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Record W2804547335 · doi:10.3389/fimmu.2018.01249

Neonatal Immunity, Respiratory Virus Infections, and the Development of Asthma

2018· review· en· W2804547335 on OpenAlexafffund
Katherine H. Restori, Bharat T. Srinivasa, Brian J. Ward, Elizabeth D. Fixman

Bibliographic record

VenueFrontiers in Immunology · 2018
Typereview
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsMcGill University Health Centre
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchMcGill University Health CentreMcGill UniversityU.S. Department of Defense
KeywordsImmunologyAsthmaImmune systemRespiratory systemVirusImmunityMedicineBiologyInternal medicine

Abstract

fetched live from OpenAlex

Infants are exposed to a wide range of potential pathogens in the first months of life. Although maternal antibodies acquired transplacentally protect full-term neonates from many systemic pathogens, infections at mucosal surfaces still occur with great frequency, causing significant morbidity and mortality. At least part of this elevated risk is attributable to the neonatal immune system that tends to favor T regulatory and Th2 type responses when microbes are first encountered. Early-life infection with respiratory viruses is of particular interest because such exposures can disrupt normal lung development and increase the risk of chronic respiratory conditions, such as asthma. The immunologic mechanisms that underlie neonatal host-virus interactions that contribute to the subsequent development of asthma have not yet been fully defined. The goals of this review are (1) to outline the differences between the neonatal and adult immune systems and (2) to present murine and human data that support the hypothesis that early-life interactions between the immune system and respiratory viruses can create a lung environment conducive to the development of asthma.

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
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.379
Teacher spread0.323 · 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

Citations68
Published2018
Admission routes2
Has abstractyes

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