Evidence map›Paper›PMID 31277513›Full record

ReviewInternational journal of molecular sciences2019

Lessons on Differential Neuronal-Death-Vulnerability from Familial Cases of Parkinson's and Alzheimer's Diseases.

Rafael Franco, Gemma Navarro, Eva Martínez-Pinilla

Open access · goldAbstract readReview
In one paragraph

Review in International journal of molecular sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.4field-weighted citation impact, top 19% 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

5 citing papers in PubMed, 18 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
  5. 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 at 2 institutions in 1 country.

Rafael FrancoChemistry School, University of Barcelona, 08028 Barcelona, Spain. rfranco@ub.edu.ORCID 0000-0003-2549-4919
Gemma NavarroCentro de Investigación Biomédica en Red Enfermedades Neurodegenerativas (CiberNed), Instituto de Salud Carlos III, 28031 Madrid, Spain.
Eva Martínez-PinillaDepartamento de Morfología y Biología Celular, Facultad de Medicina, Universidad de Oviedo, 33006 Oviedo, Spain. martinezpinillaeva@gmail.com.ORCID 0000-0002-0572-0926
Instituto de Salud Carlos III · ESUniversidad de Oviedo · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The main risk of Alzheimer's disease (AD) and Parkinson's disease (PD), the two most common neurodegenerative pathologies, is aging. In contrast to sporadic cases, whose symptoms appear at >60 years of age, familial PD or familial AD affects younger individuals. Finding early biological markers of these diseases as well as efficacious treatments for both symptom relief and delaying disease progression are of paramount relevance. Familial early-onset PD/AD are due to genetic factors, sometimes a single mutation in a given gene. Both diseases have neuronal loss and abnormal accumulations of specific proteins in common, but in different brain regions. Despite shared features, the mechanisms underlying the pathophysiological processes are not known. This review aims at finding, among the genetic-associated cases of PD and AD, common trends that could be of interest to discover reliable biomarkers and efficacious therapies, especially those aimed at affording neuroprotection, i.e., the prevention of neuronal death.

Indexed as

Alzheimer DiseaseCell DeathGene Regulatory NetworksHumansMutationNeuronsParkinson Diseasecholesterolglucocerebrosidasematernal imprintingmitochondriasteryl glycosides

Identifiers

PMID31277513
PMCPMC6651599
OpenAlexW2955613625

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.