Bibliographic record
Abstract
Highly conserved α- and β-tubulin heterodimers assemble into dynamic microtubules and perform multiple important cellular functions such as structural support, pathway for transport and force generation in cell division. Tubulin exists in different forms of isotypes expressed by specific genes with spatially- and temporally-regulated expression levels. Some tubulin isotypes are differentially expressed in normal and neoplastic cells, providing a basis for cancer chemotherapy drug development. Moreover, specific tubulin isotypes are overexpressed and localized in the nuclei of cancer cells and/or show bioenergetic functions through the regulation of the permeability of mitochondrial ion channels. It has also become clear that tubulin isotypes are involved in multiple cellular functions without being incorporated into microtubule structures. Understanding the mutations of tubulin isotypes specifically expressed in tumors and their post-translational modifications might help to identify precise molecular targets for the design of novel anti-microtubular drugs. Knowledge of tubulin mutations present in tubulinopathies brings into focus cellular functions of tubulin in brain pathologies such as Alzheimer's disease. Uncovering signaling pathways which affect tubulin functions during antigen-mediated activation of mast cells presents a major challenge in developing new strategies for the treatment of inflammatory and allergic diseases. γ-tubulin, a conserved member of the eukaryotic tubulin superfamily specialized for microtubule nucleation is a target of cell cycle and stress signaling. Besides its microtubule nucleation role, γ-tubulin functions in nuclear and cell cycle related processes. This special issue "Tubulin: Structure, Functions and Roles in Disease" contains eight articles, five of which are original research papers and three are review papers that cover diverse areas of tubulin biology and functions under normal and pathological conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".