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Record W2539646288 · doi:10.1038/srep35811

Infantile Hemangioma Originates From A Dysregulated But Not Fully Transformed Multipotent Stem Cell

2016· article· en· W2539646288 on OpenAlexfundno aff
Shaghayegh Harbi, Rong Wang, Michael Gregory, Nicole Hanson, Keith Kobylarz, Kamilah Ryan, Yan Deng, Peter Lopez, Luis Chiriboga, Paolo Mignatti

Bibliographic record

VenueScientific Reports · 2016
Typearticle
Languageen
FieldMedicine
TopicVascular Malformations and Hemangiomas
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesChina Scholarship CouncilYork UniversityNational Cancer InstituteSchool of Medicine, New York UniversityNational Institutes of HealthMemorial Sloan-Kettering Cancer CenterNYU Langone Medical Center
KeywordsVasculogenesisProgenitor cellAngiogenesisStem cellBiologyCarcinogenesisCancer researchMultipotent Stem CellLineage (genetic)Cellular differentiationCell biologyBioinformaticsGeneGenetics

Abstract

fetched live from OpenAlex

Infantile hemangioma (IH) is the most common tumor of infancy. Its cellular origin and biological signals for uncontrolled growth are poorly understood, and specific pharmacological treatment is unavailable. To understand the process of hemangioma-genesis we characterized the progenitor hemangioma-derived stem cell (HemSC) and its lineage and non-lineage derivatives. For this purpose we performed a high-throughput (HT) phenotypic and gene expression analysis of HemSCs, and analyzed HemSC-derived tumorspheres. We found that IH is characterized by high expression of genes involved in vasculogenesis, angiogenesis, tumorigenesis and associated signaling pathways. These results show that IH derives from a dysregulated stem cell that remains in an immature, arrested stage of development. The potential biomarkers we identified can afford the development of diagnostic tools and precision-medicine therapies to "rewire" or redirect cellular transitions at an early stage, such as signaling pathways or immune response modifiers.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.737

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.011
GPT teacher head0.226
Teacher spread0.215 · 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

Citations43
Published2016
Admission routes1
Has abstractyes

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