Molecular Diffusion From Bone Into Cartilage: A Study of the Osteochondral Junction
Bibliographic record
Abstract
Osteoarthritis is an idiopathic joint disease that affects 13% of Canadians.To gain a better understanding of the biochemical relationship between the bone and cartilage, molecular diffusion across the bone-cartilage junction was assessed.Passive diffusion of a small (605 Da), nutrient-sized, contrast agent across the osteonchondral junction was successfully measured using contrast-enhanced MRI.This method could be employed for future in vivo studies.The osteochondral junction was simulated using a finite difference method to assess the subchondral bone as a barrier.The average resistivity value was 1.65x10 -6 s -1 .Inclusion of resistivity in the simulation produced a better fit of the experimental data.The osteochondral junction's permeability to larger, protein-sized, solutes was assessed by attempting to diffuse fluorescent tracers (3-70 kDa).The selected fluorescent tracers were unable to passively diffuse across the osteochondral junction.This thesis provided a better understanding of the crosstalk between bone and cartilage.the diffusion chambers used throughout this research.I thank Greg Cron for his time in setting up the MR imaging and for his expertise in the magnetic resonance field.I also would like to thank Chloë van Oostende-Triplet, for her expertise in fluorescent imaging and meeting with me on a weekly basis while we tried various approaches to diffusing fluorescent tracers.I thank Dr. Paul Beaulé and his team for their work in obtaining human samples for this research.Mohammed Abdelazez, thank you for always talking me through my problems and offering solutions.Most importantly, thank you for your willingness to help and for making this degree much more enjoyable.I also thank Kevin Dick for co-chairing the Carleton chapter of the Engineers in Medicine and Biology Society with me and all the fun we had in and out of the lab.I will miss being a part of this lab.To all my friends, colleagues, professors who supported me throughout this degree, thank you.This last year was undoubtedly the hardest year for my family.Life is unpredictable and things don't always happen the way we wanted.Taking a semester off was not easy and returning to Ottawa to finish this project seemed impossible at times.Despite this and all the challenges that we still face, my family has continued to support and encourage me 5 Chapter: Discussion and Conclusions ...........
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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".