Evidence map›Paper›PMID 40656087›Full record

ArticleNeuroscience applied2024

Biomarkers of stress resilience: A review.

Pasquale Paribello, Igor Branchi, Aurelia Viglione, Giulia Federica Mancini, Maria Morena, Patrizia Campolongo, Mirko Manchia

Abstract read
In one paragraph

Article in Neuroscience applied, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–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

9 citing papers in PubMed.

  1. Article
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  3. Review
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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

7 authors.

Pasquale ParibelloUnit of Psychiatry, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy.
Igor BranchiCenter for Behavioral Sciences and Mental Health, Istituto Superiore di Sanità, Rome, Italy.
Aurelia ViglioneCenter for Behavioral Sciences and Mental Health, Istituto Superiore di Sanità, Rome, Italy.
Giulia Federica ManciniDepartment of Physiology and Pharmacology, Sapienza University of Rome, Rome, Italy.
Maria MorenaDepartment of Physiology and Pharmacology, Sapienza University of Rome, Rome, Italy.
Patrizia CampolongoDepartment of Physiology and Pharmacology, Sapienza University of Rome, Rome, Italy.
Mirko ManchiaUnit of Psychiatry, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The complex dyadic interaction of stress and resilience has received growing attention as a promising avenue for informing new diagnostic and prognostic models for human health. In this review, we present a selection of some of the most relevant data on translational models and biomarkers of stress and resilience in the field of mental health. Several critical aspects concerning the preclinical and clinical model development are addressed. The distance between preclinical and clinical disease models has widened with time across all fields of medicine, with psychiatry presenting additional hurdles represented by the inherent heterogeneity of the studied phenotypes. Capitalizing on technological advances in developing and consolidating sound theories for stress-resilience interaction models represents a promising avenue, possibly endowed with greater ecological validity compared to the sole socio-psychological assessment. Instrumental in advancing the field will be an increased level of integration between preclinical and clinical researchers' efforts in developing translational biomarkers, aiming to elucidate better the interindividual heterogeneity in the impact of stress exposure on individuals' health and behavior.

Identifiers

PMID40656087
PMCPMC12244147

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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.