Anti-IFN-γ Autoantibody Syndrome Presenting with Disseminated Nontuberculous Mycobacteria Infections: A Case Series of Therapeutic Implications and Review of Literature
Bibliographic record
Abstract
Anticytokine autoantibodies (AAbs), particularly anti-interferon-gamma (anti-IFN-γ) AAbs, disrupt cytokine functions, leading to infections, autoimmune-like diseases, and conditions resembling interleukin-12 (IL-12)/IFN-γ pathway defects. Advances in genetic testing have clarified overlaps between autoinflammatory, autoimmune disorders, and primary immunodeficiencies but reveal complex phenotypes and pathways. While these insights deepen our understanding of immune mechanisms, they also complicate diagnosis and treatment, with limited options for IFN-γ deficiencies caused by genetic mutations. The adult-onset immunodeficiency with disseminated lymphadenitis due to nontuberculous mycobacteria (NTM) and other opportunistic infections has been linked to high levels of anti-IFN-γ AAbs. This syndrome, initially identified in HIV-negative Asian patients, frequently affects individuals of Asian descent and may be associated with specific human leukocyte antigen (HLA) alleles. The presence of neutralizing anti-IFN-γ AAbs impairs the IFN-γ-dependent immune response, likely contributing to the persistent NTM infection. This study underscores the potential for late-onset anti-IFN-γ AAb syndrome to manifest with disseminated NTM (dNTM) infections, highlights the importance of timely diagnosis and considers rituximab as a potential therapeutic option.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 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".