MG-132 Next-generation sequencing in the neonatal intensive care unit: Pilot data from 12 newborns
Bibliographic record
Abstract
Background Rare disease can present in the first days and weeks of life and is associated with significant morbidity and mortality. The genetic and clinical heterogeneity of these conditions can pose a significant challenge for diagnosis. Objective To evaluate the diagnostic utility of the Illumina TruSightTM One panel, we performed Next-generation sequencing (NGS) for a series of 20 newborns presenting with features suggestive of a Mendelian disease in the Neonatal Intensive Care Unit (NICU). Design/methods Twenty patients were recruited from the NICU at the Children’s Hospital of Eastern Ontario. Inclusion criteria required a complex medical presentation (congenital anomalies, abnormalities in growth and/or neurological features). We used a family-based trio approach. Target enrichment was performed with the Illumina TruSightTM One Sequencing Panel kit. This panel targets 4,813 genes that are deemed clinically-relevant and referred to as the “clinome”. Sequencing was performed on the Illumina MiSeq. NextGene software (v2.3.4.4) was used for analyses. Time from sample acquisition to data analysis was possible in 7 working days. Results/conclusion To date, 13 trios were sequenced and analysed. A molecular diagnosis was made in 5 of 13 patients, comparable to the rate obtained by whole-exome sequencing from the literature. Positive cases included bi-allelic mutations in WDR19 and ACE, an X-linked mutation in MTM1, homozygous mutations in FTO, and a de novo mutation in SCN1A. Clinome sequencing has the potential to improve our ability to efficiently diagnose rare diseases in the NICU by providing a cost-effective tool to evaluate the clinically relevant portion of the genome in a week’s time.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".