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Record W2580067419 · doi:10.3390/ijms18020290

LOX-1 and Its Splice Variants: A New Challenge for Atherosclerosis and Cancer-Targeted Therapies

2017· review· en· W2580067419 on OpenAlexfundno aff
Barbara Rizzacasa, Elena Morini, Sabina Pucci, Michela Murdocca, Giuseppe Novelli, Francesca Amati

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2017
Typereview
Languageen
FieldImmunology and Microbiology
TopicAtherosclerosis and Cardiovascular Diseases
Canadian institutionsnot available
FundersFondazione RomaYork University
KeywordsAlternative splicingExonRNA splicingspliceGene isoformBiologyMessenger RNACancer researchBioinformaticsGenePopulationRNAMedicineGeneticsComputational biology

Abstract

fetched live from OpenAlex

Alternative splicing (AS) is a process in which precursor messenger RNA (pre-mRNA) splicing sites are differentially selected to diversify the protein isoform population. Changes in AS patterns have an essential role in normal development, differentiation and response to physiological stimuli. It is documented that AS can generate both “risk” and “protective” splice variants that can contribute to the pathogenesis of several diseases including atherosclerosis. The main endothelial receptor for oxidized low-density lipoprotein (ox-LDLs) is LOX-1 receptor protein encoded by the OLR1 gene. When OLR1 undergoes AS events, it generates three variants: OLR1, OLR1D4 and LOXIN. The latter lacks exon 5 and two-thirds of the functional domain. Literature data demonstrate a protective role of LOXIN in pathologies correlated with LOX-1 overexpression such as atherosclerosis and tumors. In this review, we summarize recent developments in understanding of OLR1 AS while also highlighting data warranting further investigation of this process as a novel therapeutic target.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.002

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 head0.098
GPT teacher head0.364
Teacher spread0.267 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations42
Published2017
Admission routes1
Has abstractyes

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