CXCL12 impact on glioblastoma cells behaviors under dynamic culture conditions: Insights for developing new therapeutic approaches
Bibliographic record
Abstract
Glioblastoma multiforme (GBM) is the most prevalent malignant brain tumor, with an average survival time of 14 to 20 months. Its capacity to invade brain parenchyma leads to the failure of conventional treatments and subsequent tumor recurrence. Recent studies have explored new therapeutic strategies using a chemoattracting gradient to attract GBM cells into a soft hydrogel trap where they can be exposed to higher doses of radiation or chemotherapy. It has been demonstrated in vitro under static conditions, that nanoparticles (NPs) encapsulating the chemoattractant CXCL12 can create a gradient to attract GBM cell. However, GBM cell invasion is also largely dependent on interstitial fluid flow (IFF). In the present study, a custom-made in vitro 3D model with indirect perfusion to mimic IFF at flow rates of 0.5 μL/min and 3 μL/min was used to examine the invasive behavior of F98-rodent-derived and U87-human-derived GBM cells. This model simulated IFF and CXCL12 gradient within an alginate:matrigel-based hydrogel mimicking brain parenchyma. Findings revealed that CXCL12 (1600 ng/mL) released from NPs significantly increased the migration of F98 GBM cells after 72 hours under IFF conditions at both 0.5 and 3 μL/min. In contrast, U87 GBM cells required a higher CXCL12 concentration (2400 ng/mL) and longer incubation time for migration (120 hours). Unlike the F98 cells, U87 GBM cells showed a CXCL12 dose-dependent proliferation. Semi-quantitative qPCR showed higher CXCR4 mRNA levels in F98 cells than in U87 cells. CXCL12 significantly increased intracellular calcium levels via CXCR4 activation, with a 2.3-fold rise in F98 cells compared to U87, consistent with observed cell behavior during perfusion. This highlights the combined influence of IFF and CXCL12 on cell migration, dependent on cell line. This 3D dynamic model is a valuable tool to analyze parameters like interstitial fluid flow (IFF) and chemokine gradients, influenced by GBM tumor diversity.
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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".