Guest Comment: Leukaemia & Lymphoma Awareness Month

Leukaemia & Lymphoma Awareness Month Guest Comment by Dr. Preety Gupta, Reader, Swami Devi Dyal Hospital and Dental College, Barwala, Panchkula, Haryana, Indi

Pleomorphic Adenoma of the Parotid Gland: A Case Report

Salivary organ tumors are uncommon, including under 3 % of all neoplasia of head and neck district. Pleomorphic adenoma is the most well-known salivary organ tumor, representing 60-80% of amiable tumors of salivary organs. Generally they are found as singular one-sided, firm and portable, effortless, moderate developing mass. The board includes careful resection by shallow or absolute parotidectomy

Artificial Intelligence in Public Health Dentistry

The educational needs must drive the development of the appropriate technology”. They should not be viewed as toys for enthusiasts. Nevertheless, the human element must never be dismissed. Scientific research will continue to offer exciting technologies and effective treatments. For the profession and the patients, it serves to benefit fully from modern science, new knowledge and technologies must be incorporated into the mainstream of dental education. The technologies of modern science have astonished and intrigued our imagination. Correct diagnosis is the key to a successful clinical practice. In this regard, adequately trained neural networks can be a boon to diagnosticians, especially in conditions having multifactorial etiology

Dental and Oral Care Under Clouds of COVID-19

Coronavirus (SARS-CoV-2) made the headlines after its initial breakout in Wuhan, China in December 2019 . Viral by genome, lethal by nature, strongly contagious by character, it succeeded in making a new chapter in everyone’s life in a very short span making it a pandemic. Despite the extensive efforts to limit its effects we stand at a point where more than 50 million people have lost their lives battling COVID. Its widespread growth has raised many concerns for global health, particularly health professionals and dentists precisely. As dentists are more prone to get affected in the course of their occupation, this review is an attempt to briefly summarize the virus and various protocols to be practiced by the dentists in their practices to protect their own health

Reasons for extraction in primary teeth among 5-12 years school children in Haryana, India- A cross-sectional study

Due to high prevalence of oral diseases extraction of primary teeth is a common and a major concern in developing countries. These teeth are given least importance as they are believed to shed off automatically, thus leading to serious problems like crowding and malocclusion. A cross sectional study was carried out among children aged 5 to 12 years among 1347 children. The data was recorded on a prestructured questionnaire. Reasons for extraction of teeth were based on Kay and Blinkhorn criteria. 20.4% children were having tooth loss due to various reasons. The main reason for extraction was found to be caries in 64.3% followed by trauma in maxillary teeth among 43.02% of children. Presence of early loss of primary teeth result in occlusal disturbances and space loss among children. Hence, proper treatment regimens must be followed by the dental professionals and should be the need of the hour

Knowledge, health seeking behavior and barriers for treatment of reproductive tract infections among married women of reproductive age in Delhi

Background: Reproductive tract infections including sexually transmitted infections are an important public health problem among women of reproductive age group in developing countries. The perceptions or taboos related to RTIs act as an obstacle for seeking treatment ultimately leading to complications. The aim of the study was to assess the knowledge, health seeking behavior and barriers for treatment of reproductive tract infections among married women of reproductive age in Delhi.Methods: A community based cross-sectional study was undertaken in an urban field practice area of department of Community Medicine of VMMC and Safdarjung Hospital, New Delhi from November 2017 to April 2019. Sample size of 270 was collected using predesigned and pre- tested questionnaire by systematic random sampling.Results: Mere 16.6% of the women knew about symptoms of RTI/STIs. Out of 81 women having RTI/STI in past 3 months 30% did not seek treatment and out of 70% who took treatment for RTI, 30% did not complete treatment. Majority of the women who sought treatment preferred government hospital. The main barrier for seeking treatment was embarrassment, not considering it as an important health problem, lack of time.Conclusions: The overall knowledge about symptoms, mode of spread of RTIs/STIs was very poor among the study participants. Women seeking treatment are not completing it. Thus, there is need to emphasize on spreading knowledge about symptoms, mode of spread, need for treatment and its completion and clearing barriers related to RTI/STI among women.

Non-Destructive Banana Ripeness Detection Using Shallow and Deep Learning: A Systematic Review

The ripeness of bananas is the most significant factor affecting nutrient composition and demand. Conventionally, cutting and ripeness analysis requires expert knowledge and substantial human intervention, and different studies have been conducted to automate and substantially reduce human effort. Using the Preferred Reporting Items for the Systematic Reviews approach, 1548 studies were extracted from journals and conferences, using different research databases, and 35 were included in the final review for key parameters. These studies suggest the dominance of banana fingers as input data, a sensor camera as the preferred capturing device, and appropriate features, such as color, that can provide better detection. Among six stages of ripeness, the studies employing the four mentioned stages performed better in terms of accuracy and coefficient of determination value. Among all the works for detecting ripeness stages prediction, convolutional neural networks were found to perform sufficiently well with large datasets, whereas conventional artificial neural networks and support vector machines attained better performance for sensor-related data. However, insufficient information on the dataset and capturing device, limited data availability, and exploitation of data augmentation techniques are limitations in existing studies. Thus, effectively addressing these shortcomings and close collaboration with experts to predict the ripeness stages should be pursued.info:eu-repo/semantics/publishedVersio

An Approach to Binary Classification of Alzheimer’s Disease Using LSTM

In this study, we use LSTM (Long-Short-Term-Memory) networks to evaluate Magnetic Resonance Imaging (MRI) data to overcome the shortcomings of conventional Alzheimer’s disease (AD) detection techniques. Our method offers greater reliability and accuracy in predicting the possibility of AD, in contrast to cognitive testing and brain structure analyses. We used an MRI dataset that we downloaded from the Kaggle source to train our LSTM network. Utilizing the temporal memory characteristics of LSTMs, the network was created to efficiently capture and evaluate the sequential patterns inherent in MRI scans. Our model scored a remarkable AUC of 0.97 and an accuracy of 98.62%. During the training process, we used Stratified Shuffle-Split Cross Validation to make sure that our findings were reliable and generalizable. Our study adds significantly to the body of knowledge by demonstrating the potential of LSTM networks in the specific field of AD prediction and extending the variety of methods investigated for image classification in AD research. We have also designed a user-friendly Web-based application to help with the accessibility of our developed model, bridging the gap between research and actual deployment

Three stage supply chain model with two warehouse, imperfect production, variable demand rate and inflation

This study develops an integrated production inventory model from the perspectives of vendor, supplier and buyer. The demand rate is time dependent for the vendor and supplier and buyer assumes the stock dependent demand rate. As per the demand, supplier uses two warehouses (rented and owned) for the storage of excess quantities. Shortages are allowed at the buyer’s part only and the unfulfilled demand is partially backlogged. The effect of imperfect production processes on lot sizing is also considered. This complete model is studied under the effect of inflation. The objective is to minimize the total cost for the system. A solution procedure is developed to find a near optimal solution for the model. A numerical example along with sensitivity analysis is given to illustrate the model