MétaCan
Menu
Back to cohort

Toxic effects of consumption of ‘hijiki’ seaweed in rats

2008· article· en· W176222734 on OpenAlexaboutno aff
Katsuhiko Yokoi, Hitomi Shizuka, Aki Konomi

Bibliographic record

VenueThe FASEB Journal · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicFluoride Effects and Removal
Canadian institutionsnot available
Fundersnot available
KeywordsAnimal scienceChemistryInorganic arsenicCholesterolFood consumptionFood scienceArsenicBiologyBiochemistry

Abstract

fetched live from OpenAlex

‘Hijiki’ ( Hijikia fusiforme ) is a sort of seaweed consumed in Japan, although it contains high amount of inorganic arsenic (As). The average portion of inorganic As in total As of ‘hijiki’ is circa 70% (Food Standards Agency, UK). Authoritative agents of Australia, New Zealand, Canada and UK are prohibiting import, sale and consumption of ‘hijiki’. We tested detrimental effects of ‘hijiki’ using rats. Twenty 3‐week‐old male Fisher rats were equally divided to two groups: control group (CON) given AIN‐93G diet and ‘hijiki’ group (HIJ) given ‘hijiki’ diet that contained 30 g ‘hijiki’ powder/kg in place of α‐cornstarch. Rats were ad libitum fed diets and deionized water for 7 weeks. The ‘hijiki’ powder contained 102±2 (mean ± SD) mg total As/kg. Food intake and body weight gain were not affected by consumption of ‘hijiki’. ‘Hijiki’ significantly increased rectal temperature by 0.5°C. Total As concentration measured by ICP‐MS was markedly and significantly increased by ‘hijiki’. Total As (ng/g) in CON vs HIJ was respectively 12±12 vs 3168±571 in liver; 177±11 vs 67600±2700 in blood. ‘Hijiki’ consumption significantly decreased plasma free fatty acids and increased plasma total and LDL‐cholesterol and phospholipids. It decreased TIBC and increased plasma ALP, choline esterase, inorganic phosphorus and Mg. These results suggest that subacute oral exposure of ‘hijiki’ is detrimental mainly due to inorganic As.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.290
Threshold uncertainty score0.306

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 science0.0000.000
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 head0.011
GPT teacher head0.217
Teacher spread0.206 · 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 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

Citations0
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueThe FASEB JournalSame topicFluoride Effects and RemovalFrench-language works237,207