<i>In vivo</i> fragmentation of heparan sulfate by heparanase overexpression renders mice resistant to amyloid protein A amyloidosis
Bibliographic record
Abstract
Amyloid diseases encompass >20 medical disorders that include amyloid protein A (AA) amyloidosis, Alzheimer's disease, and type 2 diabetes. A common feature of these conditions is the selective organ deposition of disease-specific fibrillar proteins, along with the sulfated glycosaminoglycan, heparan sulfate. We have generated transgenic mice that overexpress human heparanase and have tested their susceptibility to amyloid induction. Drastic shortening of heparan sulfate chains was observed in heparanase-overproducing organs, such as liver and kidney. These sites selectively escaped amyloid deposition on experimental induction of inflammation-associated AA amyloidosis, as verified by lack of material staining with Congo Red, as well as lack of associated polysaccharide, whereas the same tissues from control animals were heavily infiltrated with amyloid. By contrast, the spleens of transgenic mice that failed to significantly overexpress heparanase contained heparan sulfate chains similar in size to those of control spleen and remained susceptible to amyloid deposition. Our findings provide direct in vivo evidence that heparan sulfate is essential for the development of amyloid disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".