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Record W4323666708 · doi:10.3389/fimmu.2023.1097456

Extracellular vesicles as a new horizon in the diagnosis and treatment of inflammatory eye diseases: A narrative review of the literature

2023· review· en· W4323666708 on OpenAlexaff
Azam Habibi, Zeinab Zarei‐Behjani, Kimia Falamarzi, Mahdi Malekpour, Fatemeh Ebrahimi, Masood Soleimani, Mahmood Nejabat, Amir Khosravi, Zahra Moayedfard, Sara Pakbaz, Niloofar Dehdari Ebrahimi, Negar Azarpira

Bibliographic record

VenueFrontiers in Immunology · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMicrovesiclesExtracellular vesiclesExtracellular vesicleExtracellularInflammationPathogenesisVesicleBiologyCell biologyPathologyMedicineImmunologyNeurosciencemicroRNABiochemistry

Abstract

fetched live from OpenAlex

Extracellular vesicles include exosomes, microvesicles, and apoptotic bodies. Their cargos contain a diverse variety of lipids, proteins, and nucleic acids that are involved in both normal physiology and pathology of the ocular system. Thus, studying extracellular vesicles may lead to a more comprehensive understanding of the pathogenesis, diagnosis, and even potential treatments for various diseases. The roles of extracellular vesicles in inflammatory eye disorders have been widely investigated in recent years. The term "inflammatory eye diseases" refers to a variety of eye conditions such as inflammation-related diseases, degenerative conditions with remarkable inflammatory components, neuropathy, and tumors. This study presents an overview of extracellular vesicles' and exosomes' pathogenic, diagnostic, and therapeutic values in inflammatory eye diseases, as well as existing and potential challenges.

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 score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
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 head0.014
GPT teacher head0.288
Teacher spread0.274 · 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
Published2023
Admission routes1
Has abstractyes

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Same venueFrontiers in ImmunologySame topicExtracellular vesicles in diseaseFrench-language works237,207