Objective: To investigate factors associated with the stigmatization of people of Asian descent during COVID-19 in the United States and factors that can mitigate or prevent stigmatization. Design: A national sample survey of adults (N = 842) was conducted online between May 11 and May 19, 2020. Outcome variables were two dimensions of stigmatization, responsibility and persons as risk. Hierarchical regression analyses were performed. Results: Racial prejudice, maladaptive coping, and biased media use each explained stigmatization. Racial prejudice, comprising stereotypical beliefs and emotion toward Asian Americans, was a stronger predictor of stigmatization than maladaptive coping or biased media use. Fear concerning the ongoing COVID-19 situation and the use of social media and partisan cable TV also predicted stigmatization. Low self-efficacy in dealing with COVID-19, when associated with high estimated harm of COVID-19, increased stigmatization. High perceived institutional efficacy in the handling of COVID-19 increased stigmatization when linked to high estimated harm of COVID-19. On the other hand, high perceived collective efficacy in coping with COVID-19 was associated with low stigmatization. More indirect contacts with Asians via the media predicted less stigmatization. Conclusions: Efforts to reduce stigmatization should address racial stereotypes and emotions, maladaptive coping, and biased media use by providing education and resources to the public. Fostering collective efficacy and media-based contacts with Asian Americans can facilitate these efforts.

BackgroundInstagram is increasingly becoming a platform on which visual communication of cancer takes place, but few studies have investigated the content and effects. In particular, a paucity of research has evaluated the effects of visual communication of cancer on participative engagement outcomes.ObjectiveThe objective of our study was to investigate cancer-related beliefs and emotions shared on Instagram and to examine their effects on participative engagement outcomes including likes, comments, and social support.MethodsThis study analyzed the content of 441 posts of #melanomasucks on Instagram and assessed the effects of the content characteristics on outcomes, including the number of likes and comments and types of social support using group least absolute shrinkage and selection operator logistic regression.ResultsPosts about controlling melanoma were most frequent (271/441, 61.5%), followed by 240 (54.4%) posts about outcomes of having melanoma. Ninety posts (20.4%) were about the causes of melanoma. A greater number of posts expressed positive (159/441, 36.1%) than negative emotions (100/441, 22.7%). Eighty posts (18.1%) expressed hope, making it the most frequently expressed emotion; 49 posts expressed fear (11.1%), 46 were humorous (10.4%), and 46 showed sadness (10.4%). Posts about self behavior as a cause of melanoma decreased likes (P<.001) and social support comments (P=.048). Posts about physical consequences of melanoma decreased likes (P=.02) but increased comments (P<.001) and emotional social support (P<.001); posts about melanoma treatment experience increased comments (P=.03) and emotional social support (P<.001). None of the expressions of positive emotions increased likes, comments, or social support. Expression of anger increased the number of likes (P<.001) but those about fear (P<.001) and joy (P=.006) decreased the number of likes. Posts about fear (P=.003) and sadness (P=.003) increased emotional social support. Posts showing images of melanoma or its treatment on the face or body parts made up 21.8% (96/441) of total posts. Inclusion of images increased the number of comments (P=.001).ConclusionsTo our knowledge, this is the first investigation of the content and effects of user-generated visual cancer communication on social media. The findings show where the self-expressive and social engagement functions of #melanomasucks converge and diverge, providing implications for extending research on the commonsense model of illness and for developing conceptual frameworks explaining participative engagement on social media.

A large proportion of the complexity and redundancy of LC-MS metabolomics data comes from adduct formation. To reduce such redundancy, many tools have been developed to recognize and annotate adduct ions. These tools rely on pre-defined adduct lists which are learned empirically from reverse phase LC-MS studies. Meanwhile, hydrophilic interaction chromatography (HILIC) is gaining popularity in metabolomics studies due to better performance on polar compounds. HILIC methods typically use high concentration of buffer salts for improved chromatography performance. It is therefore necessary to analyze the adduct formation in HILIC metabolomics. To this end, we developed co-variant ion analysis (COVINA) to investigate the metabolite adduct formation. Using this tool, we completely annotated 201 adduct and fragment ions of 10 metabolites. Many of the metabolite adduct ions are found to contain cluster ions of mobile phase additives. We further utilized COVINA to find the major ionization forms of metabolites. Our results show that for some metabolites the adduct ion signals can be >200-fold higher than the deprotonated form, offering better sensitivity for targeted metabolomics analysis. Finally, we developed the in-source CID ramping (InCIDR) method to analyze the intensity changes of the adduct and fragment ions of the metabolites. Our analysis demonstrates a promising method to distinguish the protonated/deprotonated ions of the metabolites from the adduct and fragment ions.

Metabolic flux analysis (MFA) is an increasingly important tool to study metabolism quantitatively. Unlike the concentrations of metabolites, the fluxes, which are the rates at which intracellular metabolites interconvert, are not directly measurable. MFA uses stable isotope labeled tracers to reveal information related to the fluxes. The conceptual idea of MFA is that in tracer experiments the isotope labeling patterns of intracellular metabolites are determined by the fluxes, therefore by measuring the labeling patterns we can infer the fluxes in the network. In this review, we will discuss the basic concept of MFA using a simplified upper glycolysis network as an example. We will show how the fluxes are reflected in the isotope labeling patterns. The central idea we wish to deliver is that under metabolic and isotopic steady-state the labeling pattern of a metabolite is the flux-weighted average of the substrates’ labeling patterns. As a result, MFA can tell the relative contributions of converging metabolic pathways only when these pathways make substrates in different labeling patterns for the shared product. This is the fundamental principle guiding the design of isotope labeling experiment for MFA including tracer selection. In addition, we will also discuss the basic biochemical assumptions of MFA, and we will show the flux-solving procedure and result evaluation. Finally, we will highlight the link between isotopically stationary and nonstationary flux analysis.

Wildland firefighters are directly exposed to elevated levels of wildland fire smoke (WF smoke). Although studies demonstrate WF smoke exposure is associated with lung function changes, few studies that use invasive sample collection methods have been conducted to investigate underlying biochemical changes. These methods are also either unrepresentative of the deeper airways or capable of inducing inflammation. In the present study, levels of biomarkers of oxidative stress (8isoprostane) and pro-inflammatory response (interleukin-6 [IL-6], interleukin-8 [IL-8], C-reactive protein [CRP], and soluble intercellular adhesion molecule-1 [sICAM-1]) were determined in exhaled breath condensate (EBC) samples that were collected from firefighters before, after, and next morning of prescribed burn and regular work shifts. Results show only a marginal cross-shift increase in 8-isoprostane on burn days (0.05 < p-value < 0.1), suggesting WF smoke exposure causes mild pulmonary responses.