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Record W1998378939 · doi:10.2478/s13380-011-0037-y

Brain tumor-initiating cells and cells of origin in glioblastoma

2011· article· en· W1998378939 on OpenAlexaff
Sameer Agnihotri, Diana M. Muñoz, Gelareh Zadeh, Abhijit Guha

Bibliographic record

VenueTranslational Neuroscience · 2011
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsSickKids FoundationToronto Western HospitalHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsGlioblastomaGliomaBrain tumorBiologyStem cellCancer stem cellBrain cancerNeural stem cellCancer researchProgenitor cellCancerNeuroscienceMedicinePathologyGenetics

Abstract

fetched live from OpenAlex

Abstract Glioblastoma Multiforme (GBM) is the most malignant and devastating primary brain tumour with a median survival of ∼12–16 months. Although recent large scale sequencing projects have shed considerable light into the complexity of the disease, there remains much to be elucidated in the hopes of generating effective therapeutic strategies. Although these studies investigate the mutations and expression of bulk tumour they have limits with respect to cell of origin and the concept of brain tumour initiating cells (BTIC). Current research has challenged the old paradigm of the stochastic model as recent evidence suggests that a subset of cancer cells within a tumor is responsible for tumor initiation, maintenance, and resistance to therapy. To gain a better understanding of the different compartment of cells that GBM comprise of require careful and elegant experiments. In addition to studying GBM, exploring the role of normal neural stem cells and progenitors cells is essential to partially explain whether these GBM BTIC behave similarly or differently then their non transformed counterparts. Here we discuss the recent literature between the two models, candidate regions of glioma genesis, candidate cells of origin for GBM, and possible therapeutic avenues to explore.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.420
Threshold uncertainty score0.269

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 communication0.0000.000
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 head0.043
GPT teacher head0.276
Teacher spread0.233 · 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

Citations3
Published2011
Admission routes1
Has abstractyes

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