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A review on the evolution of methods for intestinal in vitro organ culture and its application in veterinary science

2023· review· en· W4321452371 on OpenAlexaff
Barbara Ribeiro de Souza Cortez, Roberto Maurício Carvalho Guedes

Bibliographic record

VenueVeterinary World · 2023
Typereview
Languageen
FieldNursing
TopicClinical Nutrition and Gastroenterology
Canadian institutionsUniversity of Saskatchewan
FundersFundação de Amparo à Pesquisa do Estado de Minas GeraisUniversidade Federal de Minas GeraisConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsExplant cultureOrgan cultureComputer scienceIn vitroMedicineBiology

Abstract

fetched live from OpenAlex

organ culture (IVOC). A robust compilation of all available methods is lacking in the literature, making it difficult to choose a method that corresponds to the study's demands. In this review, readers can assess the most available methods, allowing them to evaluate which is more suitable for their purposes and requirements. A simplified view of culturing intestinal explants is presented, highlighting the approachability of IVOC. Relevant findings from diverse veterinarian studies, where explants played a major role, as well as the technique used in each, are described to illustrate its applications. Finally, the strengths and limitations of the innovative intestinal IVOC methods are discussed. This review provides a collection of methods for intestinal explant culture and their possible applications in veterinary research. In this way, it aims to broaden access to IVOC techniques and aid decision-making regarding the best suited for a study's purposes.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.005
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.200
GPT teacher head0.492
Teacher spread0.292 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations4
Published2023
Admission routes1
Has abstractyes

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