Objective To observe the effect of hot compress with medicinal salt pack in preventing PICC-associated mechanical phlebitis.Method Totally 200 inpatients undergone PICC intubation were randomized into a treatment group and a control group, 100 cases in each group. After PICC intubation, the treatment group was intervened by hot compress with medicinal salt pack, while the control group was by hot compress with warm wet towel. The occurrence of mechanical phlebitis was observed at different time points and compared between the two groups. Result The occurrence rate of mechanical phlebitis was 9.0% in the treatment group versus 26.0% in the control group, and the difference was statistically significant (P<0.05). In the treatment group, the occurrence rate of mechanical phlebitis was respectively 6.0% and 3.0% when treatment≤72 h and>72 h, versus 16.0% and 10.0% in the control group, and the differences were statistically significant (P<0.05). Conclusion Hot compress with medicinal salt pack can effectively reduce the occurrence of PICC-associated mechanical phlebitis.

Objective To investigate the mechanism of anti-epileptic effect of the curcumin .Methods The SD rats were injected intraperitoneally with pentylenetetrazol kindling 25 .0 mg/kg to induce a rat epilepsy model .All of the treatments were performed once a day continuously for 28 days .The rats in blank group and model group received 5 ml of normal saline .The rats in the high and low curcumin group were given 200 mg/kg and 100 mg/kg of curcumin once a day ,respectively .The rats in the sodium valproate (VPA) group were given 400 mg/kg of VPA once a day by gavage .After treatment ,the seizures level was recorded by using the Racine′s six point grading scale ,and the expression of IL-2 and IL-6 of hippocampus were detected by the enzyme linked immunoassay (ELISA) .Results The seizures level was reduced by curcumin in epileptic rats .The ex-pressions of IL-2 and IL-6 of the model group were significantly higher than those of the blank group (P<0 .05) ,while those rats of the anti-epileptic groups ,including high dose group and low dose group ,were lower than those rats of the model group (P<0 .05) .When compared with the curcumin low dose group ,the expression of IL-2 and IL-6 of curcumin high dose group is lower (P< 0 .05) .There was no significant difference between the high dose curcumin group and VPA group (P>0 .05) . Conclusion The curcumin can reduce the seizure level in rats ,it shows some anti-epileptic effets and dose-dependently ,which may be through down-regulating the expression of IL-2 and IL-6 in hippocampus .

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.