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Record W4388832280 · doi:10.3389/frtra.2023.1261023

Comprehensive immune profiling of SARS-CoV-2 infected kidney transplant patients

2023· article· en· W4388832280 on OpenAlexafffund
Franz Fenninger, Karen Sherwood, Vivian Wu, Paaksum Wong, Mari L. DeMarco, Meng Wang, Vincent Benedicto, Krishna A. Dwarka, Oliver P. Günther, Logan Tate, Eric M. Yoshida, Paul Keown, Matthew Kadatz, James H. Lan

Bibliographic record

VenueFrontiers in Transplantation · 2023
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsVancouver Coastal HealthProvidence Health CareUniversity of British Columbia
FundersBritish Columbia Centre for Disease ControlVancouver Coastal Health Research InstituteMichael Smith Health Research BC
KeywordsImmune systemImmunologyImmunophenotypingKidney transplantationMedicineCD8SerologyAntibodyTransplantationBiologyFlow cytometryInternal medicine

Abstract

fetched live from OpenAlex

Introduction: The immune responses of kidney transplant recipients against SARS-CoV-2 remains under studied. Methods: In this prospective pilot study, we performed comprehensive immune profiling using cellular, proteomic, and serologic assays on a cohort of 9 kidney transplant recipients and 12 non-transplant individuals diagnosed with COVID-19. Results: Our data show that in addition to having reduced SARS-CoV-2 specific antibody levels, kidney transplant recipients exhibited significant cellular differences including a decrease in naïve-but increase in effector T cells, a high number of CD28+ CD4 effector memory T cells, and increased CD8 T memory stem cells compared with non-transplant patients. Furthermore, transplant patients had lower concentrations of serum cytokine MIP-1β as well as a less diverse T cell receptor repertoire. Conclusion: Overall, our results show that compared to non-transplant patients, kidney transplant recipients with SARS-CoV-2 infection exhibit an immunophenotype that is reminiscent of the immune signature observed in patients with severe COVID-19.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.598

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.044
GPT teacher head0.371
Teacher spread0.327 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2023
Admission routes2
Has abstractyes

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