Analysis of Ovariectomy and Estrogen Effects on Body Composition in Rats by X-Ray and Magnetic Resonance Imaging Techniques
Bibliographic record
Abstract
Resistance of bone to fracture--bone strength--has been shown to depend on both the amount of bone and its architectural spatial organization. In vivo magnetic resonance (MR) techniques have the capability of imaging bone tissue, including the trabecular microarchitecture and the marrow composition. We have applied in vivo and ex vivo MR methods to the tibia in an ovariectomized rat model of osteoporosis. Specifically, in vivo high-resolution three-dimensional MR imaging and localized MRS were facilitated by specialized coils and high field magnets, resulting in enhanced sensitivity of detection. As a result, in vivo and ex vivo differences in marrow composition were found between sham-ovariectomized, ovariectomized, and ovariectomized animals treated with 17-beta-estradiol. Estrogen effects were detected in vivo 7 days after surgery (3 days into treatment) as a decrease in the tibial fat signal level. The in vivo effects of ovariectomy were observed 56 days after surgery as an increase in MR image fat signal level and spectral fat/water ratio in the proximal tibia. Ex vivo measurements of tibial marrow water signal discriminated clearly between the sham and ovariectomized groups and showed increased individual variations in the treatment group. Imaging further showed that the highest fat content is observed in the epiphysis. Computed tomography confirmed ovariectomy-induced loss of bone in the proximal tibial metaphysis compared with the sham group. This loss of cancellous bone with ovariectomy is consistent with the MR observations of increases in both fat and water in the metaphysis. These data showed that MR techniques complement X-ray techniques in the bone, water, and fat compositional analysis of the appendicular skeleton in response to ovariectomy and pharmacological treatment.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".