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Record W4211029027 · doi:10.3389/fgene.2022.780764

Case Report: Two Families With HPDL Related Neurodegeneration

2022· article· en· W4211029027 on OpenAlexaff
Ieva Mičule, Baiba Lāce, Nathan T. Wright, Nicolas Chrestian, Jurģis Strautmanis, Mikus Dīriks, Janis Stavusis, Dita Kidere, Elfa Kleina, Anna Zdanovica, Nataly Laflamme, Nadie Rioux, Samarth Thonta Setty, Sander Pajusalu, Arnaud Droit, Monkol Lek, Serge Rivest, Inna Inashkina

Bibliographic record

VenueFrontiers in Genetics · 2022
Typearticle
Languageen
FieldNeuroscience
TopicNeurological diseases and metabolism
Canadian institutionsUniversité Laval
FundersLatvijas Zinātnes PadomeEesti Teadusagentuur
KeywordsProbandHereditary spastic paraplegiaDiseaseNeurodegenerationMedicineGeneticsConsanguinityBiologyBioinformaticsPediatricsGenePhenotypePathologyMutation

Abstract

fetched live from OpenAlex

There are recent reports of associations of variants in the HPDL gene with a hereditary neurological disease that presents with a wide spectrum of clinical severity, ranging from severe neonatal encephalopathy with no psychomotor development to adolescent-onset uncomplicated spastic paraplegia. Here, we report two probands from unrelated families presenting with severe and intermediate variations of the clinical course. A homozygous variant in the HPDL gene was detected in each proband; however, there was no known parental consanguinity. We also highlight reductions in citrate synthase and mitochondrial complex I activity detected in both probands in different tissues, reflecting the previously proposed mitochondrial nature of disease pathogenesis associated with HPDL mutations. Further, we speculate on the functional consequences of the detected variants, although the function and substrate of the HPDL enzyme are currently unknown.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.002
Science and technology studies0.0040.003
Scholarly communication0.0020.002
Open science0.0020.004
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.018
GPT teacher head0.238
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2022
Admission routes1
Has abstractyes

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