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Record W7027493572

Cyanobacterial toxins and liver cancer

2014· dissertation· en· W7027493572 on OpenAlexaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2014
Typedissertation
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsHepatotoxinLiver cancerHepatocellular carcinomaCarcinogenIn vivoHepatic stellate cellFibrosisLiver cellHepatocyte
DOInot available

Abstract

fetched live from OpenAlex

Hepatocellular carcinoma (HCC) is the fifth most common cancer in the world today. Over the past decade, the incidence of HCC in Canada has been increasing despite efforts to mitigate disease causing factors such as viral hepatitis B and C infection. Fresh water cyanobacteria are known to release potent hepatotoxins, such as microcystin-LR (MCLR), into drinking and recreational water sources, which have been associated with liver toxicity and HCC. Despite government efforts to limit MCRL contamination, chronic low-dose exposure continues to occur. The purpose of this thesis was to gain an understanding of the potential relationship between chronic low-dose MCLR exposure and HCC. The research contains five areas of investigation; 1) In vitro effects of low-dose MCLR exposure on various hepatocellular cell types, 2) in vitro effects of low-dose MCLR exposure on hepatic stellate cells and fibrosis, 3) in vivo effects of chronic low-dose MCLR exposure on HCC initiation and promotion, 4) in vivo effects of chronic low-dose MCLR exposure on hepatic fibrosis and 5) national and provincial epidemiological analysis investigating the potential association between cyanobacterial hepatotoxins and liver cancer. Our methodology involved; 1) exposing WB-F344, PLC and CFSC-2G hepatic cell lines to varying concentrations of MCLR and observing changes in cell proliferation, protein expression, wound healing and malignant transformation, 2) continuously exposing adult male mice to 1 µg/L of MCLR in their drinking water for seven months, and observing changes in body weight, liver histology, liver enzymes, hepatic fibrosis and HCC development, 3) conducting a national HCC epidemiological study, and determining the relative risk of developing HCC from up-stream factors contributing to cyanobacterial contamination of drinking water sources and 4) conducting a provincial epidemiological study that involved collecting liver cancer incidence data from the Manitoba cancer registry from 1985 to 2007, and temporally and spatially analyzing the association between cancer incidence and MCLR measured in Manitoba lakes. The in vitro results indicate that chronic low-dose exposure to MCLR does not significantly induce cellular characteristics associated with a malignant and/or pro-fibrotic phenotype. In vivo, there were no significant changes in liver pathology, plasma chemistry, histology or tumor development compared to negative controls. Epidemiological analyses conducted at the national and provincial levels revealed similar trends. HCC incidence in Canada was associated with urban residence, immigration and hepatitis B. However, HCC incidence was not found to be influenced by the up-stream cyanobacterial predictive factors; agriculture, cattle and swine density. In Manitoba, the increasing incidence of liver cancer did not temporally or spatially associate with MCLR contamination sites within the province. In conclusion, these results are reassuring in that they suggest that chronic low-dose exposure to MCLR does not initiate or promote the development of HCC at the cellular, systemic or epidemiological levels.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.185
Teacher spread0.178 · 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 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

Citations0
Published2014
Admission routes1
Has abstractyes

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