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Record W7133047958

The interleukin-10 knockout mouse: a model for studying bone metabolism during intestinal inflammation and the effects of flaxseed oil as a nutritional intervention

2004· dissertation· W7133047958 on OpenAlexfundno aff
Stacey Lynn Cohen

Bibliographic record

VenueTSpace · 2004
Typedissertation
Language
FieldNursing
TopicFatty Acid Research and Health
Canadian institutionsnot available
FundersDairy Farmers of Canada
KeywordsInflammationProinflammatory cytokineOsteoporosisBone remodelingKnockout mouseBone densityBone mineralInflammatory bowel disease
DOInot available

Abstract

fetched live from OpenAlex

There is a high prevalence of osteoporosis among patients with inflammatory bowel disease (IBD). Interleukin-10-/- (IL-10) knockout (KO) mice spontaneously develop IBD. The objective of study 1 was to determine if IL-10 KO mice develop abnormalities in bone metabolism during intestinal inflammation and subsequently, in study 2, to determine if a 10% flaxseed oil would attenuate these bone abnormalities. In study 1, IL-10 KO mice had greater intestinal inflammation and elevated serum proinflammatory cytokines compared to wild type (WT) mice. IL-10 KO mice had bone abnormalities including lower bone mineral content (BMC) and density (BMD). In study 2, the FO diet did not attenuate the intestinal inflammation or serum cytokines. FO diet did not improve bone metabolism. In conclusion, the IL-10 KO mouse is a good model for studying inflammation-associated bone abnormalities and potential therapeutic interventions but flaxseed oil at the level used is not an effective dietary strategy in IL-10 KO mice.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.018
GPT teacher head0.347
Teacher spread0.329 · 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 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

Citations0
Published2004
Admission routes1
Has abstractyes

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