Evidence map›Paper›PMID 42111530›Full record

ArticleRSC advances2026

Polymeric sorbent phase sorptive extraction for radiation metabolomics.

Abdul Hoque, Evan L Pannkuk, Jerome Lacombe, Sunil Bansal, Jerry Angdisen, Geraldine Vitry, Abel Encarnacion Hernandez, Nabhan M Fakrudin, Mathew Barrett, James Helton and 4 more

Abstract read
In one paragraph

Article in RSC advances, 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

14 authors.

Abdul HoqueCenter for Applied NanoBiosience & Medicine, University of Arizona Phoenix Arizona 85004 USA fzenhaus@arizona.edu.ORCID https://orcid.org/0000-0002-2508-6811
Evan L PannkukDepartment of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center Washington DC 20057 USA.
Jerome LacombeCenter for Applied NanoBiosience & Medicine, University of Arizona Phoenix Arizona 85004 USA fzenhaus@arizona.edu.
Sunil BansalDepartment of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center Washington DC 20057 USA.
Jerry AngdisenDepartment of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center Washington DC 20057 USA.
Geraldine VitryDepartment of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center Washington DC 20057 USA.
Abel Encarnacion HernandezCenter for Applied NanoBiosience & Medicine, University of Arizona Phoenix Arizona 85004 USA fzenhaus@arizona.edu.
Nabhan M FakrudinCenter for Applied NanoBiosience & Medicine, University of Arizona Phoenix Arizona 85004 USA fzenhaus@arizona.edu.ORCID https://orcid.org/0000-0002-8020-3507
Mathew BarrettCenter for Applied NanoBiosience & Medicine, University of Arizona Phoenix Arizona 85004 USA fzenhaus@arizona.edu.
James HeltonCenter for Applied NanoBiosience & Medicine, University of Arizona Phoenix Arizona 85004 USA fzenhaus@arizona.edu.
Jian GuCenter for Applied NanoBiosience & Medicine, University of Arizona Phoenix Arizona 85004 USA fzenhaus@arizona.edu.ORCID https://orcid.org/0000-0001-6081-9155
Albert J FornaceDepartment of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center Washington DC 20057 USA.
Evagelia C LaiakisDepartment of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center Washington DC 20057 USA.
Frederic ZenhausernCenter for Applied NanoBiosience & Medicine, University of Arizona Phoenix Arizona 85004 USA fzenhaus@arizona.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Novel biodosimetry assays are needed for potential radiological incidents to rapidly assess radiation exposure and guide medical treatments. Mass spectrometry-based metabolomic analysis using a sorptive phase extraction is a rapid and efficient method for radiation-induced biomarkers in biofluids. Here, we developed a chemically functionalized polymeric Sorbent Phase Sorptive Extraction (SPSE) method. This method employs polymeric thin film sorbents with tailored organic functional groups, which directly bind radiation-responsive biomolecules and increase sample absorption capacity. This microporous membrane system enables rapid, high-sensitivity extraction of metabolites spanning a wide polarity range from urine, serum, and whole blood. We characterized the surface morphology, chemical functionality, and hydrophilicity of multiple sorbent-coated cellulose membranes, including plasma-functionalized nylon-6. Matrix interference was evaluated using untargeted metabolomics, and analytical performance was assessed using a targeted multiplex radiation biomarker panel. Urine, serum, and whole blood were collected from male and female C57BL/6 mice (9 weeks old) exposed to X-rays at 1 day (0, 2, 8, 13 Gy) and 7 days (0, 2, 8 Gy) post-irradiation. The membrane types preserve metabolite stability at room temperature for up to two weeks; however, nylon-6-based cellulose paper membranes exhibited the highest surface porosity, absorption capacity, and metabolite recovery. Classifier performance evaluated using receiver operating characteristic analysis demonstrated comparable sensitivity and specificity between SPSE and conventional dilute-and-shoot workflows. Collectively, these results support further development of polymeric sorbent coated paper and fabric-based substrates to increase throughput while eliminating cold-chain requirements. These environmentally conscious design features exemplify principles of green chemistry including lowering chemical waste and operational energy demand.

Identifiers

PMID42111530
PMCPMC13154812

What OpenQuestion holds

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LicenceCC BY-NC
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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.