N-Halogenation by Vanadium-Dependent Haloperoxidases Enables 1,2,4-Oxadiazole Synthesis.

Nitrogen-containing compounds are valuable synthetic intermediates and targets in nearly every chemical industry. While methods for nitrogen-carbon and nitrogen-heteroatom bond formation have primarily relied on nucleophilic nitrogen atom reactivity, molecules containing nitrogen-halogen bonds allow for electrophilic or radical reactivity modes at the nitrogen center. Despite the growing synthetic utility of nitrogen-halogen bond-containing compounds, selective catalytic strategies for their synthesis are largely underexplored. We recently discovered that the vanadium-dependent haloperoxidase (VHPO) class of enzymes are a suitable biocatalyst platform for nitrogen-halogen bond formation. Herein, we show that VHPOs perform selective halogenation of a range of substituted benzamidine hydrochlorides to produce the corresponding N'-halobenzimidamides. This biocatalytic platform is applied to the synthesis of 1,2,4-oxadiazoles from the corresponding N-acylbenzamidines in high yield and with excellent chemoselectivity. Finally, the synthetic applicability of this biotechnology is demonstrated in an extension to nitrogen-nitrogen bond formation and the chemoenzymatic synthesis of the Duchenne muscular dystrophy drug, ataluren.

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 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.

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.

Pollution assessment and spatial distribution of roadside agricultural soils: a case study from India.

The present work studied the pH, organic carbon, phosphorus (P), calcium (Ca), magnesium (Mg), and heavy metals Cu, Cr, Co and Pb in roadside agricultural soils of Jalandhar environs of Punjab, India. A total of 120 samples in triplicates were collected from different sites for assessment of heavy metal pollution. The mean values of Cu, Cr, Co and Pb were found below the permissible limits of Indian and Swedish soil limits. Principal component analysis (PCA) showed that heavy metals have different sources of origin. The results of contamination factor (CF), geoaccumulation index (Igeo), degree of contamination (Cd) and potential ecological risk index (RI) showed low contamination and ecological risks of heavy metals in roadside agricultural soils, respectively. The maps of spatial analysis indicated that northern region of the study area is more polluted.

Prevalence and Diagnosis of Neurological Disorders Using Different Deep Learning Techniques: A Meta-Analysis.

This paper dispenses an exhaustive review on deep learning techniques used in the prognosis of eight different neuropsychiatric and neurological disorders such as stroke, alzheimer, parkinson's, epilepsy, autism, migraine, cerebral palsy, and multiple sclerosis. These diseases are critical, life-threatening and in most of the cases may lead to other precarious human disorders. Deep learning techniques are emerging soft computing technique which has been lucratively used to unravel different real-life problems such as pattern recognition (Face, Emotion, and Speech), traffic management, drug discovery, disease diagnosis, and network intrusion detection. This study confers the discipline, frameworks, and methodologies used by different deep learning techniques to diagnose different human neurological disorders. Here, one hundred and thirty-six different articles related to neurological and neuropsychiatric disorders diagnosed using different deep learning techniques are studied. The morbidity and mortality rate of major neuropsychiatric and neurological disorders has also been delineated. The performance and publication trend of different deep learning techniques employed in the investigation of these diseases has been examined and analyzed. Different performance metrics like accuracy, specificity, and sensitivity have also been examined. The research implication, challenges and the future directions related to the study have also been highlighted. Eventually, the research breaches are identified and it is witnessed that there is more scope in the diagnosis of migraine, cerebral palsy and stroke using different deep learning models. Likewise, there is a potential opportunity to use and explore the performance of Restricted Boltzmann Machine, Deep Boltzmann Machine and Deep Belief Network for diagnosis of different human neuropsychiatric and neurological disorders.

Antagonistic effects of EDTA against biochemical toxicity induced by Cr(VI) in Hordeum vulgare L. seedlings.

The present study aims at the amelioration of chromium Cr(VI) toxicity using ethylenediaminetetraacetic acid (EDTA), and to understand the interactive effects of Cr(VI) and EDTA with respect to seedling growth, lipid peroxidation as assessed from malondialdehyde, pigments and antioxidative enzymes in Hordeum vulgare L. Following multivariate statistical techniques were used to study binary interactions between Cr(VI) and EDTA: 2-way ANOVA, Tukey's multiple comparison test, multiple regression with interaction between Cr an EDTA, beta coefficients, path analysis and non-metric multidimensional scaling (NMDS). The present study revealed that the EDTA decreases lipid peroxidation induced by Cr(VI) and ameliorates the antioxidative defence system and pigment constitution of seedlings grown in Cr(VI) containing media. EDTA-Cr(VI) interaction decreased the Cr content in the seedlings which may be attributed to the chelating effect of EDTA. The root and shoot bioconcentration factors, the ratio of Cr content in the plant to that in the medium, were decreased by addition of EDTA to Cr(VI), indicating a decrease in the uptake of Cr by the seedlings from the medium. NMDS revealed that the ranking of the studied parameters is maintained by ordination on two axes. The study established that EDTA is antagonistic to Cr(VI) induced biochemical toxicity, and improves the antioxidative defence system, increases the chlorophyll content, and decreases Cr uptake in barley seedlings.

Feasibility of 4D CT simulation with synchronized intravenous contrast injection in hepatocellular carcinoma.

BACKGROUND: Delivering Stereotactic Body Radiotherapy (SBRT) for Hepatocellular Carcinoma (HCC) is challenging mainly for two reasons: first, motion of the liver occurs in six degrees of freedom and, second, delineation of the tumor is difficult owing to a similar density of HCC to that of the adjoining healthy liver tissue in a non-contrast CT scan. To overcome both these challenges simultaneously, we performed a feasibility study to synchronize intravenous contrast to obtain an arterial and a delayed phase 4D CT. MATERIALS AND METHODS: We included seven HCC patients of planned for SBRT. 4D CT simulation was performed with synchronized intravenous contrast based on the formula TSCAN DELAY = T peak - (L0/Detector Coverage × Cine Duration in Seconds). This was followed by a delayed 4D CT scan. RESULTS: We found that, with our protocol, it is feasible to obtain a 4DCT with an arterial and a delayed phase making it comparable to a diagnostic multi-phase CT. The peak HU of the 4D scan and diagnostic CT were similar (mean peak HU 134.2 vs 143.1, p value = 0.58 N.S). Whereas in comparison with a non-contrast CT a significant rise in the peak HU was seen (mean peak 134.2 vs 61.4 p value = .00003). CONCLUSION: A synchronized contrast 4D CT simulation for HCC is safe and feasible. It results in good contrast enhancement comparable to a diagnostic 3D contrast CT scan.

Diagnosis of Human Psychological Disorders using Supervised Learning and Nature-Inspired Computing Techniques: A Meta-Analysis.

A psychological disorder is a mutilation state of the body that intervenes the imperative functioning of the mind or brain. In the last few years, the number of psychological disorders patients has been significantly raised. This paper presents a comprehensive review of some of the major human psychological disorders (stress, depression, autism, anxiety, Attention-deficit hyperactivity disorder (ADHD), Alzheimer, Parkinson, insomnia, schizophrenia and mood disorder) mined using different supervised and nature-inspired computing techniques. A systematic review methodology based on three-dimensional search space i.e. disease diagnosis, psychological disorders and classification techniques has been employed. This study reviews the discipline, models, and methodologies used to diagnose different psychological disorders. Initially, different types of human psychological disorders along with their biological and behavioural symptoms have been presented. The racial effects on these human disorders have been briefly explored. The morbidity rate of psychological disordered Indian patients has also been depicted. The significance of using different supervised learning and nature-inspired computing techniques in the diagnosis of different psychological disorders has been extensively examined and the publication trend of the related articles has also been comprehensively accessed. The brief details of the datasets used in mining these human disorders have also been shown. In addition, the effect of using feature selection on the predictive rate of accuracy of these human disorders is also presented in this study. Finally, the research gaps have been identified that witnessed that there is a full scope for diagnosis of mania, insomnia, mood disorder using emerging nature-inspired computing techniques. Moreover, there is a need to explore the use of a binary or chaotic variant of different nature-inspired computing techniques in the diagnosis of different human psychological disorders. This study will serve as a roadmap to guide the researchers who want to pursue their research work in the mining of different psychological disorders.

Mean Platelet Volume, Red Cell Distribution Width to Platelet Count Ratio, Globulin Platelet Index, and 16 Other Indirect Noninvasive Fibrosis Scores: How Much Do Routine Blood Tests Tell About Liver Fibrosis in Chronic Hepatitis C?

BACKGROUND AND AIM: Many indirect noninvasive scores to predict liver fibrosis are calculated from routine blood investigations. Only limited studies have compared their efficacy head to head. We aimed to compare these scores with liver biopsy fibrosis stages in patients with chronic hepatitis C. MATERIALS AND METHODS: From blood investigations of 1602 patients with chronic hepatitis C who underwent a liver biopsy before initiation of antiviral treatment, 19 simple noninvasive scores were calculated. The area under the receiver operating characteristic curves and diagnostic accuracy of each of these scores were calculated (with reference to the Scheuer staging) and compared. RESULTS: The mean age of the patients was 41.8±9.6 years (1365 men). The most common genotype was genotype 4 (65.6%). Significant fibrosis, advanced fibrosis, and cirrhosis were seen in 65.1%, 25.6, and 6.6% of patients, respectively. All the scores except the aspartate transaminase (AST) alanine transaminase ratio, Pohl score, mean platelet volume, fibro-alpha, and red cell distribution width to platelet count ratio index showed high predictive accuracy for the stages of fibrosis. King's score (cutoff, 17.5) showed the highest predictive accuracy for significant and advanced fibrosis. King's score, Göteborg university cirrhosis index, APRI (the AST/platelet count ratio index), and Fibrosis-4 (FIB-4) had the highest predictive accuracy for cirrhosis, with the APRI (cutoff, 2) and FIB-4 (cutoff, 3.25) showing the highest diagnostic accuracy.We derived the study score 8.5 - 0.2(albumin, g/dL) +0.01(AST, IU/L) -0.02(platelet count, 10(9)/L), which at a cutoff of >4.7 had a predictive accuracy of 0.868 (95% confidence interval, 0.833-0.904) for cirrhosis. CONCLUSIONS: King's score for significant and advanced fibrosis and the APRI or FIB-4 score for cirrhosis could be the best simple indirect noninvasive scores.

Emerging role for colorectal cancer screening in Asian countries.

BACKGROUND AND AIM: Colorectal cancer (CRC) is one of the leading causes of cancer related mortality globally. Though Asia has traditionally been considered a relatively low incidence area for colorectal cancer, the incidence is reportedly increasing. The Asia Pacific Working Group for Colorectal Cancer has recommended screening of individuals at average risk starting from 50 years of age. Based on these recommendations we conducted a pilot study to assess the need and feasibility of a colorectal cancer screening program in the state of Qatar. METHODS AND RESULTS: We screened 1385 individuals by fecal immunochemical testing for occult blood, at the primary health center level and positive cases were referred for colonoscopy. Among those who tested positive for fecal occult blood, we picked up five patients with cancers and seven with neoplastic polyps. CONCLUSION: Our results compare with the yield of screening programs in western countries thus suggesting an emerging role for colorectal cancer screening in Asian countries.

Uncommon gastrointestinal complications of enteric fever in a non-endemic country.

Enteric fever is a systemic illness with varying presentation. It is an important infectious disease in developing countries and also in industrialized countries where many migrants reside. Enteric fever can result in complications in different organ systems and delay in identification and prompt treatment can be fatal. The important gastrointestinal complications of enteric fever include hepatitis, intestinal ulcers, bleeding and bowel perforation. We report three relatively uncommon complications of enteric fever in Qatar, a non-endemic country, ileal ulcer presenting with hematochezia; duodenal ulcer with polyserositis, cholestatic hepatitis and bone marrow suppression; enteric fever related peritonitis.

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.

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.