Nanomedicine: Insights from a Bibliometrics-Based Analysis of Emerging Publishing and Research Trends
Bibliographic record
Abstract
Background and Objectives: Nanomedicine, a term coined by the American engineer Eric Drexler (1955) and Robert Freitas Jr. (1952) in the nineties, can be defined as a complex, multi-disciplinary branch of medicine, in which nano-technologies, molecular biotechnologies, and other nano-sciences are applied at every step of disease management, from diagnosis (nano-diagnostics) to treatment (nano-therapeutics), prognosis, and monitoring of biological parameters and biomarkers. Nanomedicine is a relatively young discipline, which is increasingly and exponentially growing, characterized by emerging ethical issues and implications. Nanomedicine has branched out in hundreds of different sub-fields. Materials and Methods: A bibliometrics-based analysis was applied mining the entire content of PubMed/MEDLINE, using “nanomedicine” as a Medical Subject Heading (MeSH) search term. Results: A sample of 6696 articles were extracted from PubMed/MEDLINE and analyzed. Articles had been published in the period from 2003 to 2019, showing an increasing trend throughout the time. Six thematic clusters emerged (first cluster: molecular methods; second cluster: molecular biology and nano-characterization; third cluster: nano-diagnostics and nano-theranostics; fourth cluster: clinical applications, in the sub-fields of nano-oncology, nano-immunology and nano-vaccinology; fifth cluster: clinical applications, in the sub-fields of nano-oncology and nano-infectiology; and sixth cluster: nanodrugs). The countries with the highest percentages of articles in the field of nanomedicine were the North America (38.3%) and Europe (35.1%). Conclusions: The present study showed that there is an increasing trend in publishing and performing research in the super-specialty of nanomedicine. Most productive countries were the USA and European countries, with China as an emerging region. Hot topics in the last years were nano-diagnostics and nano-theranostics and clinical applications in the sub-fields of nano-oncology and nano-infectiology.
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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 | 0.011 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.167 | 0.257 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".