Most cancer causal variants are found in gene regulatory elements, e.g., enhancers. However, enhancer variants predisposing to hepatocellular carcinoma (HCC) remain unreported. Here we conduct a genome-wide survey of HCC-susceptible enhancer variants through a three-stage association study in 11,958 individuals and identify rs73613962 (T > G) within the intronic region of PRMT7 at 16q22.1 as a susceptibility locus of HCC (OR = 1.41, P = 6.02 × 10−10). An enhancer dual-luciferase assay indicates that the rs73613962-harboring region has allele-specific enhancer activity. CRISPR-Cas9/dCas9 experiments further support the enhancer activity of this region to regulate PRMT7 expression. Mechanistically, transcription factor HNF4A binds to this enhancer region, with preference to the risk allele G, to promote PRMT7 expression. PRMT7 upregulation contributes to in vitro, in vivo, and clinical HCC-associated phenotypes, possibly by affecting the p53 signaling pathway. This concept of HCC pathogenesis may open a promising window for HCC prevention/treatment.

ObjectivesErector spinae plane block (ESPB) has been used for many thoracic and abdominal surgeries. However, evidence of its analgesic efficacy following abdominal surgery, compared with that of thoracic analgesia, is insufficient. Our study explored the analgesic effect of ESPB after abdominal surgery.MethodsWe searched PubMed, Embase, Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov. Primary outcomes were pain scores at 6, 12 and 24 h and 24-h opioid consumption. Secondary outcomes included time to first rescue analgesia, length of hospital stay, and incidence of postoperative nausea and vomiting (PONV). We calculated standardized mean differences (SMDs) with 95% confidence intervals (CIs) for primary outcomes and mean differences (MDs) and risk ratios (RRs) with 95% CIs for secondary outcomes.ResultsWe systematically included 1,502 cases in 24 trials. Compared with placebo, ESPB significantly reduced pain scores at 6 h (SMD −1.25; 95% CI −1.79 to −0.71), 12 h (SMD −0.85; 95% CI −1.33 to −0.37) and 24 h (SMD −0.84; 95% CI −1.30 to −0.37) and 24-h opioid consumption (SMD −0.62; 95% CI −1.19 to −0.06) post-surgery. ESPB prolonged the time to first rescue analgesia and decreased the incidence of PONV. Compared with transversus abdominal plane block (TAPB), ESPB significantly reduced pain scores at 6, 12, and 24 h and 24-h opioid consumption and prolonged the time to first rescue analgesia postsurgically. Furthermore, subgroup analysis showed that ESPB significantly reduced pain scores at various time points and opioid consumption within 24 h after laparoscopic cholecystectomy, percutaneous nephrolithotomy and bariatric surgery.ConclusionCompared with placebo, ESPB improves the postoperative analgesic efficacy after abdominal surgery. Furthermore, our meta-analysis confirmed that ESPB provides more beneficial analgesic efficacy than TAPB.Systematic review registration[https://www.crd.york.ac.uk/PROSPEROFILES/301491_STRATEGY_20220104.pdf], identifier [CRD42022301491].

With the rapid development of computer science, there are more and more kinds of discrete dynamic systems. Computer integrated system CIMS, network communication database administrator system, and human behavior analysis system are all discrete dynamic systems. At present, many researchers have studied by adding human behavior data to discrete dynamic systems. This paper aims to study the behavior data of English learners by using the discrete dynamic modeling technology of complex systems and the discrete dynamic system modeling method of Petri nets. By adding the behavior data of learners to the discrete dynamic system of fuzzy Petri nets, the system is diagnosed and optimized. The experimental results show that the complex discrete dynamic system in this paper has achieved good experimental results according to the performance indicators selected in theory. Based on the combination of the above technologies and systems, the fuzzy Petri net discrete dynamic system studied in this paper improves the processing speed of English learners' behavior data.

The dynamic changes of grammatical functions in English teaching in different language environments are different. Based on this background, this paper studies the discrete dynamic modeling technology in the big data complex system. By analyzing the current situation of the English language, this paper studies the dynamic path of the development of English functional grammar. Different from traditional modeling algorithms, dynamic modeling of complex systems can accurately process the relevant data provided by big data. This paper discusses the influence of functional grammar on English listening teaching through dynamic modeling and predictive analysis. This study reduces the error rate of the English prediction model and determines that the change of English achievement is closely related to functional grammar. The results show that the dynamic modeling of complex systems can promote the rapid development of functional grammar in English teaching and provide an effective basis for grammar research. At the same time, the dynamic prediction model based on complex system modeling can accurately predict the actual effect of English grammatical functions on improving English proficiency.