Sex-Dependent Differences in Serum Autoantibody Levels in the 3×Tg-AD Model of Alzheimer’s Disease
Bibliographic record
Abstract
Abstract Sex-dependent discrepancies in disease prevalence and serum autoantibody levels are observed in patients and animal models of Alzheimer’s disease (AD). The present study examines whether gonadal hormones play a role in sex differences in serum autoantibody levels in the 3×Tg-AD mouse model of AD. 3×Tg-AD and wild-type (WT) mice were gonadectomised or sham-operated at 3 months of age. After behavioural phenotyping at 6 months of age, the animals were assessed for serum autoantibodies by indirect immunofluorescence for antinuclear antibodies (ANA) and by line-immunoblot assay for an additional 16 monospecific autoantibodies including anti-nucleosome antibodies. There were significant differences between the strains in ANA levels, with the major target antigens confirmed as nucleosomes. The results of ANA and anti-nucleosome assays were combined for further analysis. Further analysis revealed: 1) the level of serum autoantibodies in male 3×Tg-AD mice was higher than in female 3×Tg-AD animals, and this was not altered by orchiectomy. 2) sham-operated 3×Tg-AD female mice displayed a significantly lower level of serum autoantibodies than sham-operated WT females. 3) ovariectomy further reduced the level of serum autoantibodies in female 3×Tg-AD mice. The results suggest that dissimilar levels of serum autoantibodies in 3xTg-AD mice are a sex-dependent phenomenon and that female hormones play a role in regulation of their synthesis. Funded by grant #SVB-158618 from the Canadian Institutes of Health Research to MF.
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".