Bibliographic record
Abstract
Embryonic stem cells are cells with the ability to make more of themselves (selfrenew)and to produce all the cells of the body (pluripotency). These abilities giveembryonic stem cells much value to study for medical applications, such as regenerativemedicine for treating damaged tissues. However, a large number of specific types ofcells are required for tissue therapy. Thus embryonic stem cells must be expandedand differentiated in a controlled manner. In order to better control embryonic stemcells, the cell signalling pathways responsible for cell behaviour must be understood.Cell signalling is the molecular chain of events that occurs due to a stimulus in theenvironment. From previous studies examining embryonic stem cell behaviour underfluid flow conditions in spinner flask bioreactors, it was found that pluripotencymarkers were maintained even in media that should lead to cell differentiation intoother cell types. We hypothesized that several key signalling pathways triggeredby shear stress were responsible for this behaviour. To test this hypothesis, mouseembryonic stem cells were placed in parallel plate flow chambers and exposed touniform fluid shear stress. These embryonic stem cells were previously transfectedwith a reporter construct that detects b-catenin activity, which is related to pluripotencyof stem cells. Furthermore, these flow exposed cells were stained for L-pSmad2, asignalling protein found to increase with shear stress in another cell type. Underconfocal microscopy, it was determined that exposure to fluid shear stress influencedboth b-catenin activity and L-pSmad2 protein expression in embryonic stem cells.Overall, this study provides a better understanding of the cell signalling responsiblefor controlling the pluripotency of embryonic stem cells.
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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".