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Record W2154233527 · doi:10.1051/medsci/2006224411

Les sphingolipides : vecteurs d’agents pathogènes et cause de maladies génétiques

2006· review· fr· W2154233527 on OpenAlexaff
Caroline Fasano, Abel Hiol, Jean-Pierre Miolan, Jean-Pierre Niel

Bibliographic record

Venuemédecine/sciences · 2006
Typereview
Languagefr
FieldBiochemistry, Genetics and Molecular Biology
TopicSphingolipid Metabolism and Signaling
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMolecular biologyHumanitiesPhilosophyChemistryBiology

Abstract

fetched live from OpenAlex

Sphingolipids are present in all eukaryotic cells and share a sphingoid base : sphingosine. They were first discovered in 1884 and for a long time they were thought to participate to membrane structure only. Recently it has been established that they are mainly located in particular areas of the membrane called rafts which are signalling platforms. It has also been demonstrated that sphingolipids are receptors and second messengers. They play a crucial role in cellular functioning and are necessary to maintenance and developing of living organisms. However due to their receptor properties, they are also gateway for penetration of pathogenic agents such as virus (Ebola, HIV) or toxins (botulinium, tetanus). These agents first bind to glycosphingolipids or proteins mainly located in rafts. The complex so formed is required for the crossing of the membrane by the pathogenic agent. Sphingolipids metabolism is regulated by numerous enzymes. A failure in the activity of one of them induces an accumulation of sphingolipids known as sphingolipidoses. These are genetic diseases having severe consequences for the survival of the organism. The precise mechanisms of the sphingolipidoses are still mainly unknown which explains why few therapeutic strategies are available. These particular properties of lipids rafts and sphingolipids explain why a growing number of studies in the medical and scientific fields are devoted to them.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.935
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.077
GPT teacher head0.368
Teacher spread0.291 · 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.

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

Citations5
Published2006
Admission routes1
Has abstractyes

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