Evidence map›Paper›PMID 24733971›Full record

ReviewDialogues in clinical neuroscience2014

Proteomics, metabolomics, and protein interactomics in the characterization of the molecular features of major depressive disorder.

Daniel Martins-de-Souza

Abstract readReview
In one paragraph

Review in Dialogues in clinical neuroscience, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
42citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

42 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  10. LC-HRMS Lipidomic Fingerprints in Serbian Cohort of Schizophrenia Patients.International journal of molecular sciences · 2024
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  13. Untargeted Lipidomics Study of Bipolar Disorder Patients in Serbia.International journal of molecular sciences · 2023
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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

1 author.

Daniel Martins-de-SouzaLaboratory of Neuroproteomics, Department of Biochemistry, Institute of Biology, State University of Campinas (UNICAMP), Campinas, Brazil; Department of Psychiatry and Psychotherapy, Ludwig Maximilians University (LMU), Munich, Germany; Laboratory of Neurosciences (LIM-27), Institute of Psychiatry, University of São Paulo (USP), São Paulo, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Omics technologies emerged as complementary strategies to genomics in the attempt to understand human illnesses. In general, proteomics technologies emerged earlier than those of metabolomics for major depressive disorder (MDD) research, but both are driven by the identification of proteins and/or metabolites that can delineate a comprehensive characterization of MDD's molecular mechanisms, as well as lead to the identification of biomarker candidates of all types-prognosis, diagnosis, treatment, and patient stratification. Also, one can explore protein and metabolite interactomes in order to pinpoint additional molecules associated with the disease that had not been picked up initially. Here, results and methodological aspects of MDD research using proteomics, metabolomics, and protein interactomics are reviewed, focusing on human samples.

Indexed as

AnimalsHumansMajor Depressive DisorderMetabolomicsProteomicsbiomarkerbloodbrainCSFdepressioninteractomicsmajor depressionmass spectrometrymetabolomicsNMRproteomics

Identifiers

PMID24733971
PMCPMC3984892

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.