This article is based on the possible common mechanism of pain and depression. Recent 10 years' reports on basic and clinical studies related to acupuncture treatment for pain and depression have been retrieved to explain that acupuncture can stop pain from many aspects to relieve depression. It is hoped that acupuncture can become a green and safe alternative therapy, reduce the toxic side-effects of drugs, and be popularized and applied to clinical treatment.

Objective:To explore the effects of non-suicidal self-injury on suicidal ideation in adolescents with depressive disorder, and the mediating role of rumination and depression between them.Methods:A sample of 397 depressive disorder adolescents were recruited to complete the adolescent non-suicidal self-injury behaviour questionnaire, ruminative responses scale (RRS), self-rating depression scale (SDS), and Beck scale for suicide ideation-Chinese version(BSI-CV). All data processing and analysis were performed using SPSS 23.0.The mediating effect was tested by correlation analysis and Bootstrap analysis.Results:The non-suicidal self-injury score was (29.192±11.281), the rumination score was (65.036±12.284), the depression score was (75.770±11.278), and the suicidal ideation score was (40.681±11.626). Non-suicidal self-injury was significantly and positively correlated with suicidal ideation( r=0.403, P<0.01), rumination and depression( r=0.332, 0.470, both P<0.01). Rumination was significantly and positively correlated with depression and suicidal ideation( r=0.549, 0.181, both P<0.05). Depression was significantly and positively correlated with suicidal ideation( r=0.313, P<0.01). The direct effect value of non-suicidal self-injury on suicidal ideation was 0.341(95% CI=0.238-0.444), the indirect effect of non-suicidal self-injury on suicidal ideation through two pathways, the separate mediating effect value of depression was 0.057(95% CI=0.077-0.114), and the chain mediating effect value of rumination and depression was 0.026(95% CI=0.004-0.057). Conclusion:Non-suicidal self-injury can directly affect suicidal ideation of depressive disorder adolescents and indirectly through rumination and depression.

Objective To explore the mechanism of somatostatin in improving acute lung injury associated with acute pancreatitis.Methods Wistar rats were randomly divided into sham operation group(injection of normal saline),model group(puncture of common bile duct and injection of 5％sodium taurocholate with wire ligation),somatostatin group(injection of somatostatin into tail vein of model group),somatostatin+miR-146a-5p inhibitor group(on the basis of somatostatin group,tail vein injection of miR-146a-5p inhibitor and somatostatin+oe-angiogenin-like protein 4(ANGPTL4)group(on the basis of somatostatin group,tail vein injection of oe-ANGPTL4 plasmid).Hematoxylin-eosin(HE)staining was used to observe the pathological changes of pancreatic and lung tissues;pathological score and tissue wet-dry weight ratio were determined,real-time fluorescence quantitative polymerase chain reaction(qRT-PCR)was used to detect miR-146a-5p and ANGPTL4 mRNA expression and Western blot was used to detect the expression of related proteins in lung tissues of rats.Tumor necrosis factor-α(TNF-α)was detected by enzyme-linked immunosorbent assay(ELISA).Results In sham operation group,model group and somatostatin group,the damage degree of pancreas tissue(based on modified computed tomography severity index)were 1.25±0.28,3.20±0.34,2.15±0.31,respectively;the damage degree of lung tissue(based on the Smith lung injury score system)were 1.40±0.13,5.10±0.58,3.10±0.38,respectively.The relative expression levels of ANGPTL4 mRNA in sham operation group,model group,somatostatin group and somatostatin+miR-146a-5p inhibitor group were 1.00±0.17,1.63±0.20,1.21±0.18 and 1.73±0.28.The levels of TNF-α in sham operation group,model group,somatostatin group,somatostatin+miR-146a-5p inhibitor group and somatostatin+oe-ANGPTL4 group were(76.33±7.25),(125.05±13.56),(80.11±10.68),(118.62±14.32)and(105.32±13.52)pg·mL-1,respectively;the relative expression levels of nuclear factor E2-related factor 2(Nrf2)protein were 1.00±0.27,0.51±0.07,0.88±0.14,0.68±0.12,0.51±0.09,respectively;the relative expression levels of heme oxygenase-1(HO-1)protein were 1.00±0.25,0.58±0.11,0.79±0.18,0.48±0.07 and 0.50±0.08,respectively.The above indexes of the model group were compared with those of the sham operation group,and the above indexes of the somatostatin group were compared with those of the model group,somatostatin+miR-146a-5p inhibitor group and somatostatin+oe-ANGPTL4 group,and the differences were statistically significant(all P＜0.05).Conclusion Somatostatin has antioxidant and anti-inflammatory effects and can ameliorate acute lung injury associated with acute pancreatitis.The mechanism may be related to Nrf2/HO-1 pathway mediated by miR-146a-5p/ANGPTL4.

Objective:To establish a disease risk prediction model for the newborn screening system of inherited metabolic diseases by artificial intelligence technology.Methods:This was a retrospectively study. Newborn screening data ( n=5 907 547) from February 2010 to May 2019 from 31 hospitals in China and verified data ( n=3 028) from 34 hospitals of the same period were collected to establish the artificial intelligence model for the prediction of inherited metabolic diseases in neonates. The validity of the artificial intelligence disease risk prediction model was verified by 360 814 newborns ' screening data from January 2018 to September 2018 through a single-blind experiment. The effectiveness of the artificial intelligence disease risk prediction model was verified by comparing the detection rate of clinically confirmed cases, the positive rate of initial screening and the positive predictive value between the clinicians and the artificial intelligence prediction model of inherited metabolic diseases. Results:A total of 3 665 697 newborns ' screening data were collected including 3 019 cases ' positive data to establish the 16 artificial intelligence models for 32 inherited metabolic diseases. The single-blind experiment ( n=360 814) showed that 45 clinically diagnosed infants were detected by both artificial intelligence model and clinicians. A total of 2 684 cases were positive in tandem mass spectrometry screening and 1 694 cases were with high risk in artificial intelligence prediction model of inherited metabolic diseases, with the positive rates of tandem 0.74% (2 684/360 814)and 0.46% (1 694/360 814), respectively. Compared to clinicians, the positive rate of newborns was reduced by 36.89% (990/2 684) after the application of the artificial intelligence model, and the positive predictive values of clinicians and artificial intelligence prediction model of inherited metabolic diseases were 1.68% (45/2 684) and 2.66% (45/1 694) respectively. Conclusion:An accurate, fast, and the lower false positive rate auxiliary diagnosis system for neonatal inherited metabolic diseases by artificial intelligence technology has been established, which may have an important clinical value.