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Objective: To evaluate the effectiveness of Platelets-Rich-Plasma therapy against Steroid therapy in the treatment of Planter fasciitis. Study Design: Quasi Experimental study. Setting: Department of Orthopedic, Hayatabad Medical Complex, Peshawar. Period: January to June 2021. Material & Methods: In which participants were divided into two groups (steroid vs PRP). A total of 61 individuals with PF who have failed to respond to conservative therapy were intervened. 31 of them received steroid injection while 30 participants received PRP. The AOFAS and the VAS scoring system were recorded pre- and post-injection phases at 4 weeks, 3 months and 6 months period to evaluate the outcomes. Statistical analyses were performed to compare between the two means. Results: In both groups, the VAS, the AOFAS, and PF thickness improved significantly after injection. However, based on the available data, there was no discernible difference in improvement between the two groups for the above-mentioned factors. Conclusion: In our study, we found that both steroid and PRP injections had no statistically significant differences in VAS and AFAS scores (post treatment), we found that both were equally beneficial in treating Planter Fasciitis.

Glaucoma is an incurable eye disease that leads to slow progressive degeneration of the retina. It cannot be fully cured, however, its progression can be controlled in case of early diagnosis. Unfortunately, due to the absence of clear symptoms during the early stages, early diagnosis are rare. Glaucoma must be detected at early stages since late diagnosis can lead to permanent vision loss. Glaucoma affects the retina by damaging the Optic Nerve Head (ONH). Its diagnosis is dependent on the measurements of Optic Cup (OC) and Optic Disc (OD) in the retina. Computer vision techniques have been shown to diagnose glaucoma effectively and correctly with little overhead. These techniques measure OC and OC dimensions using machine learning based classification and segmentation algorithms. This article aims to provide a comprehensive overview of various existing techniques that use machine learning to detect and diagnose glaucoma based on fundus images. Readers would be able to understand the challenges glaucoma presents from an image processing and machine learning stand-point and will be able to identify gaps in current research.

Skin segmentation plays an important role in human activity recognition, video surveillance, hand gesture identification, face detection, human tracking and robotic surgery. The accurate segmentation of the skin is necessary to recognize the human activity. Segmentation of skin is easy to realize in ideal situations because of similar backgrounds. But it becomes complicated because of presence of skin-like pixels, background illuminations, and certain changes in environment. These problems are addressed by incorporating preprocessing stages in current studies, but this raises the total cost of the system. However, there are some limitations associated with these methods in terms of accuracy and processing speed. In this work, we propose a skin semantic segmentation network (SSS-Net) that is able to capture the multi-scale contextual information and refines the segmentation results especially along object boundaries. Moreover our network helps to reduce the cost of the preprocessing as well. We have performed experiments on the five open datasets of human activity recognition for the segmentation of skin. Experimental results show SSS-Net outperforms the state-of-the-art methods in skin segmentation in terms of accuracies.

Objective: The objective of this study was to assess the surface properties (microhardness and wear resistance) of various composites and compomer materials. In addition, the methodologies used for assessing wear resistance were compared. Materials and Methods: This study was conducted using restorative material (Filtek Z250, Filtek Z350, QuiXfil, SureFil SDR, and Dyract XP) to assess wear resistance. A custom-made toothbrush simulator was employed for wear testing. Before and after wear resistance, structural, surface, and physical properties were assessed using various techniques. Results: Structural changes and mass loss were observed after treatment, whereas no significant difference in terms of microhardness was observed. The correlation between atomic force microscopy (AFM) and profilometer and between wear resistance and filler volume was highly significant. The correlation between wear resistance and microhardness were insignificant. Conclusions: The AFM presented higher precision compared to optical profilometers at a nanoscale level, but both methods can be used in tandem for a more detailed and precise roughness analysis.

Micro-Electro-Mechanical Systems (MEMS) and Microfluidics are becoming popular solutions for sensing, diagnostics and control applications. Reliability and validation of function is of increasing importance in the majority of these applications. On-line testing strategies for these devices have the potential to provide real-time condition monitoring information. It is shown that this information can be used to diagnose and prognose the health of the device. This information can also be used to provide an early failure warning system by predicting the remaining useful life. Diagnostic and prognostic outcomes can also be leveraged to improve the reliability, dependability and availability of these devices. This work has delivered a methodology for a “lightweight” prognostics solution for a microfluidic device based on real-time diagnostics. An oscillation based test methodology is used to extract diagnostic information that is processed using a Linear Discriminant Analysis based classifier. This enables the identification of current health based on pre-defined health levels. As the deteriorating device is periodically classified, the rate at which the device degrades is used to predict the devices remaining useful life.

To find the best option to treat White Spot Lesion in existing caries treatments, and to identify the selected articles discussing etiology of caries along with White spot lesion. Null hypothesis was that "Only anticariogenic agent can cure White Spot Lesion". PRISMA guidelines were used to conduct the systematic analysis. An electronic customized search was performed using mesh terminologies on PubMed database based on inclusion criteria that included studies with; any treatment option that can treat or prevent WSL; and minimally invasive treatment options that may be altered to treat WSL. While exclusion criteria comprised studies with treatment of rampant caries, severe early childhood caries and root caries. Inclusion criteria for etiological factors incorporated studies with factors that lead to white spot lesion or carious lesion. Finally, therapeutic agents of dental caries were analyzed. Only the use of anti-cariogenic agent cannot cure White Spot Lesion. Hence study fails to prove the null hypothesis. Although combination of anti-cariogenic agents with a re-mineralizing agent can provide additional options for the treatment or prevention of WSL.

This paper proposes a solution to monitor the degradation of a multifunctional microelectromechanical systems (MEMS) sensor (MFS) and to recalibrate the sensor output accordingly. The solution is able to predict the remaining useful life based on the recalibration history. The MFS used is a dual pressure–humidity hybrid sensor where model data have been used to demonstrate the applicability and performance of the proposed method for diagnosis, self-correction, and prognosis. [2015-0316]

Lab-on-Chip devices are complex multifunctional heterogeneous microsystems that have the potential to strongly influence advances in important areas such as pharmacology, security, and environmental analysis. High reliability requirements in many of these microsystems are crucial which makes test more challenging especially given the need to validate multiple multi-domain interfaces and realise on-line solutions. Based on fault modeling and impedance analysis of the electrode/electrolyte interface and a customised prototype array structure, this paper proposes a self-test solution that targets degraded sensing microelectrodes within Multi Electrode Array's (MEA). The principle of this approach is to scan the strength of a test signal over the whole array to monitor the defective sensing electrodes. The test solution has been applied at the system level where an analogue multiplexer, an LCD, and a microcontroller have been used to achieve a real time condition monitoring technique.