Neutropenia in patients with adenosine deaminase deficiency
Bibliographic record
Abstract
Adenosine deaminase (ADA) deficiency is a disorder where the accumulation of purine metabolites, which are particularly toxic to lymphocytes, can lead to severe, life threatening infections. In addition, ADA deficiency also affects other tissues. Few reports describe the presence of neutropenia in these patients, primarily in older patients after hematopoietic stem cell transplantation (HSCT) or gene therapy. We hypothesized that abnormal purine metabolism could also affect granulopoiesis. The objective of the study was to assess the frequency and nature of neutropenia in patients with ADA deficiency in the first 180 days of life. This retrospective study analyzed all patients who were diagnosed with ADA deficiency at the Hospital for Sick Children between 1984 and 2012 and had at least one documented complete blood count with differential in the first 180 days of life. A diagnosis of ADA deficiency was made when erythrocytes ADA enzyme activity was less than 1-2% of control and/or demonstration of mutations in the ADA gene. Neutropenia was defined as the absolute neutrophil count of less than 6.0 x 10 /L for ages ≤ 6 days, less than 1.5 x 10 /L for ages 7-13 days, less than 1.0 x 10 /L for ages 14-89 days and less than 1.5 x 10 /L for ages ≥ 90 days. Patients were excluded if neutropenia was first documented after chemotherapy for HSCT. Thirteen patients with ADA deficiency were included in the study. Nine of the 13 patients had neutropenia that was first documented within the first 180 days of life (median age of first neutropenia 70 days, range 1-176 days). In 5 patients, the neutropenia was not present on the initial blood count but developed over time. The lowest neutrophil counts ranged from 0.11-1.08 x 10 /L (median 0.5 x 10 /L). The neutropenia developed in 7 patients prior to the onset of cotrimoxazole or other medications that commonly have myelosuppressive effects. The neutropenia was not associated with infections commonly causing neutropenia or with autoimmune manifestations. Bone marrow examinations in 2 patients with neutropenia were reported as normal. The neutropenia improved spontaneously in 3 patients, while in 4 additional patients it resolved after initiation of PEG-ADA replacement therapy or HSCT. Neutropenia occurs commonly in patients with ADA deficiency. Further studies are required to determine the pathogenesis of the neutropenia in ADA deficiency.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".