MétaCan
Menu
Back to cohort
Record W2079312210 · doi:10.1016/j.gene.2013.02.008

Identification of a SLC19A2 nonsense mutation in Persian families with thiamine-responsive megaloblastic anemia

2013· article· en· W2079312210 on OpenAlexaff
Aria Setoodeh, Amirreza Haghighi, Nasrollah Saleh-Gohari, Sian Ellard, Alireza Haghighi

Bibliographic record

VenueGene · 2013
Typearticle
Languageen
FieldMedicine
TopicAlcoholism and Thiamine Deficiency
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersWellcome Trust
KeywordsMegaloblastic anemiaAnemiaNonsense mutationThiamineDiabetes mellitusBiologyPopulationMedicinePediatricsGeneticsMutationInternal medicineEndocrinologyGeneMissense mutation

Abstract

fetched live from OpenAlex

Thiamine-responsive megaloblastic anemia (TRMA) is an autosomal recessive syndrome characterized by early-onset anemia, diabetes, and hearing loss caused by mutations in the SLC19A2 gene. We studied the genetic cause and clinical features of this condition in patients from the Persian population. A clinical and molecular investigation was performed in four patients from three families and their healthy family members. All had the typical diagnostic criteria. The onset of hearing loss in three patients was at birth and one patient also had a stroke and seizure disorder. Thiamine treatment effectively corrected the anemia in all of our patients but did not prevent hearing loss. Diabetes was improved in one patient who presented at the age of 8months with anemia and diabetes after 2months of starting thiamine. The coding regions of SLC19A2 were sequenced in all patients. The identified mutation was tested in all members of the families. Molecular analyses identified a homozygous nonsense mutation c.697C>T (p.Gln233*) as the cause of the disease in all families. This mutation was previously reported in a Turkish patient with TRMA and is likely to be a founder mutation in the Persian population.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.708
Threshold uncertainty score0.329

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.007
GPT teacher head0.236
Teacher spread0.229 · 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 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

Citations25
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueGeneSame topicAlcoholism and Thiamine DeficiencyFrench-language works237,207