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Record W2899952748 · doi:10.1093/geroni/igy023.871

IMMUNOGENOMIC RESPONSES TO VACCINATION IN AGING: INSIGHTS INTO IMMUNE RESILIENCE

2018· article· en· W2899952748 on OpenAlexaff
George A. Kuchel, Duygu Ucar, Jacques Banchereau, Janet E. McElhaney

Bibliographic record

VenueInnovation in Aging · 2018
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune responses and vaccinations
Canadian institutionsHealth Sciences North
Fundersnot available
KeywordsImmune systemImmunologyVaccinationStressorBiologyPsychological resilienceMedicinePsychologyNeuroscience

Abstract

fetched live from OpenAlex

Vaccination against influenza and pneumococcus serves to prevent or attenuate the severity of future infections involving these important pathogens. However, viewed from the perspective of physical resilience, vaccination also offers unique opportunities to evaluate the capacity of the immune system to respond to a defined stressor in the form an attenuated antigen. Moreover, growing evidence indicates that humoral and cell-mediated responses may be predictive of future risk of infection, disability or death by offering insights into relevant elements of physical resilience at the level of specific immune responses. In recent studies (AG048023, AG052608; UH2 AG056925), we have begun to explore these issues by leveraging technological advances involving cellular phenotyping and genome-wide sequencing of PBMCs obtained from older adults (Ucar et al. JEM 2017) with the ability to identify older flu vaccine responders versus non-responders using ex vivo responses involving inducible Granzyme B and interferon-gamma/IL10 (McElhaney et al. Front Imm. 2016).

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.301
Teacher spread0.284 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2018
Admission routes1
Has abstractyes

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