HS-GC-FID method for quantification of HFA-152a in cell culture media, and plasma from a range of species and regulatory compliant validations.

INTRODUCTION: 1,1-Difluoroethane (HFA-152a) is being developed as an alternative propellant in pressurized metered dose inhalers (pMDIs). As a part of the regulatory development pathway, pharmacology, toxicology and clinical studies have been conducted with inhaled HFA-152a. These studies require fit for purpose regulatory compliant (GxP validated) methods for quantification of HFA-152a from blood. METHODS: As HFA-152a is a gas at standard temperature and pressure, novel methods were developed to support the analysis across the wide range of species and concentrations required for regulatory filing. RESULTS: The developed methods utilized a headspace auto sampler coupled to a gas chromatograph (GC) with flame ionization detection. Key factors in the successful method included bringing together fit for purpose approaches to the head space vials, volume of matrix (blood), detection range required for species/study objective, handling / transfer of blood into head space vials and the stability/storage required for the analysis of the samples. The species-specific assays were fully validated under regulatory (GLP) conditions for mouse, rat, rabbit, canine and human and non-regulatory (non GLP) validations for guinea pig and cell culture media. DISCUSSION: Overall the novel approach of head space analysis of whole blood allowed for the development and validation of assays used to generate the toxicokinetic data that supported clinical testing of HFA-152a as a new pMDI propellant.

Composition of aerosols from thermal degradation of flavors used in ENDS and tobacco products.

Aim: The cardiovascular toxicity of unheated and heated flavorants and their products as commonly present in electronic cigarette liquids (e-liquids) was evaluated previously in vitro. Based on the results of in vitro assays, cinnamaldehyde, eugenol, menthol, and vanillin were selected to conduct a detailed chemical analysis of the aerosol generated following heating of each compound both at 250 and 750 °C. Materials and Methods: Each flavoring was heated in a drop-tube furnace within a quartz tube. The combustion atmosphere was captured using different methods to enable analysis of 308 formed compounds. Volatile organic compounds (VOCs) were captured with an evacuated Summa canister and assayed via gas chromatography interfaced with mass spectrometry (GC-MS). Carbonyls (aldehydes and ketones) were captured using a 2,4-dinitrophenylhydrazine (DNPH) cartridge and assayed via a high-performance liquid chromatography-ultra-violet (HPLC-UV) assay. Polyaromatic hydrocarbons (PAHs) were captured using an XAD cartridge and filter, and extracts were assayed using GC-MS/MS. Polar compounds were assayed after derivatization of the XAD/filter extracts and analyzed via GC-MS. Conclusion: At higher temperature, both cinnamaldehyde and menthol combustion significantly increased formaldehyde and acetaldehyde levels. At higher temperature, cinnamaldehyde, eugenol, and menthol resulted in increased benzene concentrations. At low temperature, all four compounds led to higher levels of benzoic acid. These data show that products of thermal degradation of common flavorant compounds vary by flavorant and by temperature and include a wide variety of harmful and potentially harmful constituents (HPHCs).

Promoting and countering misinformation during Australia's 2019-2020 bushfires: a case study of polarisation.

During Australia's unprecedented bushfires in 2019-2020, misinformation blaming arson surfaced on Twitter using #ArsonEmergency. The extent to which bots and trolls were responsible for disseminating and amplifying this misinformation has received media scrutiny and academic research. Here, we study Twitter communities spreading this misinformation during the newsworthy event, and investigate the role of online communities using a natural experiment approach-before and after reporting of bots promoting the hashtag was broadcast by the mainstream media. Few bots were found, but the most bot-like accounts were social bots, which present as genuine humans, and trolling behaviour was evident. Further, we distilled meaningful quantitative differences between two polarised communities in the Twitter discussion, resulting in the following insights. First, Supporters of the arson narrative promoted misinformation by engaging others directly with replies and mentions using hashtags and links to external sources. In response, Opposers retweeted fact-based articles and official information. Second, Supporters were embedded throughout their interaction networks, but Opposers obtained high centrality more efficiently despite their peripheral positions. By the last phase, Opposers and unaffiliated accounts appeared to coordinate, potentially reaching a broader audience. Finally, the introduction of the bot report changed the discussion dynamic: Opposers only responded immediately, while Supporters countered strongly for days, but new unaffiliated accounts drawn into the discussion shifted the dominant narrative from arson misinformation to factual and official information. This foiled Supporters' efforts, highlighting the value of exposing misinformation. We speculate that the communication strategies observed here could inform counter-strategies in other misinformation-related discussions. Supplementary Information: The online version contains supplementary material available at 10.1007/s13278-022-00892-x.

Amplifying influence through coordinated behaviour in social networks.

Political misinformation, astroturfing and organised trolling are online malicious behaviours with significant real-world effects that rely on making the voices of the few sounds like the roar of the many. These are especially dangerous when they influence democratic systems and government policy. Many previous approaches examining these phenomena have focused on identifying campaigns rather than the small groups responsible for instigating or sustaining them. To reveal latent (i.e. hidden) networks of cooperating accounts, we propose a novel temporal window approach that can rely on account interactions and metadata alone. It detects groups of accounts engaging in various behaviours that, in concert, come to execute different goal-based amplification strategies, a number of which we describe, alongside other inauthentic strategies from the literature. The approach relies upon a pipeline that extracts relevant elements from social media posts common to the major platforms, infers connections between accounts based on criteria matching the coordination strategies to build an undirected weighted network of accounts, which is then mined for communities exhibiting high levels of evidence of coordination using a novel community extraction method. We address the temporal aspect of the data by using a windowing mechanism, which may be suitable for near real-time application. We further highlight consistent coordination with a sliding frame across multiple windows and application of a decay factor. Our approach is compared with other recent similar processing approaches and community detection methods and is validated against two politically relevant Twitter datasets with ground truth data, using content, temporal, and network analyses, as well as with the design, training and application of three one-class classifiers built using the ground truth; its utility is furthermore demonstrated in two case studies of contentious online discussions.

Exploring the effect of streamed social media data variations on social network analysis.

To study the effects of online social network (OSN) activity on real-world offline events, researchers need access to OSN data, the reliability of which has particular implications for social network analysis. This relates not only to the completeness of any collected dataset, but also to constructing meaningful social and information networks from them. In this multidisciplinary study, we consider the question of constructing traditional social networks from OSN data and then present several measurement case studies showing how variations in collected OSN data affect social network analyses. To this end, we developed a systematic comparison methodology, which we applied to five pairs of parallel datasets collected from Twitter in four case studies. We found considerable differences in several of the datasets collected with different tools and that these variations significantly alter the results of subsequent analyses. Our results lead to a set of guidelines for researchers planning to collect online data streams to infer social networks.