Z<scp>inc</scp> F<scp>inger</scp> T<scp>ranscription</scp> F<scp>actors in</scp> S<scp>keletal</scp> D<scp>evelopment</scp>
Bibliographic record
Abstract
Cellular and molecular processes that regulate the development of skeletal tissues resemble those required for regeneration. Given the prevalence of degenerative skeletal disorders in an increasingly aging population, the molecular mechanisms of skeletal development must be understood in detail if novel strategies are to be developed in regenerative medicine. Research in this area over the past decade has revealed that cell differentiation is largely controlled at the level of gene transcription, which in turn is regulated by transcription factors. Transcription factors usually recognize and bind to specific DNA sequences in the promoter of target genes via characteristic DNA-binding domains. Although the gene family containing C2H2 zinc fingers as DNA-binding motifs is the largest family of transciptional regulators, with several hundred individual members in mammals, only a small but increasing number of zinc finger genes have been implicated in bone, cartilage, or tooth development. These zinc finger proteins (ZFPs) contain multiple structural motifs that require zinc to maintain their structural integrity and function. Interestingly, zinc deficiency is known to result in skeletal growth retardation and has been identified as a risk factor in the pathogenesis of osteoporosis. This review attempts to summarize our current state of knowledge regarding the role of ZFPs in the molecular regulation of skeletogenesis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".