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Record W4400728645 · doi:10.3390/cells13141203

Genetic Deficiencies of Hyaluronan Degradation

2024· review· en· W4400728645 on OpenAlexaff
Stephen P. Fink, Barbara L. Triggs‐Raine

Bibliographic record

VenueCells · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProteoglycans and glycosaminoglycans research
Canadian institutionsUniversity of ManitobaChildren's Hospital Research Institute of Manitoba
FundersNational Institutes of Health
KeywordsHyaluronidaseGenePolysaccharideCell biologyConnective tissueDegradation (telecommunications)BiologyChemistryBiochemistryGeneticsEnzyme

Abstract

fetched live from OpenAlex

Hyaluronan (HA) is a large polysaccharide that is broadly distributed and highly abundant in the soft connective tissues and embryos of vertebrates. The constitutive turnover of HA is very high, estimated at 5 g per day in an average (70 kg) adult human, but HA turnover must also be tightly regulated in some processes. Six genes encoding homologues to bee venom hyaluronidase (HYAL1, HYAL2, HYAL3, HYAL4, HYAL6P/HYALP1, SPAM1/PH20), as well as genes encoding two unrelated G8-domain-containing proteins demonstrated to be involved in HA degradation (CEMIP/KIAA1199, CEMIP2/TMEM2), have been identified in humans. Of these, only deficiencies in HYAL1, HYAL2, HYAL3 and CEMIP have been identified as the cause or putative cause of human genetic disorders. The phenotypes of these disorders have been vital in determining the biological roles of these enzymes but there is much that is still not understood. Deficiencies in these HA-degrading proteins have been created in mice and/or other model organisms where phenotypes could be analyzed and probed to expand our understanding of HA degradation and function. This review will describe what has been found in human and animal models of hyaluronidase deficiency and discuss how this has advanced our understanding of HA’s role in health and disease.

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 score0.990
Threshold uncertainty score0.828

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 head0.040
GPT teacher head0.335
Teacher spread0.295 · 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

Citations15
Published2024
Admission routes1
Has abstractyes

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