Evidence map›Paper›PMID 34945766›Full record

ReviewJournal of personalized medicine2021

The Potential of Gamma Secretase as a Therapeutic Target for Cardiac Diseases.

Sujoita Sen, Logan Hallee, Chi Keung Lam

Abstract readReview
In one paragraph

Review in Journal of personalized medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Review
  2. Article
  3. Protein Language Models are Accidental Taxonomists.bioRxiv : the preprint server for biology · 2025
    Article
  4. Review
  5. Review
  6. Annotation Vocabulary (Might Be) All You Need.bioRxiv : the preprint server for biology · 2024
    Article
  7. Article
  8. Article
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.

Sujoita SenDepartment of Biological Sciences, University of Delaware, Newark, DE 19716, USA.
Logan HalleeDepartment of Mathematical Sciences, University of Delaware, Newark, DE 19716, USA.ORCID 0000-0002-0426-3508
Chi Keung LamDepartment of Biological Sciences, University of Delaware, Newark, DE 19716, USA.ORCID 0000-0003-4565-9426

Funding

American Heart Association 19CDA34770040University of Delaware Research Foundation 120800
6 · The paper itself

Abstract

Heart diseases are some of the most common and pressing threats to human health worldwide. The American Heart Association and the National Institute of Health jointly work to annually update data on cardiac diseases. In 2018, 126.9 million Americans were reported as having some form of cardiac disorder, with an estimated direct and indirect total cost of USD 363.4 billion. This necessitates developing therapeutic interventions for heart diseases to improve human life expectancy and economic relief. In this review, we look into gamma-secretase as a potential therapeutic target for cardiac diseases. Gamma-secretase, an aspartyl protease enzyme, is responsible for the cleavage and activation of a number of substrates that are relevant to normal cardiac development and function as found in mutation studies. Some of these substrates are involved in downstream signaling processes and crosstalk with pathways relevant to heart diseases. Most of the substrates and signaling events we explored were found to be potentially beneficial to maintain cardiac function in diseased conditions. This review presents an updated overview of the current knowledge on gamma-secretase processing of cardiac-relevant substrates and seeks to understand if the modulation of gamma-secretase activity would be beneficial to combat cardiac diseases.

Indexed as

cardiacdiseasegamma-secretasepresenilinproteolysissignaling

Identifiers

PMID34945766
PMCPMC8703931

What OpenQuestion holds

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LicenceCC BY
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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.