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Record W3105533748 · doi:10.1139/cjpp-2020-0373

Metformin alleviates hydrogen peroxide–induced inflammation and oxidative stress via inhibiting P2X7R signaling in spinal cord tissue cells neurons

2020· article· en· W3105533748 on OpenAlexvenueno aff
Gang Wang, Shurui Chen, Zhenya Shao, Yankun Li, Wei Wang, Liang Mao, Jian Li, Xifan Mei

Bibliographic record

VenueCanadian Journal of Physiology and Pharmacology · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdenosine and Purinergic Signaling
Canadian institutionsnot available
Fundersnot available
KeywordsOxidative stressNeuroprotectionMetforminInflammationPharmacologyPurinergic receptorGene knockdownHydrogen peroxideMedicineChemistryReceptorEndocrinologyInternal medicineDiabetes mellitusApoptosisBiochemistry

Abstract

fetched live from OpenAlex

Metformin, the first medication that is often prescribed for the treatment of type 2 diabetes mellitus, was recently found to be neuroprotective. To study the mechanism underlying the neuroprotective effect of metformin, we pretreated primary spinal cord neurons with 50 µM or 100 µM metformin for 2 h prior to treatment with hydrogen peroxide (H2O2) for up to 48 h. Our results showed that H2O2 increased the expression of purinergic receptor P2X7 (P2X7R) in spinal cord neurons, which promoted the downstream pro-inflammatory cytokines release and oxidative stress. We found that metformin could reverse these pro-inflammatory and pro-oxidative effects of H2O2. Besides, P2X7R knockdown by siRNA suppressed H2O2-induced pro-inflammatory cytokine release and oxidative stress response. In conclusion, our results show that metformin can alleviate H2O2-induced inflammation and oxidative stress via modulating the P2X7R signaling pathway.

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)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.271
Teacher spread0.253 · 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

Citations9
Published2020
Admission routes1
Has abstractyes

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