Toll-like receptor 9 (TLR9) gene deletion-mediated fracture healing in type II diabetic osteoporosis associates with inhibition of the nuclear factor-kappa B (NF-κB) signaling pathway
Bibliographic record
Abstract
Diabetes is characterized by increased fracture risk. Evidence from in vivo studies is lacking for anti-fracture strategies in diabetes. Our microarray analyses predicted association of Toll-like receptor 9 (TLR9) with both diabetes and osteoporosis, which was the focus of this work in a murine model of type II diabetic osteoporosis (T2DOP). A T2DOP model with fracture was established in TLR9 knockout (TLR9−/−) mice, which were then treated with the NF-κB signaling pathway inhibitor (PDTC) and activator (TNF-α). The obtained data suggested that TLR9 knockout augmented regeneration of bone tissues and cartilage area in the callus, and diminished fibrous tissues in T2DOP mice. Moreover, TLR9 depletion significantly affected bone mineral density (BMD), bone volume/tissue volume (BV/TV), connectivity density, trabecular number, trabecular separation and trabecular thickness, thus promoting fracture recovery. Bone morphology and structure were also improved in response to TLR9 depletion in T2DOP mice. TLR9 depletion inactivated NF-κB signaling in T2DOP mice. PDTC was found to enhance fracture healing in T2DOP mice, while TNF-α negated this effect. Collectively, these data indicate that TLR9 depletion may hold anti-fracture properties, making it a potential therapeutic target for T2DOP. Abbreviations: Diabetic osteoporosis (DOP); bone mineral density (BMD); Toll-like receptors (TLRs); type 2 diabetes (T2D); Toll-like receptor 9 (TLR9); nuclear factor-kappaB (NF-κB); streptozotocin (STZ); type 2 diabetic osteoporosis (T2DOP); Gene Expression Omnibus (GEO); Kyoto encyclopedia of genes and genomes (KEGG); pyrrolidine dithiocarbamate (PDTC); computed tomography (CT); Hematoxylin–eosin (HE); bone morphogenetic protein 7 (BMP7); analysis of variance (ANOVA);
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".