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Record W2604839281 · doi:10.3389/fimmu.2017.00281

Antifungal Immunological Defenses in Newborns

2017· review· en· W2604839281 on OpenAlexafffund
Christina Michalski, Bernard Kan, Pascal M. Lavoie

Bibliographic record

VenueFrontiers in Immunology · 2017
Typereview
Languageen
FieldMedicine
TopicAntifungal resistance and susceptibility
Canadian institutionsUniversity of British ColumbiaBC Children's Hospital
FundersCanadian Institutes of Health ResearchHospital for Sick ChildrenMarch of Dimes Foundation
KeywordsContext (archaeology)Immune systemBiologyAntifungalImmunologyVulnerability (computing)MicrobiologyComputer science

Abstract

fetched live from OpenAlex

Newborns are prone to fungal infections, largely due to Candida species. The immunological basis for this vulnerability is not yet fully understood. However, useful insights can be gained from the knowledge of the maturation of immune pathways during ontogeny, particularly when placed in context with how rare genetic mutations in humans predispose to fungal diseases. In this article, we review the most current data on immune functions in human newborns, highlighting pathways most relevant to the response to Candida. While discussing these data, we propose a framework of why deficiencies in these pathways make newborns particularly vulnerable to this opportunistic pathogen.

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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.083
GPT teacher head0.373
Teacher spread0.290 · 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

Citations34
Published2017
Admission routes2
Has abstractyes

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