Evidence map›Paper›PMID 34299442›Full record

ArticleMolecules (Basel, Switzerland)2021

Personalized Metabolic Profile by Synergic Use of NMR and HRMS.

Greta Petrella, Camilla Montesano, Sara Lentini, Giorgia Ciufolini, Domitilla Vanni, Roberto Speziale, Andrea Salonia, Francesco Montorsi, Vincenzo Summa, Riccardo Vago and 3 more

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

Article in Molecules (Basel, Switzerland), 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
0.5field-weighted citation impact, top 38% 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
  2. Article
  3. A Pilot Study on theAntioxidants (Basel, Switzerland) · 2021
    Article
  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

13 authors at 4 institutions in 1 country.

Greta PetrellaDepartment of Chemical Science and Technology, University of Rome "Tor Vergata", 00133 Rome, Italy.ORCID 0000-0002-9449-9820
Camilla MontesanoChemistry Department, University of Rome "Sapienza", 00185 Rome, Italy.ORCID 0000-0001-7721-4961
Sara LentiniDepartment of Chemical Science and Technology, University of Rome "Tor Vergata", 00133 Rome, Italy.
Giorgia CiufoliniDepartment of Chemical Science and Technology, University of Rome "Tor Vergata", 00133 Rome, Italy.
Domitilla VanniDepartment of Chemical Science and Technology, University of Rome "Tor Vergata", 00133 Rome, Italy.
Roberto SpezialeIRBM S.p.A., 00071 Pomezia, Italy.ORCID 0000-0001-7309-0703
Andrea SaloniaUrological Research Institute, IRCCS Ospedale San Raffaele, 20132 Milan, Italy.
Francesco MontorsiUrological Research Institute, IRCCS Ospedale San Raffaele, 20132 Milan, Italy.
Vincenzo SummaIRBM S.p.A., 00071 Pomezia, Italy.
Riccardo VagoUrological Research Institute, IRCCS Ospedale San Raffaele, 20132 Milan, Italy.ORCID 0000-0002-3781-6770
Laura OrsattiIRBM S.p.A., 00071 Pomezia, Italy.ORCID 0000-0002-5430-9965
Edith MonteagudoIRBM S.p.A., 00071 Pomezia, Italy.
Daniel Oscar CiceroDepartment of Chemical Science and Technology, University of Rome "Tor Vergata", 00133 Rome, Italy.ORCID 0000-0001-5012-1714
University of Rome Tor Vergata · ITIRBM Science Park · ITIRCCS Ospedale San Raffaele · ITSapienza University of Rome · IT

Funding

Collezione Nazionale di Composti Chimici e Centro Screening (CNCCS) Consortium Project B, Sp 2, WP2, 2019
6 · The paper itself

Abstract

A new strategy that takes advantage of the synergism between NMR and UHPLC-HRMS yields accurate concentrations of a high number of compounds in biofluids to delineate a personalized metabolic profile (SYNHMET). Metabolite identification and quantification by this method result in a higher accuracy compared to the use of the two techniques separately, even in urine, one of the most challenging biofluids to characterize due to its complexity and variability. We quantified a total of 165 metabolites in the urine of healthy subjects, patients with chronic cystitis, and patients with bladder cancer, with a minimum number of missing values. This result was achieved without the use of analytical standards and calibration curves. A patient's personalized profile can be mapped out from the final dataset's concentrations by comparing them with known normal ranges. This detailed picture has potential applications in clinical practice to monitor a patient's health status and disease progression.

Indexed as

AdultAgedAged, 80 and overChromatography, High Pressure LiquidCystitisFemaleHumansMagnetic Resonance ImagingMagnetic Resonance SpectroscopyMaleMetabolomeMetabolomicsMiddle AgedPrecision MedicineTandem Mass SpectrometryUrinary Bladder Neoplasmsmass spectrometrynormal rangesnuclear magnetic resonancepersonalized metabolic profileurine metabolome

Identifiers

PMID34299442
PMCPMC8304707
OpenAlexW3182829554

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.