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43/100

✓ Peer-reviewed alzhub usefulness score 43 out of 100. Digital health tools

Topic: Amyloid, tau and blood-test diagnosis

Alzheimer’s disease (AD) is a major neurodegenerative dementia, with its complex pathophysiology challenging current treatments. Recent advancements have shifted the focus from the traditionally dominant amyloid hypothesis toward a multifactorial understanding of the disease. Emerging evidence suggests that while amyloid-beta (Aβ) accumulation is central to AD, it may not be the primary driver but rather part of a broader pathogenic process. Novel hypotheses have been proposed, including the role of tau protein abnormalities, mitochondrial dysfunction, and chronic neuroinflammation. Additionally, the gut–brain axis and epigenetic modifications have gained attention as potential contributors to AD progression. The limitations of existing therapies underscore the need for innovative strategies. This study explores the integration of machine learning (ML) in drug discovery to accelerate the identification of novel targets and drug candidates. ML offers the ability to navigate AD’s complexity, enabling rapid analysis of extensive datasets and optimizing clinical trial design. The synergy between these themes presents a promising future for more effective AD treatments.

Key findings

Emerging evidence suggests that while amyloid-beta (Aβ) accumulation is central to AD, it may not be the primary driver but rather part of a broader pathogenic process.

Why it matters

This paper discusses hypotheses about Alzheimer's disease, including that amyloid-beta accumulation may be part of a broader process rather than the primary driver, and explores machine learning in drug discovery. It reports no tested treatment, cure, or prevention.

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Study details

Evidence strengthLow

How this study scored 43/100

Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it.

Component	Points	Why
Journal tier	7 / 10	Tier 3 — peer-reviewed journal (International Journal of Molecular Sciences)
Journal's citation record	3 / 4	International Journal of Molecular Sciences: journal h-index 426 (OpenAlex, 2026-10-29)
Study design	0 / 12	No study design recorded — scores zero, not guessed (not stated in the record — scored 0)
Strength of evidence	1 / 5	Rated “low” in our summary
Peer-review validation	10 / 10	Peer-reviewed. Published as an article in a named peer-reviewed journal.
How recent	4 / 5	2025 — within 3 year(s) of the newest study here
Sample size	0 / 4	Participant count not stated — scores zero (not stated in the record — scored 0)
Effect reported	0 / 3	No effect size reported — scores zero (not stated in the record — scored 0)
Studied in people	0 / 3	Not stated whether this was studied in people — scores zero (not stated in the record — scored 0)
Randomised / blinded	0 / 2	No randomisation or blinding stated — scores zero (not stated in the record — scored 0)
Times cited	3 / 7	Cited 31 times (OpenAlex, 2026-10-10)
Citations per year	3 / 5	10.3 citations a year over 3 year(s) since 2025
Impact for its field and year	4 / 6	Cited 16.9× the average for papers of its field and year (OpenAlex FWCI)
Cited by studies here	0 / 6	No other study in this library cites it
Still being cited	2 / 4	Cited 31 times in 2024–2026 (2026 still in progress; OpenAlex)
Senior author's track record	1 / 4	Last-listed author Francisco Cedrón: h-index 7 (OpenAlex, whole career)
Institution's track record	0 / 3	Universidade da Coruña: institution h-index 296 (OpenAlex)
Multi-centre collaboration	1 / 3	OpenAlex lists 3 institution(s) in 2 countries
Indexed in PubMed	2 / 2	Indexed in PubMed (PMID 39940772)
Free to read	2 / 2	Free to read
Total	43 / 100	—

What each of these 20 components measures, and what this score is not →

Read the original

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

Cite this study APA, Vancouver, BibTeX · download .bib or .ris

Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.

APA 7th

Dominguez-Gortaire, J. A., Ruiz, A., Porto-Pazos, A. B., Rodríguez, S., & Cedrón, F. (2025). Alzheimer’s Disease: Exploring Pathophysiological Hypotheses and the Role of Machine Learning in Drug Discovery. International Journal of Molecular Sciences. https://doi.org/10.3390/ijms26031004

Vancouver

Dominguez-Gortaire JA, Ruiz A, Porto-Pazos AB, Rodríguez S, Cedrón F. Alzheimer’s Disease: Exploring Pathophysiological Hypotheses and the Role of Machine Learning in Drug Discovery. International Journal of Molecular Sciences. 2025. doi:10.3390/ijms26031004. PMID: 39940772.

BibTeX

@article{dominguezgortaire2025-39940772,
  author  = {Dominguez-Gortaire, Jose A and Ruiz, Alejandra and Porto-Pazos, Ana Belen and Rodríguez, Santiago and Cedrón, Francisco},
  title   = {{Alzheimer’s Disease: Exploring Pathophysiological Hypotheses and the Role of Machine Learning in Drug Discovery}},
  journal = {International Journal of Molecular Sciences},
  year    = {2025},
  doi     = {10.3390/ijms26031004},
  pmid    = {39940772},
  url     = {https://doi.org/10.3390/ijms26031004}
}

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