Phosphorus-containing nanoparticles: biomedical patents review
Bibliographic record
Abstract
INTRODUCTION: The beginning of the nano-era started with the appearance of artificial nanosized supramolecular systems called nanomaterials and nanoparticles (NPs). AREAS COVERED: In the present review, we have analyzed the patents on phosphorus-based nanomaterials (fullerenes, quantum dots [QDs], graphene, liposomes, dendrimers, gold and silver NPs) in biology and medicine. Their impact in treatment of cancer, viral infections and cardiovascular diseases is discussed. EXPERT OPINION: Liposomes and dendrimers had the highest number of biomedical patents. The third candidates were QDs and the fourth and fifth were gold and silver NPs. Fullerenes and carbon nanotubes have the fewest applications in biology and medicine. Thus, our first conclusion was about the 'unifying nanotoxicology paradigm', that 'soft' NPs are significantly more biocompatible than 'hard' NPs. There has been a trend of these nanomaterials being applied in medicine drug and gene delivery, visualization of cells and pathologic processes, using them as antivirals and antimicrobials, contrast agents, antioxidants and photosensitizers. It was unexpected that no patents were found in which phosphorus NPs were used in 3D printing of bones and other biological tissues. The conclusion reached is that nanomaterials are promising tools in future medical applications.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".