Alpha-1-antitrypsin in cell and organ transplantation
Bibliographic record
Abstract
Limited availability of donor organs and risk of ischemia-reperfusion injury (IRI) seriously restrict organ transplantation. Therapeutics that can prevent or reduce IRI could potentially increase the number of transplants by increasing use of borderline organs and decreasing discards. Alpha-1 antitrypsin (AAT) is an acute phase reactant and serine protease inhibitor that limits inflammatory tissue damage. Purified plasma-derived AAT has been well tolerated in more than 30 years of use to prevent emphysema in AAT-deficient individuals. Accumulating evidence suggests that AAT has additional anti-inflammatory and tissue-protective effects including improving mitochondrial membrane stability, inhibiting apoptosis, inhibiting nuclear factor kappa B activation, modulating pro- vs anti-inflammatory cytokine balance, and promoting immunologic tolerance. Cell culture and animal studies have shown that AAT limits tissue injury and promotes cell and tissue survival. AAT can promote tolerance in animal models by downregulating early inflammation and favoring induction and stabilization of regulatory T cells. The diverse intracellular and immune-modulatory effects of AAT and its well-established tolerability in patients suggest that it might be useful in transplantation. Clinical trials, planned and/or in progress, should help determine whether the promise of the animal and cellular studies will be fulfilled by improving outcomes in human organ transplantation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".