Immune Cell Infiltrates in Hippocampal Sclerosis: Correlation With Neuronal Loss
Bibliographic record
Abstract
Immune mechanisms have been increasingly recognized in the pathogenesis of hippocampal sclerosis (HS), but infiltration of cytotoxic T-cells and its pathological significance in patients with HS has not been explored. We examined 30 cases of surgically resected hippocampi, including 16 International League Against Epilepsy (ILAE) type 1, 9 ILAE type 2, 1 ILAE type 3 HS, and 4 ILAE No-HS, as well as 6 autopsy No-HS hippocampi. The HS hippocampi showed sparse to scattered CD8-positive T-cells, rare CD4-positive T-cells, and a modest increase in CD68-positive microglia/macrophages, which were significantly more numerous than those in the No-HS controls. The infiltration of CD8-positive T-cells was significantly greater in the CA1 subfield than other subfields of type 1 and type 2 HS. The numbers of CD8-positive T-cells positively correlated with those of CD4-positive T-cells; there was a lower ratio of CD4/CD8-positive T-cells. There were positive correlations between these cells and scores of neuronal loss but no significant correlation between the infiltration of these cells and epilepsy disease duration or age of epilepsy onset. These findings suggest that an autoimmune process may be involved in the pathogenesis of HS and infiltration of immune cells, particularly CD8-positive cytotoxic T-cells, may contribute to neuronal loss in HS.
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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 | 0.001 |
| 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 | 0.001 |
| 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 | 0.000 |
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".