Optimizing Veteran-Facing Materials for Vending Machine-Dispensed HIV Self-Testing: Formative Qualitative Study.

Alzheimer’s disease
(AD) is a leading cause of dementia
and remains the most prevalent, causing almost 60–70% of dementia
cases globally. AD has been described
as a disorder characterized centrally by Aβ plaque formation,
neurofibrillary tangles, chronic neuroinflammation, oxidative stress,
and synaptic dysfunction, causing cognitive deterioration over time.
Despite decades of research on AD, current treatments are symptomatic,
placing AD as a major unmet medical need. Recent studies are now revealing
AD as a metabolic dysfunction disorder, where lipid metabolism, mitochondrial,
and insulin signaling pathways play a crucial part in Aβ and
inflammatory signaling. In this context,
activating dual PPAR, encompassing PPAR-α and PPAR-γ,
has emerged as an exciting therapeutic strategy because of its capacity
to regulate metabolic homeostasis and neuroinflammation. PPARα supports fatty acid oxidation and
promotes Aβ clearance in glial cells, while PPARγ enhances
insulin sensitivity and suppresses NF-κB–driven inflammation,
together addressing key pathological axes of AD. Consistent with this mechanistic
rationale, preclinical studies show that activation of either receptor
can reduce amyloid burden and partially restore cognitive function
in AD models. A structured literature
survey was conducted using PubMed, Scopus,
and Web of Science databases covering studies published up to 2025.
Search terms included combinations of “Alzheimer’s disease,”
“PPARα,” “PPARγ,” “dual
PPAR agonist,” “artificial intelligence,” “machine
learning,” “deep learning,” and “drug
discovery.” Studies were included if they reported AI-driven
methodologies, PPAR-targeted therapeutic approaches, or multiomics
integration relevant to AD. Studies not directly related to neurodegeneration,
lacking AI-based approaches, or without sufficient methodological
detail were excluded. Notably, due to substantial heterogeneity in
study design and outcome measures, including cognitive assessments,
neuroimaging biomarkers, and omics-based surrogate end points, a formal
meta-analysis was not performed. Therefore, findings are presented
as a qualitative synthesis intended to highlight emerging trends and
conceptual advances. In parallel, explainable artificial intelligence
(XAI) tools will
facilitate the interpretation of models representing drug-target interaction
phenomena, while federated learning tools support the analysis of
multi-institutional AD data sets while protecting patient privacy.
The implementation of digital twin paradigms, defined as computational
models of specific molecular, cellular, and clinical states, unifies
the frameworks for simulating the dynamics of the PPAR α/γ
pathway, predicting clinical outcomes, and facilitating the implementation
strategies of precision medicine approaches in the treatment of AD.
These informatics-based technologies create a foundation for the next
generation of AI-based discovery, optimization, and lead generation
of dual PPAR α/γ modulators, which exhibit expanded translational
potential for the treatment of AD. To illustrate the high speed at
which AI technologies, research paradigms, and the biology underlying
AD, including PPAR, have evolved, we employed a bibliometric co-occurrence
analysis, where illustrates co-occurrence patterns and thematic associations between
artificial intelligence and AD research domains. These visualizations
reflect patterns of keyword comention and should be interpreted as
exploratory representations rather than statistically validated evidence
of domain convergence.