A soft-stiff patterned bioengineering model reveals kinase pathways driving directional cell migration in pulmonary arterial hypertension
Bibliographic record
Abstract
Directional cell migration by pulmonary arterial cells (PACs) is one of the important features of diseases involving arterial remodeling, such as pulmonary arterial hypertension (PAH), a disease that is often characterized by reduced arterial compliance and increased extracellular matrix (ECM) stiffening. However, there are no therapeutics that can halt the directional cell migration of PACs in PAH. The inability to identify drug targets or drugs against the directional cell migration during PAH pathogenesis stems from an incomplete understanding of the process and a lack of effective translational models for screening of candidate small molecules. Here, for the first time, we introduce a bioengineered platform suitable for screening small molecule inhibitors targeting kinase pathways that are potentially linked to ECM-mediated directed cell migration in PAH. We used a photolithographic technique to develop mechanically patterned hydrogels with alternative stripes of soft and stiff bars representing the alternating stiffness regions of PAH ECM. Employing our bioengineered platform, we demonstrated the directional cell migration capacity of PACs and found that PAH-smooth muscle cells (SMCs) showed the highest ability to migrate from soft-stiff regions. Screening of different kinase inhibitors identified the role of JAK/STAT as a mechanosensor in the PAH-SMC-specific directional cell migration. Our study highlighted the use of a mechanically patterned bioengineering platform to identify new drug targets specific to the machinery involved in directional cell migration in PAH.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".