Evidence map›Paper›PMID 35335518›Full record

ReviewPolymers2022

Nanocluster-Based Drug Delivery and Theranostic Systems: Towards Cancer Therapy.

Alma Lucia Villela Zumaya, Rosica Mincheva, Jean-Marie Raquez, Fatima Hassouna

Abstract readReview
In one paragraph

Review in Polymers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Article
  5. Review
  6. Article
  7. Review
  8. Review
  9. Review
  10. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Alma Lucia Villela ZumayaFaculty of Chemical Engineering, University of Chemistry and Technology Prague, 166 28 Prague, Czech Republic.ORCID 0000-0001-6191-1455
Rosica MinchevaLaboratory of Polymeric and Composite Materials, University of Mons (UMONS), Place du Parc 20, 7000 Mons, Belgium.ORCID 0000-0002-0479-9978
Jean-Marie RaquezLaboratory of Polymeric and Composite Materials, University of Mons (UMONS), Place du Parc 20, 7000 Mons, Belgium.ORCID 0000-0003-1940-7129
Fatima HassounaFaculty of Chemical Engineering, University of Chemistry and Technology Prague, 166 28 Prague, Czech Republic.ORCID 0000-0001-7927-8416

Funding

Czech Science Foundation 22-07164S
6 · The paper itself

Abstract

Over the last decades, the global life expectancy of the population has increased, and so, consequently, has the risk of cancer development. Despite the improvement in cancer therapies (e.g., drug delivery systems (DDS) and theranostics), in many cases recurrence continues to be a challenging issue. In this matter, the development of nanotechnology has led to an array of possibilities for cancer treatment. One of the most promising therapies focuses on the assembly of hierarchical structures in the form of nanoclusters, as this approach involves preparing individual building blocks while avoiding handling toxic chemicals in the presence of biomolecules. This review aims at presenting an overview of the major advances made in developing nanoclusters based on polymeric nanoparticles (PNPs) and/or inorganic NPs. The preparation methods and the features of the NPs used in the construction of the nanoclusters were described. Afterwards, the design, fabrication and properties of the two main classes of nanoclusters, namely noble-metal nanoclusters and hybrid (i.e., hetero) nanoclusters and their mode of action in cancer therapy, were summarized.

Indexed as

cancer therapydrug deliveryinorganic nanoparticlesnanoclusterspolymeric nanoparticlestheranostics

Identifiers

PMID35335518
PMCPMC8955999

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.