Measurement of gene expression following cryogenic mu-CT scanning of human iliac crest biopsies.
Bibliographic record
Abstract
An important consideration in interpreting indices of gene expression in human bone is relating mRNA levels to functional endpoints such as bone architecture. In the present study, a method was developed for quantitative measurement of gene expression and bone morphology in the same specimen. Three-dimensional images of iliac crest bone biopsies from healthy premenopausal women were obtained using a novel high resolution cryogenic mu-CT scanner. RNA was isolated from the biopsies and mRNA levels were measured for genes related to bone metabolism. The gene expression profile and variability of expression within iliac crest biopsies of women was similar to human osteoblastic cell lines and rat long bones. mRNA for alkaline phosphatase, bone matrix proteins, and selected cytokines and cytokine receptors were consistently detected in biopsies. As previously shown in rat bone, there was a tight correlation between mRNA levels for type 1 collagen and osteonectin, a weaker correlation between type 1 collagen and osteocalcin and no correlation between bone matrix proteins and alkaline phosphatase. The relative abundance of the mRNA for the three most prevalent transforming growth factor-beta (TGF-beta) isoforms in bone (TGF-beta(1)>> TGF-beta(3)> TGF-beta(2)) was the same as the known abundance of the corresponding TGF-beta peptides in bone matrix. The results demonstrate the feasibility of analyzing the three-dimensional architecture of a bone biopsy using cryogenic mu-CT imaging and then measuring expression of genes related to bone cell function within the same specimen following RNA extraction and analysis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".