ArticleJOM (Warrendale, Pa. : 1989)2019
Self Assembled Recombinant Proteins on Metallic Nanoparticles As Bimodal Imaging Probes.
Article in JOM (Warrendale, Pa. : 1989), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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.
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.
Who cites it
4 citing papers in PubMed.
- Spatiotemporal Imaging of Zinc Ions in Zebrafish Live Brain Tissue Enabled by Fluorescent Bionanoprobes.Molecules (Basel, Switzerland) · 2023Article
- Peptide-Enabled Nanocomposites Offer Biomimetic Reconstruction of Silver Diamine Fluoride-Treated Dental Tissues.Polymers · 2022Article
- Solid-Binding Peptide-Guided Spatially Directed Immobilization of Kinetically Matched Enzyme Cascades in Membrane Nanoreactors.ACS omega · 2021Article
- Repeatedly Applied Peptide Film Kills Bacteria on Dental Implants.JOM (Warrendale, Pa. : 1989) · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Combining multiple modalities is at the center of developing new methods for sensing and imaging that are required for comprehensive understanding of events at the molecular level. Various imaging modalities have been developed using metallic nanoparticles owning to their exceptional physical and chemical properties. Due to their localized surface plasmon resonance characteristics, gold and silver nanoparticles exhibit unique optoelectronic properties commonly used in biomedical sciences and engineering. Self assembled monolayers or physical adsorption have previously been adapted to functionalize the surfaces of nanoparticles with biomolecules for targeted imaging. However, depending on differences among the functional groups used on the nanoparticle surface, wide variation in the displayed biomolecular property to recognize its target may result. In the last decade, the properties of inorganic binding peptides have been proven advantageous to assemble selective functional nano-entities or proteins onto nanoparticles surfaces. Herein we explored formation of self-assembled hybrid metallic nano-architectures that are composed of gold and silver nanoparticles with fluorescent proteins, for use as bimodal imaging probes. We employed metal binding peptide-based assembly to self assemble green fluorescence protein onto metallic substrates of various geometries. Assembly of the green fluorescent proteins, genetically engineered to incorporate gold- or silver-binding peptides onto metallic nanoparticles, resulted in the generation of hybrid-, biomodal-imaging probes in a single step. Green fluorescent activity on gold and silver surfaces can be been monitored using both plasmonic and fluorescent signatures. Our results demonstrate a novel bimodal imaging system that can be finely tuned with respect to nanoparticle size and protein concentration. Resulting hybrid probes may mitigate the limitation of depth penetration into biological tissues as well as providing high signal-to-noise ratio and sensitivity.
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Registered trials
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.