Bibliographic record
Abstract
Most neuro-pathophysiological research involving the hippocampus uses rodents as a nonhuman model, due to the extensive experimental descriptive literature and favorable cost. The findings from the rodent hippocampus may not be generalizable to humans, though, as ontogenetically they have different hippocampal components. While the rodent model has these limitations, the use of nonhuman primates is often not feasible due to economic and ethical considerations. Pigs are a translational alternative due to the anatomic similarity of the hippocampus in humans and pigs. Materials and Methods: Eight pigs’ brains were harvested and then analyzed according to our previously established technique. Five brains were frozen and three were stored in formalin. All eight brains were then sent to an independent histology service, where they were sectioned according to the methodology established by Holm 1994. The slabs were 10 µm with 2.5 cm2 of hippocampus cross-sectional area. Results: The mean total hippocampus volume was 892.84 mm3 ± 198.91 mm3 using Holm’s methodology. The mean number of cells per sample (20X magnification settings) was 9996.75, using automated ImageJ cell counting. Discussion: In this study, the counts of hippocampus cells were divided into two regions of interest: CA1 and CA3. Our results show that the mean number of hippocampus cells observed was 5.75 million and 2.25 million, in the CA1 and CA3 regions respectively. Holm reported 4.12 million cells in the CA1 region and 1.51 million cells in the CA3 region. The results presented here indicate the CA1 and CA3 cell percentages being 23% and 9% respectively, which are similar to the percentages reported by Holm (21% and 12%). Conclusion: These results corroborate previous findings and demonstrate a novel and cost-effective way to study the hippocampus of pigs in translational neurological research.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.008 |
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".