Peripheral Ossifying Fibroma: A Case Report and Review of Literature

Peripheral ossifying fibroma (POF) is an infrequently occurring, slowly progressing, innocuous, nodular overgrowth of the gingiva, which belongs to the category of the “reactive lesions of the gingiva.” There are several such overgrowths with similar clinical manifestations, but diverse etiology and histopathological features, thus presenting a challenge for the clinician. Thorough clinical examination, radiographic and histopathological features help to establish the diagnosis which is key to the successful management of such lesions. This article describes a case of POF in a 43-year-old male patient. The clinical, radiographic, histologic features, aggressive treatment strategies, relapse and close follow-up of POF are discussed in detail. &nbsp

Observational Evidence of Sausage-Pinch Instability in Solar Corona by SDO/AIA

We present the first observational evidence of the evolution of sausage-pinch instability in Active Region 11295 during a prominence eruption using data recorded on 12 September 2011 by the Atmospheric Imaging Assembly (AIA) onboard the Solar Dynamics Observatory (SDO). We have identified a magnetic flux tube visible in AIA 304 \AA\ that shows curvatures on its surface with variable cross-sections as well as enhanced brightness. These curvatures evolved and thereafter smoothed out within a time-scale of a minute. The curved locations on the flux tube exhibit a radial outward enhancement of the surface of about 1-2 Mm (factor of 2 larger than the original thickness of the flux tube) from the equilibrium position. AIA 193 \AA\ snapshots also show the formation of bright knots and narrow regions inbetween at the four locations as that of 304 \AA\ along the flux tube where plasma emission is larger compared to the background. The formation of bright knots over an entire flux tube as well as the narrow regions in < 60 s may be the morphological signature of the sausage instability. We also find the flows of the confined plasma in these bright knots along the field lines, which indicates the dynamicity of the flux tube that probably causes the dominance of the longitudinal field component over short temporal scales. The observed longitudinal motion of the plasma frozen in the magnetic field lines further vanishes the formed curvatures and plasma confinements as well as growth of instability to stablize the flux tube.Comment: 12 pages, 5 figure

Network Performance Measurement through Machine to Machine Communication in Tele-Robotics System

Machine-to-machine (M2M) communication devices communicate and exchange information with each other in an independent manner to perform necessary tasks. The machine communicates with another machine over a wireless network. Wireless communication opens up the environment to huge vulnerabilities, making it very easy for hackers to gain access to sensitive information and carry out malicious actions. This paper proposes an M2M communication system through the internet in Tele-Robotics and provides network performance security. Tele-robotic systems are designed for surgery, treatment and diagnostics to be conducted across short or long distances while utilizing wireless communication networks. The systems also provide a low delay and secure communication system for the tele-robotics community and data security. The system can perform tasks autonomously and intelligently, minimizing the burden on medical staff and improving the quality and system performance of patient care. In the medical field, surgeons and patients are located at different places and connected through public networks. So the design of a medical sensor node network with LEACH protocol for secure and reliable communication ensures through the attack and without attack performance. Finally, the simulation results show low delay and reliable secure network transmission

Evaluation of used eye drop containers for microbial contamination in outpatient department of tertiary care teaching hospital

Background: Contaminated eyedrops are considered as serious risk factor for many iatrogenic ocular infections. Apart from the risk of infection, microbial contamination may alter the pH of the solution thereby reducing the efficacy of drugs. Presently many preservatives are added to these eye drops preparations to extend the duration of use. Hence authors aimed this study to find the contamination rates in such eye drop preparations.Methods: This was a prospective observational research conducted at Ophthalmology OPD, of tertiary care teaching hospital for the period of 2 months. Total fifty five used eyedrops were collected.Results: Authors found that 25.45% of the collected eye drops were contaminated with various organisms, viz. E. coli (10.90%), Staphylococcus aureus (9.09%), Pseudomonas aerugenosa (1.81%), Bacillus subtilis (1.81%) and Candida albicans (1.81%). Among various eyedrops, mydriatic (60%) eyedrops had the highest rate of contamination. We also found that, different preservatives in the eye drops were presents with different level of microbial contamination.Conclusions: The present study showed that there is a definite co-occurrence between eyedrop contamination and ocular infections irrespective of preservatives. This research raises a concern about questionable efficacy of preservatives.

Construction of Data Driven Decomposition Based Soft Sensors with Auto Encoder Deep Neural Network for IoT Healthcare Applications

The architecture of IoT healthcare is motivated towards the data-driven realization and patient-centric health models, whereas the personalized assistance is provided by deploying the advanced sensors. According to the procedures in surgery, in the emergency unit, the patients are monitored till they are stable physically and then shifted to ward for further recovery and evaluation. Normally evaluation done in ward doesn’t suggest continuous parameters monitoring for physiological condition and thus relapse of patients are common. In real-time healthcare applications, the vital parameters will be estimated through dedicated sensors, that are still luxurious at the present situation and highly sensitive to harsh conditions of environment. Furthermore, for real-time monitoring, delay is usually present in the sensors. Because of these issues, data-driven soft sensors are highly attractive alternatives. This research is motivated towards this fact and Auto Encoder Deep Neural Network (AutoEncDeepNN) is proposed depending on Health Framework in the internet assisting the patients with trigger-based sensor activation model to manage master and slave sensors. The advantage of the proposed method is that the hidden information are mined automatically from the sensors and high representative features are generated by multiple layer’s iteration. This goal is consistently achieved and thus the proposed model outperforms few standard approaches which are considered like Hierarchical Extreme Learning Machine (HELM), Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). It is found that the proposed AutoEncDeepNN method achieves 94.72% of accuracy, 41.96% of RMSE, 34.16% of RAE and 48.68% of MAE in 74.64 ms