Anti–nerve growth factor therapy attenuates cutaneous hypersensitivity and musculoskeletal discomfort in mice with osteoporosis
Why this work is in the frame
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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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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) | 0.000 | 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 it