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Record W3102065431 · doi:10.1016/j.actbio.2020.11.030

Biocompatible copolymer formulations to treat glioblastoma multiforme

2020· review· en· W3102065431 on OpenAlexaff
Luiza C. S. Erthal, Oliviero L. Gobbo, Eduardo Ruiz‐Hernández

Bibliographic record

VenueActa Biomaterialia · 2020
Typereview
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsTrinity College
FundersEuropean Research CouncilTrinity College DublinWellcome Trust
KeywordsCarmustineBiocompatible materialTemozolomideGlioblastomaRadiation therapySelf-healing hydrogelsGliomaMedicineChemotherapyMaterials scienceBiomedical engineeringSurgeryCancer research

Abstract

fetched live from OpenAlex

The treatment for glioblastoma multiforme (GBM) has not changed for more than 20 years while the prognosis for the patients is still poor and most of them survive less than 1 year after diagnosis. The standard of care for GBM is comprised of surgical resection followed by radiotherapy and oral chemotherapy with temozolomide. The placement of carmustine wafers in the brain after tumour removal is added in cases of recurrent glioma. Significant research is underway to improve the GBM therapy outcome and patient quality of life. Biomaterials are in the front line of the research focus for new treatment options. Specially, biocompatible polymers have been proposed in hydrogel-based formulations aiming at injectable and localized therapies. These formulations can comprise many different pharmacological agents such as chemotherapeutic drugs, nanoparticles, cells, nucleic acids, and diagnostic agents. In this manuscript, we review the most recent formulations developed and tested both in vitro and in vivo using different types of hydrogels. Firstly, we describe three common types of thermo-responsive polymers addressing the advantages and drawbacks of their formulations. Then, we focus on formulations specifically developed for GBM treatment.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.924
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.003

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 head0.048
GPT teacher head0.354
Teacher spread0.306 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations43
Published2020
Admission routes1
Has abstractyes

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