ethanol treatment, and the levels of microbiota metabolite acetate were increased. Acetate reduced arthritis severity in collagen-induced arthritis (CIA) mice, which was associated with a decrease in the articular neutrophils and the myeloperoxidase-deoxyribonucleic acid complex in serum. Meanwhile, in vitro experiments confirmed that acetate affected neutrophil activity by acting on G-protein-coupled receptor 43, which reduced endoplasmic reticulum stress in neutrophils and inhibited neutrophil extracellular traps formation. Furthermore, exogenous acetate reversed CIA mice with exacerbated gut microbial disruption, further confirming that the effect of gut microbial metabolite acetate on neutrophils in vivo is crucial for the immune regulation. Our findings illuminate the metabolic and cellular mechanisms of the gut-joint axis in the regulation of autoimmune arthritis, and may offer alternative avenues to replicate or induce the joint-protective benefits of ethanol without associated detrimental effects.

Rural micro-credit model design and credit risk assessment via improved LSTM algorithm.

Rural microcredit plays an important role in promoting rural economic development and increasing farmers' income. However, traditional credit risk assessment models may have insufficient adaptability in rural areas. This study is based on the improved Long Short Term Memory (LSTM) algorithm using self organizing method, aiming to design an optimized evaluation model for rural microcredit risk. The improved LSTM algorithm can better capture the long-term dependence between the borrower's historical behavior and risk factors with its advantages in sequential data modeling. The experimental results show that the rural microcredit risk assessment model based on the self organizing LSTM algorithm has higher accuracy and stability compared to traditional models, and can effectively control credit default risk, providing more comprehensive risk management support for financial institutions. In addition, the model also has real-time monitoring and warning functions, which helps financial institutions adjust their decisions in a timely manner and reduce credit losses. The practical application of this study is expected to promote the stable development of rural economy and the advancement of financial technology. However, future work needs to further validate the practical application effectiveness and interpretability of the model, taking into account the special circumstances of different rural areas, in order to achieve sustainable application of the model in the rural microcredit market.

Heterogeneous changes in mobility in response to the SARS-CoV-2 Omicron BA.2 outbreak in Shanghai.

The coronavirus disease 2019 (COVID-19) pandemic and the measures taken by authorities to control its spread have altered human behavior and mobility patterns in an unprecedented way. However, it remains unclear whether the population response to a COVID-19 outbreak varies within a city or among demographic groups. Here, we utilized passively recorded cellular signaling data at a spatial resolution of 1 km × 1 km for over 5 million users and epidemiological surveillance data collected during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron BA.2 outbreak from February to June 2022 in Shanghai, China, to investigate the heterogeneous response of different segments of the population at the within-city level and examine its relationship with the actual risk of infection. Changes in behavior were spatially heterogenous within the city and population groups and associated with both the infection incidence and adopted interventions. We also found that males and individuals aged 30 to 59 y old traveled more frequently, traveled longer distances, and their communities were more connected; the same groups were also associated with the highest SARS-CoV-2 incidence. Our results highlight the heterogeneous behavioral change of the Shanghai population to the SARS-CoV-2 Omicron BA.2 outbreak and the effect of heterogenous behavior on the spread of COVID-19, both spatially and demographically. These findings could be instrumental for the design of targeted interventions for the control and mitigation of future outbreaks of COVID-19, and, more broadly, of respiratory pathogens.

Assessing changes in incubation period, serial interval, and generation time of SARS-CoV-2 variants of concern: a systematic review and meta-analysis.

BACKGROUND: After the first COVID-19 wave caused by the ancestral lineage, the pandemic has been fueled from the continuous emergence of new SARS-CoV-2 variants. Understanding key time-to-event periods for each emerging variant of concern is critical as it can provide insights into the future trajectory of the virus and help inform outbreak preparedness and response planning. Here, we aim to examine how the incubation period, serial interval, and generation time have changed from the ancestral SARS-CoV-2 lineage to different variants of concern. METHODS: We conducted a systematic review and meta-analysis that synthesized the estimates of incubation period, serial interval, and generation time (both realized and intrinsic) for the ancestral lineage, Alpha, Beta, and Omicron variants of SARS-CoV-2. RESULTS: Our study included 280 records obtained from 147 household studies, contact tracing studies, or studies where epidemiological links were known. With each emerging variant, we found a progressive shortening of each of the analyzed key time-to-event periods, although we did not find statistically significant differences between the Omicron subvariants. We found that Omicron BA.1 had the shortest pooled estimates for the incubation period (3.49 days, 95% CI: 3.13-4.86 days), Omicron BA.5 for the serial interval (2.37 days, 95% CI: 1.71-3.04 days), and Omicron BA.1 for the realized generation time (2.99 days, 95% CI: 2.48-3.49 days). Only one estimate for the intrinsic generation time was available for Omicron subvariants: 6.84 days (95% CrI: 5.72-8.60 days) for Omicron BA.1. The ancestral lineage had the highest pooled estimates for each investigated key time-to-event period. We also observed shorter pooled estimates for the serial interval compared to the incubation period across the virus lineages. When pooling the estimates across different virus lineages, we found considerable heterogeneities (I2 > 80%; I2 refers to the percentage of total variation across studies that is due to heterogeneity rather than chance), possibly resulting from heterogeneities between the different study populations (e.g., deployed interventions, social behavior, demographic characteristics). CONCLUSIONS: Our study supports the importance of conducting contact tracing and epidemiological investigations to monitor changes in SARS-CoV-2 transmission patterns. Our findings highlight a progressive shortening of the incubation period, serial interval, and generation time, which can lead to epidemics that spread faster, with larger peak incidence, and harder to control. We also consistently found a shorter serial interval than incubation period, suggesting that a key feature of SARS-CoV-2 is the potential for pre-symptomatic transmission. These observations are instrumental to plan for future COVID-19 waves.

Denoising Method Based on Salient Region Recognition for the Spatiotemporal Event Stream.

Event cameras are the emerging bio-mimetic sensors with microsecond-level responsiveness in recent years, also known as dynamic vision sensors. Due to the inherent sensitivity of event camera hardware to light sources and interference from various external factors, various types of noises are inevitably present in the camera's output results. This noise can degrade the camera's perception of events and the performance of algorithms for processing event streams. Moreover, since the output of event cameras is in the form of address-event representation, efficient denoising methods for traditional frame images are no longer applicable in this case. Most existing denoising methods for event cameras target background activity noise and sometimes remove real events as noise. Furthermore, these methods are ineffective in handling noise generated by high-frequency flickering light sources and changes in diffused light reflection. To address these issues, we propose an event stream denoising method based on salient region recognition in this paper. This method can effectively remove conventional background activity noise as well as irregular noise caused by diffuse reflection and flickering light source changes without significantly losing real events. Additionally, we introduce an evaluation metric that can be used to assess the noise removal efficacy and the preservation of real events for various denoising methods.

Vitexin inhibits pain and itch behavior via modulating TRPV4 activity in mice.

Itching and pain are distinct unpleasant sensations. The transient receptor potential cation channel subfamily V member 4 (TRPV4) pathway is regarded as a shared pathway that mediates pain and itching. Vitexin (Mujingsu, MJS), a C-glycosylflavonoid, is an effective analgesic. This study aimed to explore the antinociceptive and anti-pruritic effects of MJS and whether its effects are mediated via the TRPV4 pathway. Mice were treated with MJS (7.5 mg/kg) 0.5 h prior to the initiation of the pain or itch modeling process. The results showed that MJS suppressed pain-like behavior in hot plate, thermal infiltration, glacial acetic acid twisting, and formalin tests. Administration of MJS decreased the pruritus response induced by histamine, C48/80, chloroquine and BAM8-22 within 30 min. MJS reduced scratching bouts and lessened the wiping reaction of mice under TRPV4 activation by GSK101 (10 µg/5 µl). MJS inhibited scratching behavior in acetone-ether-water (AEW)-treated mice within 60 min. An H1 receptor antagonist-chlorpheniramine (CLP, 400 mg/kg)-and a TRPV4 antagonist-HC067047 (250 ng/kg), exhibited similar effects to those of MJS. Moreover, MJS ameliorated dry skin itch-associated cutaneous barrier disruption in mice. MJS did not inhibit the expression of TRPV4 in the dorsal root ganglion neurons at L2-L3 in AEW mice. These results indicate that the analgesic and anti-pruritic effects of MJS in acute and chronic pain and itching, as well as itching caused by TRPV4 activation, could be attributed to the TRPV4 pathway modulation.

Using energy expenditure to estimate the minute ventilation and inhaled load of air pollutants: a pilot survey in young Chinese adults.

Existing equations to estimate ventilation (VE) may not represent the Chinese population. The objective is to develop regression equations to predict the basal metabolic rate (BMR) for ventilation estimation. 80 participants underwent the incremental tests on a bicycle ergometer, wearing a fitted facial mask with an airflow sensor connected to the cardiopulmonary gas analyzer, where the energy expenditure, metabolic factors and VE were monitored simultaneously. Linear regression models were established between BMR and body weight, which were used to estimate energy expenditure and VE. Extrapolation of the regression model was evaluated by the five-fold cross-validation. And we also assessed the inhaled load of air pollutants in subgroups at the same exposure levels. Regression models for males and females were BMR (kJ/d) = 107.58 × weight (kg)-172.61 and BMR (kJ/d) = 105.61 × weight (kg)-26.94, respectively. The model showed good fitness between the measured and predicted VE. Differences between the measured and predicted VE of this model are smaller than that of other models. There were significant differences in inhaled load participants in the same exposure concentrations. The regression model showed that weight and BMR are highly correlated and can be used to estimate individual VE.

Frequency of health care provider recommendations for HPV vaccination: a survey in three large cities in China.

Introduction: Human papillomavirus (HPV) infection, an important pathogenic factor for cervical cancer, can be prevented by the HPV vaccine. Health care provider (HCP) recommendations contribute to improve HPV vaccination coverage. The aim of this study was to assess the frequency of HCP recommendations for HPV vaccination and associated factors. Methods: From Nov 8 to Dec 6 in 2018, a cross-sectional study was conducted through online questionnaires among HCPs (n = 1,371) from hospitals in three large cities in China (Shanghai, Guangzhou, and Shenzhen). Data on demographic characteristics, the frequency of HPV vaccination recommendations, HPV knowledge and related attitudes were collected through the questionnaires. Results: Among 1,371 participants, only 30.2% reported that they frequently recommended HPV vaccination. Multivariate analyses indicated that female sex, being employed in obstetrics or gynecology departments and community health service centers, and having higher self-reported and actual knowledge of HPV were factors associated with a higher recommendation frequency. Factors including a self-perceived non-obligation to provide recommendations and difficulties in discussing sexual topics were significantly correlated with less frequent recommendations. Employment in a community health service center (OR = 2.068, 95% CI: 1.070-3.999) was the strongest factor associated with the frequency of HCPs' recommendations for HPV vaccination. Discussion: The frequency of HCPs' recommendations for HPV vaccination in China was much lower than that in many developed countries. To enhance the recommendation frequency, medical institutions should help HCPs gain more knowledge of HPV and master communication skills. At the same time, the government should take measures to enhance the accessibility of HPV vaccines. The media should help to alleviate people's concerns and encourage them to face up sexual health.

Vibration Detection and Degraded Image Restoration of Space Camera Based on Correlation Imaging of Rolling-Shutter CMOS.

To mitigate the influence of satellite platform vibrations on space camera imaging quality, a novel approach is proposed to detect vibration parameters based on correlation imaging of rolling-shutter CMOS. In the meantime, a restoration method to address the image degradation of rolling-shutter CMOS caused by such vibrations is proposed. The vibration parameter detection method utilizes the time-sharing and row-by-row imaging principle of rolling-shutter CMOS to obtain relative offset by comparing two frames of correlation images from continuous imaging. Then, the space camera's vibration parameters are derived from the fitting curve parameters of the relative offset. According to the detected vibration parameters, the discrete point spread function is obtained, and the rolling-shutter CMOS image degradation caused by vibration is restored row by row. The verification experiments demonstrate that the proposed detection method for two-dimensional vibration achieves a relative accuracy of less than 1% in period detection and less than 2% in amplitude detection. Additionally, the proposed restoration method can enhance the MTF index by over 20%. The experimental results demonstrate that the detection method is capable of detecting high-frequency vibrations through low-frame-frequency image sequences, and it exhibits excellent applicability in both push-scan cameras and staring cameras. The restoration method effectively enhances the evaluation parameters of image quality and yields a remarkable restorative effect on degraded images.

High-performance composite elastomers with abundant heterostructures for enhanced electromagnetic wave absorption with ultrabroad bandwidth.

Two-dimensional (2D) MXene has attracted vast attention in electromagnetic wave absorption (EWA), but there remains a contradiction between maintaining impedance matching and enhancing dielectric loss. Herein, the multi-scale architectures of ecoflex/2D MXene (Ti3C2Tx)@zero-dimensional CoNi sphere@one-dimensional carbon nanotube composite elastomers were successfully constructed by simple liquid-phase reduction and thermo-curing method. The binding between the hybrids as fillers and ecoflex as a matrix greatly enhanced the EWA capability of the obtained composite elastomer and improved its mechanical properties. Owing to its good impedance matching, abundant heterostructures, and synergistic electrical and magnetic losses, this elastomer exhibited an excellent minimum reflection loss of -67 dB at 9.46 GHz under a thickness of 2.98 mm. In addition, its ultrabroad effective absorption bandwidth reached 6.07 GHz. This achievement will pave the way for the exploitation of multi-dimensional heterostructures as high-performance electromagnetic absorbers with superior EWA ability.

4-Octyl itaconate attenuates glycemic deterioration by regulating macrophage polarization in mouse models of type 1 diabetes.

BACKGROUND: Pancreatic beta cell dysfunction and activated macrophage infiltration are early features in type 1 diabetes pathogenesis. A tricarboxylic acid cycle metabolite that can strongly activate NF-E2-related factor 2 (Nrf2) in macrophages, itaconate is important in a series of inflammatory-associated diseases via anti-inflammatory and antioxidant properties. However, its role in type 1 diabetes is unclear. We used 4-octyl itaconate (OI), the cell-permeable itaconate derivate, to explore its preventative and therapeutic effects in mouse models of type 1 diabetes and the potential mechanism of macrophage phenotype reprogramming. METHODS: The mouse models of streptozotocin (STZ)-induced type 1 diabetes and spontaneous autoimmune diabetes were used to evaluate the preventative and therapeutic effects of OI, which were performed by measuring blood glucose, insulin level, pro- and anti-inflammatory cytokine secretion, histopathology examination, flow cytometry, and islet proteomics. The protective effect and mechanism of OI were examined via peritoneal macrophages isolated from STZ-induced diabetic mice and co-cultured MIN6 cells with OI-pre-treated inflammatory macrophages in vitro. Moreover, the inflammatory status of peripheral blood mononuclear cells (PBMCs) from type 1 diabetes patients was evaluated after OI treatment. RESULTS: OI ameliorated glycemic deterioration, increased systemic insulin level, and improved glucose metabolism in STZ-induced diabetic mice and non-obese diabetic (NOD) mice. OI intervention significantly restored the islet insulitis and beta cell function. OI did not alter the macrophage count but significantly downregulated the proportion of M1 macrophages. Additionally, OI significantly inhibited MAPK activation in macrophages to attenuate the macrophage inflammatory response, eventually improving beta cell dysfunction in vitro. Furthermore, we detected higher IL-1ß production upon lipopolysaccharide stimulation in the PBMCs from type 1 diabetes patients, which was attenuated by OI treatment. CONCLUSIONS: These results provided the first evidence to date that OI can prevent the progression of glycemic deterioration, excessive inflammation, and beta cell dysfunction predominantly mediated by restricting macrophage M1 polarization in mouse models of type 1 diabetes.

Activation of the Epac/Rap1 signaling pathway alleviates blood-brain barrier disruption and brain damage following cerebral ischemia/reperfusion injury.

Blood brain barrier (BBB) destruction plays a key role in ischemia stroke, including promoting BBB leakage and brain edema, and leads to unfavorable patient prognosis. Epac/Rap1 signaling pathway is important in mediating endothelial cell barrier function. This study will investigate the regulatory role of Epac/Rap1 signaling pathway in BBB disruption after cerebral ischemia/reperfusion (CI/R) injury. CI/R model was induced by 90 min of transient middle cerebral artery occlusion (MCAO) in male C57BL/6J mice. Injection of Epac/Rap1 signaling pathway agonist was performed half an hour before the MCAO operation. The results showed that CI/R injured the tight connection of BBB and evoked the suppression of the Epac/Rap1 signaling pathway. Based on Epac activation with a cAMP analogue, 8-CPT could improve BBB disfunction by increasing the expression of tight junction protein and reducing the formation of stress fibers. In addition, 8-CPT could ameliorate neurobehavioral disorders, cerebral edema, and cerebral infarction volume in MCAO mice. Moreover, inhibition of Epac pathway with Rap1 inhibitor GGTI298 and Rac1 inhibitor NSC23766 could aggravate the damage of BBB and cerebral injury accordingly. Our results indicate that, the activation of Epac/Rap1 signaling pathway has neuroprotective effects on CI/R damaged brain, through the recovery of BBB.

Changes in cigarette consumption and intention to quit in response to the COVID-19 pandemic in China.

INTRODUCTION: Significant changes occurred in the way people socialize and interact with each other since China reported its first COVID-19 cases. However, little is known about how smoking behaviors may have changed due to the COVID-19 pandemic in China. The aim of this study was to assess changes in smoking behavior and intention to quit during the early stage of the COVID-19 pandemic in China and to investigate the associated factors. METHODS: An online cross-sectional survey was conducted among Chinese adult smokers. Participants were recruited through snowball sampling from 19 March to 2 April 2020. RESULTS: A total of 1388 smokers participated in this study. Of those, 1014 (73.0%) reported not changing their cigarette consumption, 104 (7.5%) reported smoking more and 268 (19.3%) reported smoking less due to the pandemic. Average daily cigarette consumption among all participants decreased from 15.0 (IQR: 10.0-20.0) to 13.0 (IQR: 8.0-20.0) (W=6.919, p<0.001). For intention to quit, 270 (19.5%) respondents reported becoming more willing to quit, and 91 (6.6%) reported becoming less willing to quit. Multivariate analyses showed that tobacco addiction, overall knowledge about the relationship between smoking and COVID-19, level of attention devoted to COVID-19, anxiety, living alone, and number of smokers in the family were significantly correlated with cigarette consumption and intention to quit, and living alone was the strongest factor associated with increased cigarette consumption (AOR=5.29; 95% CI: 1.51-18.56). CONCLUSIONS: The COVID-19 pandemic led to a slight decrease in cigarette consumption and an increase in quitting intention among Chinese smokers. During the early stages of the pandemic, it was important to focus on the anxiety of smokers, dispel smokers' misunderstandings of smoking and COVID-19 and create a supporting environment in the family to help smokers quit.

Assessing COVID-19 Vaccine Booster Hesitancy Using the Modified 5C Scale in Zhejiang Province, China: A Cross-Sectional Study.

Following the rollout of a booster campaign to promote immunity against COVID-19 in China, this study aimed to assess booster hesitancy among adults who were fully vaccinated with primary doses across Zhejiang Province. Firstly, the modified 5C scale developed by a German research team was assessed for reliability and validity via a pre-survey in Zhejiang Province. Then, a 30-item questionnaire was established to conduct online and offline surveys during 10 November to 15 December 2021. Demographic characteristics and information on previous vaccination experience, vaccine type of primary doses, attitudes towards booster doses and awareness of SARS-CoV-2 infection were collected. Chi-square tests, pairwise comparison and multivariate logistic regression were performed in data analysis. In total, 4039 valid questionnaires were analyzed, with booster hesitancy of 14.81%. Dissatisfaction with previous vaccination experience of primary doses (ORs = 1.771~8.025), less confidence in COVID-19 vaccines (OR = 3.511, 95%CI: 2.874~4.310), younger age compared to the elderly aged 51-60 years old (2.382, 1.274~4.545), lower education level (ORs = 1.707~2.100), weaker awareness of social responsibility of prevention and control of COVID-19 (1.587, 1.353~1.859), inconvenience of booster vaccination (1.539, 1.302~1.821), complacency regarding vaccine efficacy as well as self-health status (1.224, 1.056~1.415) and excessive trade-offs before vaccination (1.184, 1.005~1.398) were positively associated with booster hesitancy. Therefore, intelligent means should be strengthened to optimize vaccination services. More influential experts and other significant figures should be supported to promote timely evidence-based information via various media platforms to reduce public hesitancy and increase booster uptake.