Toxic effects of consumption of ‘hijiki’ seaweed in rats
Bibliographic record
Abstract
‘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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".