Evidence map›Paper›PMID 34398320›Full record

ReviewNano-micro letters2021

Recent Progress in Nanoscale Covalent Organic Frameworks for Cancer Diagnosis and Therapy.

Shuncheng Yao, Zhirong Liu, Linlin Li

Abstract readReview
In one paragraph

Review in Nano-micro letters, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
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  6. Fe-HCOF-PEGInternational journal of nanomedicine · 2024
    Article
  7. Review
  8. Biomedical Approach of Nanotechnology and Biological Risks: A Mini-Review.International journal of molecular sciences · 2023
    Review
  9. Review
  10. Review
  11. Article
  12. Review
  13. Review
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  15. Review
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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

3 authors.

Shuncheng YaoBeijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, People's Republic of China.
Zhirong LiuBeijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, People's Republic of China.
Linlin LiBeijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, People's Republic of China. lilinlin@binn.cas.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Covalent organic frameworks (COFs) as a type of porous and crystalline covalent organic polymer are built up from covalently linked and periodically arranged organic molecules. Their precise assembly, well-defined coordination network, and tunable porosity endow COFs with diverse characteristics such as low density, high crystallinity, porous structure, and large specific-surface area, as well as versatile functions and active sites that can be tuned at molecular and atomic level. These unique properties make them excellent candidate materials for biomedical applications, such as drug delivery, diagnostic imaging, and disease therapy. To realize these functions, the components, dimensions, and guest molecule loading into COFs have a great influence on their performance in various applications. In this review, we first introduce the influence of dimensions, building blocks, and synthetic conditions on the chemical stability, pore structure, and chemical interaction with guest molecules of COFs. Next, the applications of COFs in cancer diagnosis and therapy are summarized. Finally, some challenges for COFs in cancer therapy are noted and the problems to be solved in the future are proposed.

Indexed as

Cancer diagnosis and therapyCovalent organic frameworksDrug deliveryNanomedicine

Identifiers

PMID34398320
PMCPMC8368921

What OpenQuestion holds

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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.