Animal Models of Induced Apoptotic Death in the Substantia Nigra
Bibliographic record
Abstract
Apoptosis is a form of cell death in which genetically regulated programs intrinsic to the cell bring about its own demise. In recent years, there has been a tremendous growth of interest in apoptosis as a mechanism of disease in a wide range of human disorders including the neurodegenerative diseases, such as Parkinson's disease (PD) (1). This growth of interest has spawned an extraordinary number of recent discoveries about the molecular basis of apoptosis. It is important to emphasize, however, that much of this new knowledge has been attained in the study of relatively simple systems, such as invertebrate models or mammalian nonneural cell culture systems. Less is known about these mechanisms in neural cells, and much of what is known is based on studies of peripheral neural cells (such as sympathetic ganglia and PC 12 cells) in tissue culture. Much less is known about central neurons; in particular, we know little about the regulation of apoptotic death in central neurons in living animals. It is especially important to try to identify the mechanisms of cell death in central neurons of known phenotype, particularly those implicated in human neurodegenerative disease, such as the dopamine neurons in PD. The purpose of the models we have developed of induced apoptosis in dopamine neurons of the substantia nigra (SN) is to try to translate what is being learned about the molecular mechanisms of apoptosis in simpler systems to these neurons.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".