SynthesisCell reports. Medicine2022
Resolving SARS-CoV-2 CD4
Synthesis in Cell reports. Medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systemic review of T-cell epitopes defined from the proteome of SARS-CoV-2.Virus research · 2023Pooled it
- Entering the Age of Specificity in T Cell Immunity.Immunological reviews · 2026Review
- Systematic analysis of CDR contacts and pairing constraints between T cell receptor αβ chains.Bioinformatics (Oxford, England) · 2026Article
- The T Cell Receptor: Molecular Sensor, Therapeutic Mediator and Probabilistic Driver of Adaptive Immunity.Immunological reviews · 2026Review
- Article
- Article
- T-cell repertoire response in individuals with post-acute sequelae of COVID-19.bioRxiv : the preprint server for biology · 2026Article
- De novo identification of the specificities of recurrently identified human T cell receptors.Science advances · 2026Article
- T cell epitope mapping reveals immunodominance of evolutionarily conserved regions within SARS-CoV-2 proteome.iScience · 2025Article
- Article
- Phage display enables machine learning discovery of cancer antigen-specific TCRs.Science advances · 2025Article
- Distinctive evolution of alveolar T cell responses is associated with clinical outcomes in unvaccinated patients with SARS-CoV-2 pneumonia.Nature immunology · 2024Article
- Computational detection of antigen-specific B cell receptors following immunization.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Article
- De novo identification of CD4Nature methods · 2024Article
- Deep learning predictions of TCR-epitope interactions reveal epitope-specific chains in dual alpha T cells.Nature communications · 2024Article
- DNAJB1-PRKACA fusion neoantigens elicit rare endogenous T cell responses that potentiate cell therapy for fibrolamellar carcinoma.Cell reports. Medicine · 2024Article
- A comparison of clustering models for inference of T cell receptor antigen specificity.Immunoinformatics (Amsterdam, Netherlands) · 2024Article
- Article
- Activation-based repertoire analysis for T cell clonal dynamics in hybrid COVID-19 immunity.Nature immunology · 2024Article
Corrections and comments
- Update of
Authors and funding
8 authors.
Funding
Abstract
The current strategy to detect immunodominant T cell responses focuses on the antigen, employing large peptide pools to screen for functional cell activation. However, these approaches are labor and sample intensive and scale poorly with increasing size of the pathogen peptidome. T cell receptors (TCRs) recognizing the same epitope frequently have highly similar sequences, and thus, the presence of large sequence similarity clusters in the TCR repertoire likely identify the most public and immunodominant responses. Here, we perform a meta-analysis of large, publicly available single-cell and bulk TCR datasets from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected individuals to identify public CD4
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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.