ArticlemedRxiv : the preprint server for health sciences2024
Article in medRxiv : the preprint server for health sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed, 4 citations in OpenAlex.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
15 authors at 2 institutions in 2 countries.
Funding
Abstract
Background: Early host immunity to acute respiratory infections (ARIs) is heterogenous, dynamic, and critical to an individual's infection outcome. Due to limitations in sampling frequency/timepoints, kinetics of early immune dynamics in natural human infections remain poorly understood. In this nationwide prospective cohort study, we leveraged a Tasso-SST based self-blood collection and stabilization tool ( Methods: We enrolled non-symptomatic adults with recent exposure to ARIs who subsequently tested negative (exposed-uninfected) or positive for respiratory pathogens. Participants self-collected blood and nasal swabs daily for seven consecutive days followed by weekly blood collection for up to seven additional weeks. Symptom burden was assessed during each collection. Nasal swabs were tested for SARS-CoV-2 and common respiratory pathogens. 92 longitudinal blood samples spanning the presymptomatic to convalescence phase of eight SARS-CoV-2-infected participants and 40 interval-matched samples from four exposed-uninfected participants were subjected to high-frequency longitudinal profiling of 785 immune genes. Generalized additive mixed models (GAMM) were used to identify temporally dynamic genes from the longitudinal samples and linear mixed models (LMM) were used to identify baseline differences between exposed-infected (n = 8), exposed-uninfected (n = 4), and uninfected (n = 13) participant groups. Findings: Between June 2021 - April 2022, 68 participants across 26 U.S. states completed the study and self-collected a total of 691 and 466 longitudinal blood and nasal swab samples along with 688 symptom surveys. SARS-CoV-2 was detected in 17 out of 22 individuals with study-confirmed respiratory infection, of which five were still presymptomatic or pre-shedding, enabling us to profile detailed expression kinetics of the earliest blood transcriptional response to contemporaneous variants of concern. 51% of the genes assessed were found to be temporally dynamic during COVID-19 infection. During the pre-shedding phase, a robust but transient response consisting of genes involved in cell migration, stress response, and T cell activation were observed. This is followed by a rapid induction of many interferon-stimulated genes (ISGs), concurrent to onset of viral shedding and increase in nasal viral load and symptom burden. Finally, elevated baseline expression of antimicrobial peptides were observed in exposed-uninfected individuals. Interpretation: We demonstrated that unsupervised self-collection and stabilization of capillary blood can be applied to natural infection studies to characterize detailed early host immune kinetics at a temporal resolution comparable to that of human challenge studies. The remote (decentralized) study framework enables conduct of large-scale population-wide longitudinal mechanistic studies. Funding: This study was funded by R35GM128648 to ABT for in-lab developments of
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.