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Record W2969530980 · doi:10.3791/59921

Murine Appendectomy Model of Chronic Colitis Associated Colorectal Cancer by Precise Localization of Caecal Patch

2019· article· en· W2969530980 on OpenAlexaff
Yaguang Li, Junhong Liu, Gaixia Liu, Zhenhong Pan, Mingxia Zhang, Yao Ma, Qingxia Wei, Hongping Xia, Ruixue Zhang, Junjun She

Bibliographic record

VenueJournal of Visualized Experiments · 2019
Typearticle
Languageen
FieldMedicine
TopicIntraperitoneal and Appendiceal Malignancies
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsAzoxymethaneColorectal cancerColitisPathogenesisInflammatory bowel diseaseMedicineMouse model of colorectal and intestinal cancerGut floraGastroenterologyAdenocarcinomaInternal medicineDiseaseImmunologyCancer

Abstract

fetched live from OpenAlex

The human appendix has been recently implicated to play important biological roles in the pathogenesis of various complex diseases, such as colorectal cancer, inflammatory bowel disease, and Parkinson's disease. To study the function of the appendix, a gut disease-associated murine appendectomy model has been established and its step-by-step protocol is described here. This report introduces a facile protocol for caecal patch removal in mice followed by the chemical induction of chronic colitis-associated colorectal cancer using a combination of dextran sulfate sodium (DSS) and azoxymethane (AOM). IgA specific cells and IgA concentration were significantly reduced upon removal of the caecal patch in male C57BL/6 mice compared to those in the sham group. Simultaneously administering 2% DSS and AOM resulted in nearly 80% mice survival in both sham and appendectomy groups without significant body weight loss. Histological results confirmed colonic inflammation and different degrees of adenocarcinoma. This model can be used for the study of the functional role of the appendix in maintaining gut microbiota homeostasis and pathogenesis of gut colitis and malignancies, as well as for the potential development of drug targeting therapies.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.732

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.374
Teacher spread0.357 · 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

Citations7
Published2019
Admission routes1
Has abstractyes

Explore more

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