Evidence map›Paper›PMID 34589111›Full record

ReviewFrontiers in genetics2021

Applications of Aptamers in the Diagnosis and Treatment of Ovarian Cancer: Progress From 2016 to 2020.

Luoshan Ruan, Xin Li

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.3field-weighted citation impact, top 43% of its field
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

3 citing papers in PubMed, 8 citations in OpenAlex.

  1. Review
  2. Review
  3. Article
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

2 authors at 2 institutions in 1 country.

Luoshan RuanDepartment of Gynecology, Renmin Hospital of Wuhan University, Wuhan, China.
Xin LiDepartment of Gynecology, Renmin Hospital of Wuhan University, Wuhan, China.
Renmin Hospital of Wuhan University · CNWuhan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nucleic acid aptamers are short single-stranded DNA or RNA oligonucleotides selected from a random single-stranded nucleic acid library using systematic evolution of ligands by exponential enrichment technology. To allow them to bind to molecular targets with the same specificity and precision as that of antibodies, aptamers are folded into secondary or tertiary structures. However, compared to antibodies, aptamers are not immunogenic and are easier to synthesize. Furthermore, they are chemically modified, which protects them from degradation by nucleases. Hence, due to their stability and favorable targeting ability, aptamers are promising for the diagnosis and treatment of diseases. Ovarian cancer has the worst prognosis among all gynecological diseases and is usually diagnosed at the medium and advanced stages due to its nonspecific symptoms. Relapse is common, even if patients receive a standard therapeutic regimen including surgery and chemotherapy; simultaneously, drug resistance and adverse effects are reported in a several patients. Therefore, the safer and more efficient diagnostic and treatment method for ovarian cancer is imperative. Scientists have been trying to utilize aptamer technology for the early diagnosis and accurate treatment of ovarian cancer and some progress has been made in this field. This review discusses the screening of nucleic acid aptamers by targeting ovarian cancer cells and the application of aptamers in the diagnosis and treatment of ovarian cancer.

Indexed as

aptamerdiagnosisovarian cancersystematic evolution of ligands by exponential enrichmenttreatment

Identifiers

PMID34589111
PMCPMC8473910
OpenAlexW3200684373

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.