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Record W1989769320 · doi:10.1017/s0031182007002314

Re-feeding rapidly restores protection against<i>Heligmosomoides bakeri</i>(Nematoda) in protein-deficient mice

2007· article· en· W1989769320 on OpenAlexaff
Thai Hien Tu, Kristine G. Koski, Linda Wykes, Marilyn E. Scott

Bibliographic record

VenueParasitology · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsMcGill University
Fundersnot available
KeywordsBiologyAnorexiaAlkaline phosphataseParasite hostingBody weightNematodeRatónLeptinInternal medicineEndocrinologyImmunologyPhysiologyObesityBiochemistryEcologyEnzyme

Abstract

fetched live from OpenAlex

This study determined whether the timing of re-feeding of protein-deficient mice restored functional protection against the gastrointestinal nematode, Heligmosomoides bakeri. Balb/c mice were fed a 3% protein-deficient (PD) diet and then transferred to 24% protein-sufficient (PS) diet either on the day of primary infection, 10 days after the primary infection, on the day of challenge infection, or 7 days after the challenge infection. Control mice were fed either the PD or PS diet. Onset of challenge, but not primary, infection caused short-term body weight loss, anorexia and reduced feed efficiency. Weight gain was delayed in mice when re-feeding commenced on the day of challenge infection; alkaline phosphatase (ALP) was also elevated in these mice on day 28 post-challenge. In contrast, other re-feeding groups attained similar body weights to PS mice within 4 days and had similar ALP at day 28. Serum leptin was higher in PD than PS mice and positively associated with food intake. As expected, worm survival was prolonged in mice fed the PD diet. However, egg production and worm burdens were similar in all re-feeding groups to the PS mice, indicating that protein re-feeding during either the primary or challenge infection rapidly restored normal parasite clearance.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.876
Threshold uncertainty score0.331

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.027
GPT teacher head0.272
Teacher spread0.246 · 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 designBench or experimental
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

Citations22
Published2007
Admission routes1
Has abstractyes

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