Evidence map›Paper›PMID 30071418›Full record

ReviewJournal of affective disorders2018

MicroRNAs in depression and suicide: Recent insights and future perspectives.

Yogesh Dwivedi

Abstract readReview
In one paragraph

Review in Journal of affective disorders, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
48citing papers in PubMed, 1 pooled it
2.1field-weighted citation impact, top 13% 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

48 citing papers in PubMed, 1 synthesis or guideline pooled it, 70 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

1 author at 1 institution in 1 country.

Yogesh DwivediDepartment of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, SC711 Sparks Center, 1720 7th Avenue, Birmingham, AL 35294, USA. Electronic address: ydwivedi@uab.edu.
University of Alabama at Birmingham · US

Funding

Plasma Exosomal MicroRNAs as Promising Novel Biomarkers for Suicidality and Treatment OutcomeR01MH107183 · NIMH · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI DWIVEDI, YOGESH, SHELTON, RICHARD CHARLES · 2015 to 2019
$3.5M
MicroRNA Mapping in Major DepressionR01MH100616 · NIMH · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI DWIVEDI, YOGESH · 2014 to 2019
$1.8M
Perturbed cell signaling network and suicide neurobiologyR01MH101890 · NIMH · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI DWIVEDI, YOGESH · 2013 to 2017
$1.6M
Calcium Sensing Proteins in DepressionR01MH082802 · NIMH · UNIVERSITY OF ILLINOIS AT CHICAGO · PI DWIVEDI, YOGESH · 2009 to 2013
$1.4M
NIMH NIH HHS R01 MH082802NIMH NIH HHS R01 MH100616NIMH NIH HHS R01 MH101890NIMH NIH HHS R01 MH107183
6 · The paper itself

Abstract

Suicide is a major public health concern. A significant proportion of depressed individuals show suicidal ideation. The currently available medications are not optimal and a large number of depressed/suicidal patients do not respond to these medications. Thus, there is an urgent need to fully understand the neurobiological mechanisms associated with depression and suicidal behavior and to find novel targets for therapeutic interventions. In this regard, microRNAs (miRNAs), member of small non-coding RNA family, have emerged as an invaluable tool not only to understand disease pathogenesis but also to precisely pinpoint the targets that can be developed as drugs. In this review, these aspects have been discussed in a comprehensive and critical manner.

Indexed as

Suicidal IdeationDepressionHumansMicroRNAsMolecular Targeted TherapyMicroRNAsBiomarkerDepressionMiRNAsNeural plasticityStressSuicide

Identifiers

PMID30071418
PMCPMC6108934
OpenAlexW2883383846

What OpenQuestion holds

Textmetadata
LicenceTDM
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.