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Lysosomal Acid Phosphatase Biosynthesis and Dysfunction: A Mini Review Focused on Lysosomal Enzyme Dysfunction in Brain

2016· review· en· W2345256317 on OpenAlexaff
Niloufar Ashtari, Xun Jiao, Maryam Rahimi-Balaei, Sara Amiri, Shahram E. Mehr, Behzad Yeganeh, Hassan Marzban

Bibliographic record

VenueCurrent Molecular Medicine · 2016
Typereview
Languageen
FieldMedicine
TopicLysosomal Storage Disorders Research
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsLysosomeLysosomal storage diseaseNeuronal ceroid lipofuscinosisMannose 6-phosphate receptorLipofuscinAcid phosphataseGlucocerebrosidaseEnzymeBiologyPathogenesisBatten diseaseDiseaseBiochemistryCell biologyGeneMedicineInternal medicineImmunology

Abstract

fetched live from OpenAlex

Lysosomes are membrane-bound organelles that are responsible for degrading and recycling macromolecules. Lysosomal dysfunction occurs in enzymatic and non-enzymatic deficiencies, which result in abnormal accumulation of materials. Although lysosomal storage disorders affect different organs, the central nervous system is the most vulnerable. Evidence shows the role of lysosomal dysfunction in different neurodegenerative diseases, such as Niemann-Pick Type C disease, juvenile neuronal ceroid lipofuscinosis, Alzheimer's disease and Parkinson's disease. Lysosomal enzymes such as lysosomal acid phosphatase 2 (Acp2) play a critical role in mannose-6-phosphate removal and Acp2 controls molecular and cellular functions in the brain during development and adulthood. Acp2 is essential in cerebellar development, and mutations in this gene cause severe cerebellar neurodevelopmental and neurodegenerative disorders. In this mini-review, we highlight lysosomal dysfunctions in the pathogenesis of neurodevelopmental and/or neurodegenerative diseases with special attention to Acp2 dysfunction.

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 score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.003

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.

Opus teacher head0.050
GPT teacher head0.354
Teacher spread0.304 · 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

Citations36
Published2016
Admission routes1
Has abstractyes

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