A method for quantitative cell tracking using SPECT for the evaluation of myocardial stem cell therapy
Bibliographic record
Abstract
PURPOSE: A promising SPECT-based method for evaluating stem cells therapy uses (111)In-labelled cells, transfected with a reporter gene. Cells are first transplanted to the infarct, and subsequently interrogated for transgenic expression using a systemic injection of an (131)I-labelled reporter probe. The method is impeded by the physical effects of scatter, (131)I/(111)In cross-talk, and attenuation. We hypothesize that correcting for physical effects improves detection of transgenic expression in transplanted cells when (111)In localization is available. METHODS: Canine bone marrow mesenchymal cells (BMMCs), radiolabelled and transfected, were injected into infarcted myocardium. Next, a reporter probe was injected systemically, and 22 SPECT scans were acquired over 20 h. Finally, (99m)Tc-sestamibi was injected and imaged. The animal was killed, the heart sectioned, and counted for (131)I and (111)In in a well-counter ('gold standard'). Canine SPECTs were reconstructed in two ways: with corrections for physical effects and without corrections. The first (111)In reconstruction and the (99m)Tc reconstruction were used to define volumes-of-interest over the transplanted BMMC (VBMMC) and normal myocardium (VNM), respectively. RESULTS: (131)I reconstructions without corrections for physical effects had negligible differential uptake. With corrections, VBMMC was consistently higher than VNM, demonstrating transgene expression. (131)I had the following VBMMC:VNM activity ratio: without correction for physical effects=0.869; with corrections=1.23; and well-counter=1.21. VNM showed the following (131)I:(111)In activity ratio: without corrections=3.07; with corrections=1.38; and well-counter=1.58. CONCLUSIONS: In dual-isotope SPECT, corrections for physical effects were required to detect transgene expression in cells transplanted into an infarction when localization information was available.
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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 | 0.005 | 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 | 0.001 | 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".