Bibliographic record
Abstract
Gut microbiota is widely accepted to play a crucial role to host health via the regulation of many physiological functions, including metabolism, nutrition, pathogen resistance, and immune function. Over the last decades, accumulating evidence has also pinpointed a role for gut microbiota on bone metabolism and the development of metabolic bone diseases, such as osteoporosis. Emerging evidence suggests the potential of gut microbiota as a promising target for bone health management. In this contribution, we have examined the available literature to understand the role of gut microbiota on bone metabolism as well as the underlying mechanisms. Furthermore, the application and effectiveness of using probiotics/prebiotics as means to modify gut microbiota and bone health are discussed. In this relation, animal studies and human trails suggest that alternation of gut microbiota composition can exert the activity of bone metabolism and therefore lead to the change of bone quality. It is believed that gut microbiota regulates bone metabolism via host immune system, endocrine system and mineral absorption. Supplementation with probiotics and prebiotics to both animals and humans has demonstrated promising, but sometimes conflicting results, on bone health. Thus, future research is expected to reveal the influence of the variations in age, gender, dose, delivery method, and treatment duration, among others on the probiotics/prebiotics-targeted bone diseases treatment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".