Evidence map›Paper›PMID 39209944›Full record

ArticleScientific reports2024

Medium optimization to improve growth and iron uptake by Bacillus tequilensis ASFS1 using fractional factorial designs.

Naghmeh Satarzadeh, Bagher Amirheidari, Mojtaba Shakibaie, Hamid Forootanfar

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

4 authors.

Naghmeh SatarzadehDepartment of Pharmaceutical Biotechnology, Faculty of Pharmacy, Kerman University of Medical Sciences, Kerman, Iran.
Bagher AmirheidariDepartment of Pharmaceutical Biotechnology, Faculty of Pharmacy, Kerman University of Medical Sciences, Kerman, Iran. bah.articles@gmail.com.
Mojtaba ShakibaieDepartment of Pharmaceutical Biotechnology, Faculty of Pharmacy, Kerman University of Medical Sciences, Kerman, Iran. Shakiba@kmu.ac.ir.
Hamid ForootanfarDepartment of Pharmaceutical Biotechnology, Faculty of Pharmacy, Kerman University of Medical Sciences, Kerman, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Many notable applications have been described for magnetic nanoparticles in delivery of diverse drugs and bioactive compounds into cells, magnetofection for the treatment of cancer, photodynamic therapy, photothermal therapy, and magnetic particle imaging (MPI). In response to the growing demand for magnetic nanoparticles for drug delivery or biomedical imaging applications, more effective and eco-friendly methodologies are required for large-scale biosynthesis of this nanoparticles. The major challenge in the large-scale biomedical application of magnetic nanoparticles lies in its low efficiency and optimization of nanoparticle production can address this issue. In the current study, a prediction model is suggested by the fractional factorial designs. The present study aims to optimize culture media components for improved growth and iron uptake of this strain. The result of optimization for iron uptake by the strain ASFS1 is to increase the production of magnetic nanoparticles by this strain for biomedical applications in the future. In the present study, design of experiment method was used to probe the effects of some key medium components (yeast extract, tryptone, FeSO

Indexed as

Culture MediaIronBacillusBiomassMagnetite NanoparticlesCulture MediaIronMagnetite NanoparticlesBacillus tequilensisBacterial uptake ironFractional factorial designsMagnetic iron nanoparticlesMagnetotactic bacteriaMedium optimizationPrediction model

Identifiers

PMID39209944
PMCPMC11362450

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Registered trials

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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.