Cytopathology
Bibliographic record
Abstract
by dysfunction of the allograft.Hypothesis: Activation of complement in cardiac allograft recipients without allograft dysfunction may indicate that a protective mechanism exists.Design: Endomyocardial biopsies from 8 patients with complement deposition were compared.Four patients survived these episodes of AMR and 4 did not.Immunofluorescence staining of the endomyocardial biopsies was performed with the following markers: CD35 (Complement receptor 1, CR1), CD46 (Membrane Cofactor Protein, MCP), CD55 (Decay Accelerating Factor, DAF) and CD59 (Protectin).These markers are complement regulatory proteins which play a role in controlling and silencing complement activation and tissue injury or in removing complement complexes.Results: Patients with good response to therapy and resolution of the AMR episode showed intense tissue expression of these complement regulatory proteins in the endothelium of the allograft.Patiens with poor outcome had low or absent tissue expression of complement regulatory proteins.Conclusions: Local expression of complement regulatory proteins on the endothelial cell surface plays a role in controlling the deleterious effects of complement activation during episodes of AMR .Identification of these regulatory proteins in tissue and understanding of the mechanisms that control their expression will be a useful tool to predict outcome Allograft Inflammatory Factor-1 (AIF-1) Immunohistochemistry Stain in Biopsies of Human Heart Transplants
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.011 |
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".