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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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.216
Threshold uncertainty score0.356

Codex and Gemma teacher scores by category

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 communication0.0000.000
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 head0.011
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

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