Amyloid-Beta-Activated Human Microglial Cells Through ER-Resident Proteins
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".