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Record W3122335415 · doi:10.1186/s12887-021-02506-5

Facial cleft? The first case of manitoba‐oculo‐tricho‐anal syndrome with novel mutations in China: a case report

2021· article· en· W3122335415 on OpenAlexaboutno aff
Shuchen Gu, Yimin Khoong, Xin Huang, Tao Zan

Bibliographic record

VenueBMC Pediatrics · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicOcular Disorders and Treatments
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsMedicineExome sequencingCHARGE syndromeEtiologyPediatricsPopulationGeneticsGene mutationDermatologyMutationPathologyGeneBiology

Abstract

fetched live from OpenAlex

BACKGROUND: Manitoba-oculo-tricho-anal (MOTA) syndrome is a rare syndrome with only 27 cases reported worldwide so far, but none was reported in the population of Eastern Asia. Such extremely low prevalence might be contributed by misdiagnosis due to its similarities in ocular manifestations with facial cleft. In our study, we discovered the first case of MOTA syndrome in the population of China, with 2 novel FRAS1 related extracellular matrix 1 (FREM1) gene stop-gain mutations confirmed by whole exome sequencing. CASE PRESENTATION: A 12-year-old Chinese girl presented with facial cleft-like deformities including aberrant hairline, blepharon-coloboma and broad bifid nose since birth. Whole exome sequencing resulted in the identification of 2 novel stop-gain mutations in the FREM1 gene. Diagnosis of MOTA syndrome was then established. CONCLUSIONS: We discovered the first sporadic case of MOTA syndrome according to clinical manifestations and genetic etiology in the Chinese population. We have identified 2 novel stop-gain mutations in FREM1 gene which further expands the spectrum of mutational seen in the MOTA syndrome. Further research should be conducted for better understanding of its mechanism, establishment of an accurate diagnosis, and eventually the exploitation of a more effective and comprehensive therapeutic intervention for MOTA syndrome.

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: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.435
Threshold uncertainty score0.983

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 head0.010
GPT teacher head0.239
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 designCase report
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

Citations7
Published2021
Admission routes1
Has abstractyes

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