Adding a dimension to cell fate
Bibliographic record
Abstract
Cell fate specification, gene expression and spatial restriction are process finely tuned by epigenetic regulatory mechanisms.At the same time, mechanical forces have been shown to be crucial to drive cell plasticity and boost differentiation.Indeed, several studies have demonstrated that transitions along different specification states are strongly influenced by 3D rearrangement and mechanical properties of the surrounding microenvironment, that can modulate both cell potency and differentiation, through the activation of specific mechanosensing-related pathways.An overview of small molecule ability to modulate cell plasticity and define cell fate is here presented and results, showing the possibility to erase the epigenetic signature of adult dermal fibroblasts and convert them into insulin-producing cells (EpiCC) are described.The beneficial effects exerted on such processes, when cells are homed on an adequate substrate, that shows "in vivo" tissue-like stiffness are also discussed and the contribution of the Hippo signalling mechanotransduction pathway as one of the mechanisms involved is examined.In addition, results obtained using a genetically modified fibroblast cell line, expressing the enhanced green fluorescent protein (eGFP) under the control of the porcine insulin gene (INS) promoter (INS-eGFP transgenic pigs), are reported.This model offers the advantage to monitor the progression of cell conversion in real time mode.All these observations have a main role in order to allow a swift scale-up culture procedure, essential for cell therapy and tissue engineering applied to human regenerative medicine, and fundamental to ensure an efficient translation process from the results obtained at the laboratory bench to the patient bedside.Moreover, the creation of reliable in vitro model represents a key point to ensure the development of more physiological models that, in turn, may reduce the number of animals used, implementing non-invasive investigations and animal welfare and protection.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".