MétaCan
Menu
Back to cohort
Record W2331649103 · doi:10.1139/bcb-2013-0029

Clinorotation enhances autophagy in vascular endothelial cells

2013· article· en· W2331649103 on OpenAlexvenueno aff
Yongchun Wang, Dongyuan Lü, Fei Shi, Shu Zhang, Changbin Yang, Bing Wang, Xinsheng Cao, Ting‐Yuan Du, Yuan Gao, Jiangdong Zhao, Xi‐Qing Sun

Bibliographic record

VenueBiochemistry and Cell Biology · 2013
Typearticle
Languageen
FieldMedicine
TopicAutophagy in Disease and Therapy
Canadian institutionsnot available
Fundersnot available
KeywordsAutophagyCell biologyUmbilical veinHomeostasisEndothelial stem cellSpaceflightChemistryBiologyApoptosisBiochemistryIn vitro

Abstract

fetched live from OpenAlex

Individuals exposed to extended periods of spaceflight or prolonged 6° head-down-tilt bed rest often suffer from health hazards represented by cardiovascular deconditioning. Many studies have reported that alterations in vascular endothelial cells contribute to cardiovascular dysfunction induced by microgravity. Autophagy, a lysosomal degradation pathway, serves an adaptive role for survival, differentiation, and development in cellular homeostasis, and can be triggered by various environmental stimuli. However, whether autophagy can be induced in endothelial cells by real or simulated microgravity remains to be determined. This study was designed to investigate the effects of simulated microgravity on the activation of autophagy in human umbilical vein endothelial cells (HUVECs). We report here that clinorotation, a simulated model of microgravity, enhances autophagosome formation, increases LC3 and beclin-1 expression, and promotes the conversion of LC3-I to LC3-II in HUVECs. These results demonstrate that simulated microgravity for 48 h activates autophagy of vascular endothelial cells.

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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.006
GPT teacher head0.242
Teacher spread0.236 · 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

Citations18
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueBiochemistry and Cell BiologySame topicAutophagy in Disease and TherapyFrench-language works237,207