Bibliographic record
Abstract
Numerous hypotheses invoke tissue stiffness as a key parameter that regulates morphogenesis and disease progression. However, current methods are insufficient to test hypotheses that concern physical properties deep in living tissues. For example, durotaxis, a form of cell migration in which cells are guided by stiffness gradients, has only been studied in vitro owing largely to the lack of tools to measure three-dimensional tissue properties in vivo. The role of durotaxis under physiological conditions remains unknown. Targeting spatial mapping of tissue properties and investigating durotaxis in vivo, this thesis focuses on (1) developing a new tool to achieve spatial mapping of tissue properties, (2) establishing the correlation between measured tissue properties and cell migration behavior, and (3) investigating the molecular basis of tissue properties and how it regulates durotaxis behavior. A 3D magnetic device was developed which generates a uniform magnetic field gradient within a space that is sufficient to accommodate an organ-stage mouse embryo under live conditions. The method allows rapid, nontoxic measurement of the three-dimensional spatial distribution of viscoelastic properties within mesenchyme and epithelium. Using the device, an anteroproximally biased mesodermal stiffness gradient was identified in the mouse limb bud. Along the stiffness gradient, cells migrate collectively to shape the early limb bud. The stiffness gradient corresponds to a Wnt5a-dependent domain of fibronectin expression. The findings challenge the notion that Wnt5a regulates cell migration through chemotaxis. Instead, Wnt5a modifies the tissue microenvironment to promote durotaxis in vivo. By conditionally knocking out the fibronectin within the mesenchyme, the stiffness gradient was abolished and the durotaxis behavior was arrested. Three-dimensional stiffness mapping enabled by the 3D magnetic device fills an important void in the current methods repertoire of measuring tissue properties and will facilitate the generation of hypotheses and potentially the rigorous testing of mechanisms of development and disease.
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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 | 0.001 | 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".