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Record W4211134984 · doi:10.1016/j.xpro.2022.101164

Protocol to generate mouse oviduct epithelial organoids for viral transduction and whole-mount 3D imaging

2022· article· en· W4211134984 on OpenAlexafffund
Matthew J. Ford, Keerthana Harwalkar, Yojiro Yamanaka

Bibliographic record

VenueSTAR Protocols · 2022
Typearticle
Languageen
FieldMedicine
TopicReproductive Biology and Fertility
Canadian institutionsMcGill University
FundersFonds de Recherche du Québec - SantéCanadian Cancer SocietyCanadian Cardiovascular SocietyCanadian Cancer Society Research InstituteCancer Research Society
KeywordsOrganoidOviductCell biologyBiologySerous fluidCentrosomeEpitheliumCellTransduction (biophysics)Malignant transformationCancer researchCell cycle

Abstract

fetched live from OpenAlex

Epithelial cells lining the oviduct/fallopian tube are essential in reproduction and have been identified as the cell-of-origin in high-grade serous ovarian carcinoma (HGSOC). This protocol describes the generation of organoids from mouse oviduct epithelial cells, providing a powerful in vitro tool to study epithelial homeostasis and malignant transformation. We also outline a protocol for whole-mount immunofluorescence and 3D confocal imaging. In addition, we describe approaches of viral transduction to investigate gene function in organoid development and epithelial cell behavior. For complete details on the use and execution of this profile, please refer to Ford et al. (2021).

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)0.002
metaresearch head score (Gemma)0.001
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: Protocol · Consensus signal: none
Teacher disagreement score0.040
Threshold uncertainty score0.135

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0400.027

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 head0.030
GPT teacher head0.346
Teacher spread0.316 · 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
GenreProtocol

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

Citations9
Published2022
Admission routes2
Has abstractyes

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