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Record W2538290242 · doi:10.1186/s12864-016-3093-4

Impaired telomere maintenance in Alazami syndrome patients with LARP7 deficiency

2016· article· en· W2538290242 on OpenAlexaff
Brody Holohan, Wanil Kim, Tsung‐Po Lai, Hirotoshi Hoshiyama, Ning Zhang, Anas M. Alazami, Woodring E. Wright, M. Stephen Meyn, Fowzan S. Alkuraya, Jerry W. Shay

Bibliographic record

VenueBMC Genomics · 2016
Typearticle
Languageen
FieldMedicine
TopicTelomeres, Telomerase, and Senescence
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersNational Center for Research ResourcesNational Cancer InstituteNational Institute on AgingNational Institutes of Health
KeywordsTelomereTelomeraseBiologyGeneticsGene silencingAnticipation (artificial intelligence)Loss functionGenePhenotype

Abstract

fetched live from OpenAlex

BACKGROUND: Loss of function in genes required for telomere maintenance result in disorders known as telomeropathies, which are characterized by a pattern of symptoms including generalized and specific lymphocytopenias as well as very short telomere length and disease anticipation. METHODS: Because human LARP7 is the most likely ortholog of the Tetrahymena p65 protein, which is required for telomerase activity in that organism, we investigated the effects of LARP7 silencing in human cells as well as in two distinct families with Alazami syndrome (loss of function of LARP7). RESULTS: Depletion of LARP7 caused a reduction in telomerase enzymatic activity and progressively shorter telomeres in human cancer cell lines. Alazami syndrome patients from two separate cohorts exhibited very short lymphocyte telomeres. Further, wild-type offspring of LARP7 mutant individuals also had very short telomeres, comparable to what is observed in telomerase (hTERT) mutant cohorts. CONCLUSIONS: Together, these experiments demonstrate that in addition to the readily apparent developmental disorder associated with LARP7 deficiency, an underlying telomeropathy exists even in unaffected siblings of these individuals.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.648

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 head0.012
GPT teacher head0.213
Teacher spread0.200 · 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 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".

Quick stats

Citations44
Published2016
Admission routes1
Has abstractyes

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