The stealth effect from a medicinal chemist perspective: definition and updates
Bibliographic record
Abstract
In recent years, there has been a significant increase in literature on emerging nanotechnologies, including nanoparticles, nanorobots, and exosomes, for various therapeutic applications. Additionally, politically driven research initiatives aimed at accelerating COVID-19 vaccine development have further amplified interest in nanoparticles as drug delivery systems. This article provides a personal perspective on the scientific claims surrounding nanoparticles by: (i) analyzing the historical evolution of their terminology, (ii) reviewing the most cited publications in the field, and (iii) offering a professional assessment to guide the next-generation of medicinal chemists. A key aspect of this discussion is the stealth effect, which refers to the ability of nanoparticles to evade recognition and clearance by the immune system, thereby prolonging their circulation time in the bloodstream. This property is essential for enhancing the efficacy of nanoparticle-based therapeutics by improving bioavailability and ensuring targeted drug delivery to diseased tissues. Furthermore, the continuing improvement in ligand-molecules and other functional tools have developed novel strategies and brand-new definition of delivery systems, such as Trojan Horse and Nanorobots.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".