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Record W2591928899 · doi:10.1007/978-1-4939-6771-1_9

Fibro/Adipogenic Progenitors (FAPs): Isolation by FACS and Culture

2017· article· en· W2591928899 on OpenAlexafffund
Marcela Low, Christine Eisner, Fábio Rossi

Bibliographic record

VenueMethods in molecular biology · 2017
Typearticle
Languageen
FieldMedicine
TopicMesenchymal stem cell research
Canadian institutionsUniversity of British Columbia HospitalUniversity of British Columbia
FundersCanadian Institutes of Health ResearchJohns Hopkins University
KeywordsCell sortingMesenchymal stem cellProgenitor cellContext (archaeology)Isolation (microbiology)AdipogenesisCell biologyBiologyStromal cellCellComputational biologyStem cellBioinformaticsCancer researchGenetics

Abstract

fetched live from OpenAlex

Fibro/adipogenic progenitors (FAPs Fibro/adipogenic progenitors (FAPs) ) are tissue-resident mesenchymal stromal cells (MSCs). Current literature supports a role for these cells in the homeostasis and repair of multiple tissues suggesting that FAPs Fibro/adipogenic progenitors (FAPs) may have extensive therapeutic potential in the treatment of numerous diseases. In this context, it is crucial to establish efficient and reproducible procedures to purify FAP Fibro/adipogenic progenitors (FAPs) populations from various tissues. Here, we describe a protocol for the isolation and cell cultureCell culture of FAPs Fibro/adipogenic progenitors (FAPs) from murine skeletal muscle using fluorescenceFluorescence -activated cell sorting (FACS), which is particularly useful for experiments where high cell purity is an essential requirement. Identification, isolation, and cell cultureCell culture of FAPs Fibro/adipogenic progenitors (FAPs) represent powerful tools that will help us to understand the role of these cells in different conditions and facilitate the development of safe and effective new treatments for diseases.

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)0.001
metaresearch head score (Gemma)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.333
Threshold uncertainty score0.493

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.039
GPT teacher head0.473
Teacher spread0.434 · 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
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

Citations33
Published2017
Admission routes2
Has abstractyes

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