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Record W2894343896 · doi:10.1080/10409238.2018.1508276

Exosomes at a glance – common nominators for cancer hallmarks and novel diagnosis tools

2018· review· en· W2894343896 on OpenAlexfundno aff
Diana Gulei, Bogdan Petruţ, Adrian Bogdan Țigu, Anca Onaciu, Eva Fischer‐Fodor, Atanas G. Atanasov, Călin Ionescu, Ioana Berindan‐Neagoe

Bibliographic record

VenueCritical Reviews in Biochemistry and Molecular Biology · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsnot available
FundersTerry Fox Foundation
KeywordsMicrovesiclesCancerBiologyExosomeCancer cellPathologicalFunction (biology)Cancer researchExtracellularmicroRNATumor microenvironmentTumor progressionCell biologyMedicinePathologyGeneticsGene

Abstract

fetched live from OpenAlex

Cancer represents a heterogeneous disease with multiple levels of regulation and a dynamic environment that sustains the evolution of the malignant mass. This dynamic is in part sustained by a class of extracellular vesicles termed exosomes that are able to imprint the pathological state by incorporating differential cargos in order to facilitate cell-to-cell communication. Exosomes are stable within the extracellular medium and function as shuttles secreted by healthy or pathological cells, being further taken by the accepting cell with direct effects on its phenotype. The exosomal trafficking is deeply involved in multiple levels of cancer development with roles in all cancer hallmarks. Nowadays, studies are constantly exploring the ability of exosomes to sustain the malignant progression in order to attack this pathological trafficking and impair the ability of the tumor mass to expand within the organisms. As important, the circulatory characteristics of exosomes represent a steady advantage regarding the possibility of using them as minimally invasive diagnosis tools, where cancer patients' present modified exosomal profiles compared to the healthy ones. This last characteristic, as novel diagnosis tools, has the advantage of a possible rapid transition within the clinic, compared to the studies that evaluate the therapeutic meaning.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.968
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.000
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 head0.048
GPT teacher head0.407
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.

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

Citations30
Published2018
Admission routes1
Has abstractyes

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