Intravital Imaging for Tracking of Angiogenesis and Cellular Events Around Surgical Bone Implants
Bibliographic record
Abstract
Peri-implant endosseous wound healing involves cascades of cellular and molecular events that lead to the integration of the implant to the recipient bone. Currently, tools are lacking to track peri-implant healing events at high spatiotemporal resolution in vivo. A cranial window chamber model (cranial implant window chamber [CIWC]), which is compatible with confocal and multiphoton intravital microscopic imaging systems, has been developed for spatiotemporal tracking of angiogenesis, and cellular dynamics in the peri-implant wound site. In this study, we describe a step-by-step procedure for implantation of the CIWC in the calvaria of fluorescent reporter mice with endogenously labeled mesenchymal progenitor cells. The specific application of this model to track angiogenesis and cell recruitment around calvarial implants is demonstrated by in vivo images. Moreover, we developed a software platform for image processing and analysis using a MATLAB graphical user interface (GUI) and have used the created GUI for image processing and quantitative analysis of changes in vascular and cellular organization. These new experimental methods allow us to image, and quantify, angiogenesis and perivascular cell dynamics in the endosseous healing compartment. As such, the method is capable of providing a new perspective on, and unique information regarding, healing that occurs around orthopedic and dental implants.
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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 | 0.001 | 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 | 0.001 |
| 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".