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Oxidative stress increases 1-deoxysphingolipid levels in chronic kidney disease

2021· article· en· W3119307763 on OpenAlexaff
Ting Gui, Yunlun Li, Shijun Zhang, Irina Alecu, Qingfa Chen, Ying‐Yong Zhao, Thorsten Hornemann, Gerd A. Kullak‐Ublick, Zhibo Gai

Bibliographic record

VenueFree Radical Biology and Medicine · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSphingolipid Metabolism and Signaling
Canadian institutionsUniversity of Ottawa
FundersJining Medical UniversityNational Natural Science Foundation of ChinaSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsDyslipidemiaOxidative stressKidney diseaseSphingolipidInternal medicineEndocrinologyLipid metabolismMedicineLipid profileKidneyChemistryBiologyDiseaseCholesterolBiochemistry

Abstract

fetched live from OpenAlex

Chronic kidney disease (CKD) leads to deep changes in lipid metabolism and obvious dyslipidemia. The dysregulation of lipid metabolism in turn results in CKD progression and the complications of cardiovascular diseases. To obtain a profound insight into the associated dyslipidemia in CKD, we performed lipidomic analysis to measure lipid metabolites in the serum from a rat 5/6 nephrectomy (5/6 Nx) model of CKD as well as in the serum from CKD patients. HK-2 cells were also used to examine oxidative stress-induced sphingolipid changes. Totally 182 lipid species were identified in 5/6 Nx rats. We found glycerolipids, total free fatty acids, and sphingolipids levels were significantly upregulated in 5/6 Nx rats. The atypical sphingolipids, 1-deoxysphingolipids, were significantly altered in both CKD animals and human CKD patients. The levels of 1-deoxysphingolipids directly relevant to the level of oxidative stress in vivo and in vitro. These results demonstrate that 1-deoxysphingolipid levels are increased in CKD and this increase directly correlates with increased kidney oxidative stress.

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)0.003
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 score0.556
Threshold uncertainty score0.618

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.014
GPT teacher head0.286
Teacher spread0.272 · 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

Citations15
Published2021
Admission routes1
Has abstractyes

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