AMPK mediated regulation of meiotic progression in «Caenorhabditis elegans»
Bibliographic record
Abstract
Cell cycle quiescence, a state of reversible arrest, is vital for the coordinated development of an organism and is a feature shared by many cell types, including stem cells.Suggested to play a role in protecting stem cells, it may also be adopted by cancer stem cells to their advantage.To enhance our knowledge on the regulation of cell cycle quiescence, we used the G2/M arrested germline of dauer Caenorhabditis elegans larvae as a model.AMP-activated kinase (AMPK) regulates the arrest of these germ cells, where compromised AMPK function leads to cell cycle progression.Using sensitized C. elegans dauer larvae, we employed a targeted RNAi-based screening approach to identify predicted AMPK phosphorylation targets involved in arresting meiotic progression.17 genes were identified, ranging in function from protein degradation, endogenous-siRNA pathways, and post transcriptional repression or activation.Further characterization of AMPK and its predicted targets will enhance our understanding of how meiotic progression and quiescence is regulated, in turn contributing to studies linking cell cycle quiescence and malignancies.fun, friend, leader, supervisor, Dr.Richard Roy. His enthusiasm for science has always been contagious and I am sincerely grateful for having worked in his lab.His assistance in helping me think critically about my project/science and
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".