Fibromyalgia (FM) is characterized by widespread musculoskeletal pain and tenderness, cognitive dysfunction, fatigue, and insomnia. Electroacupuncture (EA) has documented efficacy against FM-associated pain, while cannabinoid receptor 1 (CB1) plays a critical role in endogenous analgesia. Herein, we examined whether pain relief initiated by EA was linked with differing cerebellar CB1 levels and signaling in an intermittent cold stress (ICS) mouse model of FM. FM-like hyperalgesia and recovery were assessed by measuring mechanical and thermal nociceptive thresholds. Compared to control mice, ICS-induced FM-model mice exhibited a significantly reduced mechanical withdrawal threshold (2.3 ± 0.1 g) and shorter thermal withdrawal latency (4.0 ± 0.5 s), indicative of mechanical and thermal hyperalgesia. Both conditions were reversed by 2 Hz EA but not sham EA. Hyperalgesia was associated with reduced CB1 receptor expression and the enhanced activity of multiple nociceptive signaling pathways (PKA, PI3K, Akt, mTOR, ERK, and NF-kB) in the mouse cerebellum. The 2 Hz EA treatment reliably reversed these abnormalities, while the sham EA treatment did not. Intracerebroventricular injection of the CB1 agonist anandamide (AEA) recapitulated the effects of EA on pain thresholds, while the analgesic effects of EA were blocked by the CB1 antagonist AM251. Precise chemogenetic stimulation at the paraventricular nucleus (PVN) of the hypothalamus reliably induced FM pain. Chemogenetic inhibition at the PVN diminished FM through the CB1 pathway in the cerebellum. Our findings suggest that dysregulation of CB1 expression and aberrant hyperactivity of nociceptive signaling pathways in the cerebellum contribute to the etiology of FM and that the upregulation of CB1 signaling mediates the analgesic efficacy of EA.

Background/Objectives: Spinal conditions, such as fractures and herniated intervertebral discs (HIVDs), are often challenging to diagnose due to overlapping clinical symptoms and the difficulty in assessing their functional impact. Accurate differentiation between these conditions is crucial for effective treatment, particularly in the context of preoperative anesthesia evaluation, where understanding the underlying condition can influence anesthesia planning and pain management. Methods and Materials: This study presents a Support Vector Machine (SVM) model designed to distinguish between spinal fractures and HIVDs using key clinical predictors, including age, gender, preoperative Visual Analog Scale (VAS) pain scores, and the number of spinal fractures. A retrospective analysis was conducted on a dataset of 199 patients diagnosed with these conditions. The SVM model, using a radial basis function (RBF) kernel, classified the conditions based on the selected predictors. Model performance was evaluated using precision, recall, accuracy, and the Kappa index, with Leave-One-Out (LOO) cross-validation applied to ensure robust results. Results: The SVM model achieved a precision of 92.1% for fracture cases and 91.2% for HIVDs, with recall rates of 98.1% for fractures and 70.5% for HIVDs. The overall accuracy was 92%, and the Kappa index was 0.76, indicating substantial agreement. The analysis revealed that age and VAS pain scores were the most critical predictors for accurately diagnosing these conditions. Conclusions: These results highlight the potential of the SVM model with an RBF kernel to reliably differentiate between spinal fractures and HIVDs using routine clinical data. Future work could enhance model performance by incorporating additional clinical parameters relevant to preoperative anesthesia evaluation.