Inherited neurodegenerative diseases: the one-hit model of neurodegeneration
Bibliographic record
Abstract
The clinical manifestations of inherited neurodegenerative diseases are often delayed for periods from years to decades. This observation has led to the idea that, in these disorders, neurons die from cumulative damage. A critical prediction of the cumulative damage hypothesis is that the probability of neuronal death increases with age. However, we recently demonstrated, in 17 examples of neurodegeneration, that the kinetics of neuronal death appear to be exponential. These examples include both monogenic disorders, such as photoreceptor degenerations, as well as others that are partly or entirely acquired (such as the clinical phase of parkinsonism and retinal detachment). Exponential kinetics indicate that (i) the risk of death is constant, (ii) death occurs randomly in time and (iii) the death of each neuron is independent of other neurons. We use the term 'one-hit model' to refer to the single catastrophic intracellular biochemical event, analogous to radioactive decay, which leads to neuronal death in the diseases we analyzed. Here, we examine the major features and implications of the one-hit model and provide preliminary evidence that amyotrophic lateral sclerosis also appears to fit this model. We also discuss a testable biochemical hypothesis, the mutant steady-state hypothesis, that we proposed to account for the one-hit model. Finally, we explore six unresolved issues that appear to challenge this model. The one-hit model appears to capture a novel principle underlying many neurodegenerations. Our findings suggest that any consideration of the biochemical basis of neurodegeneration must include a meticulous examination of the kinetics of cell death.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".