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Record W3185845224 · doi:10.3390/immuno1030015

Rescuing Immunosenescence via Non-Specific Vaccination

2021· article· en· W3185845224 on OpenAlexaff
Alexander I. Mosa

Bibliographic record

VenueImmuno · 2021
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune responses and vaccinations
Canadian institutionsToronto General HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsImmunosenescenceVaccinationImmunologyImmune systemPandemicDiseasePopulationBiologyMedicineCoronavirus disease 2019 (COVID-19)Infectious disease (medical specialty)Environmental health

Abstract

fetched live from OpenAlex

Discrepancies in lifespan and healthy-life span are predisposing populations to an increasing burden of age-related disease. Accumulating evidence implicates aging of the immune system, termed immunosenescence, in the pathogenesis of multiple age-related diseases. Moreover, immune dysregulation in the elderly increases vulnerability to infection and dampens pathogen-specific immune responses following vaccination. The health challenges manifesting from these age related deficits have been dramatically exemplified by the current SARS-CoV-2 pandemic. Approaches to either attenuate or reverse functional markers of immunosenescence are therefore urgently needed. Recent evidence suggests systemic immunomodulation via non-specific vaccination with live-attenuated vaccines may be a promising avenue to at least reduce aged population vulnerability to viral infection. This short review describes current understanding of immunosenescence, the historical and mechanistic basis of vaccine-mediated immunomodulation, and the outstanding questions and challenges required for broad adoption.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.259
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.002

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 head0.011
GPT teacher head0.250
Teacher spread0.239 · 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; both teacher heads agree on what is shown here.

Study designBench or experimental
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
Published2021
Admission routes1
Has abstractyes

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