Characterisation of immune cell function in fragment and full-length Huntington's disease mouse models
Bibliographic record
Abstract
Inflammation is a growing area of research in neurodegeneration. In Huntington's disease (HD), a fatal inherited neurodegenerative disease caused by a CAG-repeat expansion in the gene encoding huntingtin, patients have increased plasma levels of inflammatory cytokines and circulating monocytes that are hyper-responsive to immune stimuli. Several mouse models of HD also show elevated plasma levels of inflammatory cytokines. To further determine the degree to which these models recapitulate observations in HD patients, we evaluated various myeloid cell populations from different HD mouse models to determine whether they are similarly hyper-responsive, as well as measuring other aspects of myeloid cell function. Myeloid cells from each of the three mouse models studied, R6/2, HdhQ150 knock-in and YAC128, showed increased cytokine production when stimulated. However, bone marrow CD11b(+) cells did not show the same hyper-responsive phenotype as spleen and blood cells. Furthermore, macrophages isolated from R6/2 mice show increased levels of phagocytosis, similar to findings in HD patients. Taken together, these results show significant promise for these mouse models to be used to study targeting innate immune pathways identified in human cells, thereby helping to understand the role the peripheral immune system plays in HD progression.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".