Tracking transplanted cells using dual-radionuclide SPECT
Bibliographic record
Abstract
The purpose of this study was to characterize the performance of single photon emission computed tomography (SPECT) in tasks associated with tracking transplanted cells. Previous studies identified matters of hardware design, whereas we focus on biological variables impacting system performance, such as cell colony growth and non-specific radiolabelling. Using experimental data, a digital phantom was developed of in vitro 111In-radiolabelled stem cells, transfected with a reporter gene, transplanted into canine infarcted myocardium and interrogated using a peripherally injected 131I-radiolabelled reporter probe. Single- and dual-head SPECT acquisition was simulated. Performance was characterized using an estimation task, where the precision of parameter estimates (111In and 131I radiolabel quantity, cell colony size and location, and background) was tracked as the phantom evolved to simulate 111In-label efflux, cell colony growth and improved reporter probe specificity. In vitro pre-labelling of transplanted cells improved precision of parameter estimates via a priori size and location information. Precision of radiolabel quantity estimates improved with cell colony growth, despite 111In radiolabel dilution; size and location parameters were influenced little. Precision of radiolabel quantity estimates improved with reduced reporter probe non-specific uptake. The performance of SPECT in cell tracking is influenced strongly by biological variables. These should be considered when planning experiments or developing SPECT technology for cell tracking.
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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 | 0.000 | 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) | 0.000 | 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".