BACKGROUND:Adult studies have shown that quadrivalent recombinant influenza vaccines (RIV4) induce a higher antibody response than quadrivalent egg-based inactivated influenza vaccines (IIV4). METHODS:Healthcare personnel (HCP) from a 2018-2019 cohort study that assessed IIV4 immunogenicity in Israel were recruited into a 2019-2020 randomized trial of RIV4 and IIV4. 2018-2019 low titer low responders (LTLRs) had pre-vaccination hemagglutination inhibition antibody titers ≤1:40 and <4-foldrise post-vaccination. We assessed whether RIV4 versus IIV4 receipt in the 2019-2020 season improved response among 2018-2019 LTLRs using logistic regression adjusted for employment hospital. RESULTS:Of 228 HCP classified as 2018-2019 LTLRs, 2019-2020 RIV4 recipients were 3.6 (95% CI: 1.2-11.4) and 8.3 (95% CI: 3.0-26.3) times as likely to become a non-LTLR against influenza A(H1N1)pdm09 and cell-derived A(H3N2) vaccine components compared to IIV4 recipients. CONCLUSIONS:RIV4 had improved immunogenicity against influenza A viruses among previously classified low vaccine responder HCP, highlighting how recombinant influenza vaccines could improve antibody responses against influenza A for HCP at risk for poor influenza antibody response.
The proportion of SARS-CoV-2 infections diagnosed by COVID-19 tests, including home antigen tests, is unknown. We detected infections among U.S. blood donors by testing for nucleocapsid antibody (anti-N) seroconversion and administered a questionnaire to determine the proportion of those infections that were associated with a self-reported positive COVID-19 test. Among U.S. blood donors with serologic evidence of SARS-CoV-2 infection who completed a survey, 47.7% reported an associated self-reported positive COVID-19 test. This proportion changed from July-December 2020 (44.9%) to July-December 2022 (54.8%). This study suggests many SARS-CoV-2 infections in adults are not diagnosed with a test.
While sales and use of cigarettes have declined, it is important to understand how these trends differ across brands and product characteristics, and how the demographic makeup of cigarette smokers has shifted. Examine trends in U.S. market share for leading cigarette brands by brand tier and menthol status and describe the sociodemographic profile of people who use top cigarette brands. Cohort study: The Population Assessment of Tobacco and Heath (PATH) Study collects data from a nationally representative sample of U.S. youth (age 12-17) and adults (age 18+) in 1-2-year intervals from 2013-2023 (Waves 1-7). Respondents complete the questionnaire via in-person audio-computer-assisted interviews. Telephone interviews were available in 2020-2023 due to the COVID-19 pandemic. This study utilized data from 19,722 individuals (youth: N=1,201; adults: N=18,521) who smoked cigarettes in the past 30-days (P30D) Time (survey wave). Respondents were asked the cigarette brand that they usually smoke or last smoked, the number of cigarettes smoked per day, and how many days they smoked in the P30D to estimate monthly intake and market share. Cigarette brands were coded into premium and non-premium brand tiers. Demographic characteristics, other tobacco use, alcohol use, marijuana use, and mental health status were also assessed. Premium brands (overall and menthol) experienced significant market declines. Non-premium brands saw a market share increase. Characteristics of those smoking cigarettes changed: reduced number of cigarettes smoked per day, increased marijuana use, decreased alcohol use, and decreased moderate-to-high-severity mental health symptoms. While brand loyalty remains strong for the top three cigarette brands, the expanding non-premium market continues to encroach on the premium brand market. Similar patterns were observed in the menthol sector. As the marketplace changes so do the profiles of the people who use them or market potentially reflects the people who are still smoking. Continued timely surveillance of cigarette brand preferences and profiles of the people who use them will inform tobacco control policies that minimize tobacco-related mortality. Examine market share trends (2013-2023) for cigarette brands by brand tier and menthol status; describe the sociodemographic profile of people who use top cigarette brands. Premium brands experienced significant market declines overall and among the menthol category. Non-premium brands saw a market share increase. Characteristics of those smoking cigarettes changed: reduced cigarettes smoked per day, increased marijuana use, decreased alcohol use, and decreased moderate-to-high-severity mental health symptoms. While brand loyalty remains strong for top cigarette brands, the expanding non-premium market is encroaching on the premium brand market. These changes reflect shifts in sociodemographic profiles of people smoking in 2023.
Lung adenocarcinoma in never smokers (NS-LUAD) has a high mortality rate. Compared to LUAD from patients with smoking history, NS-LUAD are less sensitive to immune checkpoint blockade (ICB) potentially due to differences in tumor mutational burden and immune microenvironment. Knowledge of NS-LUAD phenotypic plasticity and cell composition can provide guidance for prognosis and treatment. However, previous studies of LUAD gene expression landscape were predominantly conducted in smokers and full transcriptomic sequencing (RNA-seq) was only examined in a few dozens of NS-LUAD. By investigating cell dynamics through RNA-seq data from 684 NS-LUAD, we identified three gene expression-based subtypes that encapsulate tumors’ clinical, morphological, and genomic features. The ‘steady’ subtype features low proliferation markers and high fraction of alveolar cells, while it is depleted of TP53 mutations and ALK fusions. With its moderate-to-well differentiated morphological features, this subtype is associated with prolonged overall survival and predicted favorable response to immune checkpoint inhibitors, independent of the PD1/PD-L1 expression levels. The ‘proliferative’ subtype features increased proliferation, and enrichment of TP53 mutations and ALK and other gene fusions. The ‘chaotic’ subtype is characterized by elevated levels of mesenchymal cell state, increased proportions of cancer associated fibroblasts and histologic features of mixed-lineage, and is associated with worst overall survival, even within stage I tumors. We derived a signature of 60 genes’ expression sufficient to recapitulate the transcriptomic classification and validated it in an independent NS-LUAD dataset. This 60-gene signature strongly predicted survival even within subgroups based on tumor stage and beyond known molecular or morphological features, confirming its importance for NS-LUAD prognostication in clinical settings. Wei Zhao, Tongwu Zhang, Xing Hua, Phuc H. Hoang, Mona Miraftab, Monjoy Saha, John P. McElderry, Jian Sang, Olivia Lee, Caleb Hartman, Azhar Khandekar, Sunandini Sharma, Frank J. Colón-Matos, Samuel Anyaso-Samuel, Defei Wang, Kristine Jones, Amy Hutchinson, Belynda Hicks, Jennifer Rosenbaum, Xiaoming Zhong, Yang Yang, Angela Pesatori, Dario Consonni, Karun Mutreja, Scott Lawrence, Nathaniel Rothman, Ludmil B. Alexandrov, Charles Leduc, Marina K. Baine, Philippe Joubert, Lynette M. Sholl, William D. Travis, Robert Homer, Qing Lan, Stephen J. Chanock, Lixing Yang, Soo-Ryum Yang, Jianxin Shi, Maria Teresa Landi. A transcriptomic signature predicts mortality risk and immune checkpoint inhibitor response beyond molecular and morphological features in lung adenocarcinoma from never smokers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_2):Abstract nr LB263.
Background The integration of biomeasures into social surveys has grown rapidly, enabling new insights into biosocial processes by combining probability-based survey data with biological indicators. However, biomeasure collection outside clinical settings introduces unique sources of error that are not fully addressed by traditional survey methodology frameworks. This paper adapts the Total Survey Error (TSE) framework to account for the distinctive challenges of biomeasure data collection in surveys. Methods We review the stages of biomeasure data collection in social surveys and systematically map them onto the TSE framework. We extend the framework to identify new error sources, including biomeasure nonresponse, data loss during transmission, laboratory and batch effects, and interviewer-related biases. The discussion draws on evidence from large-scale biosocial surveys and methodological studies, highlighting trade-offs between different sources of error and the implications for study design, data processing, and analysis. Results Our extended framework demonstrates that biomeasure collection adds multiple new opportunities for error across both selection and measurement processes. On the selection side, additional nonresponse mechanisms—such as refusal to consent to biological collection or ineligibility due to physical characteristics—can create systematic biases. On the measurement side, issues such as interviewer variability, shipment and storage conditions, laboratory practices, and batch effects can significantly influence data quality. Trade-offs emerge between maximizing measurement precision and minimizing participation bias, between standardization in clinical contexts and ecological validity in household settings, and between cost constraints and optimal data integrity. Conclusions The adapted TSE framework provides a structured approach to evaluating and minimizing errors in biomeasure data collection in social surveys. Recognizing and addressing new sources of error is essential for producing valid and generalizable biosocial research. Future work should focus on developing standardized protocols, improving interviewer and nurse training, expanding feasible self-collection methods, and quantifying the relative magnitude of different error sources. By systematically incorporating biomeasures into probability-based surveys while accounting for these challenges, researchers can strengthen the reliability and policy relevance of biosocial findings.
