Evidence map›Paper›PMID 32899160›Full record

ReviewInternational journal of molecular sciences2020

Proteostasis and Proteotoxicity in the Network Medicine Era.

Marta Lualdi, Tiziana Alberio, Mauro Fasano

Open access · goldAbstract readReview
In one paragraph

Review in International journal of molecular sciences, 2020. 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
1.4field-weighted citation impact, top 14% 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

9 citing papers in PubMed, 22 citations in OpenAlex.

  1. International journal of molecular sciences · 2021
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  2. 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

3 authors at 1 institution in 1 country.

Marta LualdiDepartment of Science and High Technology and Center of Bioinformatics, University of Insubria, I-21052 Busto Arsizio, Italy.ORCID 0000-0002-0704-3439
Tiziana AlberioDepartment of Science and High Technology and Center of Bioinformatics, University of Insubria, I-21052 Busto Arsizio, Italy.ORCID 0000-0002-6065-6699
Mauro FasanoDepartment of Science and High Technology and Center of Bioinformatics, University of Insubria, I-21052 Busto Arsizio, Italy.ORCID 0000-0003-0628-5871
University of Insubria · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neurodegenerative proteinopathies are complex diseases that share some pathogenetic processes. One of these is the failure of the proteostasis network (PN), which includes all components involved in the synthesis, folding, and degradation of proteins, thus leading to the aberrant accumulation of toxic protein aggregates in neurons. The single components that belong to the three main modules of the PN are highly interconnected and can be considered as part of a single giant network. Several pharmacological strategies have been proposed to ameliorate neurodegeneration by targeting PN components. Nevertheless, effective disease-modifying therapies are still lacking. In this review article, after a general description of the PN and its failure in proteinopathies, we will focus on the available pharmacological tools to target proteostasis. In this context, we will discuss the main advantages of systems-based pharmacology in contrast to the classical targeted approach, by focusing on network pharmacology as a strategy to innovate rational drug design.

Indexed as

ProteostasisAnimalsHomeostasisHumansNeurodegenerative DiseasesProtein Aggregation, PathologicalProteinsProteostasis DeficienciesProteinsnetwork medicinenetwork pharmacologyneurodegenerationproteostasissystems biology

Identifiers

PMID32899160
PMCPMC7503343
OpenAlexW3082098748

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.