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Record W3086804983 · doi:10.1016/j.vaccine.2020.08.079

Impact of the intestinal environment on the immune responses to vaccination

2020· review· en· W3086804983 on OpenAlexfundno aff
Koji Hosomi, Jun Kunisawa

Bibliographic record

VenueVaccine · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsnot available
FundersJapan Society for the Promotion of ScienceJapan Agency for Medical Research and DevelopmentUniversity of TokyoSwine Innovation PorcTerumo Foundation for Life Sciences and ArtsASCRS Research Foundation
KeywordsVaccinationImmune systemImmunologyGut floraImmunityBiologyVaccine efficacyHost (biology)MedicineEcology

Abstract

fetched live from OpenAlex

Vaccination has contributed greatly to the control of infectious diseases; however, regional and individual differences are occasionally observed in the efficacy of vaccination. As one explanation for these differences, much attention has focused on the intestinal environment constructed by the interaction of diet and the gut microbiota. The intestinal environment has several physiological effects on the host immune system, both locally and systemically, and consequently influences the efficacy of vaccination. In this review, we discuss the impact of the gut microbiota and dietary nutrients on systemic and oral vaccination as well as their applications in various strategies for immunoregulation, including use as vaccine adjuvants. This information could contribute to establishing methods of personalized vaccination that would optimize host immunity by changing the gut environment to maximize vaccine effects.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.028
GPT teacher head0.331
Teacher spread0.303 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations17
Published2020
Admission routes1
Has abstractyes

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