[Approval Status and Suggestions on Supervision of Class II Innovative Medical Device].

Based on the current situation of approval and publicity of class II innovative medical devices in various provinces and cities, especially after analyzing the problems found in the review of class II innovative medical devices in Shanghai and the factors affecting the listing process of innovative medical devices, this study puts forward some thoughts on supervision for reference in establishing and perfecting systems and regulations, changing the review and approval thinking, extending services to the development of innovative medical devices and the formulation of clinical trial schemes.

Delocalized photonic deep learning on the internet's edge.

Advanced machine learning models are currently impossible to run on edge devices such as smart sensors and unmanned aerial vehicles owing to constraints on power, processing, and memory. We introduce an approach to machine learning inference based on delocalized analog processing across networks. In this approach, named Netcast, cloud-based "smart transceivers" stream weight data to edge devices, enabling ultraefficient photonic inference. We demonstrate image recognition at ultralow optical energy of 40 attojoules per multiply (<1 photon per multiply) at 98.8% (93%) classification accuracy. We reproduce this performance in a Boston-area field trial over 86 kilometers of deployed optical fiber, wavelength multiplexed over 3 terahertz of optical bandwidth. Netcast allows milliwatt-class edge devices with minimal memory and processing to compute at teraFLOPS rates reserved for high-power (>100 watts) cloud computers.

[Regulation Considerations of Medical Devices with Wireless Technology].

Wireless technology has been widely used in medical devices and has brought convenience to medical care. However, wireless medical devices face risks such as data security and radio frequency interference. This study highlights safety and effectiveness evaluation of wireless medical devices, and discusses technical characteristics and regulation requirements, providing references for industry development and regulation.

Optical spectrum feature analysis and recognition for optical network security with machine learning.

Physical layer attacks threaten services transmitted through optical networks. To detect attacks, we present an investigation of optical spectrum feature analysis (OSFA) and recognition. By analyzing the spectral features of optical signals, recognition and detection of unauthorized signals can be realized. In this paper, (1) we theoretically analyzed factors influencing optical spectrum (OS) features and simulated these factors. OSs collected from the simulation are quantitatively analyzed, spectral features are extracted by principal component analysis, and the theoretical derivation is validated. (2) We proposed support vector machine (SVM) and one-dimensional convolutional neural network (1D-CNN) machine-learning OSFA methods. (3) Experimentally collected OSs from commercial small form-factor pluggable modules are used to verify the performance of the SVM and 1D-CNN methods, which achieved 98.54% and 100% recognition accuracies, respectively, demonstrating that the methods are promising solutions for optical network security.

[Status Analysis and Improvement Suggestions on the Renew Registration of Passive Medical Devices].

The article collected 250 renew registration of domestic Class Ⅱ passive medical devices from Jan. 2016 to Aug. 2017 in Shanghai. It summarized the common problems listed in the correction notifications of the technical evaluation, and then provides some relevant countermeasures or suggestions. So that we can further speed up the evaluation and approval of medical devices.

[Instruction of the technical evaluation for medical devices kits].

Recent years, the development of medical devices kits is rapid. How to make the technical evaluation of medical devices kits more perfect bases on the two major principles of safe and effective, and to make kits in the market more normative and orderly, these issues for technical evaluation have to be considered. This article makes a study on current situation of production, classification of management and registration status, combined with existing regulations and related standards, and discusses technical evaluation related issues.

Association of the expressions of platelet-derived growth factor receptor and c-Fos with the biological characteristics of bladder cancer.

OBJECTIVE: To elucidate the relation of platelet-derived growth factor receptor (PDGFR) and c-Fos protein expressions with human bladder transitional epithelial cell carcinoma (BTCC). METHODS: The expressions of PDGFR and c-Fos were investigated in 11 normal bladder tissue samples, 14 adjacent non-carcinoma tissues and 43 BTCC tissues by means of SP immunohistochemical technique. RESULTS: The c-Fos expression was found in the cell nuclei and cytoplasm, and PDGFR in the nuclear membrane, cytoplasm, and cellular membrane. PDGFR and c-Fos were detected in 81.40% and 48.83% of the BTCC tissues respectively, at the rates both significantly higher than those in normal and adjacent non-carcinoma tissues (P<0.05). Correlation between the expression of c-Fos and the tumor grading was noted (P<0.05). The expressions of PDGFR and c-Fos in tumor blood vessels were significantly higher than those in normal vessels. CONCLUSIONS: The expressions of PDGFR and c-Fos might be involved in the development of BTCC, possibly related to the angiogenesis of the tumors. c-Fos expression can indicated the cell proliferative status of the BTCC.