Clinical relevance of loss-of-function mutations of NEMO/IKBKG
Bibliographic record
Abstract
Dysfunctional inhibitor of nuclear factor-κB (NF-κB) kinase regulatory subunit gamma ( IKBKG ) is known to trigger incontinentia pigmenti (IP), anhidrotic ectodermal dysplasia with immunodeficiency (EDA-ID), immunodeficiency (ID), and IKBKG deleted exon 5 autoinflammatory syndrome (NDAS). The correlation between genotype and phenotype remains elusive because of the considerable variability in IKBKG genes. This study aimed to systematically describe IKBKG gene mutations and clinical characteristics. Cases with IKBKG mutations and thorough clinical features were gathered using PubMed, Web of Science, EMBASE, Scopus, and Cochrane databases, with a publication deadline of February 12, 2023. The Newcastle-Ottawa scale and its modified version were used to assess the quality of each study. Gene mutations and clinical manifestation data were analyzed and reviewed. 144 publications with 564 patients were included in the analysis. IP, EDA-ID, ID, and NDAS accounted for 78.0%, 15.8%, 5.0%, and 1.2% of IKBKG mutations, respectively. Skin abnormalities (89.5%), dental abnormalities (68.5%), infection (100%), and non-infectious inflammation (100%) were the most common manifestations of IP, EDA-ID, ID, and NDAS, respectively. Mutations related to EDA-ID and ID are concentrated in the zinc finger region and characterized by the most severe clinical symptoms. E390RfsX5 can cause IP, EDA-ID, and ID. c.1182_1183delTT and H413R caused the most clinical manifestations. Mycobacterium (22.7%) and Streptococcus (17.5%) were the most common pathogens. Almost all cases of hyper-IgM occurred in patients with EDA-ID. Different structural domains correspond to symptoms with varying degrees of severity. Certain mutations may correspond to unique manifestations, providing insight into disease progression.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".