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Record W3124877547

ONTARIO: Neurotoxic cyanobacterium (blue-green alga) toxicosis in Ontario

2007· article· en· W3124877547 on OpenAlexaboutno aff
Brent Hoff, Gary Thomson, Kirsten Graham

Bibliographic record

VenueEurope PMC (PubMed Central) · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsHepatotoxinNeurotoxinCyanobacteriaToxinTrichotheceneBiologyToxicityMedicineInternal medicineEndocrinologyMicrobiology
DOInot available

Abstract

fetched live from OpenAlex

In May 2006, 3 dogs (2 Labrador retrievers and a weimaraner) in a group of 11 dogs died suddenly and unexpectedly within 1 h after swimming in a local pond at a dog sitter’s farm. The dogs had spent approximately 5 min at the pond during a supervised walk around the farm, and several of the dogs were observed swimming in the pond and eating the vegetation. When the dogs arrived back at the house, 2 dogs became weak and collapsed, with shallow breathing. The 3rd dog developed similar signs within minutes. A 4th dog, a Labrador retriever-cross, subsequently developed similar signs, but survived. Hyperglycemia and acidosis were documented from 2 of the dogs from which antemortem blood was collected. The only dog submitted for postmortem examination had no gross or microscopic lesions in tissues. Gastric content was negative for organophosphates, carbamates, strychnine, and the mycotoxins penetrim A and roquefortine. Brain cholinesterase activity was reduced at 0.6 μmol/g/min (reference range, 3.2 +/− 1.6 μmol/g/min). Pond water and stomach contents were positive for the neurotoxin anatoxin-a and negative for the hepatotoxin microcystins by liquid chromatography tandem mass spectrometry (LC/MS/MS). Blue-green alga produces 4 types of toxins: hepatotoxins, neurotoxins, lipopolysaccharide endotoxins, and cytotoxins. Hepatotoxins, particularly microcystins, appear to be the most common toxins identified in toxic blooms. Neurotoxins appear to be much rarer in toxic blooms. The most common blue-green algal neurotoxin is anatoxin-a, but all the neurotoxins interfere with the transmission of signals in neurons or across the neuromuscular junction, leading to muscular paralysis and, in severe cases, death due to respiratory failure. Animal poisoning associated with toxic blooms of cyanobacterium have been reported in all continents, except Antarctica, (1,2) and there have been a few reports of domestic animals and wildlife consuming freshwater contaminated with toxic blue-green alga blooms in Canada (3). These reports have been predominantly microcystin-containing hepatotoxic blooms occurring in Saskatchewan and Alberta. Microcystins have been identified in Ontario in the past few years as causing disease in dogs and humans. The authors could find no reports of neurotoxic cyanobactrial blooms from Ontario affecting domestic animals. This report of neurotoxic cyanobacterial (blue-green alga) toxicosis in dogs is noteworthy in that it may be the first report of this in Ontario and in that the early seasonal occurrence, in May rather than in late summer, was unexpected.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
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 head0.012
GPT teacher head0.191
Teacher spread0.179 · 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

Citations13
Published2007
Admission routes1
Has abstractyes

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