Clinico-epidemiological Trends of Leprosy in 21st Century and During COVID-19 Pandemic

Leprosy is the oldest disease affecting humankind since ancient times. Despite MDT's availability for disease curability, vast pockets of multi-bacillary (MB) cases persist in the community. We conducted this study to know the clinico-epidemiological trends of leprosy over four years and five months in this era of the COVID-19 pandemic (C19P). A total of 90 cases were registered;59 (65.5%) were males, and 31 (34.5%) were females. The majority (69%) of cases were in the 15-45 age groups. Childhood leprosy was detected in 3(3.3%) cases. A history of contact with leprosy patients could be established in 16 (17.8%) cases. The cases comprised 54.5% local inhabitants and 45.5% were migrants. The MB cases 77 out of 90 (85.6%) were in higher proportion than pauci-bacillary (PB) cases. In the clinical spectrum, BL leprosy was most common in 39% of cases, followed by LL and BT leprosy. Thirty-seven (41%) patients were suffering from lepra reactions (LR), and out of them, 59.4% had type 2 reactions (T2R), and the rest had type 1 reactions (T1R). Disabilities were found in a total of 56 (62.2%) cases, and grade 2 disabilities (G2D) were recorded in 25 (44.6%) patients. Ulnar nerve (UN) was most commonly affected nerve in 64.4% of cases, followed by lateral peroneal (LPN) and posterior tibial nerve (PTN). We observed the impact of Covid 19 infection peak C19P in two ways;firstly, during the C19P peak in 2020, there was a drastic fall in total registered cases (TRC) to 4/year against 22/year in pre-C19P with a relative increase in LRs and disabilities. In post-C19P peak periods, not only was there a marked rise in TRC (20/5 months), but LR (50%) and disabilities (75%) also showed a significant rise. A high proportion of MB cases, LRs and disability rates indicate the need for population-based studies and subsequent public health measures for early diagnosis and treatment. Further large sample-sized, in-depth studies can tell the exact impact of C19P on leprosy.Copyright © Hind Kusht Nivaran Sangh, New Delhi.

Clinico-etiological Profile of COVID-19 Cases with Cavitary Lung Lesions

Background: Pulmonary cavitation as a radiological finding in COVID-19 has been documented in case reports and small case series with a variety of etiologies deemed responsible. However, there is no large data addressing the issue. Hence, we present the data of forty-two COVID-19 patients at our institute, who were diagnosed and evaluated for cavitary lung lesions. Methodology: Records of consecutive COVID-19 patients, diagnosed and evaluated for cavitary lung lesions over a period of three months from April to June 2021, were reviewed retrospectively. Result(s): 42 patients were diagnosed with cavitary lung lesions during study duration, 19 (45%) during the course of admission and 23 (55%) on readmission. Majority of patients (n=36, 86%) were detected with cavitary lung lesion between 4th to 7th week from symptom onset, while only 6 patients (14%) were detected in 2nd and 3rd week. Mean duration between symptom onset and evidence of cavity on chest tomography was 18 and 32 days in the course and readmission group, respectively. Mucor species, Aspergillus fumigatus and Candida albicans among fungal organisms and Acinetobacter baumannii and Klebsiella pneumoniae among bacterial organisms were predominantly associated with cavitary lesions. Conclusion(s): Cavitary lung lesions associated with COVID-19 are not uncommon and can be detected during the absorptive phase of disease itself or much later during readmission. We found that bacterial and fungal infections are commonly associated. Hence, prompt diagnosis and management should be initiated keeping these etiologies in mind to prevent further morbidity and mortality due to COVID-19.

Nanotheranostics to target antibiotic-resistant bacteria: Strategies and applications

Various health agencies, such as the European Medical Agency (EMA), Centers for Disease Control and Prevention (CDC), and World Health Organization (WHO), timely cited the upsurge of antibiotic resistance as a severe threat to the public health and global economy. Importantly, there is a rise in nosocomial infections among covid-19 patients and in-hospitalized patients with the delineating disorder. Most of nosocomial infections are related to the bacteria residing in biofilm, which are commonly formed on material surfaces. In biofilms, microcolonies of various bacteria live in syntropy;therefore, their infections require a higher antibiotic dosage or cocktail of broad-spectrum antibiotics, aggravating the severity of antibiotic resistance. Notably, the lack of intrinsic antibacterial properties in commercial-grade materials desires to develop newer functionalized materials to prevent biofilm formation on their surfaces. To devise newer strategies, materials prepared at the nanoscale demonstrated reasonable antibacterial properties or enhanced the activity of antimicrobial agents (that are encapsulated/chemically functionalized onto the material surface). In this manuscript, we compiled such nanosized materials, specifying their role in targeting specific strains of bacteria. We also enlisted the examples of nanomaterials, nanodevice, nanomachines, nano-camouflaging, and nano-antibiotics for bactericidal activity and their possible clinical implications.Copyright © 2023 The Author(s)

AI-based implementation of decisive technology for prevention and fight with COVID-19

The COVID-19 pandemic presents the Artificial Intelligence (AI) community with many obstacles. Healthcare organizations are in desperate need of technology for decision-making to tackle this virus and allow them to get timely feedback in real-time to prevent its spread. With the epidemic now being a global pandemic, AI tools and technology can be used to help efforts by governments, the medical community, and society as a whole to handle every stage of the crisis and its aftermath: identification, prevention, response, recovery, and acceleration of science. AI works to simulate human intellect professionally. This outcome-based technology is used to better scan, evaluate, forecast, and monitor current patients and probable patients in the future. In this proposed study, for global pandemic COVID-19, we are aiming to incorporate AI-based preventive measures such as face mask detection and image-based computed tomography scans using advanced deep learning models. © 2022 Elsevier Inc. All rights reserved.

Assessment of Fear of COVID-19 in Pateints Attending the Dermatology Outpatient Department: A Cross-sectional Study

Introduction: The entire world has been affected by Coronavirus disease 2019 (COVID-19) and experts all over the world are working hard to combat this global pandemic. There is a panic among people with resultant psychosocial consequences. Aim: To evaluate the fear factor of COVID-19 using Fear of COVID-19 Scale (FCV-19S) among two groups of patients, one with dermatological diseases managed with immunomodulators and second with dermatological diseases not requiring immunosuppression and also to counsel both the groups regarding the course of their disease and tailoring their visits to the hospital accordingly. Materials and Methods: This cross-sectional study was conducted from 16th January 2021 to 30th April 2021 in the Outpatient Department (OPD) of Dermatology of a tertiary care centre, Indira Gandhi Medical College, Shimla, Himachal Pradesh, India. Consecutive 52 patients meeting the inclusion criteria, with dermatological diseases requiring long-term immunosuppressive therapy and 49 patients with dermatological diseases or cosmetic concerns not requiring immunosuppressive treatment were enrolled for the study. The obtained data was analysed using Epi Info software version 7.2.4.0. results: A total number of 101 patients were enrolled in the study with a male to female ratio of 1.7:1. Mean age of patients was 41 years (range 18-71 years). Among them, 52 (51.49%) had chronic diseases with relapsing and remitting course requiring immunomodulator drugs and 49 (48.51%) had either cosmetic concerns or diseases not requiring immunomodulation. Seventeen (16.83%) of the total patients had other co-morbidites like diabetes mellitus, hypertension, chronic kidney disease or cardiac diseases. Out of all the study participants, 3 (2.9%) had severe fear, 16 (15.8%) had moderate fear, 36 (35.6%) had mild fear and 46 (45.5%) had no fear of COVID-19. conclusion: During this pandemic time, patients need to be counselled regarding the course and management of their diseases and stress factor should also be addressed.

Viability and Applicability of Deep Learning Approach for COVID-19 Preventive Measures Implementation

The deadliest COVID-19 (SARS-CoV-2) is expanding steadily and internationally due to which the nation economy almost come to a complete halt;citizens are locked up;activity is stagnant and this turn toward fear of government for the health predicament. Public healthcare organizations are mostly in despair need of decision-making emerging technologies to confront this virus and enable individuals to get quick and efficient feedback in real-time to prevent it from spreading. Therefore, it becomes necessary to establish auto-mechanisms as a preventative measure to protect humanity from SARS-CoV-2. Intelligence automation tools as well as techniques could indeed encourage educators and the medical community to understand dangerous COVID-19 and speed up treatment investigations by assessing huge amounts of research data quickly. The outcome of preventing approach has been used to help evaluate, measure, predict, and track current infected patients and potentially upcoming patients. In this work, we proposed two deep learning models to integrate and introduce the preventive sensible measures like face mask detection and image-based X-rays scanning for COVID-19 detection. Initially, face mask detection classifier is implemented using VGG19 which identifies those who did not wear a face mask in the whole crowd and obtained 99.26% accuracy with log loss score 0.04. Furthermore, COVID-19 detection technique is applied onto the X-ray images that used a Xception deep learning model which classifies whether such an individual is an ordinary patient or infected from COVID-19 and accomplished overall 91.83% accuracy with 0.00 log loss score. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Study on Effect of COVID-19 and AI tools to Detect Coronavirus from Chest Medical Images

In December 2019, an outbreak of a series of severe respiratory illness was found in Wuhan, Hubei Province, China. It was due to a novel coronavirus, now identified as SARS-CoV-2. The virus is human-to-human transmissible and that is why it has created a pandemic. Due to the continuous increasing death toll, several governments have been compelled to execute complete lockdown throughout the countries and followed by a social separation. The lack of tailored treatment remains an issue. Usually, patients above the age of 65 are more vulnerable to serious illnesses, according to epidemiological studies, whereas children have lesser symptoms. Depending on the present scenario of coronavirus disease, World Health Organization (WHO) advised to implement precautionary measures to defend self and other population. It has been also instructed to take legal action if some careless personnel do not abide with the guidelines of WHO and respective government of his country. A covid detection mechanism from X-ray images is presented in this paper, where a deep convolutional neural network has been utilized to determine whether a person is a covid patient or not. The proposed model accomplishes more than 96% accuracy, which proofs the goodness of the proposed work. © 2021 IEEE.

Effects of COVID-19: Redefining Work from home & Employee Engagement

Started in China's Wuhan district late last year, the coronavirus outbreak has thrown several unprecedented challenges. As per the estimates of International Labour Organisation (ILO), due to COVID 19, over 25 million people are likely to lose job. As per the report published by Gallup Foundation, (Harter, 2020) remote workers percentage jumped from 31% to 62% which is likely to impact employee engagement. Employee working remotely can become “next new normal”. Gallup research survey found a strong association between remote mode of working and employee engagement. Employees having option of work from home are more likely to be engaged in comparison to the one who do not have such options available. (Webcast, 2020). An engaged employee is considered to be the best brand ambassador of any organisation. Such an employee is always willing and interested to go “an extra mile” and gives his best at work. For the purpose of the study, data from secondary sources such as e-newspaper, articles, blogs, journal articles and research papers, reports from government organisations, company reports, review articles etc. are gathered and compiled there after a critical analysis of the same is done with respective to objective of the study. During lockdown as per the detailed review undertaken, seven parameters abbreviated as “EFFECTS” were found to be the most important and relevant for employees working from home and making them engaged with the high level of motivation and dedication. These are Employee Voice,Fun Activities,Feelings of Employees,Emotional support, Compassion, Training and Development and Supervision. The study also provides scope for future researchers. © 2021. Transnational Press London. All Rights Reserved.

Deep Neural Architecture for Face mask Detection on Simulated Masked Face Dataset against Covid-19 Pandemic

The dangerous COVID-19 (SARS-CoV-2) is rising steadily and globally, with more than 72,851,747 confirmed cases observed to WHO including 1,643,339 deaths till 17 December 2020. The country's economy is now almost fully halted, people are stuck up and investment becomes deteriorating. So, this is turning to worry of the government for a development and health. Health organizations are often desperate for evolving decision-making innovations to overcome this viral virus and encourage people to receive rapid and effective responses in real-time. Thus, it is important to create auto-mechanisms as a preventive shield to ensure healthy humanity against SARS-CoV-2. Advanced analytics methods and other strategies could also empower researchers, learners and the pharmaceutical industry to acknowledge the hazardous COVID-19 and speed it up care procedures by efficiently testing vast volumes of research data. The prevention method consequence is being used to effectively manage, calculate, forecast and monitor current infected people and future potential cases. Therefore, we proposed CNN and VGG16 based deep learning models to incorporate and enforce AI-based precautionary measures to detect the face mask on Simulated Masked Face Dataset (SMFD). This technique is capable of recognizing masked and unmasked faces to help monitor safety breaches, facilitate the use of face masks, and maintain a secure working atmosphere.

Review of the impact of COVID-19 pandemic on rheumatology services at a large tertiary care centre in Wales, UK

Background: COVID-19 pandemic had an unprecedented impact on the delivery of patient care. Rheumatology services had to rapidly adapt to virtual consultations at the onset of the pandemic. However, providing a high quality and effective service in a virtual setting can be challenging and therefore its prudent to do a formal review and gain patient feedback to ensure that these clinics are fit for purpose. Objectives: To evaluate the impact of first wave of COVID-19 pandemic on patients with autoimmune rheumatic conditions, assess delivery of rheumatology outpatient care and record patient feedback. Methods: This study included patients on the Rheumatology clinic lists between 3rd and 31st August 2020. An electronic survey questionnaire was developed and the survey link was sent to patients via a text message using secure IT platform. Data was collected on patient demographics, diagnosis, comorbidities, treatments, clinical/ laboratory confirmed COVID-19 diagnosis, treatment interruption, impact on work, personal protective measures taken and views on virtual consultations. Results: 307 patients responded with 287 complete responses. 73.1%(223) were female and 32.4% (99) were ≥65 years of age. Rheumatoid arthritis was the most common diagnosis 41.6%(127). Hypertension was the commonest comorbidity 21.4%(64) followed by Chronic lung disease 17.3%(52). 28.8%(85) were on Hydroxychloroquine, 26.7%(79) Methotrexate, 14.2%(42) Sulfasalazine and 13.2%(39) on Prednisolone. 22.3%(66) were on Biologics: Anti TNF 12.8%(38), Tocilizumab 3.7%(11) and Rituximab 3%(9). 52.6%(161) shielded, 16.9%(55) self-isolated and 30.3%(93) only maintained social distance. 197 patients self reported as being vulnerable but based on their treatment,only 167 patiemts met the clinically extremely vulnerable (CEV) criteria and all of those received government shielding letter. 3.6%(11) had lab confirmed COVID-19, 3.2%(10) had clinically suspected COVID-19 infection. 14.3% (43) had their treatment interrupted. 4.6%(14) were unable to work from home or maintain social distancing at work. 59.8%(182) had face-to-face consultation changed to virtual. 63.2%(189) were satisfied, 28%(84) neutral and 8.7%(26) reported dissatisfaction with their consultation. 50.5%(153) were happy to continue with virtual consultation but with an option of face to face only if necessary.For consultations post COVID-19, 59.4%(182) preferred a mixture of face to face and virtual appointments. Conclusion: Majority of our patients seem happy with virtual consultations as long as they are assured of a face-to-face consultation if needed. A minority(8.7%) however, were dissatisfied. Some of the suggestions were, use of video consultations and improvement in communication before the virtual appointments. Our survey also shows that our patients have adapted well to virtual consultations and many are keen to have virtual consultation in the longer term. In our survey, only 6.8%(21) patients reported definite or clinically suspected COVID-19. Possible explanations for this include strict compliance with government advice on social distancing/shielding and limited testing at the onset of the pandemic. More patients assumed themselves to be clinically CEV than those who were actually CEV based on their treatment which is not surprising because of high level of anxiety among patients due to rapidly spreading pandemic and multiple sources of information. This feedback provides useful data which will help us to plan the delivery of rheumatology services post COVID-19 pandemic. While face-to-face patient contact is needed for comprehensive disease assessment, teaching and training, a model for the future is likely to include a combination of face-to-face and virtual consultations. This could allow a greater capacity to see new patients and reduce waiting lists. Patients with uncomplicated and stable disease could be followed up in virtual clinics. There is also a need to formally incorportate the virtual consultations into the curriculum for Rheumatology trainees.

Face Mask Detection Classifier and Model Pruning with Keras-Surgeon

Multidisciplinary initiatives in the new world of coronavirus were combined to limit the spread of the pandemic. Interestingly, the AI group was a part of those efforts. This result-based approach is used to help scan, assess, predict and track current patients and possibly potential patients. Developments for tracking social distances or recognizing face masks have made headlines in particular. Most current advanced approaches to face mask recognition are built based on deep learning which is dependent on a large number of face samples. Nearly everybody wears a mask during corona virus outbreak in order to effectively avoid the spread of COVID-19 virus. Our goal is to train a customized deep learning model that helps to detect even if or not a person wears a mask and study the concept of model pruning with Keras-Surgeon. Model pruning can be efficient in reducing model size, so that it can be easily implemented and inferred on embedded systems. © 2020 IEEE.

Impact of covid-19 on education and internships of hospitality students

Introduction: As we all know COVID-19 pandemic became a problem all over the world. Every country is trying to fight with this pandemic virus, due to which many countries announced complete lockdown in their country. Schools/ colleges help in developing social skills and awareness of students which is interrupted because of lockdown due to COVID-19. Not only studies are affected but it has also affected the industrial training of the students. Hospitality students must have good communication skills, social values, teamwork and many more qualities .which are affected due to pandemic which can’t be learned through online classes. Many of the students were not able to attend online classes because of the poor network in their region, loss of focus and interest, some time does not get much space in their home for online studies due to more members in the family, sometimes they don’t have a system to excess online classes. Internships of Hospitality students were affected as their internship programmes were either reduced or cancelled due to this pandemic. Objectives: The primary objective of this research is to find out the impact of this pandemic on education and internships of hospitality students. Methods: This research paper has derived conclusions based on the feedback of different hospitality colleges situated in Deh-radun. The feedback was based on some questionnaires which were asked through an online survey. The feedback is collected on percentage bases which are then analyzed for getting results and conclusion. Results: According to the results 97% of hospitality students agreed that their Internship programmes are badly affected due to this pandemic and they face various problems during online classes.73% of students disagreed when asked will they prefer online mode of education even post this Pandemic.75% students agreed that they got full support from their teachers during online classes. Conclusion: Most of the students were facing various problems in their online classes, their internship programmes is effected, because of which their learning and skill development activities are effected but the good thing is that most of the student was getting full support from their teachers/colleges/ Universities and most of the students find the online mode of education as a better and useful tool for online studies during this pandemic. But still, most of the students prefer traditional (face to face) mode of education over the online mode of education. © IJCRR.