The Nematode Caenorhabditis elegans as a Model System to Study Neuronal Cell Death
Bibliographic record
Abstract
AbstructNormal development and homeostasis result from a tenuous balance between cell proliferation and cell death. Disruption of this balance, in favor of cell death in particular, could easily lead to pathological states in postmitotic organs such as the adult brain (see Thompson, 1995). For example, many neurodegenerative disorders are characterized by the premature death of specific subsets of neurons, which gives rise to their full clinical spectra (Coleman and Flood, 1987; Choi, 1988). Although a complete understanding of the selective cell degeneration in these conditions is still lacking, recent observations suggest that it may occur through apoptosis, a gene-directed type of cell death (Bredesen, 1995). In many cases, cell death by apoptosis requires an active role by the dying cells, because apoptosis is most often significantly blocked or delayed by inhibitors of RNA or protein synthesis (Wyllie et al., 1984). This genetic regulation of apoptosis offers a potential for therapeutic intervention and further assessment of apoptotic mechanisms in manifestations of neuropathology is warranted. However, employing conventional molecular and biochemical approaches, attempts to determine the genetic machinery responsible for specifying which cells live and which cells die have not always been successful in vertebrate systems.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".