Evidence map›Paper›PMID 42144400›Full record

ArticleScientific reports2026

Green synthesis of selenium nanoparticles using Bacillus sp. strain STG-83: optimization, characterization, and prospects for cancer radiosensitization.

Somayeh Akbari-Karadeh, Seyed Mahmoud Reza Aghamiri, Simindokht Shirvani-Arani, Parisa Tajer-Mohammad-Ghazvini, Ali Bahrami-Samani, Seyed Milad Miremad, Mohammad Reza Davarpanah

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Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Somayeh Akbari-KaradehDepartment of Medical Radiation Engineering, Shahid Beheshti University, Tehran, Iran.
Seyed Mahmoud Reza AghamiriDepartment of Medical Radiation Engineering, Shahid Beheshti University, Tehran, Iran. smr-aghamiri@sbu.ac.ir.
Simindokht Shirvani-AraniNuclear Fuel Cycle Research School, Nuclear Science and Technology Research Institute, Tehran, Iran.
Parisa Tajer-Mohammad-GhazviniNuclear Fuel Cycle Research School, Nuclear Science and Technology Research Institute, Tehran, Iran. ptajer@aeoi.org.ir.
Ali Bahrami-SamaniNuclear Fuel Cycle Research School, Nuclear Science and Technology Research Institute, Tehran, Iran.
Seyed Milad MiremadNuclear Fuel Cycle Research School, Nuclear Science and Technology Research Institute, Tehran, Iran.
Mohammad Reza DavarpanahRadiation Application Research School, Nuclear Science and Technology Research Institute, Tehran, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Selenium nanoparticles (SeNPs) have gained increasing attention due to their favorable biological properties and potential applications in cancer research. In this study, the ability of Bacillus sp. strain STG-83 to biosynthesize SeNPs was systematically investigated. This study comprehensively investigated how Bacillus sp. strain STG-83 can biosynthesize SeNPs. Response surface methodology (RSM) was utilized to optimize the bioreduction of selenite and to identify the key process parameters that play a significant role. The developed quadratic model showed a strong correlation with experimental data (R²=0.92). Statistical analysis demonstrated that time and selenium concentration significantly affected selenite reduction efficiency (P < 0.05), whereas bacterial inoculum percentage were not significant (P > 0.05). Increasing selenium concentration from 0.5 to 25 mM reduced the bioreduction efficiency from 100% to 29.37%, while extending time from 8 to 96 h increased efficiency from 42.03% to 61.51%. The biosynthesized SeNPs were characterized using UV-Vis's spectroscopy, FTIR, EDX, SEM, TEM, and XRD analyses. The nanoparticles were predominantly spherical, with sizes ranging from 80 to 140 nm, and were coated with a bioorganic surface layer. Biological evaluation revealed that SeNPs induced dose-dependent cytotoxicity in U-87 line while exerting lower toxicity toward normal fibroblast cells. Flow cytometry analysis further demonstrated a significant increase in intracellular reactive oxygen species (ROS) levels following SeNP exposure, suggesting that oxidative stress plays a central role in the observed anticancer effects. The ROS generation triggered by SeNPs suggests they might serve as effective radiosensitizing agents. Future studies that combine radiation and in vivo approaches should confirm this potential.

Indexed as

BacillusMetal NanoparticlesNanoparticlesNeoplasmsRadiation-Sensitizing AgentsSeleniumCell Line, TumorGreen Chemistry TechnologyHumansReactive Oxygen SpeciesRadiation-Sensitizing AgentsReactive Oxygen SpeciesSeleniumBacteriaBiosynthesisRadiosensitizerSeleniteSelenium nanoparticles

Identifiers

PMID42144400
PMCPMC13376643

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LicenceCC BY-NC-ND
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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.