Maternal Consumption of Hesperidin and Naringin Flavanones Exerts Transient Effects to Tibia Bone Structure in Female CD‐1 Offspring
Bibliographic record
Abstract
Hesperidin (HSP) and naringin (NAR), flavanones rich in citrus fruits, support skeletal integrity in adult and aging rodent models. This study determined whether maternal consumption of HSP and NAR favorably programs bone development, resulting in higher bone mineral density (BMD) and greater structure and biomechanical strength (i.e., peak load) in female offspring. Female CD-1 mice were fed a control diet or a HSP + NAR diet five weeks before pregnancy and throughout pregnancy and lactation. At weaning, female offspring were fed a control diet until six months of age. The structure and BMD of the proximal tibia were measured longitudinally using in vivo microcomputed tomography at 2, 4, and 6 months of age. The trabecular bone structure at two and four months and the trabecular BMD at four months were compromised at the proximal tibia in mice exposed to HSP and NAR compared to the control diet (p < 0.001). At six months of age, these differences in trabecular structure and BMD at the proximal tibia had disappeared. At 6 months of age, the tibia midpoint peak load, BMD, structure, and the peak load of lumbar vertebrae and femurs were similar (p > 0.05) between the HSP + NAR and control groups. In conclusion, maternal consumption of HSP and NAR does not enhance bone development in female CD-1 offspring.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".