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Record W7132889884

Mechanism of Deregulated Osteoclast Activity under Estrogen-withdrawal

2023· dissertation· W7132889884 on OpenAlexaff
Nisha Gandhi

Bibliographic record

VenueTSpace · 2023
Typedissertation
Language
FieldBiochemistry, Genetics and Molecular Biology
TopicBone Metabolism and Diseases
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPodosomeOsteoclastBone resorptionResorptionPopulationBone remodelingRHOAMultinucleateEstrogen
DOInot available

Abstract

fetched live from OpenAlex

Estrogen (17β - estradiol) deficiency post-menopause alters bone homeostasis whereby bone resorption by osteoclasts exceeds bone formation by osteoblasts, leading to osteoporosis in females. We established an in vitro model of estrogen-withdrawal (E2-WD) with osteoclasts derived from the mouse macrophage RAW 264.7 cell line and utilized it to investigate the mechanism behind deregulated osteoclast activity post-menopause. We found that a greater population of osteoclasts that underwent E2-WD contained a podosome belt, which is necessary for osteoclasts to adhere and resorb bone, than the osteoclasts exposed to estrogen (E2) continuously. Our results show that, compared to osteoclasts that received E2 continuously, those that underwent E2-WD had shorter podosome lifespans, reduced RhoA activation, and a faster rate of microtubule growth. Thus, altered podosome and microtubule dynamics induced by the withdrawal of estrogen supports podosome belt assembly/stability in osteoclasts, which may explain their enhanced bone resorption 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 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)0.000
metaresearch head score (Gemma)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.302
Teacher spread0.289 · 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
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

Citations0
Published2023
Admission routes1
Has abstractyes

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