From COVID-19 Vaccine Hesitancy to Vaccine Acceptance: Results of a Longitudinal Survey.

OBJECTIVES: COVID-19 vaccines are widely available, but uptake is suboptimal. To develop strategies to increase vaccination rates, we sought to (1) characterize adults initially hesitant to be vaccinated for COVID-19 who later received the vaccine and (2) identify factors associated with their vaccination decision. METHODS: In January 2021, we conducted an online survey of US adults via Prolific that assessed vaccination intent, COVID-19-related knowledge and attitudes, and demographic characteristics. In May 2021, we recontacted respondents to assess vaccination status and factors influencing their vaccination decision. We used χ2 statistics and t tests to examine associations between respondents' vaccination status and their characteristics, knowledge, and attitudes. We analyzed reasons for vaccination using thematic analysis. RESULTS: Of 756 initially vaccine-hesitant respondents, 529 (70.0%) completed the follow-up survey. Nearly half of those initially not sure about vaccination (47.3%, 112 of 237) were vaccinated at follow-up, while 21.2% (62 of 292) of those initially planning not to be vaccinated were vaccinated at follow-up. Of those initially not sure, higher educational attainment, greater knowledge of COVID-19, and a doctor's recommendation were associated with vaccination. Of those initially intending not to be vaccinated, male sex, Democratic political affiliation, receipt of an influenza shot within 5 years, being more worried about COVID-19, and having greater COVID-19 knowledge were associated with increased likelihood of being vaccinated. Of 167 respondents who gave reasons for vaccination, protecting oneself and others (59.9%), practical issues (29.9%), social influences (17.4%), and vaccine safety (13.8%) were the main reasons. CONCLUSION: Providing information on the protective value of vaccination, implementing rules that make remaining unvaccinated burdensome, making vaccination easy, and providing social support may influence vaccine-hesitant adults to accept vaccination.

Epigenetic state determines inflammatory sensing in neuroblastoma.

Immunotherapy has revolutionized cancer treatment, but many cancers are not impacted by currently available immunotherapeutic strategies. Here, we investigated inflammatory signaling pathways in neuroblastoma, a classically "cold" pediatric cancer. By testing the functional response of a panel of 20 diverse neuroblastoma cell lines to three different inflammatory stimuli, we found that all cell lines have intact interferon signaling, and all but one lack functional cytosolic DNA sensing via cGAS-STING. However, double-stranded RNA (dsRNA) sensing via Toll-like receptor 3 (TLR3) was heterogeneous, as was signaling through other dsRNA sensors and TLRs more broadly. Seven cell lines showed robust response to dsRNA, six of which are in the mesenchymal epigenetic state, while all unresponsive cell lines are in the adrenergic state. Genetically switching adrenergic cell lines toward the mesenchymal state fully restored responsiveness. In responsive cells, dsRNA sensing results in the secretion of proinflammatory cytokines, enrichment of inflammatory transcriptomic signatures, and increased tumor killing by T cells in vitro. Using single-cell RNA sequencing data, we show that human neuroblastoma cells with stronger mesenchymal signatures have a higher basal inflammatory state, demonstrating intratumoral heterogeneity in inflammatory signaling that has significant implications for immunotherapeutic strategies in this aggressive childhood cancer.

Proteomic fingerprinting of Neotropical hard tick species (Acari: Ixodidae) using a self-curated mass spectra reference library.

Matrix-assisted laser desorption/ionization (MALDI) time-of-flight mass spectrometry is an analytical method that detects macromolecules that can be used for proteomic fingerprinting and taxonomic identification in arthropods. The conventional MALDI approach uses fresh laboratory-reared arthropod specimens to build a reference mass spectra library with high-quality standards required to achieve reliable identification. However, this may not be possible to accomplish in some arthropod groups that are difficult to rear under laboratory conditions, or for which only alcohol preserved samples are available. Here, we generated MALDI mass spectra of highly abundant proteins from the legs of 18 Neotropical species of adult field-collected hard ticks, several of which had not been analyzed by mass spectrometry before. We then used their mass spectra as fingerprints to identify each tick species by applying machine learning and pattern recognition algorithms that combined unsupervised and supervised clustering approaches. Both Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) classification algorithms were able to identify spectra from different tick species, with LDA achieving the best performance when applied to field-collected specimens that did have an existing entry in a reference library of arthropod protein spectra. These findings contribute to the growing literature that ascertains mass spectrometry as a rapid and effective method to complement other well-established techniques for taxonomic identification of disease vectors, which is the first step to predict and manage arthropod-borne pathogens.