Anti–nerve growth factor therapy attenuates cutaneous hypersensitivity and musculoskeletal discomfort in mice with osteoporosis
Bibliographic record
Abstract
INTRODUCTION: The prevalence of osteoporosis is increasing with the aging population and is associated with increased risk of fracture and chronic pain. Osteoporosis is currently treated with bisphosphonate therapy to attenuate bone loss. We previously reported that improvement in bone mineral density is not sufficient to reduce osteoporosis-related pain in an ovariectomy (OVX)-induced mouse model of osteoporosis, highlighting the need for new treatments. Targeting of nerve growth factor (NGF) with sequestering antibodies is a promising new direction for the treatment of musculoskeletal pain including back pain and arthritis. Its efficacy is currently unknown for osteoporotic pain. OBJECTIVE: To investigate the efficacy of anti-NGF antibody therapy on osteoporotic pain in an OVX-induced mouse model. METHODS: Ovariectomy- and sham-operated mice were injected with an anti-NGF antibody (10 mg/kg, intraperitoneally, administered 2×, 14 days apart), and the effect on behavioural indices of osteoporosis-related pain and on sensory neuron plasticity was evaluated. RESULTS: Treatment with anti-NGF antibodies attenuated OVX-induced hypersensitivity to mechanical, cold, and heat stimuli on the plantar surface of the hind paw. The OVX-induced impairment in grip force strength, used here as a measure of axial discomfort, was partially reversed by anti-NGF therapy. No changes were observed in the rotarod or open-field tests for overall motor function and activity. Finally, anti-NGF treatment attenuated the increase in calcitonin gene-related peptide-immunoreactive dorsal root ganglia neurons observed in OVX mice. CONCLUSION: Taken together, these data suggest that anti-NGF antibodies may be useful in the treatment of prefracture hypersensitivity that is reported in 10% of patients with osteoporosis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".