MG-106 Global developmental delay and characteristic facial features associated with pacs1 gene mutation – report of two cases
Bibliographic record
Abstract
Intellectual disability (ID) affects 1%–3% of the population. While it has a strong genetic component, finding a genetic diagnosis remains challenging. Given the high rate of de novo events in ID, family-based sequencing may be an important tool. In 2012 Schuurs-Hoeijmakers et al., reported two children with ID and characteristic features associated with a heterozygote mutation in PACS1. We report the same mutation elucidated by whole exome sequencing (WES) in two additional children. Patient 1 was born at term to a 30-year-old primigravida from Bangladesh. Her birth weight and length were 3–10th% and head circumference 50–75th%. ID presented in the first year of life. She had sparse hair, a high forehead, frontal bossing, hypertelorism, deep-set eyes, a broad nasal root, full lips and a wide mouth. She had marked hypotonia, decreased muscle bulk and hyperextensible joints. WES revealed a PACS1 mutation. (NM_018026)exon4:c. C607T:p. R203W. Patient 2 was born at term to a 34-year-old primigravida from China. Birth weight and length were 10–25th% and head circumference 25–50th%. At birth, an anoplasty was performed for an ectopic anus. A right duplex kidney and undescended testes were identified. ID presented before age one. He had a short forehead, bushy eyebrows, short nose, large mouth, uplifting earlobes, bilateral single palmar creases, widely spaced nipples and an umbilical hernia. WES revealed the same mutation. Our two cases highlight the clinical utility of WES in helping establish a diagnosis of an unfamiliar clinical syndrome and supports the discovery of a now recognisable syndrome due to mutation in PACS1.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| 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".