MétaCan
Menu
← Back to cohort

New Bioaccumulations of Toxins in Resident Coastal Dolphins Signal Dangers of Human Myeloma.

2006· article· en· W2567824933 on OpenAlexaboutno aff
Brian G.M. Durie, Hardy Jones

Bibliographic record

VenueBlood · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsnot available
Fundersnot available
KeywordsEstuaryBayFisheryGeographyBioaccumulationEcologyBiologyArchaeology

Abstract

fetched live from OpenAlex

Abstract Dolphins and humans are exposed to the same toxins in seafood. Over 2 billion people worldwide rely on seafood as their major source of protein and 60% of people live in coastal areas. Resident coastal dolphins are exposed to marine pollution in the same fashion as humans who frequently consume seafood, thus any indication of disease in dolphins has implications both for humans who eat regularly from the same areas and/or are otherwise exposed to the same toxins. Although ecotoxicologic studies of marine environments are very complex, (Irwin: Aquatic Mammals 31: 195–225, 2005), the bottlenose dolphin is a sentinel species for biomonitoring purposes. Tissue levels of many known carcinogens such as DDT, DDE, dioxins (e.g. PCDDs and 2,3,7,8 TCDD), BaP, PAHs, and more recently PFC and PBDEs (water repellants and fire retardants), reflect bioaccumulation in both dolphins and humans. Target sites where human and dolphin disease have been contrasted and compared are: North America (Alaska; Puget Sound; San Francisco Bay; Gulf Coast and Florida; St. Lawrence Seaway); Japan (Osaka Bay); Sweden; Coastal UK and Hong Kong (Pearl River estuary). For Alaska, Florida, Japan, Sweden and coastal UK, there are highly significant correlations between fish contamination/consumption and excess risk of human myeloma. In Alaska, Inuit men eat contaminated fish, have high organochloride (dioxins) levels in blood and tissues and an increased risk of myeloma. Likewise for Swedish fisherman comparing Baltic (more contamination) versus west coast levels of dioxins and myeloma. In Japan, a case control study provides a highly significant odds ratio of 5.89 for agriculture/fisheries as occupational factors. A separate study gives an annual age adjusted incidence of 7.03/100,000 for the Osaka Bay fishing region. Around Lake Okeechobee Florida an incidence rate of 6.52/100,000 correlates with both contamination and commercial fishing licenses. Although dolphins share most human mammalian genes, including CYP1A and CYP2B, they lack the ability to adequately catabolize type I and II dioxins, which therefore preferentially accumulate. Unfortunately, observed results of these bioaccumulations are suppressed immunity, infections and cancers particularly B-cell lymphomas and “myeloma-like” immunoblastic lymphomas (Bossart: J. Vet Diagn Invest 9: 454–458, 1997). This pattern of diseases in turn corresponds with the local and systemic effects exemplified in Balb/c mice during pristine-induced plasmacytogenesis and in humans exposed to toxins. Newly recognized persistent organic pollutants such as water repellants (PFCs) and flame-retardants (PBDEs) are a particular concern, both because of rapid recent bioaccumulation in dolphins with associated disease manifestations plus the potential for wide global dispersal and diverse routes of human exposure. Numerous consumer goods contain PBDEs, including electronics, carpets, furniture and textiles. Genetic studies help refine probability calculations to assess risk using the union rule for independent events. Studies are now underway to correlate recent bioaccumulations in dolphins and humans, genetic predisposition and myeloma onset. Probability calculations for risk of developing myeloma will support interventions to reduce both contamination of the marine environment and elimination of human toxin exposures.

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 score0.003
Threshold uncertainty score0.005

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 studies0.0000.000
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 head0.019
GPT teacher head0.250
Teacher spread0.231 · 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
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueBlood→Same topicMarine animal studies overview→French-language works237,207→