A new bone vascular perfusion compound for the simultaneous analysis of bone and vasculature
Bibliographic record
Abstract
Bone is a highly vascular tissue, which plays an important role in bone development and healing. The ability to analyze both the bone and vasculature simultaneously can enhance the understanding of wound healing, development, and disease in bone. At present, analysis methods are limited in their ability to allow for this simultaneous analysis of bone and bone vasculature in three dimensions, without using the most recent dual-energy computed tomography (CT) techniques. In this study, we present a new barium sulfate (BaSO(4)) radiopaque vascular perfusion compound for performing postmortem microangiography with single-beam microcomputed tomography (microCT), which allows for such simultaneous analysis. This compound differs from currently available contrast mediums due to (1) the high weight-to-volume ratio of BaSO(4) achieved, (2) small BaSO(4) aggregate size (<5 microm), (3) minimal additives, and (4) its miscibility with blood and saline. Most notably, it achieves a radiodensity of 2.4x that of cortical bone, with high perfusion of both the arterial and venous systems and the intervening capillary bed, resulting in an in vivo radiodensity that ranges from that of bone to titanium. Our results, verified using a rat femoral gap-healing model, show that the compound is uniquely suited to high-contrast imaging of the vasculature in the presence of undecalcified bone, with a versatility to be used in other tissues.
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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) | 0.000 | 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) | 0.002 | 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".