Comparative Efficacy and Safety of 11 Drugs as Therapies for Adults With Neuropathic Pain After Spinal Cord Injury: A Bayesian Network Analysis Based on 20 Randomized Controlled Trials.

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].

An expanded landscape of human long noncoding RNA.

Long noncoding RNAs (lncRNAs) are emerging as key regulators of multiple essential biological processes involved in physiology and pathology. By analyzing the largest compendium of 14,166 samples from normal and tumor tissues, we significantly expand the landscape of human long noncoding RNA with a high-quality atlas: RefLnc (Reference catalog of LncRNA). Powered by comprehensive annotation across multiple sources, RefLnc helps to pinpoint 275 novel intergenic lncRNAs correlated with sex, age or race as well as 369 novel ones associated with patient survival, clinical stage, tumor metastasis or recurrence. Integrated in a user-friendly online portal, the expanded catalog of human lncRNAs provides a valuable resource for investigating lncRNA function in both human biology and cancer development.

Systematic identification and annotation of multiple-variant compound effects at transcription factor binding sites in human genome.

Understanding the functional effects of genetic variants is crucial in modern genomics and genetics. Transcription factor binding sites (TFBSs) are one of the most important cis-regulatory elements. While multiple tools have been developed to assess functional effects of genetic variants at TFBSs, they usually assume that each variant works in isolation and neglect the potential "interference" among multiple variants within the same TFBS. In this study, we presented COPE-TFBS (Context-Oriented Predictor for variant Effect on Transcription Factor Binding Site), a novel method that considers sequence context to accurately predict variant effects on TFBSs. We systematically re-analyzed the sequencing data from both the 1000 Genomes Project and the Genotype-Tissue Expression (GTEx) Project via COPE-TFBS, and identified numbers of novel TFBSs, transformed TFBSs and discordantly annotated TFBSs resulting from multiple variants, further highlighting the necessity of sequence context in accurately annotating genetic variants. COPE-TFBS is freely available for academic use at http://cope.cbi.pku.edu.cn/.

Accurately annotate compound effects of genetic variants using a context-sensitive framework.

In genomics, effectively identifying the biological effects of genetic variants is crucial. Current methods handle each variant independently, assuming that each variant acts in a context-free manner. However, variants within the same gene may interfere with each other, producing combinational (compound) rather than individual effects. In this work, we introduce COPE, a gene-centric variant annotation tool that integrates the entire sequential context in evaluating the functional effects of intra-genic variants. Applying COPE to the 1000 Genomes dataset, we identified numerous cases of multiple-variant compound effects that frequently led to false-positive and false-negative loss-of-function calls by conventional variant-centric tools. Specifically, 64 disease-causing mutations were identified to be rescued in a specific genomic context, thus potentially contributing to the buffering effects for highly penetrant deleterious mutations. COPE is freely available for academic use at http://cope.cbi.pku.edu.cn.