Delineating the expanding phenotype of <scp> <i>HERC2</i> </scp> ‐related disorders: The impact of biallelic loss of function versus missense variation
Bibliographic record
Abstract
HECT And RLD Domain-Containing E3 Ubiquitin Protein Ligase 2, or HERC2, codes an ubiquitin ligase that has an important role in key cellular processes including cell cycle regulation, DNA repair, mitochondrial functions, and spindle formation during mitosis. While HERC2 Neurodevelopmental Disorder in Old Order Amish is a well characterized human disorder involving HERC2, bi-allelic HERC2 loss of function has only been described in three families and results in a more severe neurodevelopmental disorder. Herein, we delineate the HERC2 loss of function phenotype by describing three previously unreported patients, and by summarizing the molecular and phenotypic information of all known HERC2 missense variants and biallelic loss of function patients. Collectively, these twelve individuals present with recurring features that define a syndrome with varying combinations of severe neurodevelopmental delay, structural brain anomalies, seizures, hypotonia, feeding difficulties, hearing and vision issues, and renal anomalies. This study describes a distinct neurodevelopmental disorder, emphasizing the importance of further characterization of HERC2-related disorders, as well as highlighting the importance of ongoing work into understanding these critical neurodevelopmental pathways.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".