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Activation and Regulation of Caspase-6 and Its Role in Neurodegenerative Diseases

2014· review· en· W2098663242 on OpenAlexfundno aff
Xiaojun Wang, Qin Cao, Yan Zhang, Xiao‐Dong Su

Bibliographic record

VenueThe Annual Review of Pharmacology and Toxicology · 2014
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMitochondrial Function and Pathology
Canadian institutionsnot available
FundersNational Key Research and Development Program of ChinaCanadian Institutes of Health ResearchNational Natural Science Foundation of China
KeywordsCaspaseProteasesHuntington's diseaseApoptosisHuntingtinNLRP1NeurodegenerationAlzheimer's diseaseEffectorNeuroscienceBiologyDiseaseInflammationCell biologyCaspase 2Programmed cell deathMedicineImmunologyPathologyGeneticsBiochemistryEnzyme

Abstract

fetched live from OpenAlex

Caspases, a family of cysteine proteases, are major mediators of apoptosis and inflammation. Caspase-6 is classified as an apoptotic effector, and it mediates nuclear shrinkage during apoptosis, but it possesses unique activation and regulation mechanisms that differ from those of other effector caspases. Furthermore, increasing evidence has shown that caspase-6 is highly involved in axon degeneration and neurodegenerative diseases, such as Huntington's disease and Alzheimer's disease. Cleavage at the caspase-6 site in mutated huntingtin protein is a prerequisite for the development of the characteristic behavioral and neuropathological features of Huntington's disease. Active caspase-6 is present in early stages of Alzheimer's disease, and caspase-6 activity is associated with the disease's pathological lesions. In this review, we discuss the evidence relevant to the role of caspase-6 in neurodegenerative diseases and summarize its activation and regulation mechanisms. In doing so, we provide new insight about potential therapeutic approaches that incorporate the modulation of caspase-6 function for the treatment of neurodegenerative diseases.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.982
Threshold uncertainty score0.596

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.339
Teacher spread0.321 · 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 teacher head, 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

Citations100
Published2014
Admission routes1
Has abstractyes

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