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Record W2121038174 · doi:10.1093/jpids/pit017

Implications of Age-Dependent Immune Responses to Enterovirus 71 Infection for Disease Pathogenesis and Vaccine Design

2013· review· en· W2121038174 on OpenAlexaff
Soren Gantt, Yao Li, Tobias R. Kollmann, Corey Casper, Jie Zhang, Steve Self

Bibliographic record

VenueJournal of the Pediatric Infectious Diseases Society · 2013
Typereview
Languageen
FieldMedicine
TopicViral Infections and Immunology Research
Canadian institutionsUniversity of British Columbia
FundersCenters for Disease Control and PreventionChinese Center for Disease Control and Prevention
KeywordsEnterovirus 71MedicineImmune systemImmunologyDiseaseVaccinationImmunityAcquired immune systemPandemicEnterovirusVirologyVirusInfectious disease (medical specialty)Coronavirus disease 2019 (COVID-19)

Abstract

fetched live from OpenAlex

Epidemics of enterovirus serotype 71 (EV71) infection in Asia appear to be increasing in size and severity, and there is increasing concern for pandemic spread. Efforts are underway to develop an effective EV71 vaccine. However, the immunologic correlates of protection against EV71 infection are not fully understood, and studies suggest that severe complications may result from a combination of pathological immune responses and direct viral effects. Severe disease and death typically occur only in young children, which is likely due in part to a lack of EV71-specific adaptive immunity but possibly also due to age-dependent hyperactive innate immune responses. Infants are the primary targets of EV71 vaccination strategies. Therefore, studies are needed to understand the interplay between age, immunopathology, and severity of EV71 infection to distinguish protective from harmful immune responses and to guide the development of effective EV71 vaccines. This review summarizes our current understanding and outlines the next steps forward.

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)0.001
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 score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
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 head0.057
GPT teacher head0.364
Teacher spread0.307 · 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

Citations16
Published2013
Admission routes1
Has abstractyes

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Same venueJournal of the Pediatric Infectious Diseases SocietySame topicViral Infections and Immunology ResearchFrench-language works237,207