A Monogenic Disease with a Variety of Phenotypes: Deficiency of Adenosine Deaminase 2
Bibliographic record
Abstract
Objective. Deficiency of adenosine deaminase 2 (DADA2) is an autosomal recessive autoinflammatory disorder associated with ADA2 mutations. We aimed to investigate the characteristics and ADA2 enzyme activities of patients with DADA2 compared to non-DADA2 patients. Methods. This is a descriptive study of 24 patients with DADA2 who were admitted to the Adult and Pediatric Rheumatology, Pediatric Haematology, and Pediatric Immunology Departments of Hacettepe University. All ADA2 exons were screened by Sanger sequencing. Serum ADA2 enzyme activity was measured by modified spectrophotometric method. Results. Twenty-four patients with DADA2 were included: 14 with polyarteritis nodosa (PAN)-like phenotype (Group 1); 9 with Diamond-Blackfan anemia (DBA)-like features, and 1 with immunodeficiency (Group 2). Fourteen PAN-like DADA2 patients did not have the typical thrombocytosis seen in classic PAN. Inflammatory attacks were evident only in Group 1 patients. Serum ADA2 activity was low in all patients with DADA2 except one, who was tested after hematopoietic stem cell transplantation. There was no significant difference in ADA2 activities between PAN-like and DBA-like patients. In DADA2 patients with one ADA2 mutation, serum ADA2 activities were as low as those of patients with homozygote DADA2. ADA2 activities were normal in non-DADA2 patients. ADA2 mutations were affecting the dimerization domain in Group 1 patients and the catalytic domain in Group 2 patients. Conclusion. We suggest assessing ADA2 activity along with genetic analysis because there are patients with one ADA2 mutation and absent enzyme activity. Our data suggest a possible genotype–phenotype correlation in which dimerization domain mutations are associated with PAN-like phenotype, and catalytic domain mutations are associated with hematological manifestations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.002 | 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".