Editorial: The immune system and inflammation in musculoskeletal health, aging, and disease
Bibliographic record
Abstract
Editorial on the Research TopicThe immune system and inflammation in musculoskeletal health, aging, and diseaseThe musculoskeletal and immune systems are intricately linked in anatomical space and function, with crosstalk between immune cells and musculoskeletal tissues, including bone, cartilage, muscle, and tendons, being essential for normal development and homeostasis (1-4).Such a relationship is also critical during injury and repair, with inflammation and immune cells being necessary for initiating and resolving injury-induced tissue responses and altered extracellular matrix composition and turnover, likewise regulating immune cell engagement (5-9).Over the last decade, growing evidence has demonstrated that alterations to immune cell populations caused by aging and metabolic dysfunction underly the impaired tissue repair responses seen in chronic musculoskeletal diseases and acute injuries, including those affecting synovial joints (e.g., osteoarthritis), bones (e.g., osteoporosis, fracture healing), muscles (e.g., sarcopenia), and tendons/ ligaments (e.g., tendinopathy, rupture).The burden from musculoskeletal conditions continues to rise globally, impacting patients' quality of life, independence, and health, social and economic systems due to increasing care costs and work loss.Over 1.7 billion people globally live with musculoskeletal conditions, according to the World Health Organization (10) and findings from the Lancet's Global Burden of Disease Study 2019 (11-13).Low back pain is the main contributor to this overall burden, while osteoarthritis (OA) shows the most rapid increase of these conditions.While advances have been made in treating osteoporosis in the past decade, intervertebral disc degeneration (IVDD), OA, and many others where disease pathogenesis is less understood lack disease-modifying therapeutics.Progress in understanding how diseases such as IVDD and OA progress has revealed an essential role for inflammatory dysregulation in these conditions; however, several important questions must be resolved before this can be mobilized as a therapeutic strategy.In this Frontiers of Immunology -Inflammation Section Research Topic; The Immune System and Inflammation in Musculoskeletal Health, Aging, and Disease; we present a collection of Frontiers in Immunology frontiersin.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.032 | 0.022 |
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".