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Record W4296311793 · doi:10.1016/j.mex.2022.101843

FACS-Free isolation and purification protocol of mouse prostate epithelial cells for organoid primary culture

2022· article· en· W4296311793 on OpenAlexaff
Lilianne Frégeau-Proulx, Aurélie Lacouture, Cindy Weidmann, Cynthia Jobin, Étienne Audet‐Walsh

Bibliographic record

VenueMethodsX · 2022
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsOrganoidProstate cancerProstateEx vivoFlow cytometryAndrogen receptorAndrogenIn vivoBiologyCancer researchCell biologyMedicineCancerImmunologyInternal medicineEndocrinologyHormoneBiotechnology

Abstract

fetched live from OpenAlex

The prostate is a gland that contributes to men's fertility. It is highly responsive to androgens and is often the site of carcinogenesis, as prostate cancer is the most frequent cancer in men in over a hundred countries. To study the normal prostate, few in vitro models exist, and most of them do not express the androgen receptor (AR). To overcome this issue, prostate epithelial cells can be grown in primary culture ex vivo in 2- and 3-dimensional culture (organoids). However, methods to purify these cells often require flow cytometry, thus necessitating specialized instruments and expertise. Herein, we present a detailed protocol for the harvest, purification, and primary culture of mouse prostate epithelial cells to grow prostate organoids ex vivo. This protocol does not require flow cytometry approaches, facilitating its implementation in most research laboratories, and organoids grown with this protocol are highly responsive to androgens. In summary, we present a new simple method that can be used to grow prostate organoids that recapitulate the androgen response of this gland in vivo.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.023
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0230.019

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 head0.036
GPT teacher head0.367
Teacher spread0.331 · 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
GenreMethods

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

Citations1
Published2022
Admission routes1
Has abstractyes

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