Evidence map›Paper›PMID 38276010›Full record

ReviewPharmaceuticals (Basel, Switzerland)2024

Investigation of Potential Drug Targets Involved in Inflammation Contributing to Alzheimer's Disease Progression.

Catherine Sharo, Tianhua Zhai, Zuyi Huang

Abstract readReview
In one paragraph

Review in Pharmaceuticals (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
  5. Article
  6. Review
  7. Anti-inflammatory effects ofScientific reports · 2025
    Article
  8. Article
  9. Review
  10. 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.

Catherine SharoDepartment of Chemical and Biological Engineering, Villanova University, Villanova, PA 19085, USA.ORCID 0009-0006-6158-6924
Tianhua ZhaiDepartment of Chemical and Biological Engineering, Villanova University, Villanova, PA 19085, USA.
Zuyi HuangDepartment of Chemical and Biological Engineering, Villanova University, Villanova, PA 19085, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Alzheimer's disease has become a major public health issue. While extensive research has been conducted in the last few decades, few drugs have been approved by the FDA to treat Alzheimer's disease. There is still an urgent need for understanding the disease pathogenesis, as well as identifying new drug targets for further drug discovery. Alzheimer's disease is known to arise from a build-up of amyloid beta (Aβ) plaques as well as tangles of tau proteins. Along similar lines to Alzheimer's disease, inflammation in the brain is known to stem from the degeneration of tissue and build-up of insoluble materials. A minireview was conducted in this work assessing the genes, proteins, reactions, and pathways that link brain inflammation and Alzheimer's disease. Existing tools in Systems Biology were implemented to build protein interaction networks, mainly for the classical complement pathway and G protein-coupled receptors (GPCRs), to rank the protein targets according to their interactions. The top 10 protein targets were mainly from the classical complement pathway. With the consideration of existing clinical trials and crystal structures, proteins C5AR1 and GARBG1 were identified as the best targets for further drug discovery, through computational approaches like ligand-protein docking techniques.

Indexed as

Alzheimer’s diseaseclassical complement pathwaycytokinesdrug discoverydrug targetG protein-coupled receptorsinflammationprotein interaction network

Identifiers

PMID38276010
PMCPMC10819325

What OpenQuestion holds

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Registered trials

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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.