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Record W4247702798 · doi:10.33393/jcb.2013.2036

Therapeutic Uses of Exosomes

2013· article· en· W4247702798 on OpenAlexaff
Zacharias E. Suntres, Milton G. Smith, Fatemeh Momen‐Heravi, Jie Hu, Xin Zhang, Ying Wu, Hongguang Zhu, Jiping Wang, Jian Zhou, Winston Patrick Kuo

Bibliographic record

VenueJournal of Circulating Biomarkers · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsNOSM University
FundersNational Center for Advancing Translational SciencesCentre International de Recherche sur le CancerNational Center for Research ResourcesHarvard CatalystNational Institutes of HealthHarvard University
KeywordsMicrovesiclesImmunologyAntigenDiseaseCD63CellCell typeMedicineBiologyCancer researchPathologymicroRNA

Abstract

fetched live from OpenAlex

Exosomes are membrane vesicles with a diameter of 40–100 nm that are secreted by many cell types into the extracellular milieu. Exosomes are found in cell culture supernatants and in different biological fluids and are known to be secreted by most cell types under normal and pathological conditions. Considerable research is focusing on the exploitation of exosomes in biological fluids for biomarkers in the diagnosis of disease. More recently, exosomes are being exploited for their therapeutic potential. Exosomes derived from dendritic cells, tumor cells, and malignant effusions demonstrate immunomodulatory functions and are able to present antigens to T-cells and stimulate antigen-specific T-cell responses. Exosomes have also been examined for their therapeutic potential in the treatment of infections such as toxoplasmosis, diphtheria, tuberculosis and atypical severe acute respiratory syndrome as well as autoimmune diseases. Attempts to find practical applications for exosomes continue to expand with the role of exosomes as a drug delivery system for the treatment of autoimmune/inflammatory diseases and cancers.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.011
GPT teacher head0.247
Teacher spread0.236 · 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

Citations12
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Circulating BiomarkersSame topicExtracellular vesicles in diseaseFrench-language works237,207