Collagen Type I–Gelatin Methacryloyl Composites: Mimicking the Tumor Microenvironment
Bibliographic record
Abstract
Therapeutic drugs can penetrate tissues by diffusion and advection. In a healthy tissue, the interstitial fluid is composed of an influx of nutrients and oxygen from blood vessels. In the case of cancerous tissue, the interstitial fluid is poorly drained because of the lack of lymphatic vasculature, resulting in an increase in interstitial pressure. Furthermore, cancer cells invade healthy tissue by pressing and pushing the surrounding environment, creating an increase in pressure inside the tumor area. This results in a large differential pressure between the tumor and the healthy tissue, leading to an increase in extracellular matrix (ECM) stiffness. Because of high interstitial pressure in addition to matrix stiffening, penetration and distribution of systemic therapies are limited to diffusion, decreasing the efficacy of cancer treatment. This work reports on the development of a microfluidic system that mimics in vitro healthy and cancerous microenvironments using collagen I and gelatin methacryloyl (GelMA) composite hydrogels. The microfluidic device developed here contains a simplistic design with a central chamber and two lateral channels. In the central chamber, hydrogel composites were used to mimic the ECM, whereas lateral channels simulated capillary vessels. The transport of fluorescein sodium salt and fluorescently labeled gold nanoparticles from capillary-mimicking channels through the ECM-mimicking hydrogel was explored by tracking fluorescence. By tuning the hydrogel composition and concentration, the impact of the tumor microenvironment properties on the transport of those species was evaluated. In addition, breast cancer MCF-7 cells were embedded in the hydrogel composites, displaying the formation of 3D clusters with high viability and, consequently, the development of an in vitro tumor model.
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 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 | 0.000 | 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) | 0.000 | 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 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".