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Record W2560733470 · doi:10.1139/cjpp-2016-0564

Ciliary neurotrophic factor analogue aggravates CCl<sub>4</sub>-induced acute hepatic injury in rats

2016· article· en· W2560733470 on OpenAlexvenueno aff
Mingxia Cui, Junfeng Jiang, Min Guang-ning, Wei Han, Yongjie Wu

Bibliographic record

VenueCanadian Journal of Physiology and Pharmacology · 2016
Typearticle
Languageen
FieldMedicine
TopicLiver Disease and Transplantation
Canadian institutionsnot available
FundersNatural Science Foundation of Gansu ProvinceNational Natural Science Foundation of China
KeywordsCiliary neurotrophic factorSteatosisCarbon tetrachlorideLiver injuryEndocrinologyInternal medicineMedicineHepatotoxinChemistryNeurotrophic factorsPharmacologyToxicity

Abstract

fetched live from OpenAlex

Ciliary neurotrophic factor (CNTF) and CNTF analogs were reported to have hepatoprotective effect and ameliorate hepatic steatosis in db/db or high-fat-diet-fed mice. Because hepatic steatosis and injury are also commonly induced by hepatotoxin, the aim of the present study is to clarify whether CNTF could alleviate hepatic steatosis and injury induced by carbon tetrachloride (CCl4). Unexpectedly, when combined with CCl4, CNTF aggravated hepatic steatosis and liver injury. The mechanism is associated with effects of CNTF that inhibited lipoprotein secretion and drastically impaired the ability of lipoproteins to act as transport vehicles for lipids from the liver to the circulation. While injected after CCl4 cessation, CNTF could improve liver function. These data suggest that CNTF could be a potential hepatoprotective agent against CCl4-induced hepatic injury after the cessation of CCl4 exposure. However, it is forbidden to combine recombinant mutant of human CNTF treatment with CCl4.

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 score0.002
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
Bibliometrics0.0010.000
Science and technology studies0.0000.001
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 head0.015
GPT teacher head0.267
Teacher spread0.252 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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Same venueCanadian Journal of Physiology and PharmacologySame topicLiver Disease and TransplantationFrench-language works237,207