Material properties of disulfide-crosslinked hyaluronic acid hydrogels influence prostate cancer cell growth and metabolism
Bibliographic record
Abstract
Cells reside in vivo within three dimensional environments in which they interact with extracellular matrices (ECMs) that play an integral role in maintaining tissue homeostasis and preventing tumour growth. Thus, tissue culture approaches that more faithfully reproduce these interactions with the ECM are needed to study cancer development and progression. Many materials exist for modeling tissue environments, and the effects of differing mechanical, physical, and biochemical properties of such materials on cell behaviour are often intricately coupled and difficult to tease apart. Here, an optimized protocol was developed to generate low reaction volume disulfide-crosslinked hyaluronic acid (HA) hydrogels for use in cell culture applications to relate the properties of ECM materials to cell signalling and behaviour. Mechanically, HA hydrogels are comparable to other soft hydrogel materials such as Matrigel and agarose or to tissues lacking type I collagen and other fibrillar ECM components. The diffusion of soluble materials in these hydrogels is affected by unique mass transfer properties. Specifically, HA hydrogel concentration affects the diffusion of anionic particles above 500 kDa, whereas diffusion of smaller particles appears unimpeded by HA content, likely reflecting hydrogel pore size. The HA hydrogels have a strong exclusion effect that limits the movement of proteins into and out of the material once fully formed. Such mass transfer properties have interesting implications for cell culture, as they ultimately affect access to nutrients and the distribution of signalling molecules, affecting nutrient sensing and metabolic activity. The use of disulfide-crosslinked HA hydrogels for the culture of the model prostate cancer cell lines PC3 and LNCaP reveals correlations of protein activation linked to metabolic flux, which parallel and can thus potentially provide insights into cell survival mechanisms in response to starvation that occurs in cancer cell microenvironments.
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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 | 0.000 | 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) | 0.001 | 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".