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Record W2060538136 · doi:10.3389/fphar.2013.00021

Extracellular Vesicles – Biomarkers and Effectors of the Cellular Interactome in Cancer

2013· article· en· W2060538136 on OpenAlexafffund
Janusz Rak

Bibliographic record

VenueFrontiers in Pharmacology · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsMcGill University Health CentreMontreal Children's Hospital
FundersCanadian Institutes of Health ResearchMcGill University
KeywordsMicrovesiclesCancer cellBiologyTumor microenvironmentCancerInteractomeMetastasisAngiogenesisCancer researchEffectorCell biologymicroRNAGeneticsGene

Abstract

fetched live from OpenAlex

In multicellular organisms both health and disease are defined by patterns of communication between the constituent cells. In addition to networks of soluble mediators, cells are also programed to exchange complex messages pre-assembled as multimolecular cargo of membraneous structures known extracellular vesicles (EV). Several biogenetic pathways produce EVs with different properties, and known as exosomes, ectosomes, and apoptotic bodies. In cancer, EVs carry molecular signatures and effectors of the disease, such as mutant oncoproteins, oncogenic transcripts, microRNA, and DNA sequences. Intercellular trafficking of such EVs (oncosomes) may contribute to horizontal cellular transformation, phenotypic reprograming, and functional re-education of recipient cells, both locally and systemically. The EV-mediated, reciprocal molecular exchange also includes tumor suppressors, phosphoproteins, proteases, growth factors, and bioactive lipids, all of which participate in the functional integration of multiple cells and their collective involvement in tumor angiogenesis, inflammation, immunity, coagulopathy, mobilization of bone marrow-derived effectors, metastasis, drug resistance, or cellular stemness. In cases where the EV role is rate limiting their production and uptake may represent and unexplored anticancer therapy target. Moreover, oncosomes circulating in biofluids of cancer patients offer an unprecedented, remote, and non-invasive access to crucial molecular information about cancer cells, including their driver mutations, classifiers, molecular subtypes, therapeutic targets, and biomarkers of drug resistance. New nanotechnologies are being developed to exploit this unique biomarker platform. Indeed, embracing the notion that human cancers are defined not only by processes occurring within cancer cells, but also between them, and amidst the altered tumor and systemic microenvironment may open new diagnostic and therapeutic opportunities.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.005
GPT teacher head0.252
Teacher spread0.246 · 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 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

Citations217
Published2013
Admission routes2
Has abstractyes

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