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Record W2978373462 · doi:10.29173/aar53

Too Much of a Good Thing: High T Cell Count Can Kill Newborns

2019· article· en· W2978373462 on OpenAlexaffvenue
A Capella Bustos, Lai Xu, Garett Dunsmore, Shokrollah Elahi

Bibliographic record

VenueAlberta Academic Review · 2019
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Response and Inflammation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsImmune systemAcquired immune systemImmunologyImmunityCD8SpleenBiologyFlow cytometryCytotoxic T cellListeria monocytogenesBacteria

Abstract

fetched live from OpenAlex

Neonates have a weakened immune system that could be due to low exposure to pathogens resulting in low adaptive immunity and/or purposeful immune suppression to protect the weak neonate from a robust immune response. The purpose of this project is to find preliminary data to further investigate why the immune system of neonates are weaker, and to possibly improve neonatal immunity while protecting against a powerful immune response in the future. Using processed mice spleen cells that were stained for CD4 and CD8 to be subjected to flow cytometry, an increase in the percent of helper CD4 and killer CD8 T cells were observed as the mice aged. This indicates that neonates do have a weaker immune system. Between healthy mice and mice infected with either Bordetella pertussis or Listeria, a decrease in the percent of CD4 and CD8 T cells were found, which could be because not enough time had passed for an adaptive immune response.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.806
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0020.003

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 head0.008
GPT teacher head0.239
Teacher spread0.231 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations0
Published2019
Admission routes2
Has abstractyes

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