The Regulatory Machinery of Neurodegeneration in In Vitro Models of Amyotrophic Lateral Sclerosis
Bibliographic record
Abstract
Neurodegenerative phenotypes reflect complex, time-dependent molecular processes whose elucidation may reveal neuronal class-specific therapeutic targets. The current focus in neurodegeneration has been on individual genes and pathways. In contrast, we assembled a genome-wide regulatory model (henceforth, "interactome"), whose unbiased interrogation revealed 23 candidate causal master regulators of neurodegeneration in an in vitro model of amyotrophic lateral sclerosis (ALS), characterized by a loss of spinal motor neurons (MNs). Of these, eight were confirmed as specific MN death drivers in our model of familial ALS, including NF-κB, which has long been considered a pro-survival factor. Through an extensive array of molecular, pharmacological, and biochemical approaches, we have confirmed that neuronal NF-κB drives the degeneration of MNs in both familial and sporadic models of ALS, thus providing proof of principle that regulatory network analysis is a valuable tool for studying cell-specific mechanisms of neurodegeneration.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".