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Record W2972294464 · doi:10.1093/neuonc/noz126.186

P11.40 Development of an implantable multifunctional biodevice (GlioGel) in the treatment of recurrent glioblastoma

2019· article· en· W2972294464 on OpenAlexaff
Laurence Déry, Gabriel Charest, Mohsen Akbari, David Fortin

Bibliographic record

VenueNeuro-Oncology · 2019
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsUniversity of VictoriaUniversité de Sherbrooke
Fundersnot available
KeywordsIn vivoTemozolomideChemokineGliomaIn vitroDoxorubicinCancer researchAdjuvantBrain tumorGlioblastomaChemotherapyChemotaxisMedicinePathologyBiologyImmunologyInternal medicineInflammation

Abstract

fetched live from OpenAlex

Abstract BACKGROUND Glioblastoma (GBM) is a devastating disease with a median survival of 14–16 months. This poor prognosis can be explained by 3 factors. First, the infiltrative nature of the disease prohibits a complete removal of the tumor. Second, some of the tumor cells are brain tumor stem cells, which are highly migratory and highly resistant to treatments. Finally, the presence of the blood-brain barrier prohibits entry of therapeutics. This situation implies that new treatment approaches must be directed toward the infiltrated brain surrounding the resection cavity. To bypass this problem and improve the potency of adjuvant treatment, we have designed a new “GlioGel-device” that will have the ability to: 1- attract the migrating tumor cells into or nearby the device, and 2- subsequently deliver chemotherapy to the locally pooled tumor cells and 3- irradiate these cells with radioisotopes embedded in the GlioGel. MATERIAL AND METHODS In vitro proof of principle of chemoattraction was investigated by agarose drops method releasing chemokines molecules (CCL2, CCL11, CXCL10) with F98 and U87MG GBM cells. In vivo experiments evaluated the efficiency of chemokines and doxorubicin released by the implanted GlioGel on the tumor behaviour in our Fischer-F98 rat glioma model. An histology of tumour behaviour exposed to chemokines and survival of GBM rats treated with doxorubicin were analysed. RESULTS In vitro preliminary results for chemoattraction assays show that up to 2 times more cells invade the gel when it releases chemoattractant compared to PBS. The In vivo chemotherapy experiments with a fast, medium and slow release of doxorubicin from the GlioGel show that a local dose that represent a 1300-fold smaller dose than a normal intravenous systemic dose gave a significant reduction in tumour growth (median survival) compared to a control group. We investigated the effect provided by the GlioGel impregnated with chemokines on tumor cells migration, after implantation in the Fischer-F98 rat glioma model. CONCLUSION This preliminary study shows the ability of GlioGel releasing chemokines and doxorubicin to respectively attract and kill orthotopic glioblastoma cells. These encouraging results will be completed with a combination of short-range (high LET) radiation by embedded radioisotope into the GlioGel aiming for synergistic combination to eradicate as much tumour cells as possible, while limiting systemic side effects.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.026
GPT teacher head0.306
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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