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Record W2988590615 · doi:10.3389/fonc.2019.01208

Therapeutic Prospects of mRNA-Based Gene Therapy for Glioblastoma

2019· review· en· W2988590615 on OpenAlexaff
Xiangjun Tang, Shenqi Zhang, Rui Fu, Li Zhang, Kuan-Ming Huang, Hao Peng, Long‐Jun Dai, Qianxue Chen

Bibliographic record

VenueFrontiers in Oncology · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsUniversity of British Columbia
FundersNatural Science Foundation of Hubei ProvinceNational Natural Science Foundation of China
KeywordsGlioblastomaGenetic enhancementMedicineCancer researchGeneMessenger RNAOncologyBioinformaticsComputational biologyBiologyGenetics

Abstract

fetched live from OpenAlex

The treatment of glioblastoma has been a big challenge for decades in the oncological field mainly owing to its unique biological characteristics, such as high heterogeneity, diffusing invasiveness, and capacity to resist conventional therapies. The mRNA-based therapeutic modality holds many superior features, including easy manipulation, rapid and transient expression, and adaptive convertibility without mutagenesis, which is suitable for dealing with glioblastoma’s complexity and variability. Synthetic anticancer mRNAs carried by various vehicles act as the ultimate attackers of the tumor across biological barriers. In this modality, specifically targeted glioblastoma treatment can be guaranteed by adding targeting molecules at certain levels. The choice of mRNA-bearing vehicle and administration method is a fully patient-tailored selection. This review covers the advantages and possible limitations of mRNA-based gene therapy, the in vitro synthesis of mRNA, the feasible methods for synthetic mRNA delivery and clinical therapeutic prospects of mRNA-based gene therapy for glioblastoma.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.038
GPT teacher head0.344
Teacher spread0.305 · 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

Citations70
Published2019
Admission routes1
Has abstractyes

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