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Record W2896868353 · doi:10.1080/09540105.2018.1508424

Magnesium isoglycyrrhizinate positively affects concanavalin A-induced liver damage by regulating macrophage polarization

2018· article· en· W2896868353 on OpenAlexfundno aff
Rui Lin, Yun Liu, Meiyu Piao, Yan Song

Bibliographic record

VenueFood and Agricultural Immunology · 2018
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacological Effects of Natural Compounds
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaAlberta Innovates - Health Solutions
KeywordsConcanavalin AProinflammatory cytokineMacrophage polarizationImmunologyHepatitisLiver injuryLiver functionPhenotypeBiologyPharmacologyMedicineInternal medicineBiochemistryInflammationIn vitroGene

Abstract

fetched live from OpenAlex

The deficient functional polarization of macrophages is implicated in the disease progression of autoimmune hepatitis (AIH). This study aims to evaluate the impact of Magnesium isoglycyrrhizinate (MgIG) on concanavalin A (Con A)-induced hepatitis in a mouse model, thereby clarifying the molecular mechanisms with which it is associated. MgIG was periodically administered to C57BL/6 mice before one intravenous injection of Con A (20 mg/kg). The MgIG treatment demonstrated a protective function in mice for Con A-induced AIH, the expression of proinflammatory cytokines, and the serum levels of alanine aminotransferase and aspartate aminotransferase. In addition, the MgIG pre-treatment had a significant effect on the number of F4/80+ cells entering the liver. MgIG efficiently facilitated macrophage polarization toward an M2 phenotype. The results indicate that a relationship may exist between the protective impacts of MgIG with respect to Con A-induced liver injury and the capability of the hepatoprotective agent to regulate macrophage polarization.

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)0.000
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 score0.102
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.040
GPT teacher head0.342
Teacher spread0.302 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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