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Record W4312086912 · doi:10.1002/alz.068708

Improving Vascular Functions by Bioreactive Nanoparticles for Treatment of Alzheimer’s Disease

2022· article· en· W4312086912 on OpenAlexaff
Elliya Park, Lily Yi Li, Chunsheng He, Azhar Z. Abbasi, Taksim Ahmed, Warren D. Foltz, Andrew M. Rauth, Paul E. Fraser, Jeffrey T. Henderson, Xiao Yu Wu

Bibliographic record

VenueAlzheimer s & Dementia · 2022
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsOxidative stressNeuroinflammationMicrogliaChemistryPharmacologyReactive oxygen speciesHippocampusBlood–brain barrierPathologyMedicineInflammationImmunologyInternal medicineBiochemistryCentral nervous system

Abstract

fetched live from OpenAlex

Abstract Background Developing effective disease‐modifying treatment for Alzheimer’s disease (AD) remains a tremendous challenge due to its multifactorial nature involving multiple pathologic signaling pathways in addition to ineffective drug delivery through the blood‐brain barrier (BBB).1 With this in mind our group has developed multifunctional bioreactive nanoparticles (Ab‐TP‐MDNPs), consisting of anti‐amyloid β antibody (Ab) linked to brain‐penetrating terpolymer (TP) and manganese dioxide (MnO2) nanoparticles (MDNPs), that are shown to reduce oxidative stress in AD brains.2 Given the early occurrence of oxidative stress, hypoxia, and vascular dysfunction in AD brains,3,4 we investigated the therapeutic effects of Ab‐TP‐MDNPs on reducing neuroinflammation and vascular dysfunction in an AD mouse model. Method A transgenic mouse model of AD (TgCRND8 species) and wildtype littermates (WT) were treated with intravenous (i.v.) injection of Ab‐TP‐MDNPs (twice/week, 100 µmol Mn/kg b.w.) or vehicle for 2‐weeks. Oxidative and inflammatory biomarkers were examined using immunohistochemistry and enzyme‐linked immunosorbent assay (ELISA). Vascular function before and after the treatment was studied via high resolution magnetic resonance imaging (MRI). Cerebral blood flow (CBF) was assessed using FAIR (flow‐sensitive alternating inversion recovery) technique. BBB permeability was measured via T1 mapping re‐acquisition prior to and following i.v. injection of gadolinium‐diethylenetriamine penta‐acetate (Gd‐DTPA) at 1.2 mmol/kg Result Ab‐TP‐MDNPs treatment significantly decreased inflammatory cytokines and activation of microglia and astrocytes markers (reactive microglia: hippocampus by 69% and cortex by 59%, reactive astrocytes: hippocampus by 32% and cortex by 33%). In addition, Ab‐TP‐MDNPs treatment improved CBF (cortex by 19% and subcortex by 35%) and vessel leakage by 29% in the cortex of AD mouse brains. Conclusion Ab‐TP‐MDNPs treatment reduced neuroinflammation and vascular dysfunction in an AD mouse model. These findings suggest a new multimodal strategy for AD treatment and encourage further development of such approach for complex neurologic diseases. Reference: 1. Panza F, Lozupone M, Logroscino G, Imbimbo BP. Nature Reviews Neurology. 2019;15(2):73‐88. 2. He C, Ahmed T, Abbasi AZ, et al. Nano Today. 2020;35:100965. 3. Sweeney MD, Montagne A, Sagare AP, et al. Alzheimer’s & Dementia. 2019;15(1):158‐167. 4. Nortley R, Korte N, Izquierdo P, et al. Science. 2019:eaav9518.

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 score0.003

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 head0.035
GPT teacher head0.300
Teacher spread0.265 · 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
Published2022
Admission routes1
Has abstractyes

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