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Pneumonia is a serious respiratory infection that can lead to severe complications if not diagnosed and treated promptly. Chest X-rays are a widely used diagnostic tool to identify pneumonia, but the accurate and timely interpretation of these images is often challenging for healthcare professionals. This research presents a novel approach to automate pneumonia detection from chest X-rays using Convolutional Neural Networks (CNN). The proposed model leverages the power of CNN, a deep learning architecture specifically designed for image analysis, to automatically learn and extract relevant features from the chest X-ray images. The dataset consists of a large number of annotated chest X-rays collected from diverse patient populations, including both pneumonia-positive and pneumonia-negative cases. Preprocessing techniques are applied to standardize the images and reduce noise, ensuring the CNN's robustness to variations in image quality and positioning. The CNN model is trained using a transfer learning strategy, utilizing a pre-trained model with weights learned from a large-scale dataset. During the training process, the CNN learns to differentiate between normal and pneumonia-infected lung patterns, thereby enabling accurate classification of pneumonia cases in unseen chest X-rays. The performance of the model is evaluated using various metrics, such as sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC-ROC). Experimental results demonstrate the effectiveness of the CNN-based approach in accurately identifying pneumonia cases from chest X-rays, achieving high sensitivity and specificity rates. The proposed model shows promising potential as a reliable and efficient tool for aiding radiologists and healthcare practitioners in pneumonia diagnosis. Furthermore, this research contributes to the ongoing efforts in developing AI-based medical systems that can assist healthcare professionals in making accurate and timely diagnoses, ultimately improving patient outcomes and reducing healthcare costs. Nonetheless, further validation on larger and more diverse datasets is essential to establish the generalization and scalability of the CNN-based model for pneumonia detection in real-world clinical settings. [ABSTRACT FROM AUTHOR]

Context: The ongoing pandemic has affected all the spheres of life and one of the severely affected avenues is the education of a child. The online education has seen an upward curve since the start of COVID-19 pandemic. Schools globally have adopted online class tutorials as the main method to impart education and directly increasing the screen time for a child. Aim: The aim of the present study was to evaluate the cytological effects of prolonged mobile phone usage on the buccal mucosa of children. Settings and Design: Stratified sampling was used for the selection of subjects for the study. After a questionnaire regarding the usage of a mobile phone was distributed among the parents of children. Among them, 90 children were selected on the basis of pattern and frequency of mobile phone usage in the child. Materials and Methodology: The children were divided into three groups based on the per day hours of viewing of mobile phone, i.e., Group 1: Usage of 1–2 h a day, Group 2: Usage of 3–6 h a day, and Group 3: Usage of >6 h a day. The time frame taken into consideration was 1 year after the pandemic started. This was specifically to understand the impact of the online education. Swab was obtained by using the conventional ice-cream stick method from the buccal mucosa. Statistical Analysis: The samples were subjected to histological and microscopical analysis to observe for cytological changes. One-way ANOVA was used to determine the statistical significance if any. Results: The results obtained clearly showed that Group 3 (>6 h usage per day) showed the highest number of cellular and chromosomal aberrations which was significant. Conclusion: The results indicated that impact due to the prolonged screen time on the buccal mucosa is significant. A direct proportionality was seen between the apoptotic changes and chromosomal aberrations and the number of daily hour usage. [ABSTRACT FROM AUTHOR]

Space maintenance is of particular importance following the premature loss of deciduous molars to prevent development of malocclusion. The usage of fixed space maintainers is more common in clinical practice. Traditionally fixed unilateral space maintainers were advocated following the premature loss of primary molars but they have certain clinical disadvantages such as loss of arch length, improper eruption of permanent successors and primarily the loss of function of mastication which cannot be achieved with the available fixed unilateral space maintainers. The paper is about three cases with an interesting design of fixed functional space maintainers which might overcome the disadvantages in the existing strategies in space maintainers and space maintenance and restore the primary function of mastication. To incorporate functionality into the conventional space maintainers a stainless-steel crown or an acrylic tooth of customised adequate dimension is incorporated into the traditional design. [ABSTRACT FROM AUTHOR]