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Record W7006162506

Studies on the role of extracellular vesicles in horizontal transfer of oncogenes in cancer

2015· dissertation· en· W7006162506 on OpenAlexfundno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2015
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell Image Analysis Techniques
Canadian institutionsnot available
FundersMcGill University
KeywordsExtracellularMutantCancer cellExtracellular vesiclesEndocytosisIntracellularDNAVesicleExtracellular vesicle
DOInot available

Abstract

fetched live from OpenAlex

Extracellular vesicles (EVs) are spherical or cup-shaped membrane structures that originate from 'donor' cells and are released into their pericellular space, travelling considerable distances in the interstitial space until they undergo uptake, fusion or interaction with a range of 'acceptor' cells.EVs have been documented to harbour cancer-associated molecules, including oncogenic proteins, transcripts and genomic sequences containing mutant oncogenes, and to participate in their intercellular trafficking that results in horizontal transformation.Here, we address the questions about EV isolation techniques, their DNA cargo and barriers to EV-mediated transformation.We show that while ultracentrifugation approach is sufficient to eliminate the rare cancer cells that may enter EV preparations, certain highly transformed cells enter EV preparations having survived the extreme centrifugal force.We also show that oncogenic H-ras drives vesiculation of intestinal epithelial cells resulting in shedding of small EVs that contain double-stranded chromatin-associated DNA sequences, which are representative of the entire host genome and includes the transforming H-ras oncogene.In addition, we demonstrate that EV formation is affected antithetically by both oncogenes and tumour suppressors, and is further modified by hypoxia and calcium signalling pathways within human glioma cells.Lastly, we reveal that selective, transient nature of EV uptake and EV-induced toxicity are natural barriers x 8.2.Future Directions -Unanswered Questions………………………..…………...........

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.004

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.291
Teacher spread0.271 · 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 designBench or experimental
Domainnot available
GenreMethods

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
Published2015
Admission routes1
Has abstractyes

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