MétaCan
Menu
Back to cohort
Record W1967410304 · doi:10.1517/13543776.2015.1010512

Phosphorus-containing nanoparticles: biomedical patents review

2015· review· en· W1967410304 on OpenAlexaff
Dzmitry Shcharbin, Natallia Shcharbina, Antos Shakhbazau, Serge Mignani, Jean‐Pierre Majoral, Maria Bryszewska

Bibliographic record

VenueExpert Opinion on Therapeutic Patents · 2015
Typereview
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsNanotechnologyPhosphorusNanoparticleChemistryBusinessMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.987
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.009

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.

Opus teacher head0.142
GPT teacher head0.380
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations7
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueExpert Opinion on Therapeutic PatentsSame topicNanoparticle-Based Drug DeliveryFrench-language works237,207