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Record W2978443606 · doi:10.3791/60387

Generation of Porcine Testicular Organoids with Testis Specific Architecture using Microwell Culture

2019· article· en· W2978443606 on OpenAlexaff
Sadman Sakib, Yang Yu, Anna Voigt, Mark Ungrin, Ina Dobrinski

Bibliographic record

VenueJournal of Visualized Experiments · 2019
Typearticle
Languageen
FieldMedicine
TopicSperm and Testicular Function
Canadian institutionsUniversity of AlbertaUniversity of Calgary
Fundersnot available
KeywordsOrganoidBiologyCell biologyDevelopmental biologyComputational biology

Abstract

fetched live from OpenAlex

Organoids are three dimensional structures composed of multiple cell types that are capable of recapitulating tissue architecture and functions of organs in vivo. Formation of organoids has opened up different avenues of basic and translational research. In recent years, testicular organoids have garnered interest in the field of male reproductive biology. Testicular organoids allow for the study of cell-cell interactions, tissue development, and the germ cell niche microenvironment and facilitate high throughput drug and toxicity screening. A method is needed to reliably and reproducibly generate testicular organoids with testis specific tissue architecture. The microwell culture system contains a dense array of pyramid-shaped microwells. Testicular cells derived from pre-pubertal testes are centrifuged into these microwells and cultured to generate testicular organoids with testis-specific tissue architecture and cell associations. Thousands of homogeneous organoids can be generated via this process. The protocol reported here will be of broad interest to researchers studying male reproduction.

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 score0.033
Threshold uncertainty score0.503

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)0.0000.000

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 head0.041
GPT teacher head0.367
Teacher spread0.326 · 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

Citations25
Published2019
Admission routes1
Has abstractyes

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