The importance of unambiguous cell origin determination in neuronal repopulation studies
Bibliographic record
Abstract
Neuronal repopulation achieved through transplantation or transdifferentiation from endogenous sources holds tremendous potential for restoring function in chronic neurodegenerative disease or acute injury. Key to the evaluation of neuronal engraftment is the definitive discrimination of new or donor neurons from preexisting cells within the host tissue. Recent work has identified mechanisms by which genetically encoded donor cell reporters can be transferred to host neurons through intercellular material transfer. In addition, labeling transplanted and endogenously transdifferentiated neurons through viral vector transduction can yield misexpression in host cells in some circumstances. These issues can confound the tracking and evaluation of repopulated neurons in regenerative experimental paradigms. Using the retina as an example, we discuss common reasons for artifactual labeling of endogenous host neurons with donor cell reporters and suggest strategies to prevent erroneous conclusions based on misidentification of cell origin.
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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".