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Record W3167688804 · doi:10.1139/bcb-2020-0599

Aberrant sphingomyelin <sup>31</sup>P-NMR signatures in giant cell tumour of bone

2021· article· en· W3167688804 on OpenAlexvenueno aff
Tayde Quiroz‐Acosta, Yazmin Montserrat Flores-Martinez, Elvia Becerra‐Martínez, Elizabeth Pérez‐Hernández, Nury Pérez‐Hernández, Angel E. Bañuelos‐Hernández

Bibliographic record

VenueBiochemistry and Cell Biology · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSphingolipid Metabolism and Signaling
Canadian institutionsnot available
FundersConsejo Nacional de Ciencia y Tecnología
KeywordsSphingomyelinCeramideSphingolipidPhosphocholineBiochemistryChemistrySphingomyelin phosphodiesteraseBiosynthesisGlycerophospholipidEnzymeMetabolomicsCell biologyMetabolismPhospholipidBiologyCholesterolBioinformaticsApoptosis

Abstract

fetched live from OpenAlex

An understanding of the biochemistry of the giant cell tumour of bone (GCTB) provides an opportunity for the development of prognostic markers and identification of therapeutic targets. Based on metabolomic analysis, we proposed glycerophospholipid metabolism as the altered pathway in GCTB., The objective of this study was to identify these altered metabolites. Using phosphorus-31 nuclear magnetic resonance spectroscopy (31P-NMR), sphingomyelin was determined to be the most dysregulated phospholipid in tissue samples from six patients with GCTB. Enzymes related to its biosynthesis and hydrolysis were examined using immunodetection techniques. High expression of sphingomyelin synthases 1 and 2, but low expression of neutral sphingomyelinase 2 (nSMase2) was found in GCTB tissues compared to non-neoplastic bone tissues. Sphingomyelin/ceramide biosynthesis is dysregulated in GCTB due to alterations in the expression of SMS1, SMS2, and nSMase2.

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 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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.006
GPT teacher head0.215
Teacher spread0.209 · 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 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

Citations7
Published2021
Admission routes1
Has abstractyes

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