IFN-γ Production by Amyloid β–Specific Th1 Cells Promotes Microglial Activation and Increases Plaque Burden in a Mouse Model of Alzheimer’s Disease
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Alzheimer's disease (AD) is characterized by the presence of amyloid-β (Aβ)-containing plaques, neurofibrillary tangles, and neuronal loss in the brain. Inflammatory changes, typified by activated microglia, particularly adjacent to Aβ plaques, are also a characteristic of the disease, but it is unclear whether these contribute to the pathogenesis of AD or are a consequence of the progressive neurodegenerative processes. Furthermore, the factors that drive the inflammation and neurodegeneration remain poorly understood. CNS-infiltrating T cells play a pivotal role in the pathogenesis of multiple sclerosis, but their role in the progression of AD is still unclear. In this study, we examined the role of Aβ-specific T cells on Aβ accumulation in transgenic mice that overexpress amyloid precursor protein and presenilin 1 (APP/PS1). We found significant infiltration of T cells in the brains of APP/PS1 mice, and a proportion of these cells secreted IFN-γ or IL-17. Aβ-specific CD4 T cells generated by immunization with Aβ and a TLR agonist and polarized in vitro to Th1-, Th2-, or IL-17-producing CD4(+) T cells, were adoptively transferred to APP/PS1 mice at 6 to 7 mo of age. Assessment of animals 5 wk later revealed that Th1 cells, but not Th2 or IL-17-producing CD4(+) T cells, increased microglial activation and Aβ deposition, and that these changes were associated with impaired cognitive function. The effects of Th1 cells were attenuated by treatment of the APP/PS1 mice with an anti-IFN-γ Ab. Our study suggests that release of IFN-γ from infiltrating Th1 cells significantly accelerates markers of diseases in an animal model 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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it