Bibliographic record
Abstract
Osteoimmunology is an interdisciplinary research field combining the exciting fields of osteology and immunology. An observation that contributed enormously to the emergence of osteoimmunology was the accelerated bone loss caused by inflammatory diseases such as rheumatoid arthritis. Receptor activator of nuclear factor kappaB ligand (RANKL), which is the main regulator of osteoclastogenesis, was found to be the primary culprit responsible for the enhanced activation of osteoclasts: activated T cells directly and indirectly increased the expression of RANKL, and thereby promoted osteoclastic activity. Excessive bone loss is not only present in inflammatory diseases but also in autoimmune diseases and cancer. Furthermore, there is accumulating evidence that the very prevalent skeletal disorder osteoporosis is associated with alterations in the immune system. Meanwhile, numerous connections have been discovered in osteoimmunology beyond merely the actions of RANKL. These include the importance of osteoblasts in the maintenance of the hematopoietic stem cell niche and in lymphocyte development as well as the functions of immune cells participating in osteoblast and osteoclast development. Furthermore, research is being done investigating cytokines, chemokines, transcription factors and co-stimulatory molecules which are shared by both systems. Research in osteoimmunology promises the discovery of new strategies and the development of innovative therapeutics to cure or alleviate bone loss in inflammatory and autoimmune diseases as well as in osteoporosis. This review gives an introduction to bone remodeling and the cells governing that process and summarizes the most recent discoveries in the interdisciplinary field of osteoimmunology. Furthermore, an alternative large animal model will be discussed and the pathophysiological alterations of the immune system in osteoporosis will be highlighted.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".