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Record W2507736308 · doi:10.2147/ijn.s95077

Microglial AGE-albumin is critical for neuronal death in Parkinson's disease: a possible implication for theranostics

2016· article· en· W2507736308 on OpenAlexaff
Seong Soo A. An, Bong‐Hee Lee, Kyunghee Byun, Enkhjargal Bayarsaikhan, Delger Bayarsaikhan, Jaesuk Lee, Myeongjoo Son, Jeongsik Moon, Hye‐Jeong Park, Arivazhagan Roshini, Seung Up Kim, Byoung‐Joon Song, Seung‐Mook Jo, Seyeon Oh

Bibliographic record

VenueInternational Journal of Nanomedicine · 2016
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsUniversity of British Columbia
FundersKorea Health Industry Development InstituteGachon University
KeywordsParkinson's diseaseAlbuminMedicineDiseaseNeurosciencePathologyInternal medicineBiology

Abstract

fetched live from OpenAlex

Advanced glycation end products (AGEs) are known to play an important role in the pathogenesis of neurodegenerative diseases, including Parkinson's disease (PD), by inducing protein aggregation and cross-link, formation of Lewy body, and neuronal death. In this study, we observed that AGE-albumin, the most abundant AGE product in the human PD brain, is synthesized in activated microglial cells and accumulates in the extracellular space. AGE-albumin synthesis in human-activated microglial cells is distinctly inhibited by ascorbic acid and cytochalasin treatment. Accumulated AGE-albumin upregulates the receptor to AGE, leading to apoptosis of human primary dopamine (DA) neurons. In animal experiments, we observed reduced DA neuronal cell death by treatment with soluble receptor to AGE. Our study provides evidence that activated microglial cells are one of the main contributors in AGE-albumin accumulation, deleterious to DA neurons in human and animal PD brains. Finally, activated microglial AGE-albumin could be used as a diagnostic and therapeutic biomarker with high sensitivity for neurodegenerative disorders, including PD.

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)0.001
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 score0.520
Threshold uncertainty score0.466

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0000.000

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 head0.038
GPT teacher head0.357
Teacher spread0.319 · 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

Citations36
Published2016
Admission routes1
Has abstractyes

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