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The proliferation of digital communication has profoundly transformed the landscape of persuasive discourse. Online platforms have amplified the reach and impact of persuasive techniques. However, they have also enabled the rapid spread of manipulative content, targeted propaganda, and divisive rhetoric. Consequently, a wide range of computational approaches has emerged to address the multifaceted nature of digital persuasion, to detect and mitigate its harmful practices. In light of this, the paper surveys computational methods for detecting persuasive means in digital communication, focusing on how they integrate humanistic knowledge to operationalize this construct. Additionally, special emphasis is placed on models' explainability, a pivotal aspect considering these models are used by institutions to influence societal interactions. For the analysis, two primary perspectives in persuasion are defined: linguistic and argumentative. The linguistic approach analyzes specific textual features, allowing for highly accountable algorithms based on explicit rules. The argumentative approach focuses on broader persuasive mechanisms, offering greater scalability but often resulting in less explainable models due to their complexity. This tension between model sophistication and explainability presents a key challenge in developing effective and transparent persuasion detection systems. The results highlight the spectrum of methodologies for studying persuasion, ranging from analyzing stylistic elements to detecting explicitly propagandist messages. Our findings highlight two key challenges in using these algorithms to tackle societal issues of persuasion misuse: the opacity of deep learning models and the absence of a theoretically grounded distinction between vicious and virtuous persuasion. To address these challenges, we propose integrating social sciences and humanities theories to enhance the effectiveness and ethical robustness of persuasion detection systems. This interdisciplinary approach enables a more nuanced characterization of text, facilitating the differentiation between vicious and virtuous persuasion through analysis of rhetorical, argumentative, and emotional aspects. We emphasize the potential of hybrid approaches that combine rule-based methods with deep learning techniques, as these offer a promising avenue for implementing this interdisciplinary framework. The paper concludes by outlining future challenges, including the importance of multimodal and multilingual analysis, ethical considerations in handling user-generated data and the growing challenge of distinguishing between human and AI-generated persuasive content.

Introduction: The World Health Organization (WHO)'s Research and Development (R&D) Blueprint for Action to Prevent Epidemics, a plan of action, highlighted several infectious diseases as crucial targets for prevention. These infections were selected based on a thorough assessment of factors such as transmissibility, infectivity, severity, and evolutionary potential. In line with this blueprint, the VACCELERATE Site Network approached infectious disease experts to rank the diseases listed in the WHO R&D Blueprint according to their perceived risk of triggering a pandemic. VACCELERATE is an EU-funded collaborative European network of clinical trial sites, established to respond to emerging pandemics and enhance vaccine development capabilities. Methods: Between February and June 2023, a survey was conducted using an online form to collect data from members of the VACCELERATE Site Network and infectious disease experts worldwide. Participants were asked to rank various pathogens based on their perceived risk of causing a pandemic, including those listed in the WHO R&D Blueprint and additional pathogens. Results: A total of 187 responses were obtained from infectious disease experts representing 57 countries, with Germany, Spain, and Italy providing the highest number of replies. Influenza viruses received the highest rankings among the pathogens, with 79 % of participants including them in their top rankings. Disease X, SARS-CoV-2, SARS-CoV, and Ebola virus were also ranked highly. Hantavirus, Lassa virus, Nipah virus, and henipavirus were among the bottom-ranked pathogens in terms of pandemic potential. Conclusion: Influenza, SARS-CoV, SARS-CoV-2, and Ebola virus were found to be the most concerning pathogens with pandemic potential, characterised by transmissibility through respiratory droplets and a reported history of epidemic or pandemic outbreaks.

An in-depth analysis of antibody epitopes following vaccination with different regimens provides important insight for developing future vaccine strategies. B-cell epitopes conserved across virus variants may be ideal targets for vaccine-induced antibodies and therapeutic drugs. However, challenges lie in identifying these key antigenic regions, and directing the immune system to target them. We previously evaluated the immunogenicity of two candidate DNA vaccines encoding the unmodified spike protein of either the SARS-CoV-2 Index strain or the Beta variant of concern (VOC). As a follow-on study, we characterized here the antibody binding profiles of three groups of mice immunized with either the DNA vaccine encoding the SARS-CoV-2 Index strain spike protein only, the Beta VOC spike protein only, or a combination of both as an antigen-heterologous prime-boost regimen. The latter induced an antibody response targeting overlapping regions that were observed for the individual vaccines but with additional high levels of antibody directed against epitopes in the SD2 region and the HR2 region. These heterologous-vaccinated animals displayed improved neutralization breadth. We believe that a broad-focused vaccine regimen increases neutralization breadth, and that the in-depth analysis of B-cell epitope targeting used in this study can be applied in future vaccine research.

The proliferation of digital communication has profoundly transformed the landscape of persuasive discourse. Online platforms have amplified the reach and impact of persuasive techniques. However, they have also enabled the rapid spread of manipulative content, targeted propaganda, and divisive rhetoric. Consequently, a wide range of computational approaches has emerged to address the multifaceted nature of digital persuasion, to detect and mitigate its harmful practices. In light of this, the paper surveys computational methods for detecting persuasive means in digital communication, focusing on how they integrate humanistic knowledge to operationalize this construct. Additionally, special emphasis is placed onmodels' explainability, a pivotal aspect considering these models are used by institutions to influence societal interactions. For the analysis, two primary perspectives in persuasion are defined: linguistic and argumentative. The linguistic approach analyzes specific textual features, allowing for highly accountable algorithms based on explicit rules. The argumentative approach focuses on broader persuasive mechanisms, offering greater scalability but often resulting in less explainable models due to their complexity. This tension between model sophistication and explainability presents a key challenge in developing effective and transparent persuasion detection systems. The results highlight the spectrum of methodologies for studying persuasion, ranging from analyzing stylistic elements to detecting explicitly propagandist messages. Our findings highlight two key challenges in using these algorithms to tackle societal issues of persuasionmisuse: the opacity of deep learning models and the absence of a theoretically grounded distinction between vicious and virtuous persuasion. To address these challenges, we propose integrating social sciences and humanities theories to enhance the effectiveness and ethical robustness of persuasion detection systems. This interdisciplinary approach enables a more nuanced characterization of text, facilitating the differentiation between vicious and virtuous persuasion through analysis of rhetorical, argumentative, and emotional aspects. We emphasize the potential of hybrid approaches that combine rule-based methods with deep learning techniques, as these offer a promising avenue for implementing this interdisciplinary framework. The paper concludes by outlining future challenges, including the importance of multimodal and multilingual analysis, ethical considerations in handling user-generated data and the growing challenge of distinguishing between human and AI-generated persuasive content. [ABSTRACT FROM AUTHOR]

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) not only caused the COVID-19 pandemic but also had a major impact on farmed mink production in several European countries. In Denmark, the entire population of farmed mink (over 15 million animals) was culled in late 2020. During the period of June to November 2020, mink on 290 farms (out of about 1100 in the country) were shown to be infected with SARS-CoV-2. Genome sequencing identified changes in the virus within the mink and it is estimated that about 4000 people in Denmark became infected with these mink virus variants. However, the routes of transmission of the virus to, and from, the mink have been unclear. Phylogenetic analysis revealed the generation of multiple clusters of the virus within the mink. Detailed analysis of changes in the virus during replication in mink and, in parallel, in the human population in Denmark, during the same time period, has been performed here. The majority of cases in mink involved variants with the Y453F substitution and the H69/V70 deletion within the Spike (S) protein; these changes emerged early in the outbreak. However, further introductions of the virus, by variants lacking these changes, from the human population into mink also occurred. Based on phylogenetic analysis of viral genome data, we estimate, using a conservative approach, that about 17 separate examples of mink to human transmission occurred in Denmark but up to 59 such events (90% credible interval: (39–77)) were identified using parsimony to count cross-species jumps on transmission trees inferred using Bayesian methods. Using the latter approach, 136 jumps (90% credible interval: (117–164)) from humans to mink were found, which may underlie the farm-to-farm spread. Thus, transmission of SARS-CoV-2 from humans to mink, mink to mink, from mink to humans and between humans were all observed. Author summary: In addition to causing a pandemic in the human population, SARS-CoV-2 also infected farmed mink. In Denmark, after the first identification of infection in mink during June 2020, a decision was made in November 2020 to cull all the farmed mink. Within this outbreak, mink on 290 farms (out of about 1100 in the country) were found to have been infected. We showed, by analysis of the viruses from the mink, that the viruses on the farms were mainly of three different, but closely related, types (termed Clusters 2, 3 and 4) that shared certain distinctive features. Thus, we found that many outbreaks in mink resulted from transmission of the virus between mink farms. However, we identified that new introductions of other virus variants, presumably from infected humans, also occurred. Furthermore, we showed that spread of the virus from infected mink to humans also happened on multiple occasions. Thus, transmission of these viruses from humans to mink, mink to mink, from mink to humans and between humans were all observed. [ABSTRACT FROM AUTHOR]

Background: It is mandatory to label food products with the 14 main allergens in the EU. Reasonable allergen labeling requires knowledge of population‐based thresholds derived from food challenges. The aim of this study was to evaluate the threshold‐distribution in clinically verified food allergic patients for allergens mandatory for labeling. Methods: All positive open oral food challenges and double‐blind placebo‐controlled food challenges (DBPCFC) performed at the Allergy Center, Odense University Hospital, Denmark (2000–2022) were included. For each included challenge, the cumulative threshold (LOAEL) was obtained and NOAEL estimated. Data were modelled as an interval censored log‐normal distribution. Results: Overall, 38 of all 2612 challenges (1.5%) in 1229 patients (717 male, 986 children) reacted to <5 mg protein. The majority of the most sensitive patients reacted with a Sampson severity score of 2–3. Using interval censored log‐normal models only five groups (hens´ egg, fish, peanut, milk, tree‐nuts) elicited reactions after ingestion of 0.5 mg protein and in low frequencies of the population. Hen's egg was the most potent allergen, with reactivity to <0.5 mg protein in 0.24% [0.13–0.44%] of egg allergic patients while the estimated fraction of allergic patients reacting to a eliciting dose on 0.5 mg protein for most other allergens were below 0.04%. Conclusion: Our data demonstrates that the majority of food allergic patients as expected tolerating traces of allergenic foods without developing severe allergic symptoms and signs. Hen's egg appears to be the food most likely to elicit reactions in the most sensitive individuals at very low doses. [ABSTRACT FROM AUTHOR]

Introduction: Venom immunotherapy (VIT) and adrenaline autoinjector (AAI) are important therapies in venom anaphylaxis. Adherence to VIT and AAI in patients with venom allergy has been evaluated in a few studies; however, solid data are lacking. This study aimed to evaluate VIT and AAI retrieval rates in patients with venom allergy with a special focus on adherence to treatment. Adherence was compared to subcutaneous immunotherapy (SCIT) with inhalant allergens. Methods: This was a retrospective study among patients registered for allergen immunotherapy at the Allergy Center, Odense University Hospital, Denmark, from January 1, 2010, to December 31, 2014. Data on purchased immunotherapy and AAI were obtained from the Danish National Health Service Prescription Database. Multivariable logistic regression was used to analyze if allergen, age, sex, mastocytosis, and treatment site affected adherence. Results: The 3-year adherence to VIT was 92.4% (244/264) compared to 87.4% (215/246) in SCIT with inhalant allergens, and the 5-year adherence to VIT was 84.1% (222/264) compared to 74.8% (184/246) in SCIT with inhalant allergens (p = 0.045). Females treated with VIT were more adherent than males (p = 0.45 [3-year], p = 0.008 [5-year]), whereas allergen, age, mastocytosis, or treatment site did not significantly affect adherence. Only 28.6% of patients (12/42) purchased an AAI after premature termination of VIT. Conclusion: In this register-based study, we found that the 3- and 5-year adherences to VIT and SCIT with inhalant allergens are at the upper end of the spectrum hitherto reported. Patients' 5-year adherence to VIT was higher than patients' 5-year adherence to SCIT with inhalant allergens. If VIT was prematurely terminated, less than 1/3 would have purchased an AAI. [ABSTRACT FROM AUTHOR]

This article examines the factors related to side effects, re-sting reactions, and adherence in venom immunotherapy (VIT) for venom allergies. The study reveals that side effects during VIT are more likely in females, with honeybee venom, and in cases where the initial sting reaction was severe. Re-sting reactions after completing VIT are also associated with side effects during treatment. The study emphasizes the importance of considering these factors when determining the duration of VIT and the need for lifelong treatment in certain cases. The study also explores adherence to VIT and the risk factors for allergic reactions in patients with insect venom allergy. It finds that re-sting during VIT is reported in a significant number of cases, and side effects during VIT are linked to allergic reactions after re-sting. The study also reveals high adherence to VIT and adrenaline autoinjector (AAI) use, but low adherence to AAI when VIT is prematurely terminated. Risk factors for side effects during VIT include honeybee venom, female sex, and severe initial sting reactions. The study acknowledges limitations such as self-reported data and non-standardized procedures for re-sting. [Extracted from the article]

SARS‐CoV‐2 variants of concern (VOC), such as Delta and Omicron have harbored mutations, which increased viral infectivity or ability to evade neutralizing antibodies. Immunocompromised patients might be a source of some of these emerging variants. In this study, we sequenced 17 consecutive samples from an immunocompromised patient with a long‐term SARS‐CoV‐2 infection with the pre‐VOC era lineage B.1.177.35. We here describe the emergence of 73 nonsynonymous minority variants in this patient and show that 10 of these mutations became dominant in the viral population during the treatment period. Four of these were seen throughout the infection period and had a very low global prevalence, although three of them were also observed later in the Alpha, Delta, and Omicron lineages. We also found that two adjacent nsp12 variants (M785I and S786P) belonged to different quasi‐species and competed during the early stages of infection and remdesivir administration. This emphasizes the importance of ongoing genome surveillance of SARS‐CoV‐2 among immunocpromised patients. [ABSTRACT FROM AUTHOR]