Evidence map›Paper›PMID 28050890›Full record

ReviewDrug delivery and translational research2017

Nanoengineered strategies for siRNA delivery: from target assessment to cancer therapeutic efficacy.

Dinesh Kumar Mishra, Neelam Balekar, Pradyumna Kumar Mishra

Abstract readReview
PubMed Publisher
In one paragraph

Review in Drug delivery and translational research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
2.4field-weighted citation impact, top 11% 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

19 citing papers in PubMed, 33 citations in OpenAlex.

  1. siRNA-based strategies to combat drug resistance in gastric cancer.Medical oncology (Northwood, London, England) · 2024
    Review
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  8. Nanotechnology in reproductive medicine: Opportunities for clinical translation.Clinical and experimental reproductive medicine · 2020
    Article
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  15. Article
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  17. [Construction and verification of anti-MM scFv-tP fusion protein expression vector].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2017
    Article
  18. Review
  19. 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

3 authors at 2 institutions in 1 country.

Dinesh Kumar MishraIPS Academy, College of Pharmacy, A. B. Road, Indore, MP, 452 012, India. dineshdops@gmail.com.
Neelam BalekarIPS Academy, College of Pharmacy, A. B. Road, Indore, MP, 452 012, India.
Pradyumna Kumar MishraDepartment of Molecular Biology, National Institute for Research in Environmental Health, Indian Council of Medical Research (ICMR), Bhopal, India.
IPS Academy · INIndian Council of Medical Research · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The promise of RNA interference (RNAi) technology in cancer therapeutics aims to deliver small interfering RNA (siRNA) for silencing of gene expression in cell type-specific pathway. However, the challenge for the delivery of stable siRNA is hindered by an immune-hostile tumor microenvironment and physiological barriers of the circulatory system. Therefore, the development and validation of safe, stable, and efficient nanoengineered delivery systems are highly essential for effective delivery of siRNA into cancer cells. This review focuses on gene-silencing mechanisms, challenges to siRNA delivery, design and delivery of nanocarrier systems, ongoing clinical trials, and translational prospects for siRNA-mediated cancer therapeutics.

Indexed as

Gene SilencingHumansNanotechnologyNeoplasmsRNA, Small InterferingTreatment OutcomeRNA, Small InterferingDrug deliveryNanomedicineOncotherapyRNA interferenceTranslational research

Identifiers

PMID28050890
OpenAlexW2565544011

What OpenQuestion holds

Textmetadata
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.