Evidence map›Paper›PMID 34744497›Full record

ArticleInternational journal of mass spectrometry2021

Toward improvement of screening through mass spectrometry-based proteomics: ovarian cancer as a case study.

Gordon T Luu, Laura M Sanchez

Open access · greenAbstract read
In one paragraph

Article in International journal of mass spectrometry, 2021. 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
0.7field-weighted citation impact, top 34% 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, 8 citations in OpenAlex.

  1. Article
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  3. Review
  4. Article
  5. 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

2 authors at 1 institution in 1 country.

Gordon T LuuDepartment of Chemistry and Biochemistry, University of California Santa Cruz, 1156 High St. Santa Cruz, CA, 95064.
Laura M SanchezDepartment of Chemistry and Biochemistry, University of California Santa Cruz, 1156 High St. Santa Cruz, CA, 95064.
University of California, Santa Cruz · US

Funding

Imaging mass spectrometry methodologies for studying the metabolites of cancer metastasisR01CA240423 · NCI · UNIVERSITY OF ILLINOIS AT CHICAGO · PI BURDETTE, JOANNA E, SANCHEZ, LAURA MARGARET · 2020 to 2024
$2.1M
MALDI-TOF MS/IDBac to discover antibiotics from the Great Lakes sponge microbiomeR01GM125943 · NIGMS · UNIVERSITY OF ILLINOIS AT CHICAGO · PI MURPHY, BRIAN THACHER, SANCHEZ, LAURA MARGARET · 2018 to 2021
$2.1M
NCI NIH HHS R01 CA240423NIGMS NIH HHS R01 GM125943
6 · The paper itself

Abstract

Ovarian cancer is one of the leading causes of cancer related deaths affecting United States women. Early-stage detection of ovarian cancer has been linked to increased survival, however, current screening methods, such as biomarker testing, have proven to be ineffective in doing so. Therefore, further developments are necessary to be able to achieve positive patient prognosis. Ongoing efforts are being made in biomarker discovery towards clinical applications in screening for early-stage ovarian cancer. In this perspective, we discuss and provide examples for several workflows employing mass spectrometry-based proteomics towards protein biomarker discovery and characterization in the context of ovarian cancer; workflows include protein identification and characterization as well as intact protein profiling. We also discuss the opportunities to merge these workflows for a multiplexed approach for biomarkers. Lastly, we provide our insight as to future developments that may serve to enhance biomarker discovery workflows while also considering translational potential.

Identifiers

PMID34744497
PMCPMC8570641
OpenAlexW3192127699

What OpenQuestion holds

Textmetadata
LicenceTDM
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.