MétaCan
Menu
← Back to cohort
Record W7132935102

Investigating Sialic Acid Deficiency on Promoting Renal Thrombosis

2020· dissertation· W7132935102 on OpenAlexaff
Justin Pham

Bibliographic record

VenueTSpace · 2020
Typedissertation
Language
FieldBiochemistry, Genetics and Molecular Biology
TopicGlycosylation and Glycoproteins Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSialic acidSialyltransferaseGlycoproteinThrombosisPhenotypeKidney
DOInot available

Abstract

fetched live from OpenAlex

Atypical Hemolytic Uremic Syndrome (aHUS) is a rare condition which causes thrombosis in the renal microvasculature. Pneumococcal aHUS (P-aHUS) is a rare subtype resulting from severe Streptococcus pneumoniae infections. Although the pathophysiology of P-aHUS remains unknown, patients often present with high blood levels of sialidase, enzymes which remove terminal sialic acids from cell surface glycans. Recently, recessive mutations resulting in loss-of-function in the sialyltransferase ST3GAL1 were identified in three siblings with aHUS. To determine how sialic acid-deficiency promotes thrombosis in the renal microvasculature, the desialylated glycoproteins expressed on sialic acid-deficient endothelial cells were identified and the renal phenotypes of sialic acid-deficient rats were characterized. In total, 5 desialylated glycoproteins of interest were identified by mass spectrometry proteomics, while sialic acid-deficient rats were revealed to have significant renal microvasculature injury. The data collected provides evidence for sialic acid-deficiency as a mechanism for promoting renal thrombosis, and potentially novel targets for future study.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.041
GPT teacher head0.365
Teacher spread0.324 · 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 designBench or experimental
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

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueTSpace→Same topicGlycosylation and Glycoproteins Research→French-language works237,207→