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Background: FNAC is a rapid, simple, cost-effective, and minimally invasive technique used to diagnose different types of swelling occurring in head and neck masses, and assess its need for biopsy. Methods: The study included 650 patients, who presented with palpable head and neck swelling over a period of 18 months, at our hospital. FNAC procedure has been performed and smears are stained with Papanicolaou stain, Gemisa stain, and H and E stain. Cyto-histopathological correlations were done to evaluate the need for a biopsy. Results: Out of 650 cases, lymph node lesions constitute 370 cases (56.90%) most commonly involved. Thyroid gland lesions constitute 189 cases (29.10%) followed by salivary gland lesions 38 cases (5.80%), and other 53 cases (8.20%). Histo-pathological correlation of 116 cases were done out of 650. Conclusions: FNAC serves as a guide to appropriate therapeutic management whether to locally excise a benign tumor or plan radical surgery and helps as an adjunct to histopathology.

The brain tumor is the 22nd most common cancer worldwide, with 1.8% of new cancers. It is likely the most severe ailment that necessitates early discovery and treatment, and it requires the competence of neurosubject-matter experts and radiologists. Because of their enormous increases in data search and extraction speed and accuracy, as well as individualized treatment suggestions, machine and deep learning techniques are being increasingly commonly applied throughout healthcare industries. The current study depicts the methodologies and procedures used to detect a tumor inside the brain utilizing machine and deep learning techniques. Initially, data were preprocessed using contrast limited adaptive histogram equalization. Then, features were extracted using principal component analysis and independent component analysis (ICA). Next, the image was smoothed using multiple optimization techniques such as firefly and cuckoo search, lion, and bat optimization. Finally, Naïve Bayes and recurrent neural networks were utilized to classify the improved results. According to the findings, the ICA with cuckoo search and Naïve Bayes has the best mean square error rate of 1.02. With 64.81% peak signal-to-noise and 98.61% accuracy, ICA with hybrid optimization and a recurrent neural network (RNN) proved to better than the other algorithms. Furthermore, a Smartphone application is designed to perform quick and decisive actions. It helps neurologists and patients identify the tumor from a brain image in the early stages.

Two different categories of bioactive glasses prepared from biomass using SiO2 and P2O5 as glass formers are reported in this study. These glasses are prepared by melt-quench technique. The glasses are evaluated in-vitro for their bioactivity assessment with the help of simulated body fluid (SBF). The formation of hydroxyapatite (HAp) above the glass surface is taken as an indicator for the glasses to be bioactive. Hence, various experimental techniques like X-Ray diffraction, Scanning electron microscopy, Energy dispersive X-ray spectroscopy, Fourier transform infrared spectroscopy, and Microwave plasma atomic emission spectroscopy are employed to assess the formation of HAp layer above the glass surface. All these results confirmed the formation of HAp layer. Further, drug loading and antibacterial studies were carried out to investigate the application of glass samples as drug delivery vehicles and antibacterial agents, respectively. These investigations proved that the as-prepared glass samples have high drug loading efficiency and antibacterial activity.

In the present work, silica extracted from the agricultural waste material; rice husk (RH) was utilized for the synthesis of biocompatible glass of general composition SiO2-P2O5-CaO-MgO-MoO3. In the synthesized glasses P2O5 (5%) and CaO (25%) was kept constant whereas MgO and MoO3 was varied from 10% to 20% and 0% to 5% respectively. The structural, morphological, elemental and functional properties of silica as well as the derived glasses were analyzed by X-Ray Diffraction (XRD), Field Emission Scanning Electron Microscopy (FE-SEM), Energy Dispersive X-ray spectroscopy (EDX) and Fourier Transform Infrared (FTIR) spectroscopy techniques. The effect of MoO3 on the structural and thermal properties of silicate phosphate glasses has been studied in details. The bioactivity of as-synthesized glass samples were further evaluated after immersion in Simulated Body Fluid (SBF) solution which shows bioactive properties thus enabling them to be used as scaffolds in implant materials.

Due to dynamic topology and non-wired infrastructure of Wireless Sensor Networks (WSN), they are prone to attacks. Wormhole attack is one of the most popular and serious attack in WSNs. In Wormhole attack two or more malicious nodes makes a covert channel which attracts the traffic towards itself by depicting a low latency link and then start dropping and replaying packets in the multi-path route. This paper proposes promiscuous mode method to detect and isolate the malicious node during wormhole attack by using Ad-hoc on demand distance vector routing protocol (AODV) with omnidirectional antenna. This paper proposes that the nodes which are not participating in multi-path routing generates an alarm message during delay and then detects and isolate the malicious node from network.

A Web-services based grid infrastructure is evolving to be readily available in the near future. In this approach, the Web services are inherited (encapsulated or functioned) into the same existing Grid services class. In practice there is not much difference between the existing Web and grid infrastructure. Grid services emerged as stateful web services. In this paper, we present the key components of web-services based grid and also how the resource discovery is performed on web-services based grid considering resource discovery, as a critical service, to be provided by any type of grid.