Calcium signaling as an integrator and decoder of niche factors to control somatic stem cell quiescence and activation
Bibliographic record
Abstract
Somatic stem cells (SSCs) play a major role in tissue homeostasis and respond to a panoply of micro‐environmental cues by adjusting their quiescence and activation profiles. How these cells integrate and decode multiple niche signals remains elusive. In recent years, Ca2+ signaling has emerged as one of the key intracellular pathways that allow stem cells to dynamically adjust their fate and either to remain quiescent for future needs or to become activated to generate new progeny. Interestingly, not only distinct Ca2+ signatures are associated with the quiescence and activation states of stem cells, but also various extracellular cues impinge on Ca2+ pathways to dynamically regulate the responses of stem cells to different niche signals. This Viewpoint article deals with how Ca2+ signaling may be used to decode and integrate different niche factors and how Ca2+ fluctuations of distinct amplitudes, frequencies, and overall intracellular levels may trigger the differential gene transcription program. Knowledge about mechanisms that allow SSCs to translate the complexity of extracellular niche signaling into intrinsic states of cell quiescence and activation is crucial for understanding life‐long tissue homeostasis and regeneration.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".