Cell tracking and therapy evaluation of bone marrow monocytes and stromal cells using SPECT and CMR in a canine model of myocardial infarction
Bibliographic record
Abstract
BACKGROUND: The clinical application of stem cell therapy for myocardial infarction will require the development of methods to monitor treatment and pre-clinical assessment in a large animal model, to determine its effectiveness and the optimum cell population, route of delivery, timing, and flow milieu. OBJECTIVES: To establish a model for a) in vivo tracking to monitor cell engraftment after autologous transplantation and b) concurrent measurement of infarct evolution and remodeling. METHODS: We evaluated 22 dogs (8 sham controls, 7 treated with autologous bone marrow monocytes, and 7 with stromal cells) using both imaging of 111Indium-tropolone labeled cells and late gadolinium enhancement CMR for up to12 weeks after a 3 hour coronary occlusion. Hearts were also examined using immunohistochemistry for capillary density and presence of PKH26 labeled cells. RESULTS: In vivo Indium imaging demonstrated an effective biological clearance half-life from the injection site of ~5 days. CMR demonstrated a pattern of progressive infarct shrinkage over 12 weeks, ranging from 67-88% of baseline values with monocytes producing a significant treatment effect. Relative infarct shrinkage was similar through to 6 weeks in all groups, following which the treatment effect was manifest. There was a trend towards an increase in capillary density with cell treatment. CONCLUSION: This multi-modality approach will allow determination of the success and persistence of engraftment, and a correlation of this with infarct size shrinkage, regional function, and left ventricular remodeling. There were overall no major treatment effects with this particular model of transplantation immediately post-infarct.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".