Extracellular vesicles: the growth as diagnostics and therapeutics; a survey
Bibliographic record
Abstract
This article aims to document the growth in extracellular vesicle (EV) research. Here, we report the growth in EV-related studies, patents, and grants as well as emerging companies with major intent on exosomes. Four different databases were utilized for electronic searches of published literature: two general databases - Scopus/Elsevier and Web of Science (WoS), as well as two specialized US government databases - the USA Patent and Trademark Office and National Institutes of Health (NIH) of the Department of Health and Human Services. The applied combination of key words was carefully chosen to cover the most commonly used terms in titles of publications, patents and grants dealing with conceptual areas of EVs. Within the time frame from 1 January 2000 to 31 December 2016, limited to articles published in English, we identified output using search strategies based upon Scopus/Elsevier and WoS, patent filings and NIH Federal Reports of funded grants. Consistently, USA and UK universities are the most frequent among the top 15 affiliations/organizations of the authors of the identified records. There is clear evidence of upward streaming of EV-related publications. By documenting the growth of the EV field, we hope to encourage a roster of independent authorities skilled to provide peer review of manuscripts, evaluation of grant applications, support of foundation initiatives and corporate long-term planning. It is important to encourage EV research to further identify biomarkers in diseases and allow for the development of adequate diagnostic tools that could distinguish disease subpopulations and enable personalized treatment of patients.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.031 | 0.046 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".