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Objective: To provide an updated analysis of the efficacy and safety of drugs for the management of neuropathic pain (NP) after spinal cord injury (SCI) based on Bayesian network analysis. Methods: A Bayesian network meta-analysis of literature searches within PubMed, Cochrane Library, Embase, and Web of Science databases from their inception to February 21 2021 was conducted without language restrictions. Paired and network meta-analyses of random effects were used to estimate the total standardized mean deviations (SMDs) and odds ratios (ORs). Results: A total of 1,133 citations were identified and 20 RCTs (including 1,198 patients) involving 11 drugs and placebos for post-SCI NP selected. The 5 outcomes from all 11 drugs and placebos had no inconsistencies after Bayesian network analysis. BTX-A gave the most effective pain relief for the 4 weeks, following a primary outcome. No significant differences were found among drugs with regard to adverse events of the primary outcome. Gabapentin, BTX-A, and pregabalin were found to be the most helpful in relieving secondary outcomes of mental or sleep-related symptoms with differences in SMDs, ranging from -0.63 to -0.86. Tramadol triggered more serious adverse events than any of the other drugs with differences in ORs ranging from 0.09 to 0.11. Conclusion: BTX-A, gabapentin, pregabalin, amitriptyline, ketamine, lamotrigine, and duloxetine were all effective for NP management following SCI. Lamotrigine and gabapentin caused fewer side effects and had better efficacy in relieving mental or sleep-related symptoms caused by SCI-related NP. Tramadol, levetiracetam, carbamazepine, and cannabinoids could not be recommended due to inferior safety or efficacy. Systematic Review Registration: [https://inplasy.com/inplasy-2020-7-0061/], identifier [INPLASY202070061].
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BACKGROUND: Sciatic nerve injury (SNI), which frequently occurs under the traumatic hip and hip fracture dislocation, induces serious complications such as motor and sensory loss, muscle atrophy, or even disabling. The present work aimed to determine the regulating factors and gene network related to the SNI pathology. METHODS: Sciatic nerve injury dataset GSE18803 with 24 samples was divided into adult group and neonate group. Weighted gene co-expression network analysis (WGCNA) was carried out to identify modules associated with SNI in the two groups. Moreover, differentially expressed genes (DEGs) were determined from every group, separately. Subsequently, co-expression network and protein-protein interaction (PPI) network were overlapped to identify hub genes, while functional enrichment and Reactome analysis were used for a comprehensive analysis of potential pathways. GSE30165 was used as the test set for investigating the hub gene involvement within SNI. Gene set enrichment analysis (GSEA) was performed separately using difference between samples and gene expression level as phenotype label to further prove SNI-related signaling pathways. In addition, immune infiltration analysis was accomplished by CIBERSORT. Finally, Drug-Gene Interaction database (DGIdb) was employed for predicting the possible therapeutic agents. RESULTS: 14 SNI status modules and 97 DEGs were identified in adult group, while 15 modules and 21 DEGs in neonate group. A total of 12 hub genes was overlapping from co-expression and PPI network. After the results from both test and training sets were overlapped, we verified that the ten real hub genes showed remarkably up-regulation within SNI. According to functional enrichment of hub genes, the above genes participated in the immune effector process, inflammatory responses, the antigen processing and presentation, and the phagocytosis. GSEA also supported that gene sets with the highest significance were mostly related to the cytokine-cytokine receptor interaction. Analysis of hub genes possible related signaling pathways using gene expression level as phenotype label revealed an enrichment involved in Lysosome, Chemokine signaling pathway, and Neurotrophin signaling pathway. Immune infiltration analysis showed that Macrophages M2 and Regulatory T cells may participate in the development of SNI. At last, 25 drugs were screened from DGIdb to improve SNI treatment. CONCLUSIONS: The gene expression network is determined in the present work based on the related regulating factors within SNI, which sheds more light on SNI pathology and offers the possible biomarkers and therapeutic targets in subsequent research.