Bibliographic record
Abstract
In mammalian cells, protein degradation is an essential and dynamic process that is crucial for survival, growth, differentiation and proliferation of cells. Tellingly, the majority of intracellular proteins are degraded via the ubiquitin–proteasome system (UPS). UPS-mediated protein degradation serves qualitative and quantitative roles within the cellular proteome. For instance, UPS specifically targets misfolded, aggregated, toxic, mutant and otherwise structurally abnormal proteins for destruction and hence prevent aggregation and accumulation of toxic proteins. Furthermore, several cellular regulatory proteins, including cell cycle regulators, transcription factors, DNA replication and DNA repair proteins are selectively targeted for degradation via UPS and thus contribute to maintaining protein homeostasis (proteostasis) and proper functional proteome. Concomitantly, the deregulation of proteostasis may lead to several pathological disorders including aging-associated pathologies. Remarkably, augmenting the proteasomal activity has been linked to longevity in model organisms and protect these organisms from symptoms linked to protein homeostasis disorders. Herein I comment briefly on the recent work revealing the pivotal role of ubiquitin–proteasome-mediated protein degradation with respect to regulating aging process in model organisms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".