(Micro)managing the mechanical microenvironment
Bibliographic record
Abstract
Mechanical forces are critical components of the cellular microenvironment and play a pivotal role in driving cellular processes in vivo. Dissecting cellular responses to mechanical forces is challenging, as even "simple" mechanical stimulation in vitro can cause multiple interdependent changes in the cellular microenvironment. These stimuli include solid deformation, fluid flows, altered physical and chemical surface features, and a complex transfer of loads between the various interacting components of a biological culture system. The active mechanical and biochemical responses of cells to these stimuli in generating internal forces, reorganizing cellular structures, and initiating intracellular signals that specify cell fate and remodel the surrounding environment further complicates cellular response to mechanical forces. Moreover, cells present a non-linear response to combinations of mechanical forces, materials, chemicals, surface features, matrix properties and other effectors. Microtechnology-based approaches to these challenges can yield key insights into the mechanical nature of cellular behaviour, by decoupling stimulation parameters; enabling multimodal control over combinations of stimuli; and increasing experimental throughput to systematically probe cellular response. In this critical review, we briefly discuss the complexities inherent in the mechanical stimulation of cells; survey and critically assess the applications of present microtechnologies in the field of experimental mechanobiology; and explore opportunities and possibilities to use these tools to obtain a deeper understanding of mechanical interactions between cells and their environment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".