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Record W7117314730 · doi:10.1002/alz70859_104173

Targeting Neuroinflammation by Bioreactive Nanoparticles for Early Detection and Intervention of Alzheimer’s Disease

2025· article· en· W7117314730 on OpenAlexaff
Liting Wang, Elliya Park, Chunsheng He, Azhar Z. Abbasi, R Rajakumar, Shudi Huang, Paul E Fraser, Jeffrey T. Henderson, Xiao Yu Wu

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNeuroinflammationDiseaseNeurodegenerationIntervention (counseling)Inflammation

Abstract

fetched live from OpenAlex

Abstract Background Neuroinflammation plays a causal role in neurodegenerative Alzheimer’s disease (AD); it occurs long before clinical onset of AD. 1 Therefore, early detection of neuroinflammation is critical for early intervention before the irreversible neurodegeneration happens. To address this pressing need, our group has developed multifunctional bioreactive nanoparticles, consisting of blood‐brain barrier‐penetrating terpolymer and MnO 2 nanoparticles and conjugated anti‐Ab antibody (Ab‐TP‐MDNP). The system reduced oxidative stress and produced oxygen and paramagnetic Mn 2+ ions, thereby remodeling the brain microenvironment and enabling sensitive detection of early neuroinflammation prior to Ab plague formation in an APP transgenic TgCRND8+ AD mouse model. 2 We also demonstrated its effects on improving vascular functions, Ab elimination, energy metabolism, neuronal activity and cognitive function. 3‐4 Built on the foundation of previous findings, we investigate whether the TP‐MDNP is able enhance early detection of neuroinflammation regardless of Aß or tau expression and evaluate its therapeutic effect in AD mouse model of tauopathy. Method Three types of transgenic mouse model of AD, TgCRND8+, PS19 with tauopathy, and APP/PS1 were used in the MRI study. The diagnostic performance of Ab‐TP‐MDNP in MRI was also compared with PET imaging using F18‐florobetaben in TgCRND8+ mice. PS19 mice were treated with IV injection of TP‐MDNP for two weeks (2/week, 100 μmol Mn/kg b.w.) and the biomarkers for ROS, neuroinflammation, and p‐Tau expression were examined using immunohistochemistry and ELISA. Result Ab‐TP‐MDNP enhanced MRI signal significantly outperformed PET imaging by Ab‐targeted F18‐florobetaben in TgCRND8+ mice of 3 months and 6 months of age. Similar MRI imaging sensitivity was observed in PS19 and APP/PS1 mice with or without conjugated antibody against Ab or tau protein, suggesting the neuroinflammation activated MRI contrast enhancement as a common mechanism. In PS19, TP‐MDNP treatment significantly reduced total ROS, CA9 (a hypoxia marker) and p‐tau levels. Conclusion The results suggest that TP‐MDNP can enable MRI detection of neuroinflammation and reduce neurodegeneration pathogenetic factors such as ROS, hypoxia, and p‐tau in AD mouse brains. References 1. Zhang W, et al. Sig Transduct Target Ther 2023;8, 267. 2. He C, et al. Nano Today . 2020;35:100965. 3. Park E,. et al. Advanced Science . 2023 Apr;10(12):2207238. 4. Park E, et al. Biomaterials . 2025 Jan 24:123142.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.305
Teacher spread0.285 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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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