U-Net based Semantic Segmentation for Touchless Fingerprint Technology: A Reflective Review

Touch-based fingerprints are widely used in today's world;even with all the success, the touch-based nature of these is a threat, especially in this COVID-19 period. A solution to the same is the introduction of Touchless Fingerprint Technology. The workflow of a touchless system varies vastly from its touch-based counterpart in terms of acquisition, pre-processing, image enhancement, and fingerprint verification. One significant difference is the methods used to segment desired fingerprint regions. This literature focuses on pixel-level classification or semantic segmentation using U-Net, a key yet challenging task. A plethora of semantic segmentation methods have been applied in this field. In this literature, a spectrum of efforts in the field of semantic segmentation using U-Net is investigated along with the components that are integral while training and testing a model, like optimizers, loss functions, and metrics used for evaluation and enumeration of results obtained. © 2022 IEEE.

Deep learning-based COVID-19 triage tool: An observational study on an X-ray dataset

Background: Easy availability, low cost, and low radiation exposure make chest radiography an ideal modality for coronavirus disease 2019 (COVID-19) detection. Objective(s): In this study, we propose the use of an artificial intelligence (AI) algorithm to automatically detect abnormalities associated with COVID-19 on chest radiographs. We aimed to evaluate the performance of the algorithm against the interpretation of radiologists to assess its utility as a COVID-19 triage tool. Material(s) and Method(s): The study was conducted in collaboration with Kaushalya Medical Trust Foundation Hospital, Thane, Maharashtra, between July and August 2020. We used a collection of public and private datasets to train our AI models. Specificity and sensitivity measures were used to assess the performance of the AI algorithm by comparing AI and radiology predictions using the result of the reverse transcriptase-polymerase chain reaction as reference. We also compared the existing open-source AI algorithms with our method using our private dataset to ascertain the reliability of our algorithm. Result(s): We evaluated 611 scans for semantic and non-semantic features. Our algorithm showed a sensitivity of 77.7% and a specificity of 75.4%. Our AI algorithm performed better than the radiologists who showed a sensitivity of 75.9% and specificity of 75.4%. The open-source model on the same dataset showed a large disparity in performance measures with a specificity of 46.5% and sensitivity of 91.8%, thus confirming the reliability of our approach. Conclusion(s): Our AI algorithm can aid radiologists in confirming the findings of COVID-19 pneumonia on chest radiography and identifying additional abnormalities and can be used as an assistive and complementary first-line COVID-19 triage tool.Copyright © Cancer Research, Statistics, and Treatment.

Pharmacovigilance-Based Drug Repurposing

Pharmacovigilance involves evaluation of adverse effects of drugs in the interest of patient safety. Large-scale application of pharmacovigilance generates big datasets that are mined to identify previously unknown drug–event combinations, and, as an extension, may help in identifying new indications for old drugs. The therapeutic potential of a drug using pharmacovigilance-based drug repurposing can be assessed in one of the four ways—serendipity, mechanistic profiling, signature matching, and inverse signaling. Serendipity is the phenomenon of discovery of some valuable information for an already known drug, by chance, like minoxidil. Mechanistic profiling proposed the use of sulfonylureas for diabetes mellitus, based on the observation of their hypoglycemic effect. Signature matching is puzzling out new indications of drugs based on similarity of characteristics in a network of other drugs which are already approved for any condition. Inverse signaling approach takes cues from data mining approaches, applied to pharmacovigilance databases. Currently, this approach is being tried to evaluate existing compounds for Raynaud's phenomenon, COVID-19, Alzheimer' disease, etc. In this chapter, we discuss these pharmacovigilance-based methods as they have immense translational potential for drug repurposing. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

Scientific rationale of Indian AYUSH Ministry advisory for COVID-19 prevention, prophylaxis, and immunomodulation

The current outbreak of COVID-19 is caused by the SARS-CoV-2 virus that has affected > 210 countries. Various steps are taken by different countries to tackle the current war-like health situation. In India, the Ministry of AYUSH released a self-care advisory for immunomodulation measures during the COVID-19 and this review article discusses the detailed scientific rationale associated with this advisory. Authors have spotted and presented in-depth insight of advisory in terms of immunomodulatory, antiviral, antibacterial, co-morbidity associated actions, and their probable mechanism of action. Immunomodulatory actions of advised herbs with no significant adverse drug reaction/toxicity strongly support the extension of advisory for COVID-19 prevention, prophylaxis, mitigations, and rehabilitation capacities. This advisory also emphasized Dhyana (meditation) and Yogasanas as a holistic approach in enhancing immunity, mental health, and quality of life. The present review may open-up new meadows for research and can provide better conceptual leads for future researches in immunomodulation, antiviral-development, psychoneuroimmunology, especially for COVID-19.Copyright © 2021, Institute of Korean Medicine, Kyung Hee University.

Epidemiological and Demographic Profile of COVID-19 Suspects in a Tertiary Care Hospital at Jaipur, Rajasthan

Background: The coronavirus disease (COVID-19) was a pandemic which spread to various countries and originated in Wuhan, China. For appropriate response, planning, and allocation of resources demographic data play an important role in understanding the impact of COVID-19 across the country. Aim(s): To estimate epidemiological and demographic parameters like age, sex, area, sample type etc. of samples reported in COVID-19 diagnostic laboratory of RUHS College of Medical Sciences, Jaipur, Rajasthan. Material(s) and Method(s): The study was conducted retrospectively in a tertiary care hospital at Jaipur. Data like age, gender, urban or rural, IPD/ICU or OPD etc. were collected between January 1, 2021 to June 30, 2021. The collected data were expressed in number, counts and percentage. The data of six months were analysed using Microsoft Excel. Result(s): From January to June 2021, April and May 2021 showed highest positivity 13084 (27.42%) and 10968 (23.06%) respectively. February 2021 and June 2021 showed least positivity 156 (2.39%) and 163 (0.8%) respectively. Total COVID-19 positive cases during 6 months were 25134 and deaths were 357 with highest deaths were during May 2021 (n=270). Males (64.28% to 72.20%) were affected most. In April and May 2021 positivity in urban area was 6053 (46.26%) and 5712 (52.07%) respectively, while in rural area 7031 (53.74%) and 5256 (47.93%) respectively. The positivity in OPD patient during April and May was 93.58% (12245) and 95.26 % (10449) respectively. Nineteen to forty years was most affected age group. Conclusion(s): During second wave both urban and rural population was affected. Males and working age group were affected more. Among COVID-19 suspects' positivity rate was low in IPD patients as compared to OPD patients. Critical factors for an effective public health response are surveillance and contact tracing.Copyright © 2023, Dr Yashwant Research Labs Pvt Ltd. All rights reserved.

Acknowledgment of patient in sense behaviors using bidirectional ConvLSTM

Recognizing patient activity in real-time from video or images collected by a CCTV camera available in the hospital during a Covid-19 situation has proven challenging. The dilemma of patient activity recognition is identifying and recognizing a patient's various actions in a series of videos. The process presented in our paper needs to achieve unrestricted, generic behavior in videos. Detecting events in any video is often difficult because we use Bidirectional ConvLSTM to create a robust patient in the sense behaviors (PSB) framework capable of eliminating certain barriers. To begin this paper by proposing a new Bidirectional ConvLSTM for establishing a stable PSB scheme. Our proposed model is capable of accurately predicting patient's behaviors like seated, standing, and so on. Using Bidirectional ConvLSTM, learning information from a pre-trained model is an excellent place to start for rapidly developing a new PSB system using a current PSB database, as both the source and target datasets are critical. All parameters are frozen in a pre-trained PSB device. Then, using the UCI and HMDB51 dataset to train the model, variables and local relations are progressively fixed. A novel PSB framework is developed using the target dataset. Relevant tests are conducted using commonly used research indices to assess prediction precision accuracy. They acknowledge six patient's behavior with a weighted accuracy rate of 92%. For recognizing novel activity, laying, the precision of a corresponding prediction is the best, 91%, of all six test results. The proposed work uses bidirectional ConvLSTM with modified activation layers to sense the patients' behavior. This article may be a patient activity recognition system to identify an individual. It takes a clip of COVID-19 patients as input and looks for matches inside the hold-on images.

Recent Trends and Characteristics of Genitourinary Fistulas in Rwanda

INTRODUCTION AND OBJECTIVE: Genitourinary fistulas (GU) in Rwanda have significantly increased in recent years. We previously reported an increase in the proportion of vesicouterine, vesicocervical and uterovaginal fistulas, with the majority occurring after Cesarean section. Our goal is to examine the characteristics of our the most recent cohort. METHOD(S): A cross-sectional study was conducted of women presenting for evaluation to the International Organization for Women and Development (IWOD) in Kigali, Rwanda, from 2018 to 2019, and 2022. No data was collected during years 2020 and 2021, due to the COVID-19 pandemic. Data was collected from medical records and included region of residence, surgical history, presence of fistula, and type. RESULT(S): A total of 434 women were evaluated, of these 194 (44.7%) were diagnosed with GU fistula. In 2018, fistula types were 40 (52%) vesicovaginal, 5 (6%) urethral, 5 (6%) ureterovaginal, 23 (30%) vesicoureterine or vesicocervical, and 7 (9%) juxtacervical. In 2019, the fistula types were 26 (41%) vesicovaginal, 4 (6%) urethral, 6 (10%) ureterovaginal, 17 (27%) vesicoureterine or vesicocervical, and 10 (16%) juxtacervical. In 2022, the fistula types were 33 (61%) vesicovaginal, no urethral fistula reported, 7 (13%) ureterovaginal, 8 (15%) vesicoureterine or vesicocervical, and 6 (11%) juxtacervical. CONCLUSION(S): In comparison to our prior cohort, recent data shows a stable proportion of types of fistulas. The increased number of vesicouterine, vesicocervical, and juxtacervical fistula may be higher due to increased performance of Cesarean sections.

Predictive Modeling of Stress in the Healthcare Industry During COVID-19: A Novel Approach Using XGBoost, SHAP Values, and Tree Explainer

There was a substantial medicine shortage and an increase in morbidity due to the second wave of the COVID-19 pandemic in India. This pandemic has also had a drastic impact on healthcare professionals' psychological health as they were surrounded by suffering, death, and isolation. Healthcare practitioners in North India were sent a self-administered questionnaire based on the COVID-19 Stress Scale (N = 436) from March to May 2021. With 10-fold cross-validation, extreme gradient boosting (XGBoost) was used to predict the individual stress levels. XGBoost classifier was applied, and classification accuracy was 88%. The results of this research show that approximately 52.6% of healthcare specialists in the dataset exceed the severe psychiatric morbidity standards. Further, to determine which attribute had a significant impact on stress prediction, advanced techniques (SHAP values), and tree explainer were applied. The two most significant stress predictors were found to be medicine shortage and trouble in concentrating.

Bitesize Epidemiology for General Awareness of All Students — II

This is the second part of a two-part series article. Recently, we have been in the middle of a difficult time due to the Covid-19 pandemic. Pandemics or global epidemics are not new to humankind;they have occurred many times in history. The discourse of epidemiology describes mainly the causal factors which need to be mitigated to prevent or combat the effects of epidemics. In epidemiology, we are not concerned for a person, but rather every individual globally, to make life healthier for all. In this article, we will discuss the basics of epidemiological practice that scientists have used for centuries to prevent epidemics with great results. Overall, we plan for better global health aided by epidemiology. © 2023, Indian Academy of Sciences.

Knowledge Practice Gap Among Nurses Towards COVID-19 Patients' Dead Body Care in a Tertiary Care Hospital

COVID-19 due to SARS-CoV-2 is a global pandemic that presents a serious challenge from many angles for healthcare professionals. The virus causes a potentially fatal disease that is easily transmitted among patients and caregivers, hence specific dead body care is required for such patients. Our study was conducted to identify knowledge, attitude, and practice regarding COVID-19 dead body care among hospital nursing personnel. A cross sectional survey-based study was performed involving 282 nurses who worked in COVID-19 units during data collection from July 2020 to September 2020. The online structured questionnaire was based on world health organization guidelines, institutional infection control protocols, and course material regarding emerging respiratory diseases including COVID-19. We found that work experience in the COVID-19 unit had a significant impact on knowledge and practice regarding COVID-19 dead body care. Similarly, we observed that training improved the knowledge and practice of nursing personnel regarding dead body care. Good knowledge, attitude, and practice were observed in experienced and trained nurses (p-value <0.005). No significant changes were observed with age, gender, and education qualification. Overall knowledge, attitude, and practice regarding COVID-19 dead body care were moderate to good. Adequate training among nurses should prevent the transmission of disease due to occupational exposure.Copyright © 2023, Science Publications. All rights reserved.

Acute hemorrhagic leukoencephalitis after administration of the first dose of ChAdOx1 nCoV-19 SARS-CoV-2 (COVISHIELDTM) vaccine

Background: Many central and peripheral nervous system complications, following COVID-19 vaccination, have been described. We report an unusual case of central demyelinating disorder, following the administration of the ChAdOx1 nCoV-19 SARS-CoV-2 (COVISHIELDTM) vaccine. Case-report: The 28-year female developed sudden onset headache followed by weakness of the left upper and lower limbs, and gait ataxia. Neurological symptoms developed two weeks after administration of the first dose of the ChAdOx1 nCoV-19 SARS-CoV-2 (COVISHIELDTM) vaccine. Magnetic resonance imaging brain revealed T2/FLAIR hyperintense lesions involving bilateral subcortical white matter, splenium of the corpus callosum, and both cerebellar hemispheres. Few lesions showed blooming on gradient echo sequence suggestive of a hemorrhagic component. Post-contrast T1 images showed mild enhancement of demyelinating lesions. The patient was treated intravenously with methylprednisolone. After 12 weeks of follow-up, there was a substantial improvement in her symptoms. She became independent in all her activities of daily living. Conclusion(s): In conclusion, this is an unusual case of acute hemorrhagic leukoencephalitis following ChAdOx1 nCoV-19 SARS-CoV-2 (COVISHIELDTM) vaccination.Copyright © 2022 The Author(s)

Assessment of Lifestyle Changes and Their Effect on Health among Children of <=15 Years during Covid-19 Pandemic, in North India

Objective: The objectives of the study were: (1) To assess life style changes among children of <=15 years of age during COVID-19 pandemic and (2) to find out the effect of the life style changes on health of children of <=15 years of age. Method(s): The cross-sectional comparative study conducted at department of pediatrics, Vivekananda Polyclinic and Institute of Medical Sciences, Lucknow for duration of 1 year and sample size found to be 276 on calculation by applying the formula. Result(s): Out of 278 children, about 39% (108) were female children. Most of children were studying in primary level classes (52.51%) and most of enrolled children had joint family (66.18%). Level of physical activity reduced significantly due to closure of school and restriction on outdoor activities. Weight of children increased significantly during COVID-19 pandemic seems to be due to decreased in physical activities and consumption of more fast food/fried food (high calorie intake) and sedentary life style. Conclusion(s): During COVID-19 pandemic due to closure of schools and restricted outdoor activities results in decrease level of physical activities, increased consumption of high calorie food and sedentary behavior lead to increase in weight of children and changes in sleeping pattern of children.Copyright © 2023 The Authors. Published by Innovare Academic Sciences Pvt Ltd.

A Comparative Study of Screen Time and Its Effects in Pre-Covid and Covid-Era in Children of 5 to 15 Years

Objective: The objective of this study was to compare the screen time (ST) in pre-COVID and COVID era in children aged 5-15 years and to analyse the ST effect in pre-COVID and COVID era in the children. Method(s): The study was done at Vivekananda Polyclinic and Institute of Medical Sciences, Lucknow. Two hundred and seventy-six children aged between 5 and 15 years, attending outpatient department or inpatient department were enrolled in the study. Result(s): It was observed that the ST was significantly increased in post-COVID as compared to pre-COVID time and the difference was statistically significant (p<0.0001*). It was also observed that the screening time was significantly increased in post-COVID as compared to pre-COVID time and the difference was statistically significant (p<0.0001*). Conclusion(s): The present study found that when screening duration was analysed, the screening time during COVID-19 was significantly longer than the screening time before COVID-19 which may be associated with the various health problems reported among children during COVID-19 pandemic.Copyright © 2023 The Authors. Published by Innovare Academic Sciences Pvt Ltd.

Health informatics system using machine learning techniques

A global pandemic is the cause of concern for humanity. The data collection and their analytics are a critical part of research and clinical studies for decision-making activities in the healthcare sector. Healthcare informatics systems and analytics (HCI&A) is a rapidly emerging technology in the medical domain that could be explored for analyzing pandemics like coronavirus disease 2019 (COVID-19). The ethical, legal, and privacy issues to be considered during data collection for research activities. Data governance and data stewardship are required to be addressed during interoperability and interpretation while sharing and reusing the data in collaborative research. The sharing of comprehensive records of clinical data collected by EHRs, also known as electronic health records, to be stored and analyzed on a time-to-time basis. The emerging area of information technology, represented by big data and artificial intelligence (AI) technology, has been widely studied in recent circumstances like COVID-19 for pandemic management. The possibility of using machine learning is explored for better predictive diagnostics and treatment. This chapter discusses the application of artificial intelligence in pandemic management including prevention, diagnosis, treatment, and also critical policy decisions in the COVID-19 pandemic. The methods to collect the digital data of health records are categorized along with few constraints as most of the electronic records related to clinical and epidemiological data are obtained through a shared database such as national and international collaborative informatics infrastructure. The necessity of digital technologies for pandemic emergencies including medical infrastructure reorganization and data workflow model is highlighted. A comparative study of different machine learning models is discussed in the subsequent sections. The digital healthcare informatics envisage a decentralized network architecture and better privacy and security such as blockchain and heterogeneous data collection with machine learning capability are also emphasized. © 2022 River Publishers.

Self-Protection of Medical Workers in Traditional Hospitals and Clinics Against SARS-CoV-2 Infection

Coronavirus Disease 2019 (COVID-19) is the most prevalent infectious human disease spreading in several parts of the world caused by SARS Coronavirus 2 (SARS-CoV-2). COVID-19 transmission is mainly spreading via the respiratory tract, personal contact, digestive tract and hospital-acquired infections. Health care workers particularly working in clinics practicing traditional medicine need to be in close contact with patients, so they have a higher risk of SARS-CoV-2 infection. In this paper, therefore, the personal-protective measures need to be followed by healthcare workers in traditional medicine clinics during COVID-19 pandemic are emphasized, to enlighten them about self-protection and to improve the safety of such a special group of traditional healers.Copyright © 2021 Bentham Science Publishers.

Ayurvedic Herbs Advised for COVID-19 Management: Therapeutic Potential and Clinical Relevance

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide. There is no effective medication for COVID-19 as of now, so it would be good to take preventive measures that not only boost our immunity but also fight against infections. The use of traditional Chinese medicine in China to treat COVID-19 patients sets the prototype demonstrating that traditional medicines can contribute to prevention and treatment successfully. In India, the Ministry of AYUSH (Ayurveda, Yoga, Unani, Siddha, Homeop-athy) released a self-care advisory during the COVID-19 crisis as a preventive aspect. This review article discusses the therapeutic potential and clinical relevance of some herbs [(Tulsi (Ocimum sanctum), Haridra (Curcuma longa), Tvaka (Cinnamon), Maricha (Piper longum), Shunthi (Zingi-ber officinale), Munakka (Dried grapes), Lavang (Syzigiumaromaticum), Pudina (Mentha arvensis), and Ajwain (Trachyspermum ammi)] advised by AUYSH to take during COVID-19 infection. They are effective in COVID-19 management, therefore, authors have discussed their detailed traditional uses as therapeutics and spotted scientific insight and clinical significance of the herbs mentioned above along with their mechanistic viewpoint, adequately, on a single platform. Provided information could be a treasure to open up a new research arena on natural products to manage human health crises effectively, caused not only by COVID-19 but also by other infectious diseases.Copyright © 2023 Bentham Science Publishers.

Role of Liver And Kidneys In The COVID-19 Patients In A Tertiary Care Hospital

Corona virus pandemic started in 2019, is due to severe acute respiratory syndrome coronavirus 2 (SARS-Co V-2), that belongs to the family of coronaviruses and primarily affects the respiratory system. This disease affected millions of people across the world since 2020. This has wide spectrum clinical infection leading to affect in liver and Kidney. So, this study was conducted to illustrate severity of affect of this virus to liver in kidneys in CO VID-19 patients. Blood samples was collected from COVID-19 patients after their consent and access for Liver and Kidney function test in hospital laboratory. The obtained data was statistically analysed with control subjects. Two sample unpaired t-test was done for comparing data with control values obtained. The study was conducted on 50 non co-morbidity COVID-19 patients arriving at tertiary care hospital, Bareilly. And it is compared with LFT & KFT values of 50 healthy subjects. It was obtained that ALT, AS T & BUN values are significantly (P<=0.05) higher in COVID-19 patients. Though the mean values were not high at critical level.By this study we conclude that patients of COVID-19 without co-morbidity don't have critical severity on liver and kidney damage. ALT, AST and BUN could be independent factors for predicting the severity of CO VID-19. Further studies need to be done on these factors.

Need for metacognition and critical thinking in the e-learning ecosystem: The new normal in post Covid era

Present paper aims to extend the legitimacy of social cognition theory in the context of e-learning by examining the relationship between metacognition and critical thinking. In addition, we test the moderating effect of internet self-efficacy. We contextualize this study to e-learning as digital medium has become the new normal in post-Covid era. A sample set of 357 management professionals who have participated in e-learning recently was obtained, and collected data were analyzed using regression analysis. Our analysis confirms that metacognition is positively associated to critical thinking. The findings establish the need to provide management professionals with tools to develop metacognition as it promotes critical thinking prowess in the context of e-learning. © 2023 Wiley Periodicals LLC.

ALPINE: Improving Remote Heart Rate Estimation using Contrastive Learning

Heart rate (HR) is a crucial physiological indicator of human health and can be used to detect cardiovascular disorders. The traditional HR estimation methods, such as electrocardiograms (ECG) and photoplethysmographs, require skin contact. Due to the increased risk of viral in- fection from skin contact, these approaches are avoided in the ongoing COVID-19 pandemic. Alternatively, one can use the non-contact HR estimation technique, remote photo- plethysmography (rPPG), wherein HR is estimated from the facial videos of a person. Unfortunately, the existing rPPG methods perform poorly in the presence of facial deformations. Recently, there has been a proliferation of deep learning networks for rPPG. However, these networks require large-scale labelled data for better generalization. To alleviate these shortcomings, we propose a method ALPINE, that is, A noveL rPPG technique for Improving the remote heart rate estimatioN using contrastive lEarning. ALPINE utilizes the contrastive learning framework during training to address the issue of limited labelled data and introduces diversity in the data samples for better network generalization. Additionally, we introduce a novel hybrid loss comprising contrastive loss, signal-to-noise ratio (SNR) loss and data fidelity loss. Our novel contrastive loss maximizes the similarity between the rPPG information from different facial regions, thereby minimizing the effect of local noise. The SNR loss improves the quality of temporal signals, and the data fidelity loss ensures that the correct rPPG signal is extracted. Our extensive experiments on publicly available datasets demonstrate that the proposed method, ALPINE outperforms the previous well-known rPPG methods. © 2023 IEEE.

Artificial Intelligence in Pharmaceutical and Healthcare Research

Artificial intelligence (AI) is a branch of computer science that allows machines to work efficiently, can analyze complex data. The research focused on AI has increased tremendously, and its role in healthcare service and research is emerging at a greater pace. This review elaborates on the opportunities and challenges of AI in healthcare and pharmaceutical research. The literature was collected from domains such as PubMed, Science Direct and Google scholar using specific keywords and phrases such as ‘Artificial intelligence', ‘Pharmaceutical research', ‘drug discovery', ‘clinical trial', ‘disease diagnosis', etc. to select the research and review articles published within the last five years. The application of AI in disease diagnosis, digital therapy, personalized treatment, drug discovery and forecasting epidemics or pandemics was extensively reviewed in this article. Deep learning and neural networks are the most used AI technologies;Bayesian nonparametric models are the potential technologies for clinical trial design;natural language processing and wearable devices are used in patient identification and clinical trial monitoring. Deep learning and neural networks were applied in predicting the outbreak of seasonal influenza, Zika, Ebola, Tuberculosis and COVID-19. With the advancement of AI technologies, the scientific community may witness rapid and cost-effective healthcare and pharmaceutical research as well as provide improved service to the general public. © 2023 by the authors.