Evidence map›Paper›PMID 42569409›Full record

ArticleCell signaling2026

Generation of selenium nano- and micro-materials using abiotic and biotic processes.

Michael Wells, Kyle Rugg, Humaira Urmee, Jenna Etheridge, Caitlyn Boudreaux, Natalie Fairley, Mark A DeCoster

Abstract read
In one paragraph

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

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Michael WellsBiological Sciences, Louisiana Tech University, USA.
Kyle RuggCellular Neuroscience Lab, Biomedical Engineering, Louisiana Tech University, USA.
Humaira UrmeeCellular Neuroscience Lab, Biomedical Engineering, Louisiana Tech University, USA.
Jenna EtheridgeCellular Neuroscience Lab, Biomedical Engineering, Louisiana Tech University, USA.
Caitlyn BoudreauxCellular Neuroscience Lab, Biomedical Engineering, Louisiana Tech University, USA.
Natalie FairleyCellular Neuroscience Lab, Biomedical Engineering, Louisiana Tech University, USA.
Mark A DeCosterCellular Neuroscience Lab, Biomedical Engineering, Louisiana Tech University, USA.

Funding

STABILITY' (symptomatic review during biologic therapy) Review in Inflammatory Bowel DiseaseP20GM103424 · NIGMS · LOUISIANA STATE UNIV A&M COL BATON ROUGE · PI VLADIMIR N CHOULJENKO · 2012 to 2026
$57.4M
NIGMS NIH HHS P20 GM103424
6 · The paper itself

Abstract

We have previously described the generation of Metal Organic Biohybrids (MOBs) as self-assembling structures that are formed by a combination of a biological component (amino acid), and a metal, such as copper. This MOBs synthesis process has now been distilled to be purely biochemical (a green chemistry process), or abiotic, not requiring the presence of cells. Building on these discoveries, in this commentary we describe the use of selenium for the first time using MOBs synthesis techniques, as well as using biotic techniques, that is, selenium in the presence of living microbes. New and different materials are described using the respective techniques, which have different nano- and micro-scale properties. We anticipate that the abiotic and biotic techniques may provide parallel methods for generating new MOBs and new nanomaterials, respectively. One potential application of the generated materials could include treatments for cancer, since in the case of selenium-containing MOBs, we demonstrate 100 percent biodegradability.

Indexed as

CancerCopperMetal-Organic Biohybrids (MOBs)MicrobesNanomaterialsSelenium

Identifiers

PMID42569409
PMCPMC13449840

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.