Clinical effect of neovascular glaucoma treated by vitrectomy and cyclophotocoagulation

AIM: To observe the postoperative intraocular pressure(IOP)and operation safety in the eyes of the neovascular glaucoma pateints treated by intraocular cyclophotocoagulation which needed vitrectomy at the same time. <p>METHODS: A total of 12 neovascular glaucoma cases(14 eyes)secondary to diabetic retinopathy, retinal detachment surgery and trauma were reviewed in our study. This procedure mainly used intraocular photocoagulation catheter to highlight the ciliary processes until the ciliary became white atrophy or plosion after vitreous surgery treatment. The intraocular photocoagulation catheter was performed at a power of 300-500mW, for a duration of 0.1-0.2ms. Postoperative follow-up was at least for 6mo. The observation of 14 postoperative neovascular glaucoma was performed at 1wk, 1, 6mo observing the IOP and complications. <p>RESULTS: IOP of the 11 eyes was significantly declined and controlled in normal. After cyclophotocoagulation, average IOP at 1wk was 16.7±14.4mmHg, 15.7±8.8mmHg at 1mo and 12.9±4.5mmHg at 6mo, which compared with untreatment(39.6 ±10.0mmHg)was statistically significant different(<i>P</i><0.01). In follow up time 3 cases were relapsed which were supplied with transscleral or endoscope cyclophotocoagulation. During the follow-up period no endophthalmitis and complications such as eyeball atrophy were found. <p>CONCLUSION: The intraocular cyclophotocoagulation and vitrectomy simultaneously can deal with the primary disease and secondary neovascular glaucoma. The operation can be accurately performed under direct cyclophotocoagulation and it is a safe and effective way for neovascular glaucoma which needs vitreous surgery

Deepen electronic health record diffusion beyond breadth: game changers and decision drivers

Cloud computing, financial incentive and patient-centered care are the game changers that deepen EHR diffusion beyond breadth. Based on the innovation diffusion theory (IDT), technology-organization-environment (TOE) framework and alignment literature, this study examines how these changes shape business requirement, service value and society need that drive different phases of EHR diffusion in terms of planning, adoption, usage and upgrade. A longitudinal analysis with the USA National Ambulatory Medical Care Survey (NAMCS) reveals the impacts of different drivers on EHR diffusion. In addition to quantitative results, interview observations corroborate the relationships among game changers, decision drivers and EHR diffusion. The findings provide healthcare providers, system vendors and policy-makers the insights on the best practices of promoting EHR diffusion for long-term success

Investigating the Relationship between the Effectiveness of App Evolution and App Continuance Intention: An Empirical Study of the U.S. App Market

Researchers have shown app evolution to continuously lead to app success from the developer perspective. However, few studies have explored app success from the user perspective, which limits our knowledge about the role that app evolution has in app success. Building on app evolution literature and the technology acceptance model (TAM), we investigate the influence that effectiveness of app evolution has on perceived app usefulness, perceived ease of use, and app continuance intention (a proxy for app success from the user perspective). We collected survey data from 299 app users on both the Google Play and Apple’s App Store platforms in the United States. Our findings indicate that effectiveness of strategic evolution and effectiveness of evolution speed directly affect perceived app usefulness, while effectiveness of operational evolution and effectiveness of evolution speed directly affect perceived app ease of use. In addition, perceived app usefulness and perceived app ease of use constitute two key factors that lead to app continuance intention. Perceived ease of use affects users’ app continuance intention both directly and indirectly through perceived app usefulness. This study enhances our knowledge about the relationship between effectiveness of app evolution and app continuance intention. Such knowledge has particular importance in helping small firms or startups with limited resources understand how to retain app users. We also discuss limitations and directions for future research

Investigating the Relationship between Effectiveness of App Evolution and App Continuance Intention: An Empirical Study of the U.S. App Market

App evolution has been shown to continuously lead to app success from the developer perspective. However, few studies have explored app success from the user perspective, which limits our understanding of the role of app evolution in app success. Building on app evolution literature and the technology acceptance model (TAM), the authors investigate the influence of the effectiveness of app evolution on users’ perceived app usefulness and ease of use and their app continuance intention, which is a proxy of app success from the user perspective. Survey data were collected from 299 app users on both the Google Play and AppStore platforms in the U.S. The findings indicate that the effectiveness of strategic evolution and effectiveness of evolution speed directly affect a user’s perceived app usefulness, while effectiveness of operational evolution and effectiveness of evolution speed directly affect a user’s perceived app ease of use. In addition, perceived app usefulness and perceived app ease of use are two keys that lead to users’ app continuance intention. A user’s perceived app ease of use affects app continuance intention both directly and indirectly through perceived app usefulness. This study enhances our understanding of the relationship between effectiveness of app evolution and app continuance intention. This is especially important in helping app developers that are small firms or startups with limited resources understand how to retain app users. Limitations and directions for future research are also discussed

Degree correlation effect of bipartite network on personalized recommendation

In this paper, by introducing a new user similarity index base on the diffusion process, we propose a modified collaborative filtering (MCF) algorithm, which has remarkably higher accuracy than the standard collaborative filtering. In the proposed algorithm, the degree correlation between users and objects is taken into account and embedded into the similarity index by a tunable parameter. The numerical simulation on a benchmark data set shows that the algorithmic accuracy of the MCF, measured by the average ranking score, is further improved by 18.19% in the optimal case. In addition, two significant criteria of algorithmic performance, diversity and popularity, are also taken into account. Numerical results show that the presented algorithm can provide more diverse and less popular recommendations, for example, when the recommendation list contains 10 objects, the diversity, measured by the hamming distance, is improved by 21.90%.Comment: 9 pages, 3 figure

Spherical Message Passing for 3D Graph Networks

We consider representation learning from 3D graphs in which each node is associated with a spatial position in 3D. This is an under explored area of research, and a principled framework is currently lacking. In this work, we propose a generic framework, known as the 3D graph network (3DGN), to provide a unified interface at different levels of granularity for 3D graphs. Built on 3DGN, we propose the spherical message passing (SMP) as a novel and specific scheme for realizing the 3DGN framework in the spherical coordinate system (SCS). We conduct formal analyses and show that the relative location of each node in 3D graphs is uniquely defined in the SMP scheme. Thus, our SMP represents a complete and accurate architecture for learning from 3D graphs in the SCS. We derive physically-based representations of geometric information and propose the SphereNet for learning representations of 3D graphs. We show that existing 3D deep models can be viewed as special cases of the SphereNet. Experimental results demonstrate that the use of complete and accurate 3D information in 3DGN and SphereNet leads to significant performance improvements in prediction tasks.Comment: 16 pages, 8 figures, 8 table