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Record W1974943937 · doi:10.1371/journal.pone.0002829

Human Microglial Cells Synthesize Albumin in Brain

2008· article· en· W1974943937 on OpenAlexaff
Sung‐Min Ahn, Kyunghee Byun, Kun Cho, Jin Young Kim, Jong Shin Yoo, Deokhoon Kim, Sun Ha Paek, Seung Up Kim, Richard J. Simpson, Bong‐Hee Lee

Bibliographic record

VenuePLoS ONE · 2008
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsUniversity of British Columbia
FundersMedical Research CouncilKorea Science and Engineering FoundationKorea Basic Science Institute
KeywordsAlbuminSerum albuminMicrogliaSecretionHuman serum albuminInflammationBiologyBiochemistryChemistryMedicinePharmacologyImmunology

Abstract

fetched live from OpenAlex

Albumin, an abundant plasma protein with multifunctional properties, is mainly synthesized in the liver. Albumin has been implicated in Alzheimer's disease (AD) since it can bind to and transport amyloid beta (Abeta), the causative agent of AD; albumin is also a potent inhibitor of Abeta polymerization. Despite evidence of non-hepatic transcription of albumin in many tissues including kidney and pancreas, non-hepatic synthesis of albumin at the protein level has been rarely confirmed. In a pilot phase study of Human Brain Proteome Project, we found evidence that microglial cells in brain may synthesize albumin. Here we report, for the first time, the de novo synthesis of albumin in human microglial cells in brain. Furthermore, we demonstrate that the synthesis and secretion of albumin from microglial cells is enhanced upon microglial activation by Abeta(1-42)- or lipopolysaccharide (LPS)-treatment. These data indicate that microglial cells may play a beneficial role in AD by secreting albumin that not only inhibits Abeta polymerization but also increases its clearance.

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 score0.002
Threshold uncertainty score0.008

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)0.0020.001

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 head0.076
GPT teacher head0.292
Teacher spread0.217 · 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

Citations87
Published2008
Admission routes1
Has abstractyes

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Same venuePLoS ONESame topicAlzheimer's disease research and treatmentsFrench-language works237,207