scholarly journals Biomarker discovery in systemic sclerosis: state of the art

2015 ◽  
pp. 47 ◽  
Author(s):  
Francesco Bonella ◽  
Giuseppe Patuzzo ◽  
Claudio Lunardi
Keyword(s):  
Systemic Sclerosis ◽  
Biomarker Discovery ◽  
State Of The Art

An update on biomarker discovery and use in systemic sclerosis

2017 ◽  
Vol 17 (9) ◽  
pp. 823-833 ◽  
Author(s):  
Takashi Matsushita ◽  
Kazuhiko Takehara
Keyword(s):  
Systemic Sclerosis ◽  
Biomarker Discovery

scholarly journals Current state of the art for enhancing urine biomarker discovery

2016 ◽  
Vol 13 (6) ◽  
pp. 609-626 ◽  
Author(s):  
Michael Harpole ◽  
Justin Davis ◽  
Virginia Espina
Keyword(s):  
Biomarker Discovery ◽  
State Of The Art ◽  
Current State ◽  
Urine Biomarker

scholarly journals Proteomics and Metabolomics for Cystic Fibrosis Research

2020 ◽  
Vol 21 (15) ◽  
pp. 5439 ◽  
Author(s):  
Nara Liessi ◽  
Nicoletta Pedemonte ◽  
Andrea Armirotti ◽  
Clarissa Braccia
Keyword(s):  
Cystic Fibrosis ◽  
Biomarker Discovery ◽  
State Of The Art ◽  
Review Article ◽  
Future Scenarios ◽  
Transmembrane Conductance Regulator ◽  
Transmembrane Conductance ◽  
Post Translational Modifications ◽  
New Applications ◽  
Cystic Fibrosis Transmembrane

The aim of this review article is to introduce the reader to the state-of-the-art of the contribution that proteomics and metabolomics sciences are currently providing for cystic fibrosis (CF) research: from the understanding of cystic fibrosis transmembrane conductance regulator (CFTR) biology to biomarker discovery for CF diagnosis. Our work particularly focuses on CFTR post-translational modifications and their role in cellular trafficking as well as on studies that allowed the identification of CFTR molecular interactors. We also show how metabolomics is currently helping biomarker discovery in CF. The most recent advances in these fields are covered by this review, as well as some considerations on possible future scenarios for new applications.

scholarly journals Transparent exploration of machine learning for biomarker discovery from proteomics and omics data

2021 ◽  
Author(s):  
Furkan M. Torun ◽  
Sebastian Virreira Winter ◽  
Sophia Doll ◽  
Felix M. Riese ◽  
Artem Vorobyev ◽  
...  
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Biomarker Discovery ◽  
Expert Knowledge ◽  
State Of The Art ◽  
Open Science ◽  
Easy Access ◽  
Health State ◽  
List Type ◽  
Web Based

AbstractBiomarkers are of central importance for assessing the health state and to guide medical interventions and their efficacy, but they are lacking for most diseases. Mass spectrometry (MS)-based proteomics is a powerful technology for biomarker discovery, but requires sophisticated bioinformatics to identify robust patterns. Machine learning (ML) has become indispensable for this purpose, however, it is sometimes applied in an opaque manner, generally requires expert knowledge and complex and expensive software. To enable easy access to ML for biomarker discovery without any programming or bioinformatic skills, we developed ‘OmicLearn’ (https://OmicLearn.com), an open-source web-based ML tool using the latest advances in the Python ML ecosystem. We host a web server for the exploration of the researcher’s results that can readily be cloned for internal use. Output tables from proteomics experiments are easily uploaded to the central or a local webserver. OmicLearn enables rapid exploration of the suitability of various ML algorithms for the experimental datasets. It fosters open science via transparent assessment of state-of-the-art algorithms in a standardized format for proteomics and other omics sciences.Graphical AbstractHighlightsOmicLearn is an open-source platform allows researchers to apply machine learning (ML) for biomarker discoveryThe ready-to-use structure of OmicLearn enables accessing state-of-the-art ML algorithms without requiring any prior bioinformatics knowledgeOmicLearn’s web-based interface provides an easy-to-follow platform for classification and gaining insights into the datasetSeveral algorithms and methods for preprocessing, feature selection, classification and cross-validation of omics datasets are integratedAll results, settings and method text can be exported in publication-ready formats

scholarly journals Systemic Sclerosis Pathogenesis and Emerging Therapies, beyond the Fibroblast

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Andrea Sierra-Sepúlveda ◽  
Alexia Esquinca-González ◽  
Sergio A. Benavides-Suárez ◽  
Diego E. Sordo-Lima ◽  
Adrián E. Caballero-Islas ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Immune System ◽  
Systemic Sclerosis ◽  
Autoimmune Disease ◽  
State Of The Art ◽  
Therapeutic Targets ◽  
Organ Damage ◽  
The State ◽  
Pathogenic Mechanism ◽  
Emerging Therapies

Systemic sclerosis (SSc) is a complex rheumatologic autoimmune disease in which inflammation, fibrosis, and vasculopathy share several pathogenic pathways that lead to skin and internal organ damage. Recent findings regarding the participation and interaction of the innate and acquired immune system have led to a better understanding of the pathogenesis of the disease and to the identification of new therapeutic targets, many of which have been tested in preclinical and clinical trials with varying results. In this manuscript, we review the state of the art of the pathogenesis of this disease and discuss the main therapeutic targets related to each pathogenic mechanism that have been discovered so far.

Adipose-Derived Cell Transplantation in Systemic Sclerosis: State of the Art and Future Perspectives

2016 ◽  
Vol 2 (1) ◽  
pp. 33-41 ◽  
Author(s):  
Nicoletta Del Papa ◽  
Eleonora Zaccara ◽  
Gabriele Di Luca ◽  
Romina Andracco ◽  
Wanda Maglione ◽  
...  
Keyword(s):  
Systemic Sclerosis ◽  
Cell Transplantation ◽  
State Of The Art ◽  
Future Perspectives

scholarly journals State of the Art in Tumor Antigen and Biomarker Discovery

Cancers ◽  
2011 ◽  
Vol 3 (2) ◽  
pp. 2554-2596 ◽  
Author(s):  
Klervi Even-Desrumeaux ◽  
Daniel Baty ◽  
Patrick Chames
Keyword(s):  
Tumor Antigen ◽  
Biomarker Discovery ◽  
State Of The Art

scholarly journals CLINICAL AND SEROLOGICAL PARAMETERS OF PROGRESSION AND PROGNOSIS IN PATIENTS WITH SYSTEMIC SCLEROSIS – A STATE OF THE ART REVIEW

2020 ◽  
Vol 73 (7) ◽  
pp. 1528-1532
Author(s):  
Ewa Wielosz ◽  
Maria Majdan
Keyword(s):  
Systemic Sclerosis ◽  
Connective Tissue ◽  
Rna Polymerase ◽  
Connective Tissue Disease ◽  
State Of The Art ◽  
Rna Polymerase Iii ◽  
Vascular Complications ◽  
Skin Lesions ◽  
Tendon Ruptures ◽  
Generalized Type

Systemic sclerosis (SSc) is a multi-organ connective tissue disease that leads to the dysfunction and the impaired morphology of blood vessels due to non-specific inflammation and progressive fibrosis. Due to the diversity of SSc and even though the factors predisposing to the severe course of SSc are known, it is not always possible to predict the disease progression and to determine the prognosis. Ideally, the group of patients with faster progression of organ lesions and a worse course of the disease should be identified and the early intensive treatment should be instituted. The aim of the article, is an attempt to identify the factors that worsen the prognosis in the course of SSc. The analysis of numerous studies demonstrated that patients with short-lasting SSc, with the presence of anti-RNA polymerase III antibodies, with a generalized type of SSc with quickly progressing skin lesions and males should be most strictly monitored. Moreover, vascular complications, tendon ruptures and fast capillaries loss observed in nailfold capillaroscopy are the factors deteriorating the prognosis in SSc. In conclusion, despite the known, the factors that worsen the prognosis, it is difficult to predict the course of systemic sclerosis. Due to its incompletely elucidated etiopathology as well as the diverse and unpredictable nature of the disease, reliable markers to determine the prognosis in SSc have not been found.

Pathophysiology of systemic sclerosis: State of the art in 2014

2014 ◽  
Vol 43 (10) ◽  
pp. e267-e278 ◽  
Author(s):  
Nicolas Dumoitier ◽  
Sébastien Lofek ◽  
Luc Mouthon
Keyword(s):  
Systemic Sclerosis ◽  
State Of The Art
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