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Record W2766583610 · doi:10.5604/01.3001.0010.5288

Long-Term, Low-Dose Exposure to Microcystin-LR Does not Cause or Increase the Severity of Liver Disease in Rodents

2017· article· en· W2766583610 on OpenAlexafffund
Meaghan Labine, Yuewen Gong, Gerald Y. Minuk

Bibliographic record

VenueAnnals of Hepatology · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsUniversity of Manitoba
FundersPublic Health AgencyUniversity of ManitobaManitoba Health Research Council
KeywordsHepatotoxinThioacetamideMedicineHepatocyteLiver diseaseHepatic stellate cellInternal medicineFibrosisInflammationHistologyGastroenterologyEndocrinologyToxicityIn vitroBiologyBiochemistry

Abstract

fetched live from OpenAlex

BACKGROUND: Acute exposure to high concentrations of microcystin-LR (MC-LR) can cause significant hepatocyte injury. AIM: To document the effects of long-term, low-dose MC-LR exposure on hepatic inflammation and fibrosis in mice with healthy and diseased livers. MATERIAL AND METHODS: Male CD1 mice (N = 20/group) were exposed to 1.0 μg/L of MC-LR in drinking water; 1.0 μg/L MC-LR plus 300 mg/L of the hepatotoxin thioacetamide (MC-LR/TAA); or 300 mg/L TAA alone for 28 weeks. Liver biochemistry and histology were documented at the end of the study period. In addition, hepatic stellate cells (HSCs), were exposed in vitro to MC-LR (0.1-10,000 μg/L) and monitored for changes in cell metabolism, proliferation and activation. RESULTS: Liver biochemistry and histology were essentially normal in MC-LR alone exposed mice. MC-LR/TAA and TAA alone exposed mice had significant hepatic inflammation and fibrosis but the extent of the changes were similar in the two groups. In vitro, MC-LR had no effect on HSC metabolism, proliferation or activation. CONCLUSION: Long-term, low-dose exposure to MC-LR is unlikely to lead to chronic liver disease in the setting of a normal liver or exacerbate existing liver disease in the setting of ongoing hepatitis.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.995

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.037
GPT teacher head0.303
Teacher spread0.266 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations8
Published2017
Admission routes2
Has abstractyes

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