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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.

In the present work the crystallization and densification behavior of Lithium-Disilicate (LD) glass compositions were studied with the presence of various nucleating agent and ceramic oxides. A 30 mol% Li2O-70 mol% SiO2 LD system was modified using Al2O3, P2O5 and ZrO2 by keeping SiO2/ Li2O ratio constant at 2.33. The role of 2 to 5 mol% Al2O3, P2O5 and ZrO2 were studied in terms of Differential Scanning Calorimetry, XRD and SEM. The DSC results suggest that glass transition temperature increased with the inclusion of Al2O3 and ZrO2. It is also found that, inclusion of Al2O3 and ZrO2 helped lowering melting temperatures which reduced energy cost for melt processing. The study also suggested that densification was aided by the inclusion of P2O5 and ZrO2. Keywords: Densification; Crystallization; Glass-Ceramics; DSC.

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.

The present work describes the synthesis of biocompatibe alkali-free glasses utilizing the agricultural waste rice husk and eggshells. A series of glasses with basic composition SiO2–P2O5–MgO–CaO has been synthesized using melt-quench technique, where P2O5 is partially replaced by MnO. These glasses are characterized to assess their in-vitro reactivity with the help of X-Ray diffraction, scanning electron microscopy, energy dispersive X-ray spectroscopy, fourier transform infrared spectroscopy, and microwave plasma atomic emission spectroscopy techniques before and after soaking in the simulated body fluid solution. Further, drug loading and release studies are carried out with the help of UV–Visible spectrophotometer. Wherefore, a multivariable non-linear regression model has been developed to predict the release rate at different manganese oxide concentrations. The proposed model shows a high Pearson correlation coefficient between the observed and predicted absorbance. These results designate that low-cost agro-waste derived alkali-free glasses have potential in medical applications in drug delivery as well as it helps in environmental pollution management.