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Record W2069905236 · doi:10.1515/jpem.2010.096

Correlates of Mucosal Immunity and Upper Respiratory Tract Infections in Girls

2010· article· en· W2069905236 on OpenAlexaff
Lauren Corbett, Cameron Muir, Izabella A. Ludwa, Matthew Yao, Brian W. Timmons, Bareket Falk, Panagiota Klentrou

Bibliographic record

VenueJournal of Pediatric Endocrinology and Metabolism · 2010
Typearticle
Languageen
FieldMedicine
TopicExercise and Physiological Responses
Canadian institutionsMcMaster UniversityBrock University
Fundersnot available
KeywordsSalivaMedicineInternal medicineTestosterone (patch)ImmunityHormoneMucosal immunityEndocrinologyPhysiologyImmunoglobulin AImmune systemImmunologyImmunoglobulin G

Abstract

fetched live from OpenAlex

In this study we examined whether salivary hormones, physical activity and adiposity were correlated with secretory immunoglobulin A (sIgA) and frequency of upper respiratory tract infections (URTI) in 43 early-pubertal and 59 late-pubertal girls. Physical activity was measured using accelerometers and relative body fat was assessed using bioelectrical impendence. Resting saliva samples were obtained between 1500 and 1800 hr and assayed for sIgA, cortisol and testosterone. Participants completed a one-month health log to record URTI frequency. Early-pubertal girls were more physically active, had less adiposity, but lower concentrations of sIgA than late-pubertal adolescents (122.7 +/- 91.6 vs 201.9 +/- 102.9 pg/ml, respectively). The frequency of URTI was similar in the two groups. Neither sIgA nor URTI were correlated with salivary hormones, physical activity or adiposity within the early-pubertal girls. In the late-pubertal group, sIgA was negatively associated (r = -0.44; p < 0.05) with cortisol, and positively associated (r = 0.41; p < 0.05) with the testosterone to cortisol ratio. These results suggest that mucosal immunity increases with pubertal maturation, while higher cortisol is associated with lower mucosal immunity in adolescents.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.410

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.299
Teacher spread0.282 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations17
Published2010
Admission routes1
Has abstractyes

Explore more

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