Histone deacetylases (<scp>HDACs</scp>) in frontotemporal lobar degeneration
Bibliographic record
Abstract
AIMS: Frontotemporal lobar degeneration (FTLD) is clinically and pathologically heterogeneous. Although associated with variations in MAPT, GRN and C9ORF72, the pathogenesis of these, and of other nongenetic, forms of FTLD, remains unknown. Epigenetic factors such as histone regulation by histone deacetylases (HDAC) may play a role in the dysregulation of transcriptional activity, thought to underpin the neurodegenerative process. METHODS: The distribution and intensity of HDACs 4, 5 and 6 was assessed semi-quantitatively in immunostained sections of temporal cortex with hippocampus, and cerebellum, from 33 pathologically confirmed cases of FTLD and 27 controls. RESULTS: We found a significantly greater intensity of cytoplasmic immunostaining for HDAC4 and HDAC6 in granule cells of the dentate gyrus in cases of FTLD overall compared with controls, and specifically in cases of FTLD tau-Picks compared with FTLD tau-MAPT and controls. No differences were noted between FTLD-TDP subtypes, or between the different genetic and nongenetic forms of FTLD. No changes were seen in HDAC5 in any FTLD or control cases. CONCLUSIONS: Dysregulation of HDAC4 and/or HDAC6 could play a role in the pathogenesis of FTLD-tau associated with Pick bodies, although their lack of immunostaining implies that such changes do not contribute directly to the formation of Pick bodies.
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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) | 0.001 | 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".