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Record W3201801936 · doi:10.30683/1927-7229.2021.10.02

Metastatic Model of Cerebellar Medulloblastoma Cells to Peritoneal Cavity: Exploration of Circulating Tumor Cells

2021· article· en· W3201801936 on OpenAlexvenueno aff
Parvin Mehdipour, Firoozeh Javan, Morteza Faghih Jouibari, Mehdi Khaleghi

Bibliographic record

VenueJournal of Analytical Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsnot available
FundersTehran University of Medical Sciences and Health Services
KeywordsCirculating tumor cellImmunofluorescenceMedicinePathologyCancer researchMetastasisInternal medicineCancerImmunologyAntibody

Abstract

fetched live from OpenAlex

Background: Circulating Tumor Cells (CTCs) are the reliable key for an early detection. The cell-based/classified/personalized diagnostic approaches are unavailable. Therefore, it was aimed to explore the expression behavior of tumor (T) cells in brain, peritoneal cavity (PC) and genomic level to deliver the hypothetical model through the metastatic events.
 Patients and Methods: The focal assay included protein expression (PE) by immunofluorescence in T-cells of cerebellarmeduloblastoma (CM), PC, and CTCs in a metastatic patient. The CCL2, VEGF, EGF, CD133/Cyclin E/ P21/Neuronal marker (NM), and CD45 were explored.
 Result: Frequency of T-cells lacking PE and the Ratio of T/CTCs in different sections of CM- tumor cells in brain and the metastatic PC revealed the diverse expression and co-expression of the involved proteins. The poor prognosis is offered upon the value of PE at T/CTCs ratio. High PE and harmonic co-expression played the influential role in the metastatic process and manner of evolution.
 Conclusions: Single cell- based analysis of expression and co-expression is the directive channel to unmask the heterogeneity through the metastatic process at genomic and somatic levels for providing the metastatic model. Present findings deliver the somatic/genomic ratio-based prognosis for further clinical managements.

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 score0.045
Threshold uncertainty score0.435

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.062
GPT teacher head0.345
Teacher spread0.283 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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