Bibliographic record
Abstract
Purine nucleoside phosphorylase (PNP) is a key enzyme required for the degradation of purine nucleosides into uric acid or their salvage into nucleic acids. Patients who are deficient in PNP suffer from progressive T cell immunodeficiency, with increased susceptibility to infections, autoimmunity, and neurologic abnormalities. In the absence of successful treatment to restore immune function, these patients rarely survive to adulthood. Hematopoietic stem cell transplantation is the only known cure for PNP deficiency. Use of an HLA-matched donor is preferable as the outcome with alternative donors have been variable; however, this option is rarely available. Gene therapy represents a therapeutic option that bypasses the need for a donor, and thus associated complications. Although first generation γ-retroviral vectors have been successful in some immunodeficiencies, in others, evidence of insertional mutagenesis prompted a halt in their use. More recently, the introduction of safer lentiviral vectors holds promise in offering a viable option to treat immunodeficiency. Here, we present a clinical trial protocol utilizing self-inactivating lentiviral vectors to treat PNP deficiency. Patients will be evaluated up to 3 years post-transplantation to determine the safety of lentiviral-treated stem cell infusion, as well as the extent of immune reconstitution. Statement of novelty: This protocol describes the novel treatment of PNP deficiency using lentiviral-based gene therapy.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 0.009 |
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".