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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".