Evidence map›Paper›PMID 34568735›Full record

ArticleInternational journal of reproductive biomedicine2021

Aromatic amino acids play a harmonizing role in prostate cancer: A metabolomics-based cross-sectional study.

Ziba Akbari, Roghayeh Taghipour Dijojin, Zahra Zamani, Reza Haji Hosseini, Mohammad Arjmand

Open access · goldAbstract read
In one paragraph

Article in International journal of reproductive biomedicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.9field-weighted citation impact, top 27% 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

8 citing papers in PubMed, 13 citations in OpenAlex.

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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

5 authors at 1 institution in 1 country.

Ziba AkbariBiochemistry Department, Metabolomics Lab, Pasture Institute of Iran, Tehran, Iran.
Roghayeh Taghipour DijojinBiochemistry Department, Metabolomics Lab, Pasture Institute of Iran, Tehran, Iran.
Zahra ZamaniBiochemistry Department, Metabolomics Lab, Pasture Institute of Iran, Tehran, Iran.
Reza Haji HosseiniBiology Department, Payame Noor University, Tehran, Iran.
Mohammad ArjmandBiochemistry Department, Metabolomics Lab, Pasture Institute of Iran, Tehran, Iran.ORCID https://orcid.org/0000-0003-0560-0745
Payame Noor University · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundProstate cancer (PCa) is a common health problem worldwide. The rate of this disease is likely to grow by 2021. PCa is a heterogeneous disorder, and various biochemical factors contribute to the development of this disease. The metabolome is the complete set of metabolites in a cell or biological sample and represents the downstream end product of the omics. Hence, to model PCa by computational systems biology, a preliminary metabolomics-based study was used to compare the metabolome profile pattern between healthy and PCa men.

objectiveThis study was carried out to highlight energy metabolism modification and assist the prognosis and treatment of disease with unique biomarkers. MATERIALS AND

methodsIn this cross-sectional research, 26 men diagnosed with stage-III PCa and 26 healthy men with normal PSA levels were enrolled. Urine was analyzed with proton nuclear magnetic resonance (

resultsOur findings reveal that modifications in aromatic amino acid metabolism and some of their metabolites have a high potential for use as urinary PCa biomarkers. Tryptophan metabolism (p

conclusionThe discriminated metabolites and their pathways play an essential role in PCa causes and harmony.

Indexed as

Aromatic amino acidsMetabolomicsProstate cancer

Identifiers

PMID34568735
PMCPMC8458921
OpenAlexW3200980159

What OpenQuestion holds

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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.