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Record W4387826571 · doi:10.1016/j.actbio.2023.10.022

Implantable SDF-1α-loaded silk fibroin hyaluronic acid aerogel sponges as an instructive component of the glioblastoma ecosystem: Between chemoattraction and tumor shaping into resection cavities

2023· article· en· W4387826571 on OpenAlexfundno aff
Rodolfo Molina-Peña, Natália Helen Ferreira, Charlotte Roy, Loris Roncali, Mathie Najberg, Sylvie Avril, Mariana Zarur, William Bourgeois, Alba Ferreirós, Chiara Lucchi, Francesco Cavallieri, François Hindré, Giovanni Tosi, Giuseppe Biagini, Franco Valzania, François Berger, Miguel Abal, Audrey Rousseau, Frank Boury, Carmen Alvarez‐Lorenzo, Emmanuel Garcion

Bibliographic record

VenueActa Biomaterialia · 2023
Typearticle
Languageen
FieldEngineering
TopicNanoplatforms for cancer theranostics
Canadian institutionsnot available
FundersInstituto de Salud Carlos IIIInstitut National de Recherche pour l'Agriculture, l'Alimentation et l'EnvironnementUniversidade de Santiago de CompostelaMinistère de l'Enseignement supérieur, de la Recherche et de l'InnovationCancéropôle Grand OuestLigue Contre le CancerUniversite AngersEuroNanoMed IIIFederación Española de Enfermedades RarasCanadian Institute for Theoretical AstrophysicsInstitut National de la Santé et de la Recherche MédicaleConseil Régional des Pays de la LoireAgence Nationale de la RechercheLabex Iron
KeywordsFibroinHyaluronic acidSpongeStromal cellIn vivoBiomedical engineeringImplantScaffoldCXCR4AerogelMaterials scienceCancer researchSILKMedicineBiologySurgeryNanotechnologyAnatomyInflammationImmunologyChemokine

Abstract

fetched live from OpenAlex

In view of inevitable recurrences despite resection, glioblastoma (GB) is still an unmet clinical need. Dealing with the stromal-cell derived factor 1-alpha (SDF-1α)/CXCR4 axis as a hallmark of infiltrative GB tumors and with the resection cavity situation, the present study described the effects and relevance of a new engineered micro-nanostructured SF-HA-Hep aerogel sponges, made of silk fibroin (SF), hyaluronic acid (HA) and heparin (Hep) and loaded with SDF-1α, to interfere with the GB ecosystem and residual GB cells, attracting and confining them in a controlled area before elimination. 70 µm-pore sponges were designed as an implantable scaffold to trap GB cells. They presented shape memory and fit brain cavities. Histological results after implantation in brain immunocompetent Fischer rats revealed that SF-HA-Hep sponges are well tolerated for more than 3 months while moderately and reversibly colonized by immuno-inflammatory cells. The use of human U87MG GB cells overexpressing the CXCR4 receptor (U87MG-CXCR4+) and responding to SDF-1α allowed demonstrating directional GB cell attraction and colonization of the device in vitro and in vivo in orthotopic resection cavities in Nude rats. Not modifying global survival, aerogel sponge implantation strongly shaped U87MG-CXCR4+ tumors in cavities in contrast to random infiltrative growth in controls. Overall, those results support the interest of SF-HA-Hep sponges as modifiers of the GB ecosystem dynamics acting as "cell meeting rooms" and biocompatible niches whose properties deserve to be considered toward the development of new clinical procedures. STATEMENT OF SIGNIFICANCE: Brain tumor glioblastoma (GB) is one of the worst unmet clinical needs. To prevent the relapse in the resection cavity situation, new implantable biopolymer aerogel sponges loaded with a chemoattractant molecule were designed and preclinically tested as a prototype targeting the interaction between the initial tumor location and its attraction by the peritumoral environment. While not modifying global survival, biocompatible SDF1-loaded hyaluronic acid and silk fibroin sponges induce directional GB cell attraction and colonization in vitro and in rats in vivo. Interestingly, they strongly shaped GB tumors in contrast to random infiltrative growth in controls. These results provide original findings on application of exogenous engineered niches that shape tumors and serve as cell meeting rooms for further clinical developments.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.012
Threshold uncertainty score0.874

Codex and Gemma teacher scores by category

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.236
Teacher spread0.221 · 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 teacher head, 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

Citations14
Published2023
Admission routes1
Has abstractyes

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