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Record W2054775081 · doi:10.1017/s0031182013002308

Protein deficiency alters impact of intestinal nematode infection on intestinal, visceral and lymphoid organ histopathology in lactating mice

2014· article· en· W2054775081 on OpenAlexaff
Lisa Starr, Maurice R. Odiere, Kristine G. Koski, Marilyn E. Scott

Bibliographic record

VenueParasitology · 2014
Typearticle
Languageen
FieldImmunology and Microbiology
TopicParasites and Host Interactions
Canadian institutionsMcGill University
Fundersnot available
KeywordsBiologySpleenLactationLymphatic systemKidneyImmune systemHistopathologyInflammationInternal medicineImmunologyEndocrinologyPregnancyPathology

Abstract

fetched live from OpenAlex

Protein deficiency impairs local and systemic immune responses to Heligmosomoides bakeri infection but little is known about their individual and interactive impacts on tissue architecture of maternal lymphoid (thymus, spleen) and visceral (small intestine, kidney, liver, pancreas) organs during the demanding period of lactation. Using a 2 × 2 factorial design, pregnant CD1 mice were fed a 24% protein sufficient (PS) or a 6% protein deficient (PD) isoenergetic diet beginning on day 14 of pregnancy and were infected with 100 H. bakeri larvae four times or exposed to four sham infections. On day 20 of lactation, maternal organs were examined histologically and serum analytes were assayed as indicators of organ function. The absence of villus atrophy in response to infection was associated with increased crypt depth and infiltration of mast cells and eosinophils but only in lactating dams fed adequate protein. Infection-induced lobular liver inflammation was reduced in PD dams, however, abnormalities in the kidney caused by protein deficiency were absent in infected dams. Bilirubin and creatinine were highest in PD infected mice. Infection-induced splenomegaly was not due to an increase in the lymphoid compartment of the spleen. During lactation, infection and protein deficiency have interactive effects on extra-intestinal pathologies.

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 score0.409
Threshold uncertainty score0.776

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 integrity0.0000.000
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 head0.012
GPT teacher head0.321
Teacher spread0.308 · 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

Citations5
Published2014
Admission routes1
Has abstractyes

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