Recent advances in understanding and managing adenosine deaminase and purine nucleoside phosphorylase deficiencies
Bibliographic record
Abstract
PURPOSE OF THE REVIEW: To review the recent advances in the understanding and management of the immune and nonimmune effects of inherited adenosine deaminase (ADA) and purine nucleoside phosphorylase (PNP) deficiencies. RECENT FINDINGS: Abnormal thymocyte development and peripheral T-cell activation in ADA-deficient and PNP-deficient patients cause increased susceptibility to infections and immune dysregulation. The impaired purine homeostasis also damages many other cell types and tissues. Animal studies suggest that defects in surfactant metabolism by alveolar macrophages cause the pulmonary alveolar proteinosis commonly seen in ADA-deficient infants, while toxicity of purine metabolites to cerebellar Purkinje cells may lead to the ataxia frequently observed in PNP deficiency. Patients' outcome with current treatments including enzyme replacement and stem cell transplantations are inferior to those achieved in most severe immunodeficiency conditions. New strategies, including intracellular enzyme replacement, gene therapy and innovative protocols for stem cell transplantations hold great promise for improved outcomes in ADA and PNP deficiency. Moreover, newborn screening and early diagnosis will allow prompt application of these novel treatment strategies, further improving survival and reducing morbidity. SUMMARY: Better understanding of the complex immune and nonimmune effects of ADA and PNP deficiency holds great promise for improved patients' outcome.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.002 |
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".