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Record W3001396177 · doi:10.3390/jcm9020344

Molecular Bases of Neurodegeneration and Cognitive Decline, the Major Burden of Sanfilippo Disease

2020· review· en· W3001396177 on OpenAlexafffund
Rachel Heon‐Roberts, Annie L. A. Nguyen, Alexey V. Pshezhetsky

Bibliographic record

VenueJournal of Clinical Medicine · 2020
Typereview
Languageen
FieldMedicine
TopicLysosomal Storage Disorders Research
Canadian institutionsUniversité de MontréalMcGill UniversityCentre Hospitalier Universitaire Sainte-Justine
FundersCanadian Institutes of Health Research
KeywordsNeurodegenerationDiseaseMedicineNeuroinflammationOxidative stressNeuroscienceLysosomal storage diseaseSandhoff diseaseCognitive declineSomatic cellBioinformaticsDementiaGeneticsPathologyBiologyInternal medicineGene

Abstract

fetched live from OpenAlex

The mucopolysaccharidoses (MPS) are a group of diseases caused by the lysosomal accumulation of glycosaminoglycans, due to genetic deficiencies of enzymes involved in their degradation. MPS III or Sanfilippo disease, in particular, is characterized by early-onset severe, progressive neurodegeneration but mild somatic involvement, with patients losing milestones and previously acquired skills as the disease progresses. Despite being the focus of extensive research over the past years, the links between accumulation of the primary molecule, the glycosaminoglycan heparan sulfate, and the neurodegeneration seen in patients have yet to be fully elucidated. This review summarizes the current knowledge on the molecular bases of neurological decline in Sanfilippo disease. It emerges that this deterioration results from the dysregulation of multiple cellular pathways, leading to neuroinflammation, oxidative stress, impaired autophagy and defects in cellular signaling. However, many important questions about the neuropathological mechanisms of the disease remain unanswered, highlighting the need for further research in this area.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.118
GPT teacher head0.477
Teacher spread0.359 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations66
Published2020
Admission routes2
Has abstractyes

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