MG-124 The investicate project: Identification of new variation, establishment of stem cells, and tissue collection advancing treatment efforts
Bibliographic record
Abstract
Objective Neurodevelopmental disorders (NDDs) are a large and complex group of disorders with varied etiologies. Recent advances in sequencing, induced stem cells, and small molecule screening technologies provide an opportunity to develop personalised treatment for NDDs. Methods The INVESTICATE project recruits patients with NDDs from Children’s Hospitals in Canada and internationally. Patients are enrolled if they have a similarly affected sibling and negative genetic tests, or a de novo balanced chromosomal rearrangement (BCR). We use Next-Generation Sequencing tools to find variation and structural variant breakpoints. Fibroblasts from patients undergo rapid induced pluripotent stem cell (iPSC) re-programming, neural progenitor cell (NPC) differentiation, CRSIPR/Cas9 mutation correction, and finally cell phenotyping. Where feasible, we collect brains from cases with reduced life expectancy. Patient-derived NPCs undergo high-throughput small molecule screening to reverse cell phenotypes associated with disease. Results INVESTICATE has recruited six families, and we identified mutations in genes not previously associated with NDDs. We identified a stop codon altering single base deletion in one family, a 51-basepair promoter deletion in another family, and a gene truncating chromosomal translocation implicating chromatin remodelling, netrins, and maintenance of brain pH in NDDs. Functional assays using iPSC-NPCs of these rare variants support their role in disease. Conclusions INVESTICATE is a rapid bedside-to-bench and back again pipeline capable of finding variants missed using standard methodology, and complements variant detection with a full battery of cell phenotyping assays, brain collection, and high-throughput screening. INVESTICATE is well positioned to attempt to provide cost-effective, personalised care to children with NDDss.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".