Decarboxylative halogenation of indoles by vanadium haloperoxidases.

Halogenated heteroarenes are key building blocks across numerous chemical industries. Here, we report that vanadium haloperoxidases are capable of producing 3-haloindoles through decarboxylative halogenation of 3-carboxyindoles. This biocatalytic method is applicable to decarboxylative chlorination, bromination, and iodination in moderate to high yields and with excellent chemoselectivity.

A NOVEL AND HYBRID WHALE OPTIMIZATION WITH RESTRICTED CROSSOVER AND MUTATION BASED FEATURE SELECTION METHOD FOR ANXIETY AND DEPRESSION.

BACKGROUND: Anxiety and depression are two leading human psychological disorders. In this work, several swarm intelligence-based metaheuristic techniques have been employed to find an optimal feature set for the diagnosis of these two human psychological disorders. SUBJECTS AND METHODS: To diagnose depression and anxiety among people, a random dataset comprising 1128 instances and 46 attributes has been considered and examined. The dataset was collected and compiled manually by visiting the number of clinics situated in different cities of Haryana (one of the states of India). Afterwards, nine emerging meta-heuristic techniques (Genetic algorithm, binary Grey Wolf Optimizer, Ant Colony Optimization, Particle Swarm Optimization, Artificial Bee Colony, Firefly Algorithm, Dragonfly Algorithm, Bat Algorithm and Whale Optimization Algorithm) have been employed to find the optimal feature set used to diagnose depression and anxiety among humans. To avoid local optima and to maintain the balance between exploration and exploitation, a new hybrid feature selection technique called Restricted Crossover Mutation based Whale Optimization Algorithm (RCM-WOA) has been designed. RESULTS: The swarm intelligence-based meta-heuristic algorithms have been applied to the datasets. The performance of these algorithms has been evaluated using different performance metrics such as accuracy, sensitivity, specificity, precision, recall, f-measure, error rate, execution time and convergence curve. The rate of accuracy reached utilizing the proposed method RCM-WOA is 91.4%. CONCLUSION: Depression and Anxiety are two critical psychological disorders that may lead to other chronic and life-threatening human disorders. The proposed algorithm (RCM-WOA) was found to be more suitable compared to the other state of art methods.

A holistic approach to remote patient monitoring, fueled by ChatGPT and Metaverse technology: The future of nursing education.

ChatGPT and Metaverse are contemporary artificial intelligence tools that are increasingly being used in healthcare professional training, particularly for remote patient monitoring. These technologies offer immersive and personalized learning experiences for nurses, improving their skills and confidence in managing remote patient care. ChatGPT can create simulated patient interactions that mimic real-life scenarios, while the Metaverse can provide virtual reality simulations and scenarios for nurses to practice and learn in a safe and controlled environment. The unification of ChatGPT and Metaverse technology in nursing education can enrich the learning experience and equip nurses with the necessary skills for remote patient monitoring, ultimately leading to improved patient outcomes and quality of care.

Transforming Maritime Health with ChatGPT-Powered Healthcare Services for Mariners.

The maritime industry is vital to international trade, however, it also poses inimitable challenges to the health and well-being of mariners. Long voyages at sea might make it grim to receive high-quality healthcare. This is a descriptive study that highlights the use of ChatGPT in providing healthcare amenities to mariners. AI technologies can revolutionize maritime healthcare to tackle this issue. ChatGPT, a state-of-the-art AI system developed by OpenAI can provide valuable support for the health and welfare of seafarers'. By harnessing the extensive expertise and conversational capacities of ChatGPT, maritime industries can provide personalized and prompt healthcare services to their stakeholders. This research work will highlight how ChatGPT-powered healthcare services can boost the health and well-being of seafarers. ChatGPT has the potential to revolutionize the marine sector by enabling virtual consultations with healthcare professionals for the analysis of health data. The assimilation of ChatGPT technology into maritime healthcare has the potential to revolutionize the way seafarers receive care and support. Certainly, some challenges need to be taken into consideration.

Unusual Magnetic Resonance Imaging Features of Scrub Typhus Encephalitis.

Scrub typhus is an acute febrile illness caused by Orientia tsutsugamushi. The diagnosis of scrub typhus relies on the patient's history of exposure, clinical manifestations, and results of serological tests. Our patient had a history of altered sensorium, inability to walk, and macular rashes predominantly distributed over the chest and bilateral upper limbs. Post serological testing, the patient was referred to the radiology department for MRI brain. Radiologically, MRI being a superior modality helps in the evaluation of lesions in depth, helping to simplify the diagnosis of meningitis, scrub typhus encephalitis, and other related conditions. Various findings have been described in scrub typhus encephalitis in MR brain imaging, and our case shows an unusual finding in brain imaging.

Research and Google Trend for Human Neuropsychiatric Disorders and Machine Learning: A Brief Report.

OBJECTIVES: The key characteristics of this study are to highlight the research trend pertaining to the use of machine learning in the diagnosis and management of neuropsychiatric conditions. METHODS: The last ten years (2011-2020) Scopus data related to the use of machine learning techniques in the diagnosis and management of neuropsychiatric disorders in human beings have been collected and examined using VOSviewer. The global internet trend for neuropsychiatric disorders and machine learning techniques during the observation period (1-Jan-2010 to 30-Nov-2020) has been also explored using Google Trend. RESULTS: The mean values of the Google trend for neuropsychiatric disorders and machine learning are 52.09 and 40.00 respectively. Moreover, the correlation coefficient for the Google trend of USA, UK and the world found to be significantly (0.98) higher. Likewise, the mean values of web trend for USA, UK, and China are 42.17, 38.55, and 30.90. Additionally, the Google trend for the term 'machine learning' in the observation period (1-Jan-2010 to 30-Nov-2020) has been also explored. CONCLUSION: It is observed that the researchers from the US (32.4%), UK (9.2%) and China (7.4%) are the prime contributors as far as mining and management of the neuropsychiatric disorders using machine learning is concerned. Moreover, the study revealed that neuropsychiatric disorders (seizure, eating, mood, sleep, conduct, and intellectual) need more attention as far as machine learning is concerned.

Consequences of Stress on Academic Fraternity: A Meta-analytical Review.

INTRODUCTION: The modern lifestyle and the dynamic environment are progressively inducing stress among a wide range of individuals like students, healthcare professionals, bankers, engineers, businessmen, administrators and teachers. This vitriolic human psychiatric disorder has put the academic fraternity in a shabby state. METHODS: The methodology adopted in this article is comprised of three phases such as review planning, review conducting, and review reporting. RESULTS: The main emphasis of this extensive study is to highlight the major stressors and consequences of stress among the academic fraternity. Firstly, different categories of stress and their repercussions on the human body have been outlined. Additionally, the comprehensive publishing trend of the related manuscripts pertinent to stress among the academic fraternity has also been reported. The research implications and future directions have been also outlined. CONCLUSION: Finally, some major research breaches are analyzed and it is witnessed that there is prospective scope in the diagnosis of stress among students and teachers using emerging soft computing and deep learning techniques.

A comprehensive review and analysis of supervised-learning and soft computing techniques for stress diagnosis in humans.

Stress is the most prevailing and global psychological condition that inevitably disrupts the mood and behavior of individuals. Chronic stress may gravely affect the physical, mental, and social behavior of victims and consequently induce myriad critical human disorders. Herein, a review has been presented where supervised learning (SL) and soft computing (SC) techniques used in stress diagnosis have been meticulously investigated to highlight the contributions, strengths, and challenges faced in the implementation of these methods in stress diagnostic models. A three-tier review strategy comprising of manuscript selection, data synthesis, and data analysis was adopted. The issues in SL strategies and the potential possibility of using hybrid techniques in stress diagnosis have been intensively investigated. The strengths and weaknesses of different SL (Bayesian classifier, random forest, support vector machine, and nearest neighbours) and SC (fuzzy logic, nature-inspired, and deep learning) techniques have been presented to obtain clear insights into these optimization strategies. The effects of social, behavioral, and biological stresses have been highlighted. The psychological, biological, and behavioral responses to stress have also been briefly elucidated. The findings of the study confirmed that different types of data/signals (related to skin temperature, electro-dermal activity, blood circulation, heart rate, facial expressions, etc.) have been used in stress diagnosis. Moreover, there is a potential scope for using distinct nature-inspired computing techniques (Genetic Algorithm, Particle Swarm Optimization, Ant Colony Optimization, Whale Optimization Algorithm, Butterfly Optimization, Harris Hawks Optimizer, and Crow Search Algorithm) and deep learning techniques (Deep-Belief Network, Convolutional-Neural Network, and Recurrent-Neural Network) on multimodal data compiled using behavioral testing, electroencephalogram signals, finger temperature, respiration rate, pupil diameter, galvanic-skin-response, and blood pressure. Likewise, there is a wider scope to investigate the use of SL and SC techniques in stress diagnosis using distinct dimensions such as sentiment analysis, speech recognition, handwriting recognition, and facial expressions. Finally, a hybrid model based on distinct computational methods influenced by both SL and SC techniques, adaption, parameter tuning, and the use of chaos, levy, and Gaussian distribution may address exploration and exploitation issues. However, factors such as real-time data collection, bias, integrity, multi-dimensional data, and data privacy make it challenging to design precise and innovative stress diagnostic systems based on artificial intelligence.

Visualisation and bibliometric analysis of worldwide research trend of stress among seafarer: an extensive publication analysis.

Stress is the most predominant and worldwide psychological human condition that undoubtedly impedes the state of mind of the individuals. During the last few years, a tremendous change and growth in the global publishing trend for human stress have been observed. Like other professionals (doctors, bankers, teachers, and businessmen), the mariners or seafarers are also being the most common victims of this human psychological disorder (stress) these days. The keywords "seafarer" and "stress" have been exposed to mine the research trend of the articles exploring the stress among the seafarers. From 2001 to 2020, more than six thousand (6211) human stress-based articles have been reported in Scopus indexing database. However, the number of articles related to the stress among seafarer is 136 only i.e. 2.18% of the human-stress based articles. The research related to the psychological disorder (stress) among seafarer has been increased during the last two decades. Germany, United Kingdom and Italy found to be the topmost countries for exploring the stress among the seafarer. The study concluded that the human psychological conditions like bulimia, anorexia nervosa, obsessive-compulsive disorder, schizophrenia, generalised anxiety disorder, cyclothymia, mood, conduct, and personality disorders along with stress still need to be extensively mined to diagnose the state of mind of seafarers.

A global investment framework for the elimination of hepatitis B.

BACKGROUND & AIMS: More than 292 million people are living with hepatitis B worldwide and are at risk of death from cirrhosis and liver cancer. The World Health Organization (WHO) has set global targets for the elimination of viral hepatitis as a public health threat by 2030. However, current levels of global investment in viral hepatitis elimination programmes are insufficient to achieve these goals. METHODS: To catalyse political commitment and to encourage domestic and international financing, we used published modelling data and key stakeholder interviews to develop an investment framework to demonstrate the return on investment for viral hepatitis elimination. RESULTS: The framework utilises a public health approach to identify evidence-based national activities that reduce viral hepatitis-related morbidity and mortality, as well as international activities and critical enablers that allow countries to achieve maximum impact on health outcomes from their investments - in the context of the WHO's 2030 viral elimination targets. CONCLUSION: Focusing on hepatitis B, this health policy paper employs the investment framework to estimate the substantial economic benefits of investing in the elimination of hepatitis B and demonstrates how such investments could be cost saving by 2030. LAY SUMMARY: Hepatitis B infection is a major cause of death from liver disease and liver cancer globally. To reduce deaths from hepatitis B infection, we need more people to be tested and treated for hepatitis B. In this paper, we outline a framework of activities to reduce hepatitis B-related deaths and discuss ways in which governments could pay for them.

Global hepatitis C elimination: an investment framework.

WHO has set global targets for the elimination of hepatitis B and hepatitis C as a public health threat by 2030. However, investment in elimination programmes remains low. To help drive political commitment and catalyse domestic and international financing, we have developed a global investment framework for the elimination of hepatitis B and hepatitis C. The global investment framework presented in this Health Policy paper outlines national and international activities that will enable reductions in hepatitis C incidence and mortality, and identifies potential sources of funding and tools to help countries build the economic case for investing in national elimination activities. The goal of this framework is to provide a way for countries, particularly those with minimal resources, to gain the substantial economic benefit and cost savings that come from investing in hepatitis C elimination.