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Record W4324375426 · doi:10.3389/fnmol.2023.1048985

Roxadustat (FG-4592) abated lipopolysaccharides-induced depressive-like symptoms via PI3K signaling

2023· article· en· W4324375426 on OpenAlexaff
Axiang Li, Zizhen Liu, Tahir Ali, Ruyan Gao, Yanhua Luo, Qichao Gong, Chenyou Zheng, Weifen Li, Hongling Guo, Xinshe Liu, Shupeng Li, Tao Li

Bibliographic record

VenueFrontiers in Molecular Neuroscience · 2023
Typearticle
Languageen
FieldNeuroscience
TopicTryptophan and brain disorders
Canadian institutionsUniversity of Toronto
FundersNational Natural Science Foundation of China
KeywordsNeuroinflammationWortmanninHypoxia (environmental)PI3K/AKT/mTOR pathwayProinflammatory cytokinePharmacologyCytokineInflammationMedicineSignal transductionEndocrinologyInternal medicineChemistryCell biologyBiology

Abstract

fetched live from OpenAlex

Background: Despite its role in inflammation and the redox system under hypoxia, the effects and molecular mechanisms of hypoxia-inducible factor (HIF) in neuroinflammation-associated depression are poorly explored. Furthermore, Prolyl hydroxylase domain-containing proteins (PHDs) regulate HIF-1; however, whether and how PHDs regulate depressive-like behaviors under Lipopolysaccharides (LPS)-induced stress conditions remain covered. Methods: To highlight the roles and underlying mechanisms of PHDs-HIF-1 in depression, we employed behavioral, pharmacological, and biochemical analyses using the LPS-induced depression model. Results: Lipopolysaccharides treatment induced depressive-like behaviors, as we found, increased immobility and decreased sucrose preference in the mice. Concurrently, we examined increased cytokine levels, HIF-1 expression, mRNA levels of PHD1/PHD2, and neuroinflammation upon LPS administration, which Roxadustat reduced. Furthermore, the PI3K inhibitor wortmannin reversed Roxadustat-induced changes. Additionally, Roxadustat treatment attenuated LPS-induced synaptic impairment and improved spine numbers, ameliorated by wortmannin. Conclusion: PI3K signaling.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.051
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.253
Teacher spread0.234 · 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 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
Published2023
Admission routes1
Has abstractyes

Explore more

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