RECENT RESEARCH ON THE GROWTH PLATE: Impact of inflammatory cytokines on longitudinal bone growth
Bibliographic record
Abstract
Children with inflammatory diseases usually display abnormal growth patterns as well as delayed puberty. This is a result of several factors related to the disease itself, such as malnutrition, hypercortisolism, and elevated levels of pro-inflammatory cytokines. These factors in combination with glucocorticoid treatment contribute to growth retardation during chronic inflammation by systemically affecting the major regulator of growth, the GH/IGF1 axis. However, recent studies have also shown evidence of a direct effect of these factors at the growth plate level. In conditions of chronic inflammation, pro-inflammatory cytokines are upregulated and released into the circulation. The most abundant of these, tumor necrosis factor α, interleukin 1β (IL1β), and IL6, are all known to directly act on growth plate cartilage to induce apoptosis and thereby suppress bone growth. Both clinical and experimental studies have shown that growth retardation can partly be rescued when these cytokines are blocked. Therefore, therapy modulating the local actions of these cytokines may be effective for preventing growth failure in patients with chronic inflammatory disorders. In this review, we report the current knowledge of inflammatory cytokines and their role in regulating bone growth.
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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 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.003 | 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".