Mandibular condyle tissue reaction to low intensity pulsed ultrasound in young adult rats: Micro computed tomographic and histomorphometric datasets
Bibliographic record
Abstract
Mandibular condyle (MC) in postnatal life, grows mainly by endochondral bone growth which is a multistep process and the condylar cartilage plays a vital role in its regional adaptive growth. Hence, for determining the exact effect of a treatment such as low Intensity pulsed ultrasound (LIPUS) on the MC growth in animal models, it is important to reliably and reproducibly detect changes at different tissue levels and correct regions of the condyle. To this aim, micro computed tomography (µCT), as well as Alcian Blue-Pas staining, in vivo flourochrome labeling via calcein green, and Goldner's Trichrome staining on proper decalcified and undecalcified sections was performed for the harvested samples from young adult rats. Standardized procedures were used to determine volumes or regions of interest for microstructural evaluations in the middle and posterior areas of the MC. In the condylar cartilage, the thickness of fibrous, proliferative, chondroblastic, and total fibrocartilage layers; also the cell population in proliferative and chondroblastic layers were precisely measured. On the other side, using accurate methods percentage of calcifying cartilage and newly formed bone areas/bone area, bone volume fraction and specific surface, trabecular number, thickness, and separation, degree of anisotropy, bone mineral density; furthermore, the amount of actual endochondral bone growth and the osteoid thickness were quantified in subchondral cancellous bone subjacent to condylar cartilage. Data provided herein present the robust µCT and histomorphometric evaluations of the control and LIPUS treated adult MCs at cartilage and bone level. Data also highlights the difference in tissue response to the stimuli between the middle and posterior regions of the condyle. Further interpretation of these datasets can be found in https://doi.org/10.1016/j.bonr.2021.101122[1].
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".