ArticleThe Journal of infectious diseases2024
Diagnosing Viral Infections Through T-Cell Receptor Sequencing of Activated CD8+ T Cells.
Article in The Journal of infectious diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 10 citations in OpenAlex.
- T-cell repertoire response in individuals with post-acute sequelae of COVID-19.bioRxiv : the preprint server for biology · 2026Article
- Medical software for precision diagnostics of infection with immunoprofiling and artificial intelligence.Journal of translational medicine · 2026Review
- Peptide-driven identification of TCRs reveals dynamics and phenotypes of CD4 T cells in tuberculosis.Journal of immunology (Baltimore, Md. : 1950) · 2026Article
- Review: application and opportunities for machine learning and artificial intelligence in preclinical immunogenicity risk assessment.Frontiers in immunology · 2026Review
- TCR β CDR3 repertoire signatures in paired PBMC and BALF from children with mycoplasma pneumoniae pneumonia.Frontiers in immunology · 2026Article
- Machine learning in AIRR diagnostics: Advances and applications.Immunoinformatics (Amsterdam, Netherlands) · 2025Article
- Phage display enables machine learning discovery of cancer antigen-specific TCRs.Science advances · 2025Article
- Celluloepidemiology-A paradigm for quantifying infectious disease dynamics on a population level.Science advances · 2025Article
- Simulation of adaptive immune receptors and repertoires with complex immune information to guide the development and benchmarking of AIRR machine learning.Nucleic acids research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
20 authors at 3 institutions in 2 countries.
Funding
Abstract
T-cell-based diagnostic tools identify pathogen exposure but lack differentiation between recent and historical exposures in acute infectious diseases. Here, T-cell receptor (TCR) RNA sequencing was performed on HLA-DR+/CD38+CD8+ T-cell subsets of hospitalized coronavirus disease 2019 (COVID-19) patients (n = 30) and healthy controls (n = 30; 10 of whom had previously been exposed to severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2]). CDR3α and CDR3β TCR regions were clustered separately before epitope specificity annotation using a database of SARS-CoV-2-associated CDR3α and CDR3β sequences corresponding to >1000 SARS-CoV-2 epitopes. The depth of the SARS-CoV-2-associated CDR3α/β sequences differentiated COVID-19 patients from the healthy controls with a receiver operating characteristic area under the curve of 0.84 ± 0.10. Hence, annotating TCR sequences of activated CD8+ T cells can be used to diagnose an acute viral infection and discriminate it from historical exposure. In essence, this work presents a new paradigm for applying the T-cell repertoire to accomplish TCR-based diagnostics.
Indexed as
Identifiers
What OpenQuestion holds
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.