Evidence map›Paper›PMID 38409422›Full record

ArticleAdvances in experimental medicine and biology2024

Multi-omics Investigations in Endocrine Systems and Their Clinical Implications.

Rodrigo Antonio Peliciari-Garcia, Carolina Fonseca de Barros, Ayla Secio-Silva, Diogo de Barros Peruchetti, Renata Marino Romano, Paula Bargi-Souza

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Article in Advances in experimental medicine and biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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

6 authors at 3 institutions in 1 country.

Rodrigo Antonio Peliciari-GarciaDepartment of Biological Sciences, Morphophysiology and Pathology Sector, Federal University of São Paulo (UNIFESP), Diadema, SP, Brazil. peliciari.garcia@unifesp.br.
Carolina Fonseca de BarrosDepartment of Physiology and Biophysics, Institute of Biological Sciences, Federal University of Minas Gerais (UFMG), Belo Horizonte, MG, Brazil.
Ayla Secio-SilvaDepartment of Physiology and Biophysics, Institute of Biological Sciences, Federal University of Minas Gerais (UFMG), Belo Horizonte, MG, Brazil.
Diogo de Barros PeruchettiDepartment of Physiology and Biophysics, Institute of Biological Sciences, Federal University of Minas Gerais (UFMG), Belo Horizonte, MG, Brazil.
Renata Marino RomanoDepartment of Medicine, State University of Central-West (UNICENTRO), Guarapuava, Parana, Brazil.
Paula Bargi-SouzaDepartment of Physiology and Biophysics, Institute of Biological Sciences, Federal University of Minas Gerais (UFMG), Belo Horizonte, MG, Brazil. bargisouzap@ufmg.br.
Universidade Federal de Minas Gerais · BRUniversidade Estadual do Centro-Oeste · BRUniversidade Federal de São Paulo · BR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Innovative techniques such as the "omics" can be a powerful tool for the understanding of intracellular pathways involved in homeostasis maintenance and identification of new potential therapeutic targets against endocrine-metabolic disorders. Over the last decades, proteomics has been extensively applied in the study of a wide variety of human diseases, including those involving the endocrine system. Among the most endocrine-related disorders investigated by proteomics in humans are diabetes mellitus and thyroid, pituitary, and reproductive system disorders. In diabetes, proteins implicated in insulin signaling, glucose metabolism, and β-cell activity have been investigated. In thyroid diseases, protein expression alterations were described in thyroid malignancies and autoimmune thyroid illnesses. Additionally, proteomics has been used to investigate the variations in protein expression in adrenal cancers and conditions, including Cushing's syndrome and Addison's disease. Pituitary tumors and disorders including acromegaly and hypopituitarism have been studied using proteomics to examine changes in protein expression. Reproductive problems such as polycystic ovarian syndrome and endometriosis are two examples of conditions where alterations in protein expression have been studied using proteomics. Proteomics has, in general, shed light on the molecular underpinnings of many endocrine-related illnesses and revealed promising biomarkers for both their detection and treatment. The capacity of proteomics to thoroughly and objectively examine complex protein mixtures is one of its main benefits. Mass spectrometry (MS) is a widely used method that identifies and measures proteins based on their mass-to-charge ratio and their fragmentation pattern. MS can perform the separation of proteins according to their physicochemical characteristics, such as hydrophobicity, charge, and size, in combination with liquid chromatography. Other proteomics techniques include protein arrays, which enable the simultaneous identification of several proteins in a single assay, and two-dimensional gel electrophoresis (2D-DIGE), which divides proteins depending on their isoelectric point and molecular weight. This chapter aims to summarize the most relevant proteomics data from targeted tissues, as well as the daily rhythmic variation of relevant biomarkers in both physiological and pathophysiological conditions within the involved endocrine system, especially because the actual modern lifestyle constantly imposes a chronic unentrained condition, which virtually affects all the circadian clock systems within human's body, being also correlated with innumerous endocrine-metabolic diseases.

Indexed as

Endocrine System DiseasesMultiomicsBiomarkersEndocrine SystemHumansMass SpectrometryProteinsBiomarkersProteinsDiabetes mellitusEndocrine disordersOmicsReproductive systemThyroid

Identifiers

PMID38409422
OpenAlexW4392165888

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.