Perception and Experience of Medical Students Regarding Hybrid Problem-based Learning Technique at a Medical College in West Bengal, India: A Cross-sectional Study

Introduction: Hybrid Problem-Based Learning (h-PBL) is a type of teaching-learning technique that incorporates both in-person learning and virtual learning via hybrid classroom tools. It reportedly increases student engagement, positively impact their learning process and improve communication skills. During Coronavirus Disease 2019 (COVID-19) times, its applicability was further enhanced as it allowed the flexibility of teaching as well as learning from home to both teachers and students. Aim: To assess the perception and experience of 2nd phase MBBS students after undergoing training by the h-PBL method. Materials and Methods: A cross-sectional study was conducted on 2nd phase MBBS students in the Department of Microbiology at College of Medicine and Sagore Dutta Hospital, Kolkata from 15th March to 14th April 2022. A total of 111 students of 2nd phase MBBS of the college gave an informed consent to be part of the study. All inductees underwent a structured training by h-PBL technique following which their perception and experience about the exercise was sought via questionnaire. Data were presented in frequency and percentage. Association between mean scores of male and female participants was calculated by Chi-square test. Results: Out of the 111 participants, 58 (52.2%) were male and 53 (47.8%) were female with mean age of 19.5±0.5 years (range 18-22 years). The h-PBL technique was perceived to be motivating for self-directed learning by 97 (85.6%) of the respondents. A total of 107 (96.4%) students agreed that h-PBL is more effective than traditional teaching for acquiring both theoretical and practical knowledge, learning and understanding topics correctly and also identifying and rectifying their deficiencies in knowledge and skills. More than 90% participants (102 of 111) felt that h-PBL has more potential than traditional teaching to establish fruitful student-teacher interaction and provide better feedback opportunities. Overall student satisfaction in our study showed 96.4% agreement (107 of 111). Conclusion: The students considered h-PBL model to be better than traditional teaching to help them acquire theoretical knowledge and practical skills. They also felt that it improved their communication skills, teamwork ethics and motivated them to undertake self-directed learning.

Content-Based Retrieval of COVID-19 Affected Chest X-rays with Siamese CNN

In December 2019, the first reported case of COVID-19 was brought to notice in Wuhan, China. The virus has novel characteristics, its harshness is unpredictable, its transmission ability is extremely powerful, and its incubation period is comparatively larger. Thus the outbreak emerged as a pandemic worldwide. World health and socio-economy is getting continually affected by COVID-19 since its outbreak. It will be easier to handle the situation if an automated diagnostic system is developed, capable of separating COVID-19 affected images from bulk images obtained from a mass screening process. Kaggle's online chest X-Ray image dataset has been considered for this work evaluation. Healthy and COVID-19 affected chest X-Ray images were used for evaluating the performance of content-based image retrieval. Image retrieval has been carried out based on the absolute difference between the encoded features of twin images obtained from the Siamese Convolutional Neural Network (SCNN). The retrieval performance is awe-inspiring as the Siamese network used for retrieval is a relatively shallow network. SCNN does not require resource-hungry training with huge samples as part of its underlying implementation characteristics. The execution time is also very encouraging as the simplicity of the method is concerned. The method achieves 94% average precision and 100% average reciprocal rank while rank = 5 has been considered. Till now, no work has been reported on content-based retrieval of COVID-19 chest X-Ray images. Thus, a comparative study of evaluation metrics and execution time requirements of similar work could not be provided.

Smart Intravenous Drip Monitoring System with Bubble Detection Indicator using IoT

With the increasing emergence of new diseases and ailments, health care facilities are gradually becoming an integral part of our lives. The most important thing in these healthcare facilities is safety of the patients. With an ever increasing population, automatic health monitoring systems are gaining importance too. They are highly sought after since they provide exact information while remaining cost-effective and minimizing medical practitioners' and patients' stress. Among various health procedures, intravenous therapy is an often used medical method to deliver fluids, medications and nutrients into a patient's veins. It requires manual monitoring by the medical staff and negligence can cause fatal risks. Further, with COVID-19, the burden and pressure on medical professionals increased manifold which enhanced the chances of negligence. The aim of this paper is to create a practical, budget-friendly and Internet of Things based monitoring device for intravenous therapy. The proposed system consists of a light dependent resistor for detection of fluid level in the IV drip bottle and air bubble formation in IV tube. When the fluid in the IV drip bottle reaches a critical level or air bubble is detected, a notification will be received by the concerned staff. Also, an auditory and visual alert will notify the people present in the patient's room. This will enable the medical professionals to immediately attend to the patient without the hassle of periodic monitoring. © 2022 IEEE.

An Update of Pharmacological and Non-pharmacological Therapies for COVID-19 Pandemic Trivia

Background: The whole planet is facing one of the scariest pandemic situations in this era. On 11th February, 2020 the World Health Organization announced the name of an unknown disease as COVID-19, which is caused by the ssRNA virus SARS-CoV-2 (formally recognized as a sister of SARS-CoV and MERS-CoV). The epicenter of this disease is Wuhan, Hubei Province, China. COVID-19 can affect all age groups, but particularly affects immune compromised and aged persons with co-morbid conditions. It is highly contagious disease that involves mild to severe respiratory symptoms along with breathing difficulties. Objectives: As SARS-CoV-2 is a new strain of β-coronavirus that spreads from animals to humans via an unknown intermediate host, no vaccines have been developed yet and only supportive treatment is given to the infected patients. The review paper highlights the pharmacological therapy as a supportive treatment given to the COVID-19 patients and nonpharmacological therapeutic approaches for the prevention. Methods: Methods: Authors were surveyed and reviewed numerous articles, magazines, news papers, conference proceedings from different search engines and made the review successful. Results: Some drugs of different categories are approved and prescribed to the patients and some others are still under investigation and have gone through clinical trials. Conclusion: As no specific treatment or drugs for this disease have been developed till the date;therefore, social distancing, home quarantine, and proper healthy lifestyle management are the best current short-term options to avoid further spread of this pervasive virus.

Covid-19 (Pandemic): A Review Article

Coronaviruses are a group of enveloped viruses with nonsegmented, single-stranded, and positive-sense RNA genomes. Coronaviruses belong to the "Coronaviridae family", which causes various diseases, from the common cold to SARS and MERS. In March 2020 the World Health Organization declared the SARS-Cov-2 virus a global pandemic. We performed a review to describe existing literature about Corona Virus Disease 2019 (COVID-19) history, Symptoms, Epidemiology, Clinical features, Clinical manifestations, Diagnosis, Treatment, Prevention.

Object tracking and counting in a zone using YOLOv4, DeepSORT and TensorFlow

The main objective of this research work is to solve multiple object tracking problems in a given frame, wherein the proposed model intends to identify and track various objects. The problem has been solved in three stages viz. detecting, identifying, and tracking the object in a particular zone, i.e., but it is observed that something more could be done in this field, mostly the MOT-A score was not up to the mark;hence the proposed research work utilized Kalman filters for obtaining enhanced results and compared the obtained MOT-A metric with previous works, and the results were good. Object detection and recognition occur via the YOLO algorithm, which enables us to classify the objects into 80 classes. Then, Motion Prediction and feature generation occur in which an estimation model is created, and Kalman filters are used to model these states for capturing moving objects in the frame. Finally, tracking takes place with Kalman filters in the previous frame, and newly detected objects are placed in the current frame, after which an association is made for new detection. All this is done via the DeepSORT algorithm, which is essentially a Deep association metric with the SORT algorithm. Here, Kalman filters are used as they improved the accuracy of the proposed model and yielded better results. On the same lines, YOLO is used to perform object detection and recognition at the same time. It is also a detector, which by applying a single neural network, it can predict the bounding boxes and perform multi-class classification. This problem can have various applications, especially in traffic management. It can also prevent people from gathering during COVID times and raising an alert for all the authorities. Hence, this is a multidisciplinary approach wherein the work of object detection is being used in various fields like crowd assembling, Surveillance, Animal management in Zoo/Biodiversity parks as well as in the case of traffic systems as well. The real motivation behind this work was using the state-of-the-art technology to solve the modern-day problems. Manually human vigilance in large areas is a utopian task and especially when surveillance and security is big threat out there. Adding to it is the COVID Pandemic which has claimed millions of lives and yet vaccination is a still a dream yet to be realized. Hence, cluster identification problem is considered and also the ways to solve this challenge using the state-of-the-art technology. Once it has been achieved, it is initiated further to find more applications of this technology in various other domains and found out how it is applied in various use cases in case of crowd gathering at a single point and how this is a similar problem in case of animal gathering at a point and how forest rangers can solve the problem in a more efficient way. © 2021 IEEE.

Hand hygiene and adverse skin reactions: Covid-19 prospect

Hand hygiene is the most primary, but a very necessary practice for maintaining health hygiene. The pandemic attack of COVID-19 is highly contagious that primarily spreads through the hands. Hand washing and hand rubbing protect against not only from SARS-CoV-2 (Virus of COVID-19 disease), but other harmful microbes also. The authors have found by surveying and reviewing various literature that, frequent hand washing with sanitizer or soap and detergents as well cause vandalism of the skin's outermost layer, dryness of skin, rough skin, erythomatous scaly patches, allergic contact dermatitis, destruction of skin proteins, and epidermal keratin denaturation or hand hygiene associated other dermal adverse reactions especially in health care workers (HCW) along with common mass. So, prolonged use of sanitizer, soap or detergent and their adverse dermal reaction may be minimized by the use of alternative herbal ingredients in alcohol-based sanitizer. © 2020, Advanced Scientific Research. All rights reserved.