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Record W4388589481 · doi:10.1093/neuonc/noad179.0105

CNSC-21. INVESTIGATING THE EVOLUTION OF NEURON-GLIOMA CIRCUIT DYNAMICS USING AN IN VIVO IMAGING METHOD

2023· article· en· W4388589481 on OpenAlexaff
Kiarash Shamardani, Michael B. Keough, Michelle Monje

Bibliographic record

VenueNeuro-Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGliomaNeuroscienceNeuronBiological neural networkCalcium imagingPremovement neuronal activityBiologySynaptogenesisIn vivoCalciumMedicineCancer researchInternal medicine

Abstract

fetched live from OpenAlex

Abstract Pediatric high-grade gliomas (pHGG) are aggressive primary brain neoplasms with a dismal prognosis, making them the leading cause of brain tumor-related deaths in children. pHGGs occur in specific anatomical locations at specific ages underscoring their origin in neurodevelopment and the critical importance of the brain tumor microenvironment. Activity-regulated mechanisms are major regulators of neural development and plasticity. Many pHGGs originate from oligodendroglial precursor cells (OPC) and, like OPCs, neuronal activity promotes pHGG proliferation. Recent work has demonstrated that pHGG cells form calcium-permeable AMPAR-mediated synapses with neurons analogous to the axo-glial synapses that form between neurons and OPCs. Neuronal activity drives pHGG growth through secretion of activity-regulated mitogens and through electrochemical communication with pHGG cells that integrate into neural circuits. In turn, pHGG increases neuronal excitability and remodels functional neural circuits. The interconnected network of glioma cells and neurons is fundamental to pHGG progression. However, how these neuron-glioma networks evolve over time remains to be fully understood. We hypothesize that as gliomas progress, the neuron-glioma malignant circuitry evolves chiefly through synaptogenesis to promote activity that fosters glioma progression. Thus, we developed a two-color in vivo imaging method to study neuron-glioma cell interactions over time in freely behaving mice enabling us to record the frequency, pattern, and synchronicity of calcium transients in neurons, glioma cells, and their coactivity giving us a comprehensive view of the changes in neuron-glioma circuit dynamics over time. We have observed increasing neuronal activity throughout the disease, consistent with increasing neural hyperexcitability over time, and have observed that different types of gliomas exhibit unique patterns of calcium transients (rise time, peak amplitude, decay time, and half-width). Our imaging paradigm offers a unique ability to study neuron-glioma interactions at the systems level and will allow us to study how pharmacological inhibitors and neuronal experience shape neuron-glioma malignant circuit dynamics.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.050
GPT teacher head0.347
Teacher spread0.297 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2023
Admission routes1
Has abstractyes

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