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Record W4406153376 · doi:10.14740/jnr869

Molecular Signaling Pathways in Alzheimer’s Disease and Their Therapeutic Implications

2025· article· en· W4406153376 on OpenAlexvenueno aff
Jahangir Alam, Anushka Kalash

Bibliographic record

VenueJournal of Neurology Research · 2025
Typearticle
Languageen
FieldMedicine
TopicCholinesterase and Neurodegenerative Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSignal transductionMechanism (biology)DiseaseBioinformaticsNeuroscienceDrug discoveryComputational biologyBiologyGeneticsPathology

Abstract

fetched live from OpenAlex

Alzheimer’s disease (AD) poses significant health challenges for the elderly due to its complex origins and heterogeneous etiology. Currently, there are no medications specifically aimed at halting its progression through any preventive signaling mechanism. Therefore, there is a pressing need to explore alternative pathways that could guide researchers towards new avenues in drug development for managing AD sustainability. This article is the first to simultaneously review several potential signaling pathways that were previously reported as individual signaling conduits in AD. Through meticulous analysis, we assess the pathophysiological roles of these pathways in AD. Extensive literature searches across databases including PubMed, Cochrane Library, Web of Science, Scopus, ResearchGate, Google Scholar, X-Mol, EBSCO, Loop, and Google, encompassing both research articles and highly cited reviews, were conducted. In addition to the cholinergic and N-methyl D-aspartate (NMDA) glutamate signaling pathways, we have observed four other less-explored, but promising molecular pathways relevant to AD onset and progression. We delved into the detailed signaling mechanisms of these pathways, spanning from preclinical to clinical studies. Remarkably, each pathway demonstrates distinct molecular signaling patterns and offers diverse perspectives on AD treatment. This review highlights six pivotal signaling pathways that may hold promise as future targeting pathways in managing AD.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.727
Threshold uncertainty score0.254

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.140
GPT teacher head0.419
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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