Lycopene Improves Bone Quality and Regulates AGE/RAGE/NF‐кB Signaling Pathway in High‐Fat Diet‐Induced Obese Mice
Bibliographic record
Abstract
OBJECTIVE: This study was aimed at examining the effects of lycopene on bone metabolism in high-fat diet (HFD)- induced obese mice and to identify the potential underlying mechanisms. METHODS: Mice were fed a HFD for 12 weeks and then continue with or without lycopene intervention (15 mg/kg) for additional 10 weeks. The effects of lycopene on blood glucose and lipid metabolism, as well as serum levels of total antioxidant capacity (T-AOC), superoxide dismutase (SOD), and malondialdehyde (MDA) were determined by biochemical assays. Bone histomorphological features and osteoclast activity were assessed by hematoxylin/eosin and tartrate-resistant acid phosphatase staining. Bone microstructure at the proximal tibial metaphysis and diaphysis was determined by microcomputed tomography. Tibial biomechanical strength and material profiles were measured by a three-point bending assay and Fourier transform infrared spectroscopy. Protein expressions involved in the AGE/RAGE/NF-кB signaling pathway were determined by western blot and/or immunohistochemical staining. RESULTS: Lycopene consumption reduced body weight gain and improved blood glucose and lipid metabolism in HFD-induced obese mice. In addition, lycopene treatment preserved bone biomechanical strength, material profiles, and microarchitecture in obese mice. Moreover, these alterations were associated with an increase in serum levels of T-AOC and SOD, and a decline in serum levels of MDA, as well as a reduction of AGEs, RAGE, cathepsin K, and p-NF-кBp65 and NF-кBp65 expressions in the femurs and tibias of obese mice. CONCLUSION: Lycopene may improve bone quality through its antioxidant properties, which may be linked with the regulation of the AGE/RAGE/NF-кB signaling pathway in obese mice. These results suggest that lycopene consumption may be beneficial for the management of obesity-induced osteoporosis.
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) | 0.000 | 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) | 0.001 | 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".