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Record W2142071589 · doi:10.1136/gut.46.3.344

Interleukin 10 gene transfer prevents experimental colitis in rats

2000· article· en· W2142071589 on OpenAlexaff
Giovanni Barbara

Bibliographic record

VenueGut · 2000
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsMcMaster UniversityHamilton Health Sciences
Fundersnot available
KeywordsColitisLeukotriene B4MyeloperoxidaseInflammatory bowel diseaseMedicineInflammationImmunologyUlcerative colitisCytokinePharmacologyPathologyDisease

Abstract

fetched live from OpenAlex

BACKGROUND: The development of colitis in interleukin 10 (IL-10) deficient mice, together with the known anti-inflammatory and immunomodulatory properties of this cytokine have prompted consideration of IL-10 as a treatment for inflammatory bowel disease (IBD). However, studies using hrIL-10 in IBD models have yielded inconsistent results. AIMS: To examine the therapeutic potential of overexpressing the IL-10 gene before and after the induction of experimental colitis in rats. METHODS: Gene transfer was achieved by intraperitoneal injection of non-replicating human type 5 adenovirus bearing the IL-10 gene, either 24 hours before or one hour after intrarectal administration of dinitrobenzene sulphonic acid in rats. Colonic damage and inflammation was assessed macroscopically and by measuring myeloperoxidase activity and leukotriene B4 concentrations. RESULTS: Gene transfer increased IL-10 protein in serum for up to six days. IL-10 gene transfer prior to colitis improved colitis macroscopically and histologically, and significantly reduced colonic myeloperoxidase activity and leukotriene B4 concentrations. In contrast, IL-10 gene transfer after the onset of colitis had no beneficial effect. CONCLUSIONS: Gene therapy using an adenovirus-IL-10 construct was successful in preventing but not in reversing experimental colitis in the rat.

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 categoriesInsufficient payload (model declined to judge)
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 score0.101
Threshold uncertainty score0.999

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)0.0020.000

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 head0.007
GPT teacher head0.240
Teacher spread0.233 · 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.

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

Citations150
Published2000
Admission routes1
Has abstractyes

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