A new case of spastic paraplegia type 64 due to a missense mutation in the ENTPD1 gene
Bibliographic record
Abstract
Abstract Spastic paraplegia type 64 (SPG64; OMIM 615683) is a complicated form of hereditary spastic paraplegia (HSP) recently identified in individuals diagnosed with suspected neurodegenerative disease. Affected patients carry homozygous mutations in the ectonucleoside triphosphate diphosphohydrolase 1 gene (ENTPD1). Although they share common characteristics, affected individuals show slight discrepancies in some clinical aspects. At present, only two different cases of SPG64 have been diagnosed. More findings of genetic variation would be helpful to better understand the effect of mutations in the ENTPD1 gene on the neurological condition of affected individuals. In this study, we examined a family with an individual diagnosed with suspected HSP based on clinical findings. DNA samples from the proband, her affected sister, and both parents were analyzed using next-generation sequencing. We used an in-house automated pipeline to detect potential neuromuscular disease-causing variants. Variants were confirmed by Sanger sequencing. After cosegregation analysis, the variant NM_001776.5:c.401T>G (p.M134R) of the ENTPD1 gene was identified as a novel missense mutation linked to the phenotype of SPG64 in the proband and her sister, who showed similar and distinct clinical features compared with the two cases previously described in the literature.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".