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Molecular Basis of Von Willebrand Disease in Patients from India

2015· article· en· W2575041000 on OpenAlexaff
Sumitha Elayaperumal, Eunice Sindhuvi Edison, Sankari Devi Govindanattar, Surendar Singh, Aby Abraham, Biju George, Auro Viswabandya, Sukesh C. Nair, Alok Srivastava

Bibliographic record

VenueBlood · 2015
Typearticle
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsVon Willebrand diseaseVon Willebrand factorMedicinePopulationHaplotypeInternal medicineGeneticsImmunologyGastroenterologyBiologyGenotypeGenePlatelet

Abstract

fetched live from OpenAlex

Abstract Introduction: Type 3 Von Willebrand Disease (VWD) is an autosomal recessive bleeding disorder with a prevalence of about 0.5 to 1 per million in western countries. In India there lies no epidemiological data on the prevalence of different subtypes. However type-3 VWD outnumbers the other types mainly due to (i) high degree of consanguinity and (ii) under diagnosis of mild to moderate subtypes. Clinically being a severe subtype, there are very few studies describing mutation spectrum and molecular pathology of the disease in Indian population. Hence we screened for mutations in patients with type III(VWD). More than 724 mutations have been reported in the literature. Identification of mutations is important for offering genetic testing to families affected by this disorder and to understand the biology of von willebrand factor. Methods: A total of one hundred patients from 86 families were diagnosed with type 3 VWD from 2012-2015 were included in the study. Clinical data was collected by Condensed Molecular and Clinical Markers for the Diagnosis and Management of Type 1 von Willebrand Disease (MCMDM-1VWD) bleeding questionnaire. Laboratory diagnosis was based on prolonged clotting times, reduced antigen(vWF:Ag), ristocetin co-factor activity (vWF:RCo) and FVIII levels(FVIII:C). DNA was screened for mutations in the VWF gene by PCR, CSGE and sequencing. For ascribing causality to novel mutations, we performed in silico analysis. Gene dosage analysis was done to detect deletions and to confirm the carrier status in females. Haplotype analysis was carried out using polymorphic markers in patients with recurrent mutations. Results: The median age at presentation was 3 years (0-60years). All these patients presented with history of variable skin and mucosal bleeding with a mean MCMDM-1VWD bleeding score of 10(3-25). A total of 55 mutations were identified in 91 patients of which 43(78%) were novel. These included frame shift (n=17, 30.9%) missense (n=14, 25.5%), nonsense (n=10, 18.18%), large deletion (n=2, 3.63%) gene conversion (n=3, 5.45%) and splice site mutation (n=9, 18.4%). Among the fourteen missense mutations, 8(57%) were novel. The mutations p.Asp47 and p.Gly74, are highly conserved across multiple species and mutations in this region are known to impair the polymerization of the multimers. The residue p.Cys370Tyr lies in D1 domain which could affect the extracellular secretion of VWF. The residue p.Met1055Lys lies in the D3 domain which may impair FVIII binding. The residue Ala1150Pro, p.Gln2266His, p.Cys2184Tyr on insilico analysis is predicted to impair the protein stability, which may affect the extracellular secretion of VWF proteins (SIFT score: 0.0). The residues p. Cys2257Arg are predicted to disturb dimerization. The functional significance of some of these mutations has to be further evaluvated and confirmed. Three common mutations accounting to 22% (p.Trp2107*, n=6; c.2443-1G>C, n=12; c.3675+1G>C, n=4) of the patients were highly prevelant in the study were haplotype analysis was carried out. A common haplotype was shared among different ethinic group from India only in patients with p.Trp2107*. Conclusions: The mutations identified in patients with VWD are as heterogeneous as reported in other populations. The molecular data presented here adds significantly to the mutation database of this condition and also useful for its genetic diagnosis in India. Disclosures No relevant conflicts of interest to declare.

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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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.217
Teacher spread0.210 · 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 designObservational
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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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