Evidence map›Paper›PMID 35884634›Full record

ArticleBrain sciences2022

Computational Analysis of Pathogenetic Pathways in Alzheimer's Disease and Prediction of Potential Therapeutic Drugs.

Maria Cristina Petralia, Katia Mangano, Maria Catena Quattropani, Vittorio Lenzo, Ferdinando Nicoletti, Paolo Fagone

Open access · goldAbstract read
In one paragraph

Article in Brain sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.9field-weighted citation impact, top 23% 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

3 citing papers in PubMed, 6 citations in OpenAlex.

  1. Review
  2. Article
  3. 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

6 authors at 3 institutions in 1 country.

Maria Cristina PetraliaDepartment of Clinical and Experimental Medicine, University of Messina, 98122 Messina, Italy.
Katia ManganoDepartment of Biomedical and Biotechnological Sciences, University of Catania, Via S. Sofia 97, 95123 Catania, Italy.ORCID 0000-0001-5920-4620
Maria Catena QuattropaniDepartment of Educational Sciences, University of Catania, 95124 Catania, Italy.
Vittorio LenzoDepartment of Social and Educational Sciences of the Mediterranean Area, University for Foreigners "Dante Alighieri" of Reggio Calabria, 89125 Reggio Calabria, Italy.ORCID 0000-0001-8391-5123
Ferdinando NicolettiDepartment of Biomedical and Biotechnological Sciences, University of Catania, Via S. Sofia 97, 95123 Catania, Italy.ORCID 0000-0002-4570-8462
Paolo FagoneDepartment of Biomedical and Biotechnological Sciences, University of Catania, Via S. Sofia 97, 95123 Catania, Italy.ORCID 0000-0002-6694-1992
University of Catania · ITUniversità per Stranieri Dante Alighieri · ITUniversity of Messina · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAlzheimer's disease (AD) is a chronic and progressive neurodegenerative disease which affects more than 50 million patients and represents 60-80% of all cases of dementia. Mutations in the APP gene, mostly affecting the γ-secretase site of cleavage and presenilin mutations, have been identified in inherited forms of AD.

methodsIn the present study, we performed a meta-analysis of the transcriptional signatures that characterize two familial AD mutations (APP

resultsOverall, the meta-analysis identified 1479 differentially expressed genes (DEGs), 684 downregulated genes, and 795 upregulated genes. Additionally, we found 14 drugs with a significant anti-similarity to the AD signature, with the top five drugs being naftifine, moricizine, ketoconazole, perindopril, and fexofenadine.

conclusionsThis study aimed to integrate the transcriptional profiles associated with common familial AD mutations in neurons in order to characterize the pathogenetic mechanisms involved in AD and to find more effective drugs for AD.

Indexed as

Alzheimer’s diseasedementiain silico pharmacologytherapeutic targets

Identifiers

PMID35884634
PMCPMC9313152
OpenAlexW4283461982

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.