Anoikis Regulation: Complexities, Distinctions, and Cell Differentiation
Bibliographic record
Abstract
Cell survival and apoptosis (a type of regulated cell death) are processes that involve an expanding complexity of regulating and executing molecular, as well as signaling, actors. Such an intricacy of process-controlling and -implementing players likewise administers, itself a subroutine of caspase-dependent apoptosis that is predominantly regulated by integrin-mediated, cell-ECM interactions. Interestingly, the complexities in regulating cell survival, apoptosis and anoikis implicate additional mechanistic distinctions depending on specific tissues, cell types and species. To these, the existence of further mechanistic distinctions according to the state of cell differentiation constitute yet another level of intricacy in the control of cell survival and anoikis. Considering the roles of anoikis in tissue development and homeostasis, as well as the physiopathological consequences of a deregulation of anoikis, there is a growing necessity for furthering analyses of differentiation state-distinct mechanisms in the control of anoikis, in order to improve our understanding of physiopathologies that are mainly caused, or driven, by a dysregulation of anoikis - such as specific degenerative diseases, as well as cancer progression and metastasis.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".