Evidence map›Paper›PMID 36745806›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2023

Decoding the metabolic response of

Hong Wei, Yixin Huang, Peter J Santiago, Khachik E Labachyan, Sasha Ronaghi, Martin Paul Banda Magana, Yen-Hsiang Huang, Sunny C Jiang, Allon I Hochbaum, Regina Ragan

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
2.3field-weighted citation impact, top 12% of its field
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

5 citing papers in PubMed, 21 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. Review
  5. Article
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

10 authors at 1 institution in 1 country.

Hong WeiDepartment of Materials Science and Engineering, University of California, Irvine, CA 92697-2585.ORCID 0000-0002-2359-8522
Yixin HuangDepartment of Chemical and Biomolecular Engineering, University of California, Irvine, CA 92697-2580.
Peter J SantiagoDepartment of Materials Science and Engineering, University of California, Irvine, CA 92697-2585.
Khachik E LabachyanDepartment of Pharmaceutical Sciences, University of California, Irvine, CA 92697-3958.
Sasha RonaghiSage Hill School, Newport Coast, CA 92657.
Martin Paul Banda MaganaDepartment of Molecular Biology and Biochemistry, University of California, Irvine, CA 92697-2525.
Yen-Hsiang HuangDepartment of Civil and Environmental Engineering, University of California, Irvine, CA 92697-2175.ORCID 0000-0002-7603-5897
Sunny C JiangDepartment of Civil and Environmental Engineering, University of California, Irvine, CA 92697-2175.ORCID 0000-0002-4993-8038
Allon I HochbaumDepartment of Materials Science and Engineering, University of California, Irvine, CA 92697-2585.ORCID 0000-0002-5377-8065
Regina RaganDepartment of Materials Science and Engineering, University of California, Irvine, CA 92697-2585.ORCID 0000-0002-8694-5683
University of California, Irvine · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Heavy metal contamination due to industrial and agricultural waste represents a growing threat to water supplies. Frequent and widespread monitoring for toxic metals in drinking and agricultural water sources is necessary to prevent their accumulation in humans, plants, and animals, which results in disease and environmental damage. Here, the metabolic stress response of bacteria is used to report the presence of heavy metal ions in water by transducing ions into chemical signals that can be fingerprinted using machine learning analysis of vibrational spectra. Surface-enhanced Raman scattering surfaces amplify chemical signals from bacterial lysate and rapidly generate large, reproducible datasets needed for machine learning algorithms to decode the complex spectral data. Classification and regression algorithms achieve limits of detection of 0.5 pM for As

Indexed as

Metals, HeavyWater Pollutants, ChemicalAgricultureAnimalsEnvironmental MonitoringEscherichia coliHumansWater QualityMetals, HeavyWater Pollutants, Chemicalbacterial metabolismenvironmental sensorsmachine learningvibrational spectroscopy

Identifiers

PMID36745806
PMCPMC9963153
OpenAlexW4319302925

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.