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Record W3117534446 · doi:10.1101/2020.12.17.20248401

Longitudinal analysis of COVID-19 patients shows age-associated T cell changes independent of ongoing ill-health

2020· preprint· en· W3117534446 on OpenAlexfundno aff
Liam Townsend, Adam H. Dyer, Aifric Naughton, Rachel Kiersey, Dean Holden, Mary Gardiner, Joanne Dowds, Kate O’Brien, Ciarán Bannan, Parthiban Nadarajan, Jean Dunne, Ignacio Martín‐Loeches, Padraic G. Fallon, Colm Bergin, Cliona O’Farrelly, Clíona Ní Cheallaigh, Nollaig M. Bourke, Niall Conlon

Bibliographic record

VenuemedRxiv · 2020
Typepreprint
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsnot available
FundersScience Foundation IrelandHealth Research BoardHealth Service ExecutiveWellcome TrustCanadian Institute for Theoretical Astrophysics
KeywordsConvalescenceImmunophenotypingCD8Coronavirus disease 2019 (COVID-19)MedicineImmunologyT cellInternal medicineImmune systemFlow cytometryDisease

Abstract

fetched live from OpenAlex

Abstract The trajectory of immunological and inflammatory changes following acute COVID-19 infection are unclear. We investigate immunological changes in convalescent COVID-19 and interrogate their potential relationships with persistent symptoms, termed long COVID . We performed paired immunophenotyping at initial SARS-CoV-2 infection and convalescence (n=40, median 68 days) and validated findings in 71 further patients at median 101 days convalescence. Results were compared to 40 pre-pandemic controls. Fatigue and exercise tolerance were assessed and investigated their relationship with convalescent results. We demonstrate persistent expansion of intermediate monocytes, effector CD8+, activated CD4+ and CD8+ T cells, and reduced naïve CD4+ and CD8+ T cells at 68 days, with activated CD8+ T cells remaining increased at 101 days. Patients >60 years also demonstrate reduced naïve CD4+ and CD8+ T cells and expanded activated CD4+ T cells at 101 days. Ill-health, fatigue, and reduced exercise tolerance were common but were not associated with immunological changes. Graphical Abstract

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.045
GPT teacher head0.334
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2020
Admission routes1
Has abstractyes

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