[Establishment of an auxiliary diagnosis system of newborn screening for inherited metabolic diseases based on artificial intelligence technology and a clinical trial].

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.

MiR-17-5p promotes cervical cancer cell proliferation and metastasis by targeting transforming growth factor-ß receptor 2.

OBJECTIVE: MicroRNAs (miRNAs) play critical roles in post-translational gene expression. The aim of the current study was to investigate the effects of miR-17-5p in cervical cancer. PATIENTS AND METHODS: Fifteen clinical cervical cancer tissue samples, as well as their paired adjacent noncancerous tissues, were collected. The microarray was performed to identify differential miRNAs in cervical cancer. Luciferase reporter assay was conducted to identify the target gene of selected miRNA. SiHa was transfected with mimics, inhibitors as well as negative controls of miR-17-5p and Targeting Transforming Growth Factor-ß Receptor 2 (TGFBR2) open reading frame or siRNA. Cell counting kit-8 (CCK-8) assay and transwell experiment were performed to detect the proliferation rate and metastasis, respectively. Western blotting and quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR) analysis were used to analyze TGFBR2 expression. Balb/c nude mice were utilized to verify the effect of miR-17-5p in vivo. RESULTS: Microarray analysis identified miR-17-5p as our interesting miRNA, and luciferase reporter assay identified TGFBR2 as its target gene. MiR-17-5p overexpression significantly enhanced cervical cancer cell proliferation and metastasis. In-vivo study also verified that miR-17-5p overexpression stimulated cervical cancer growth. CONCLUSIONS: MiR-17-5p enhances cervical cancer proliferation and metastasis via targeting TGFBR2. It is proposed that targeting miR-17-5p may be a promising therapeutic approach for cervical cancer.

The role of leaky plasmon waves in the directive beaming of light through a subwavelength aperture.

We show that the enhanced directivity phenomenon for light passing through a subwavelength aperture in a silver film with corrugations on the exit face, is due to a leaky wave that decays exponentially from the aperture. We show quantitatively that the field along the interface of the silver film is dominated by the leaky wave, and that the radiation of the leaky wave, supported by the periodic structure, yields the directive beam. The leaky wave propagation and attenuation constants parameterize the physical radiation mechanism, and provide important design information for optimizing the structure. Maximum directivity occurs when the phase and attenuation constants are approximately equal.