Humoral Proinflammatory Cytokine and SAA Generation Profiles and Spatio‐Temporal Relationship Between SAA and Lysosomal Cathepsin B and D in Murine Splenic Monocytoid Cells During AA Amyloidosis
Bibliographic record
Abstract
Evidence shows that tissue macrophages (MPhis), in mice undergoing AA amyloidosis, endocytose acute-phase humoral serum amyloid A (SAA) and traffic it to lysosomes where it is degraded. Incomplete degradation of SAA leads to intracellular nascent AA fibril formation. In vitro, cathepsin (Cat) B is known to generate amyloidogenic SAA derivatives, whereas Cat D generates non-amyloidogenic SAA derivatives, and interferon (IFN-gamma)-treated MPhis show selective increase in Cat B concentration, a factor conducive to AA amyloidogenesis. To understand the cumulative effect of these factors in AA amyloidosis, humoral levels of SAA, IFN-gamma, tumour necrosis factor (TNF-alpha) and granulocyte-macrophage colony-stimulating factor were determined in azocasein (AZC)-treated CD-1 mice. We correlated these responses with the spatio-temporal distribution of SAA, Cat B- and Cat D-immunoreactive splenic reticuloendothelial (RE) cells. AZC-treated CD-1 mice similar to that of A/J mice showed partial amyloid resistance; their peak humoral IFN-gamma and SAA responses overlapped during the pre-amyloid phase. Unexpectedly, Cat D immunoreactivity (IR), instead of Cat B IR, was predominant in the splenic RE cells, indicating an apparent lack of causal relationship between IFN-gamma-mediated increase in Cat B expression. Partial amyloid resistance in CD-1 mice, probably a genetic trait, may be linked to high levels of Cat D expression, causing a delay in nascent AA fibril formation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".