<p>Publication Trends in Exosomes Nanoparticles for Cancer Detection</p>
Bibliographic record
Abstract
BACKGROUND: Exosomes are small vesicles produced by almost all cells in the body and found in all biofluids. Cancer cell-derived exosomes are known to have distinct, measurable signatures, applicable for early cancer diagnosis. Despite the present bibliometric studies on "Cancer detection" and "Nanoparticles", no single study exists to deal with "Exosome" bibliometric study. METHODS: This bibliometric work investigated the publication trends of "Exosomes" nanoparticles and its application in cancer detection, for the literature from 2008 to July 2019. The data were collected from the Web of Science Core Collection. There were variant visual maps generated to show annual publication, most- relevant authors, sources, countries, topics and keywords. The network analysis of these studies was investigated to evaluate the research trends in the field of exosomes. In addition, the data were qualitatively analyzed according to 22 top-cited articles, illustrating the frequently used subjects and methods in exosomes research area. RESULTS: The results showed that the documents in this field have improved the citation rate. The top-relevant papers are mostly published in Scientific Reports journal which has lost its popularity after 2017, while today, Analytical Chemistry is leading in publishing the most articles related to exosomes. The documents containing keywords of plasma, cells, cancer, biomarkers, and vesicles as keywords plus, are more likely to be published in PLoS One journal. The clustering of the keywords network showed that the keyword theme of "extracellular vesicles" has the highest centrality rate. In global research, USA is the most corresponding country, followed by China, Korea and Australia. Based on the qualitative analysis, the published documents with at least 50 citations have used exosome release, cargo, detection, purification and secretion, as their targets and applied cell culture or isolation as their methods. CONCLUSION: The bibliometric study on exosomes nanoparticles for cancer detection provides a clear vision of the future research direction and identifies the potential opportunities and challenges. This may lead new researchers to select the proper subfields in exosome-related research fields.
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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.048 | 0.077 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.008 |
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".