The role of pore size and mechanical properties on the accumulation, retention and distribution of F98 glioblastoma cells in macroporous hydrogels
Bibliographic record
Abstract
Glioblastoma (GBM) accounts for half of all central nervous system tumors. Once the tumor is removed, many GBM cells remain present near the surgical cavity and infiltrate the brain up to a distance of 20-30 mm, resulting in recurrence a few months later. GBM remains incurable due to the limited efficiency of current treatments, a result of the blood-brain barrier and sensitivity of healthy brain tissues to chemotherapy and radiation. A new therapeutic paradigm under development to treat GBM is to attract and accumulate GBM cells in a cancer cell trap inserted in the surgical cavity after tumor resection. In this work, porous gels were prepared using porous polylactide molds obtained from melt-processed co-continuous polymer blends of polystyrene and polylactide, with an average pore size ranging from 5 μm to over 500 μm. In order to efficiently accumulate and retain GBM brain cancer cells within a macroporous sodium alginate-based hydrogel trap, the pores must have an average diameter superior to 100 μm, with the best results obtained at 225 μm. In that case, the accumulation and retention of F98 GBM cells were more homogeneous, especially when functionalized with RGD adhesion peptides. At an alginate concentration of 1% w/v, the compression modulus reaches 15 kPa, close to the average value of 1-2 kPa reported for brain tissues, while adhesion and retention were also superior compared to 2% w/v gels. Overall, 1% w/v gels with 225 μm pores functionalized with the RGD peptide display the best performances.
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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) | 0.000 | 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".