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Record W2242972064 · doi:10.1021/pr500926r

Amyloid-Beta-Activated Human Microglial Cells Through ER-Resident Proteins

2014· article· en· W2242972064 on OpenAlexaff
YongCheol Yoo, Kyunghee Byun, Taewook Kang, Delger Bayarsaikhan, Jin Young Kim, Seyeoun Oh, Young Hye Kim, Se Young Kim, Wonil Chung, Seung Up Kim, Bong‐Hee Lee, Young Mok Park

Bibliographic record

VenueJournal of Proteome Research · 2014
Typearticle
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsUniversity of British Columbia
FundersKorea Basic Science Institute
KeywordsMicrogliaAmyloid (mycology)BETA (programming language)Amyloid betaAmyloid precursor proteinChemistryCell biologyNeuroscienceInflammationAlzheimer's diseaseBiochemistryBiologyMedicineImmunologyPathologyComputer scienceDiseasePeptide

Abstract

fetched live from OpenAlex

Microglial activation in the central nervous system is a key event in the neuroinflammation that accompanies neurodegenerative diseases such as Alzheimer's disease (AD). Among cytokines involved in microglial activation, amyloid β (Aβ) peptide is known to be a key molecule in the induction of diverse inflammatory products, which may lead to chronic inflammation in AD. However, proteomic studies of microglia in AD are limited due to lack of proper cell or animal model systems. In this study, we performed a proteomic analysis of Aβ-stimulated human microglial cells using SILAC (stable isotope labeling with amino acids in cell culture) combined with LC-MS/MS. Results showed that 60 proteins increased or decreased their abundance by 1.5 fold or greater. Among these, ER-resident proteins such as SERPINH1, PDIA6, PDIA3, and PPIB were revealed to be key molecular biomarkers of human microglial activation by validation of the proteomic results by immunostaining, PCR, ELISA, and Western blot. Taken together, our data suggest that ER proteins play an essential role in human microglial activation by Aβ and may be important molecular therapeutic targets for treatment of 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)0.003
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.665

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.154
GPT teacher head0.389
Teacher spread0.236 · 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 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

Citations17
Published2014
Admission routes1
Has abstractyes

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