EA Improves the Motor Function in Rats with Spinal Cord Injury by Inhibiting Signal Transduction of Semaphorin3A and Upregulating of the Peripheral Nerve Networks.

Peripheral nerve networks (PNNs) play a vital role in the neural recovery after spinal cord injury (SCI). Electroacupuncture (EA), as an alternative medicine, has been widely used in SCI and was proven to be effective on neural functional recovery. In this study, the interaction between PNNs and semaphrin3A (Sema3A) in the recovery of the motor function after SCI was observed, and the effect of EA on them was evaluated. After the establishment of the SCI animal model, we found that motor neurons in the ventral horn of the injured spinal cord segment decreased, Nissl bodies were blurry, and PNNs and Sema3A as well as its receptor neuropilin1 (NRP1) aggregated around the central tube of the gray matter of the spinal cord. When we knocked down the expression of Sema3A at the damage site, NRP1 also downregulated, importantly, PNNs concentration decreased, and tenascin-R (TN-R) and aggrecan were also reduced, while the Basso-Beattie-Bresnahan (BBB) motor function score dramatically increased. In addition, when conducting EA stimulation on Jiaji (EX-B2) acupoints, the highly upregulated Sema3A and NRP1 were reversed post-SCI, which can lessen the accumulation of PNNs around the central tube of the spinal cord gray matter, and simultaneously promote the recovery of motor function in rats. These results suggest that EA may further affect the plasticity of PNNs by regulating the Sema3A signal and promoting the recovery of the motor function post-SCI.

Identification of copper death-associated molecular clusters and immunological profiles for lumbar disc herniation based on the machine learning.

Lumbar disc herniation (LDH) is a common clinical spinal disorder, yet its etiology remains unclear. We aimed to explore the role of cuproptosis-related genes (CRGs) and identify potential diagnostic biomarkers. Our analysis involved interrogating the GSE124272 and GSE150408 datasets for differential gene expression profiles associated with CRGs and immune characteristics. Molecular clustering was performed on LDH samples, followed by expression and immune infiltration analyses. Using the WGCNA algorithm, specific genes within CRG clusters were identified. After selecting the most predictive genes from the optimal model, four machine learning models were constructed and validated. This study identified nine CRGs associated with copper-regulated cell death. Two copper-containing molecular clusters linked to death were detected in LDH samples. Elevated expression and immune infiltration levels were found in LDH patients, particularly in CRG cluster C2. Utilizing XGB, five genes were identified for constructing a diagnostic model, achieving an area under the curve values of 0.715. In conclusion, this research provides valuable insights into the association between LDH and copper-regulated cell death, alongside proposing a promising predictive model.