Influence of lung extracellular matrix from non-IPF and IPF donors on primary human lung fibroblast biology
Bibliographic record
Abstract
ECM, we studied both the concentration-dependent effects and the intrinsic biochemical cues of ECM on cell morphology, protein expression, mechanobiology biomarkers, and gene expression. Morphological analysis revealed that HLFs displayed altered spreading, shape, and nuclear characteristics in response to dECM coatings relative to control plastic, indicating a response to the physical and biochemical cues. Protein expression studies showed an upregulation of α-smooth muscle actin (α-SMA) in cells interacting with both non-IPF and IPF dECM coatings, that was more prominent at IPF dECM-coated surface. In addition, YAP localization, a marker of mechanotransduction, was also dysregulated on dECM coatings, reflecting changes in mechanical signaling pathways. Gene expression profiles were differentially regulated by the different dECM coatings. The developed dECM coating strategy in this work facilitates the integration of tissue-specific biochemical cues onto standard cell culture platforms, which is ideal for high-throughput screening. Importantly, it minimizes the requirement for human tissue samples, especially when compared to more sample-intensive 3D models like dECM-based hydrogels.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".