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Record W2889024639 · doi:10.3791/57870

Stereotactic Adoptive Transfer of Cytotoxic Immune Cells in Murine Models of Orthotopic Human Glioblastoma Multiforme Xenografts

2018· article· en· W2889024639 on OpenAlexaff
Ulrich Jarry, Noémie Joalland, Cynthia Chauvin, Béatrice Clemenceau, Claire Pecqueur, Emmanuel Scotet

Bibliographic record

VenueJournal of Visualized Experiments · 2018
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsHotel Dieu Hospital
FundersUniversité de NantesLigue Contre le CancerInstitut National de la Santé et de la Recherche MédicaleAgence Nationale de la Recherche
KeywordsMedicineAdoptive cell transferRadioresistanceImmune systemCancer researchCancerCytotoxic T cellBrain tumorGlioblastomaImmunotherapyImmunologyU87Radiation therapyT cellPathologyBiologyInternal medicine

Abstract

fetched live from OpenAlex

Glioblastoma multiforme (GBM), the most frequent and aggressive primary brain cancer in adults, is generally associated with a poor prognosis, and scarce efficient therapies have been proposed over the last decade. Among the promising candidates for designing novel therapeutic strategies, cellular immunotherapies have been targeted to eliminate highly invasive and chemo-radioresistant tumor cells, likely involved in a rapid and fatal relapse of this cancer. Thus, administration(s) of allogeneic GBM-reactive immune cell effectors, such as human Vϒ9Vδ2 T lymphocytes, in the vicinity of the tumor would represents a unique opportunity to deliver efficient and highly concentrated therapeutic agents directly into the site of brain malignancies. Here, we present a protocol for the preparation and the stereotaxic administration of allogeneic human lymphocytes in immunodeficient mice carrying orthotopic human primary brain tumors. This study provides a preclinical proof-of-concept for both the feasibility and the antitumor efficacy of these cellular immunotherapies that rely on stereotactic injections of allogeneic human lymphocytes within intrabrain tumor beds.

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 score0.019
Threshold uncertainty score0.752

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.036
GPT teacher head0.394
Teacher spread0.359 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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