Evidence map›Paper›PMID 35361774›Full record

ArticleScientific data2022

Songyeon Lee, Byung-Joon Seung, In Seok Yang, Jueun Lee, Taewoong Ha, Hee-Myung Park, Jae-Ho Cheong, Sangwoo Kim, Jung-Hyang Sur, Geum-Sook Hwang and 1 more

Open access · goldAbstract readDataset
In one paragraph

Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.6field-weighted citation impact, top 37% 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

4 citing papers in PubMed, 7 citations in OpenAlex.

  1. Review
  2. Article
  3. Omics Technologies in Veterinary Medicine: Literature Review and Perspectives in Transfusion Medicine.Transfusion medicine and hemotherapy : offizielles Organ der Deutschen Gesellschaft fur Transfusionsmedizin und Immunhamatologie · 2023
    Review
  4. Scientific data · 2022
    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

11 authors at 5 institutions in 1 country.

Songyeon LeeSchool of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005, South Korea.ORCID 0000-0003-1312-2464
Byung-Joon SeungDepartment of Veterinary Pathology, College of Veterinary Medicine, Konkuk University, Seoul, 05029, South Korea.
In Seok YangDepartment of Biomedical Systems Informatics and Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, 03722, South Korea.
Jueun LeeIntegrated Metabolomics Research Group, Western Seoul Center, Korea Basic Science Institute, Seoul, 03759, South Korea.
Taewoong HaIntegrated Metabolomics Research Group, Western Seoul Center, Korea Basic Science Institute, Seoul, 03759, South Korea.
Hee-Myung ParkDepartment of Veterinary Internal Medicine, College of Veterinary Medicine, Konkuk University, Seoul, 05029, South Korea.
Jae-Ho CheongDepartment of Biomedical Systems Informatics and Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, 03722, South Korea.
Sangwoo KimDepartment of Biomedical Systems Informatics and Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, 03722, South Korea.ORCID 0000-0001-5356-0827
Jung-Hyang SurDepartment of Veterinary Pathology, College of Veterinary Medicine, Konkuk University, Seoul, 05029, South Korea.
Geum-Sook HwangIntegrated Metabolomics Research Group, Western Seoul Center, Korea Basic Science Institute, Seoul, 03759, South Korea. gshwang@kbsi.re.kr.ORCID 0000-0002-1600-1556
Hojung NamSchool of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005, South Korea. hjnam@gist.ac.kr.ORCID 0000-0002-5109-9114
Konkuk University · KRYonsei University · KRGwangju Institute of Science and Technology · KRKorea Basic Science Institute · KREwha Womans University · KR

Funding

Korea Basic Science Institute (KBSI) C170200National Research Foundation of Korea (NRF) 2016M3A9B6903439National Research Foundation of Korea (NRF) 2017M3C9A6047625
6 · The paper itself

Abstract

The identification of efficient and sensitive biomarkers for non-invasive tests is one of the major challenges in cancer diagnosis. To address this challenge, metabolomics is widely applied for identifying biomarkers that detect abnormal changes in cancer patients. Canine mammary tumors exhibit physiological characteristics identical to those in human breast cancer and serve as a useful animal model to conduct breast cancer research. Here, we aimed to provide a reliable large-scale metabolite dataset collected from dogs with mammary tumors, using proton nuclear magnetic resonance spectroscopy. We identified 55 metabolites in urine samples from 20 benign, 87 malignant, and 49 healthy control subjects. This dataset provides details of mammary tumor-specific metabolites in dogs and insights into cancer-specific metabolic alterations that share similar molecular characteristics.

Indexed as

DogsMammary Neoplasms, AnimalAnimalsFemaleMetabolomicsProton Magnetic Resonance Spectroscopy

Identifiers

PMID35361774
PMCPMC8971436
OpenAlexW4220741339

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.