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The unknownness and dread potential of a risk event shapes its perceived risk. A public health emergency of international concern (PHEIC) declaration by the World Health Organisation (WHO) is a signal for such an event. Understanding perceived risk then shapes risk-avoiding behaviours, important for health prevention. The review aims to consolidate the determinants of risk perception during a PHEIC, underscoring the need for grounding in context and theory. Studies published from 2010 until end-2020, searching PubMed, PsycINFO, MedlinePlus, PubPsych, and CINAHL, were included. Studies with only biological conceptualisations of risk, or no association to risk perception, were excluded. A total of 65 studies were included. Quality of the cross-sectional studies was assessed using Newcastle Ottawa Scale (NOS), yielding an average of 5.4 stars (out of 10). Factors were classified into three broad categories – individual, contextual, and media. Individual risk factors include emotions; beliefs, trust, and perceptions; immutable physical traits (sex, age, ethnicity); mutable traits (education, income, etc . ); and knowledge, with no definitive correlation to risk perception. Contextual traits include pandemic experience, time, and location, with only time negatively correlated to risk perception. Media traits include exposure, attention, and framing of media, with no clear association to risk perception. One limitation is excluding a portion of COVID-19 studies due to censoring. Still, this lack of consensus highlights the need to better conceptualise “risk perception”. Specifying the context and timing is also important since jurisdictions experience different outbreaks depending on outbreak histories. Using theories to ground risk perception research assists with these tasks.

The rapid diffusion of medical technologies is widely recognized as a key driver of healthcare cost escalation. The excessive duplication of technologies gives rise to the so-called medical arms race. Conventional wisdom tends to explain this phenomenon by external reimbursement mechanisms and hospitals' competitive strategies, but has largely neglected the role played by health regulations that may also affect hospitals' technology adoption decisions. This study sheds new light on the medical arms race with evidence from China, which has witnessed an unprecedented expansion of big tertiary hospitals and a keen pursuit of expensive medical technologies. Chinese hospitals aggressively pursue high-tech medical equipment as an opportunistic reaction to the peculiar health regulatory environment. By analysing a panel dataset collected from Shenzhen City, this study reveals a series of important impacts of the medical arms race in Chinese public hospitals. High-tech medical equipment is found to lead to an increase in hospital revenues and patient volumes, but no significant impact is noted on unit costs. While high-tech medical equipment is associated with a discernible improvement in clinical outcomes, no contribution to hospitals' operational efficiency is noted. These findings are interpreted in the context of the broader health regulatory framework and China's public hospital reforms.

Background Accurately assessing biopsychosocial risk and vulnerability are the building blocks for better interventions and tailoring care for older people. Methods Building on the previously published Community for Successful Ageing (ComSA) BioPsychoSocial Risk Version I Screener (BPS-RS I), we report a re-specified version (BPS-RS II) derived from interviews with older people ( n  = 40) in Singapore. A cross-sectional survey ( n  = 1,107) and Factor Analysis (FA) were used to identify items for BPS-RS II. Associations with health-related and quality of life (QoL) and comparisons to Version I were examined. Results Fourteen questions generated from the qualitative findings clustered above the stipulated Kaiser–Meyer–Olkin (KMO) value of greater than 0.5. FA retained the BPS three factor structure. Items in the B domain were largely consistent with Version I, though new items in the P and S domains were identified. Adjusted multi-variate regression analyses replicated additive dose response effects found in BPS-RS I, with smoother gradients and narrower Confidence Intervals (CIs). Stronger associations with Length of Hospital Stay and QoL were detected in BPS-RS II compared to BPS-RS I for participants with BPS risk consisting of having ‘overwhelming problems’. The relationship between this category and poor QoL was particularly evident (OR of 39.20, 95% CI: 37.73–40.66, p  < 0.01) compared to no BPS risk. Conclusions Using BPS-RS II over I is recommended for biopsychosocial risk assessment and stratifying older people to interventions for population health management. The updated tool is theory-based, culturally sensitive to multi-ethnic contexts in Singapore, and shows improved associations with health-related outcomes, and strong associations with QoL.

We report the experimental observations of large-scale pattern formation in soft quasicrystals activated by mechanical loading. The soft material system utilizes localized transformations to give rise to large-scale organized patterns. The complex multiscale mechanisms lead to an initial loss of microscale quasicrystalline order, which ultimately reemerges in the large-scale patterns with rotational symmetry. Our experiments demonstrate that the resulting large-scale patterns can be pre-designed by fine-tuning initial local chirality and porosity at small length scales. Furthermore, different initial configurations result in patterns exhibiting high shape similarity, with characteristic lengths falling within a discrete sequence governed by the silver ratio. This experimentally observed phenomenon opens a new class of transformative materials with switchable self-similarity propagating across length scales. The study of quasicrystals has revealed unique material properties that challenge traditional notions of order and symmetry in solids, prompting interest in their potential applications in advanced materials. Here, authors demonstrate the experimental realization of large-scale pattern formation in soft quasicrystals through mechanical loading, revealing a multiscale transformation process that can be finely tuned by adjusting initial local chirality and porosity.

Immunotherapy with immune checkpoint blockade (ICB) has shown limited benefits in hepatocellular carcinoma (HCC) and other cancers, mediated in part by the immunosuppressive tumor microenvironment (TME). As p53 loss of function may play a role in immunosuppression, we herein examine the effects of restoring p53 expression on the immune TME and ICB efficacy. We develop and optimize a CXCR4-targeted mRNA nanoparticle platform to effectively induce p53 expression in HCC models. Using p53 -null orthotopic and ectopic models of murine HCC, we find that combining CXCR4-targeted p53 mRNA nanoparticles with anti-PD-1 therapy effectively induces global reprogramming of cellular and molecular components of the immune TME. This effect results in improved anti-tumor effects compared to anti-PD-1 therapy or therapeutic p53 expression alone. Thus, our findings demonstrate the reversal of immunosuppression in HCC by a p53 mRNA nanomedicine when combined with ICB and support the implementation of this strategy for cancer treatment. The p53 tumor suppressor gene is frequently mutated in liver cancer. Here the authors show that restoration of p53 expression with a mRNA nanoparticle platform elicits anti-tumor immune responses and promotes response to immune checkpoint blockade in preclinical models of p53-null hepatocellular carcinoma.

Prevalence and spread of misinformation are a concern for the exacerbation of vaccine hesitancy and a resulting reduction in vaccine intent. However, few studies have focused on how vaccine misinformation diffuses online, who is responsible for the diffusion, and the mechanisms by which that happens. In addition, researchers have rarely investigated this in non-Western contexts particularly vulnerable to misinformation. This study aims to identify COVID-19 vaccine misinformation, map its diffusion, and identify the effect of echo chamber users on misinformation diffusion on a Taiwanese online forum. The study uses data from a popular forum in Taiwan called PTT. A crawler scraped all threads on the most popular subforum from January 2021 until December 2022. Vaccine-related threads were identified through keyword searching (n=5818). Types of misinformation, including misleading, disinformation, conspiracy, propaganda, and fabricated content, were coded by 2 researchers. Polarity was proposed as a proxy for measuring an individual's level of involvement in the echo chamber, one of the mechanisms responsible for the viral misinformation on social media. Factors related to information diffusion, including misinformation type and polarity, were then assessed with negative binomial regression. Of 5818 threads, 3830 (65.8%) were identified as true information, and 1601 (27.5%) contained misinformation, yielding 5431 boards for analysis. Misinformation content did not vary much from other contexts. Propaganda-related information was most likely to be reposted (relative risk: 2.07; P<.001) when comparing to true information. However, the more polarized a user was, the less likely his or her content was to be reposted (relative risk: 0.22; P<.001). By removing the nodes with a high level of indegree, outdegree, and betweenness centrality, we found that the core network and the entire network demonstrated a decreasing trend in average polarity score, which showed that influential users contributed to the polarization in misinformation consumption. Although the forum exhibits a resilience to echo chambering, active users and brokers contribute significantly to the polarization of the community, particularly through propaganda-style misinformation. This popularity of propaganda-style misinformation may be linked to the political nature of the forum, where public opinion follows \"elite cues\" on issues, as observed in the United States. The work in this study corroborates this finding and contributes a data point in a non-Western context. To manage the echo chambering of misinformation, more effort can be put into moderating these users to prevent polarization and the spread of misinformation to prevent growing vaccine hesitancy.

Media studies are important for vaccine hesitancy research, as they analyze how the media shapes risk perceptions and vaccine uptake. Despite the growth in studies in this field owing to advances in computing and language processing and an expanding social media landscape, no study has consolidated the methodological approaches used to study vaccine hesitancy. Synthesizing this information can better structure and set a precedent for this growing subfield of digital epidemiology. This review aimed to identify and illustrate the media platforms and methods used to study vaccine hesitancy and how they build or contribute to the study of the media's influence on vaccine hesitancy and public health. This study followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. A search was conducted on PubMed and Scopus for any studies that used media data (social media or traditional media), had an outcome related to vaccine sentiment (opinion, uptake, hesitancy, acceptance, or stance), were written in English, and were published after 2010. Studies were screened by only 1 reviewer and extracted for media platform, analysis method, the theoretical models used, and outcomes. In total, 125 studies were included, of which 71 (56.8%) used traditional research methods and 54 (43.2%) used computational methods. Of the traditional methods, most used content analysis (43/71, 61%) and sentiment analysis (21/71, 30%) to analyze the texts. The most common platforms were newspapers, print media, and web-based news. The computational methods mostly used sentiment analysis (31/54, 57%), topic modeling (18/54, 33%), and network analysis (17/54, 31%). Fewer studies used projections (2/54, 4%) and feature extraction (1/54, 2%). The most common platforms were Twitter and Facebook. Theoretically, most studies were weak. The following five major categories of studies arose: antivaccination themes centered on the distrust of institutions, civil liberties, misinformation, conspiracy theories, and vaccine-specific concerns; provaccination themes centered on ensuring vaccine safety using scientific literature; framing being important and health professionals and personal stories having the largest impact on shaping vaccine opinion; the coverage of vaccination-related data mostly identifying negative vaccine content and revealing deeply fractured vaccine communities and echo chambers; and the public reacting to and focusing on certain signals-in particular cases, deaths, and scandals-which suggests a more volatile period for the spread of information. The heterogeneity in the use of media to study vaccines can be better consolidated through theoretical grounding. Areas of suggested research include understanding how trust in institutions is associated with vaccine uptake, how misinformation and information signaling influence vaccine uptake, and the evaluation of government communications on vaccine rollouts and vaccine-related events. The review ends with a statement that media data analyses, though groundbreaking in approach, should supplement-not supplant-current practices in public health research.

In this study we modeled secondary organic aerosol (SOA) mass loadings from the oxidation (by O3, OH and NO3) of five representative biogenic volatile organic compounds (BVOCs): isoprene, endocyclic bond-containing monoterpenes (α-pinene and limonene), exocyclic double-bond compound (β-pinene) and a sesquiterpene (β-caryophyllene). The simulations were designed to replicate an idealized smog chamber and oxidative flow reactors (OFRs). The Master Chemical Mechanism (MCM) together with the peroxy radical autoxidation mechanism (PRAM) were used to simulate the gas-phase chemistry. The aim of this study was to compare the potency of MCM and MCM + PRAM in predicting SOA formation. SOA yields were in good agreement with experimental values for chamber simulations when MCM + PRAM was applied, while a stand-alone MCM underpredicted the SOA yields. Compared to experimental yields, the OFR simulations using MCM + PRAM yields were in good agreement for BVOCs oxidized by both O3 and OH. On the other hand, a stand-alone MCM underpredicted the SOA mass yields. SOA yields increased with decreasing temperatures and NO concentrations and vice versa. This highlights the limitations posed when using fixed SOA yields in a majority of global and regional models. Few compounds that play a crucial role (>95 % of mass load) in contributing to SOA mass increase (using MCM + PRAM) are identified. The results further emphasized that incorporating PRAM in conjunction with MCM does improve SOA mass yield estimation.

Mitigation surface ozone pollution becomes increasingly pivotal in improving China’s air quality. However, the impact of global sea surface temperature anomalies (SSTA) on the long-term predictability of China’s surface ozone remains challenging. In this study, we employ eigen techniques to effectively characterize dominant surface ozone patterns over China, and establish cross-correlations between the dominant patterns and global SSTA time series. Our findings reveal that China’s summer ozone pollution is strongly associated with crucial SSTA clusters linked to atmospheric circulations, i.e., the West Pacific Subtropical High and the Pacific-North American teleconnection pattern. For winter, ozone pollution is attributed to SSTA clusters related to the Southern Oscillation, the Madden-Julian Oscillation and others. We propose a multivariate regression model capable of predicting surface ozone patterns with a lead time of at least 3 months. Evaluation of our model using a testing dataset yields an R-value of around 0.5 between predicted and observed data, surpassing statistical significance threshold. This suggests the viability and potential applicability of our predictive model in surface ozone forecasting and mitigation strategies in China.

Hydroxyl (OH) radicals, nitrate (NO3) radicals and ozone (O3) play central roles in the troposphere because they control the lifetimes of many trace gases that result from anthropogenic and biogenic origins. To estimate the air chemistry, the atmospheric reactivity and oxidation capacity were comprehensively analyzed based on a parameterization method at a suburban site in Xianghe in the North China Plain from 6 July 2018 to 6 August 2018. The total OH, NO3 and O3 reactivities at the site varied from 9.2 to 69.6, 0.7 to 27.5 and 3.3×10-4 to 1.8×10-2 s−1 with campaign-averaged values of 27.5±9.7, 2.2±2.6 and 1.2±1.7×10-3 s−1 (± standard deviation), respectively. NOx (NO+NO2) was by far the main contributor to the reactivities of the three oxidants, with average values of 43 %–99 %. Alkenes dominated the OH, NO3 and O3 reactivities towards total nonmethane volatile organic compounds (NMVOCs), accounting for 42.9 %, 77.8 % and 94.0 %, respectively. The total OH, NO3 and O3 reactivities displayed similar diurnal variations with the lowest values during the afternoon but the highest values during rush hours, and the diurnal profile of NOx appears to be the major driver for the diurnal profiles of the reactivities of the three oxidants. A box model (a model to Simulate the concentrations of Organic vapors, Sulfuric Acid and Aerosols; SOSAA) derived from a column chemical transport model was used to simulate OH and NO3 concentrations during the observation period. The calculated atmospheric oxidation capacity (AOC) reached 4.5×108 moleculescm-3s-1, with a campaign-averaged value of 7.8×107 moleculescm-3s-1 dominated by OH (7.7×107 moleculescm-3s-1, 98.2 %), O3 (1.2×106 moleculescm-3s-1, 1.5 %) and NO3 (1.8×105 moleculescm-3s-1, 0.3 %). Overall, the integration of OH, NO3 and O3 reactivities analysis could provide useful insights for NMVOC pollution control in the North China Plain. We suggest that further studies, especially direct observations of OH and NO3 radical concentrations and their reactivities, are required to better understand trace gas reactivity and AOC.