Hippocampal damage in mouse and human forms of systemic autoimmune disease
Bibliographic record
Abstract
Systemic lupus erythematosus (SLE) is frequently accompanied by neuropsychiatric (NP) and cognitive deficits of unknown etiology. By using autoimmune MRL-lpr mice as an animal model of NP-SLE, we examine the relationship between autoimmunity, hippocampal damage, and behavioral dysfunction. Fluoro Jade B (FJB) staining and anti-ubiquitin (anti-Ub) immunocytochemistry were used to assess neuronal damage in young (asymptomatic) and aged (diseased) mice, while spontaneous alternation behavior (SAB) was used to estimate the severity of hippocampal dysfunction. The causal relationship between autoimmunity and neuropathology was tested by prolonged administration of the immunosuppressive drug cyclophosphamide (CY). In comparison to congenic MRL +/+ controls, SAB acquisition rates and performance in the "reversal" trial were impaired in diseased MRL-lpr mice, suggesting limited use of the spatial learning strategy. FJB-positive neurons and anti-Ub particles were frequent in the CA3 region. Conversely, CY treatment attenuated the SAB deficit and overall FJB staining. Similarly to mouse brain, the hippocampus from a patient who died from NP-SLE showed reduced neuronal density in the CA3 region and dentate gyrus, as well as increased FJB positivity in these regions. Gliosis and neuronal loss were observed in the gray matter, and T lymphocytes and stromal calcifications were common in the choroid plexus. Taken together, these results suggest that systemic autoimmunity induces significant hippocampal damage, which may underlie affective and cognitive deficits in NP-SLE.
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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 | 0.000 |
| 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".