Effects of Herring Milt Hydrolysates and Fractions in a Diet-Induced Obesity Model
Bibliographic record
Abstract
Over the past years, promising results from studies have shown that herring milt hydrolysates (HMH) can counter immune-metabolic disorders associated with obesity. However, more studies must corroborate these results. Thus, three commercial hydrolysates (HMH1, HMH2, and HMH3) as well as the fractions of two of them (HMH4 and HMH5) obtained by electrodialysis with ultrafiltration membranes (EDUF) were evaluated in vivo at higher doses compared to a previous study. To achieve this, seven groups of mice were fed for 8 weeks with either a control Chow diet or an obesogenic diet rich in fat and sucrose (HFHS) and supplemented by daily gavage with water or 312.5 mg/kg of one of the five HMH products. In summary, HMH supplements had no impact on weight gain. In the insulin tolerance test (ITT), HMH2 and its HMH5 fraction significantly reduced the blood sugar variation (p < 0.05). However, during the glucose tolerance (OGTT), HMH2 supplement increased the hyperinsulinemia variation (p < 0.05) induced by the HFHS diet. HMH1, HMH2, and HMH5 supplements generated potentially beneficial changes for health in the gut microbiota. These results reveal that HMH do not counteract obesity effects but may decrease certain physiological effects induced by obesity.
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 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) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".