Evidence map›Paper›PMID 37422485›Full record

ArticleScientific reports2023

Codelivery of resveratrol melatonin utilizing pH responsive sericin based nanocarriers inhibits the proliferation of breast cancer cell line at the different pH.

Faranak Aghaz, Zahra Asadi, Soraya Sajadimajd, Khosrow Kashfi, Elham Arkan, Zohreh Rahimi

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
3.3field-weighted citation impact, top 7% 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

22 citing papers in PubMed, 33 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. Stimuli-responsive bioengineered platforms for precision cancer therapy.Frontiers in bioengineering and biotechnology · 2026
    Review
  5. Review
  6. Review
  7. Article
  8. Article
  9. Sericin-coated MnOScientific reports · 2025
    Article
  10. Article
  11. Review
  12. Article
  13. Review
  14. Article
  15. Sericin Protein: Structure, Properties, and Applications.Journal of functional biomaterials · 2024
    Review
  16. Article
  17. Article
  18. Article
  19. Review
  20. 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

6 authors at 3 institutions in 2 countries.

Faranak AghazNano Drug Delivery Research Center, Health Technology Institute, Kermanshah University of Medical Sciences, Kermanshah, Iran.
Zahra AsadiStudents Research Committee, Kermanshah University of Medical Sciences, Kermanshah, Iran.
Soraya SajadimajdDepartment of Biology, Faculty of Science, Razi University, Kermanshah, Iran.
Khosrow KashfiDepartment of Molecular, Cellular and Biomedical Sciences, Sophie Davis School of Biomedical Education, City University of New York School of Medicine, New York, USA.
Elham ArkanNano Drug Delivery Research Center, Health Technology Institute, Kermanshah University of Medical Sciences, Kermanshah, Iran. ElhamArkan@yahoo.com.
Zohreh RahimiDepartment of Clinical Biochemistry, Medical School, Kermanshah University of Medical Sciences, Kermanshah, Iran. Zrahimi@yahoo.com.
Kermanshah University of Medical Sciences · IRCity University of New York · USRazi University · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Protein-based nanocarriers have demonstrated good potential for cancer drug delivery. Silk sericin nano-particle is arguably one of the best in this field. In this study, we developed a surface charge reversal sericin-based nanocarrier to co-deliver resveratrol and melatonin (MR-SNC) to MCF-7 breast cancer cells as combination therapy. MR-SNC was fabricated with various sericin concentrations via flash-nanoprecipitation as a simple and reproducible method without complicated equipment. The nanoparticles were subsequently characterized for their size, charge, morphology and shape by dynamic light scattering (DLS) and scanning electron microscope (SEM). Nanocarriers chemical and conformational analysis were done by fourier transform infrared spectroscopy (FT-IR) and circular dichroism (CD) respectively. In vitro drug release was determined at different pH values (7.45, 6.5 and 6). The cellular uptake and cytotoxicity were studies using breast cancer MCF-7 cells. MR-SNC fabricated with the lowest sericin concentration (0.1%), showed a desirable 127 nm size, with a net negative charge at physiological pH. Sericin structure was preserved entirely in the form of nano-particles. Among the three pH values we applied, the maximum in vitro drug release was at pH 6, 6.5, and 7.4, respectively. This pH dependency showed the charge reversal property of our smart nanocarrier via changing the surface charge from negative to positive in mildly acidic pH, destructing the electrostatic interactions between sericin surface amino acids. Cell viability studies demonstrated the significant toxicity of MR-SNC in MCF-7 cells at all pH values after 48 h, suggesting a synergistic effect of combination therapy with the two antioxidants. The efficient cellular uptake of MR-SNC, DNA fragmentation and chromatin condensation was found at pH 6. Nutshell, our result indicated proficient release of the entrapped drug combination from MR-SNC in an acidic environment leading to cell apoptosis. This work introduces a smart pH-responsive nano-platform for anti-breast cancer drug delivery.

Indexed as

Antineoplastic AgentsMelatoninNanoparticlesNeoplasmsSericinsCell ProliferationDrug CarriersDrug Delivery SystemsHumansHydrogen-Ion ConcentrationMCF-7 CellsResveratrolSpectroscopy, Fourier Transform InfraredAntineoplastic AgentsDrug CarriersMelatoninResveratrolSericins

Identifiers

PMID37422485
PMCPMC10329705
OpenAlexW4383619738

What OpenQuestion holds

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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.