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Record W4404552333 · doi:10.21769/bioprotoc.5155

In Vitro Assay to Examine Osteoclast Resorptive Activity Under Estrogen Withdrawal

2024· article· en· W4404552333 on OpenAlexafffund
Cara Fiorino, Safia Omer, Nisha Gandhi, Rene E. Harrison

Bibliographic record

VenueBIO-PROTOCOL · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBone Metabolism and Diseases
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsOsteoclastIn vitroEstrogenChemistryMedicineInternal medicineBiochemistry

Abstract

fetched live from OpenAlex

The bone is a highly dynamic organ that undergoes continuous remodeling through an intricate balance of bone formation and degradation. Hyperactivation of the bone-degrading cells, the osteoclasts (OCs), occurs in disease conditions and hormonal changes in females, resulting in osteoporosis, a disease characterized by altered microarchitecture of the bone tissue, and increased bone fragility. Thus, building robust assays to quantify OC resorptive activity to examine the molecular mechanisms underlying bone degradation is critical. Here, we establish an in vitro model to investigate the effect of estrogen withdrawal on OCs derived from the mouse macrophage RAW 264.7 cell line in a bone biomimetic microenvironment. This simple and robust model can also be adapted to examine the effect of drugs and genetic factors influencing OC resorptive activity in addition to being compatible with fluorescent imaging. Key features • A robust in vitro protocol that allows molecular and functional studies of mature osteoclasts in response to estrogen and its withdrawal. • Generation of inorganic bone-mimetic substrates for culturing and examining osteoclast resorptive behavior. • This quantitative image-based approach is compatible with brightfield and fluorescence microscopy to assess osteoclast resorptive activity.

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: Protocol · Consensus signal: Protocol
Teacher disagreement score0.289
Threshold uncertainty score0.784

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 head0.013
GPT teacher head0.308
Teacher spread0.296 · 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
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

Citations1
Published2024
Admission routes2
Has abstractyes

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