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Record W2980345122 · doi:10.1002/iub.2182

Exosomes and cancer: From oncogenic roles to therapeutic applications

2019· review· en· W2980345122 on OpenAlex
Soheila Mohammadi, Fatemeh Yousefi, Zahra Shabaninejad, Ahmad Movahedpour, Maryam Mahjoubin‐Tehran, Alimohammad Shafiee, Sanaz Moradizarmehri, Sarah Hajighadimi, Amir Savardashtaki, Hamed Mirzaei

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIUBMB Life · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsToronto General Hospital
Fundersnot available
KeywordsMicrovesiclesmicroRNACarcinogenesisBiologyCancer cellCancerCell biologyCellCell growthRNAComputational biologyCancer researchGeneGenetics

Abstract

fetched live from OpenAlex

Exosomes belong to extracellular vehicles that were produced and secreted from most eukaryotic cells and are involved in cell-to-cell communications. They are an effective delivery system for biological compounds such as mRNAs, microRNAs (miRNAs), proteins, lipids, saccharides, and other physiological compounds to target cells. In this way, they could influence on cellular pathways and mediate their physiological behaviors including cell proliferation, tumorigenesis, differentiation, and so on. Many research studies focused on their role in cancers and also on potentially therapeutic and biomarker applications. In the current study, we reviewed the exosomes' effects on cancer progression based on their cargoes including miRNAs, long noncoding RNAs, circular RNAs, DNAs, mRNAs, proteins, and lipids. Moreover, their therapeutic roles in cancer were considered. In this regard, we have given a brief overview of challenges and obstacles in using exosomes as therapeutic agents.

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.

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 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 score0.997
Threshold uncertainty score1.000

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 head0.039
GPT teacher head0.353
Teacher spread0.314 · 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