Hippocampal dentate granule cells in temporal lobe epilepsy: A morphometry and transcriptomic study
Bibliographic record
Abstract
The dentate gyrus (DG) plays a critical role in hippocampal circuitry, providing a "gate-like" function to the downstream cornu ammonis (CA) sectors. Despite this critical role, pathologies in DG are less commonly described than those in the CA sectors in the diagnosis of mesial temporal lobe epilepsy (mTLE). To elucidate the role of the DG in mTLE, we analysed hippocampal sclerosis (HS), no-HS, non-TLE epilepsy control, and non-epilepsy control cohorts using morphometry and gene expression profiling techniques. Morphometry techniques analysed DG cell spacing, nucleus size, and nucleus circularity. Our data show distinct DG morphometry and RNA expression profiles between HS and No-HS. Dentate granule cells are more dispersed in patients with HS, and the DG shows an elevated expression of the complement system, apoptosis, and extracellular matrix remodelling-related RNA. We also observe an overall decrease in neurogenesis-related RNA in HS DG. Interestingly, regardless of the pathological diagnosis, the DG morphometry correlates with post-operative outcomes. Increased cell spacing is observed in the DG of mTLE cases that achieve seizure freedom post-operatively. This study reveals the possible prognostic value of DG morphometry, as well as supporting the notion that HS and no-HS TLE may be distinct disease entities with differing contributing mechanisms.
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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 | 0.001 |
| 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) | 0.001 | 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".