Septins: New Microtubule Interacting Partners
Bibliographic record
Abstract
Originally characterized as regulators of cytokinesis, septins were later implicated in other cellular processes. Recent studies show that septins have a broader role in microtubule-dependent processes, such as karyokinesis, exocytosis, and maintenance of cell shape. Many members of the septin family have been shown to colocalize or interact with the microtubule cytoskeleton, suggesting that these might be general properties of septins. Septins could play an important role in regulating microtubule dynamics by interacting with microtubule-associated proteins (MAPs) that modulate microtubule stability. Being able to associate with both microtubules and actin, septins can play an important role as adaptors between the two cytoskeletons and as regulators of processes in which both actin and microtubules are involved. As septins are associated with various neurodegenerative diseases and cancer, a better understanding of the biology of septins and their interactions with microtubules is important in order to develop possible therapeutic strategies for these diseases.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".