UBE4B: A Promising Regulatory Molecule in Neuronal Death and Survival
Bibliographic record
Abstract
Neuronal survival and death of neurons are considered a fundamental mechanism in the regulation of the nervous system during early development of the system and in adulthood. Defects in this mechanism are highly problematic and are associated with many neurodegenerative diseases. Because neuronal programmed death is apoptotic in nature, indicating that apoptosis is a key regulatory process, the p53 family members (p53, p73, p63) act as checkpoints in neurons due to their role in apoptosis. The complexity of this system is due to the existence of different naturally occurring isoforms that have different functions from the wild types (WT), varying from apoptotic to anti-apoptotic effects. In this review, we focus on the role of UBE4B (known as Ube4b or Ufd2a in mouse), an E3/E4 ligase that triggers substrate polyubiquitination, as a master regulatory ligase associated with the p53 family WT proteins and isoforms in regulating neuronal survival. UBE4B is also associated with other pathways independent of the p53 family, such as polyglutamine aggregation and Wallerian degeneration, both of which are critical in neurodegenerative diseases. Many of the hypotheses presented here are gateways to understanding the programmed death/survival of neurons regulated by UBE4B in normal physiology, and a means of introducing potential therapeutic approaches with implications in treating several neurodegenerative diseases.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".