Co‐pathologies modify hippocampal protein accumulation patterns in neurodegenerative diseases
Bibliographic record
Abstract
INTRODUCTION: Limited research has extensively analyzed neurodegenerative disease-related protein deposition patterns in the hippocampus. METHODS: This study examined the distribution of proteins in hippocampal subregions across major neurodegenerative diseases and explored their relation to each other. The area density of phosphorylated tau (p-tau), amyloid beta (Aβ), α-synuclein, and phosphorylated TDP-43 protein deposits together with pyramidal cell density in each hippocampal subregion, including CA1-4, prosubiculum (ProS), and subiculum was assessed in 166 cases encompassing various neurodegenerative diseases. RESULTS: Alzheimer's disease-associated p-tau predominated in ProS, Aβ in the CA1, and Lewy body-related α-synuclein in the CA2. The area density of protein deposits increased with the pathological stage until a peak, then decreased in cases with high pathology stages along with pyramidal cell density. Comorbid protein pathology influenced protein deposition patterns. DISCUSSION: This comprehensive evaluation reveals characteristic neurodegenerative disease-related protein accumulation patterns in hippocampal subregions modified by co-pathologies. HIGHLIGHTS: Alzheimer's disease-related phosphorylated tau predominates in the prosubiculum. Amyloid beta predominates in the CA1 and Lewy body-related α-synuclein in the CA2. The area density of protein deposition increases with the disease stage up to a peak. In the high pathology stage, protein deposition and pyramidal cell density decreases. Comorbid protein pathology affects the pattern of protein accumulation.
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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".