Rapid and inexpensive bedside diagnosis of RAN binding protein 2-associated acute necrotizing encephalopathy
Bibliographic record
Abstract
Acute necrotizing encephalopathy 1 (ANE1) is a very rare disorder associated with a dominant heterozygous mutation in the RANBP2 (RAN binding protein 2) gene. ANE1 is frequently triggered by a febrile infection and characterized by serious and irreversible neurological damage. Although only a few hundred cases have been reported, mutations in RANBP2 are only partially penetrant and can occur de novo , suggesting that their frequency may be higher in some populations. Genetic diagnosis is a lengthy process, potentially delaying definitive diagnosis. We therefore developed a rapid bedside qPCR-based tool for early diagnosis and screening of ANE1 mutations. Primers were designed to specifically assess RANBP2 and not RGPD (RANBP2 and GCC2 protein domains) and discriminate between wild-type or mutant RANBP2 . Nasal epithelial cells were obtained from two individuals with known RANBP2 mutations and two healthy control individuals. RANBP2 -specific reverse transcription followed by allele-specific primer qPCR amplification confirmed the specific detection of heterozygously expressed mutant RANBP2 in the ANE1 samples. This study demonstrates that allele-specific qPCR can be used as a rapid and inexpensive diagnostic tool for ANE1 using preexisting equipment at local hospitals. It can also be used to screen non-hospitalized family members and at risk-population to better establish the frequency of non-ANE-associated RANBP2 mutations, as well as possible tissue-dependent expression patterns. Systematic review registration The protocol was registered in the international prospective register of systematic reviews (PROSPERO– CRD42023443257 ).
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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 | 0.014 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".