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Ayurveda is called Mother of all medical sciences. It's the oldest therapeutic and medicinal treatment invented in ancient India. Ayurveda or Ayurvedic treatment is bit different from modern medical science. It believes in Nadi Pariksha and many subjective parameters are included to start diagnosis of disease. Whereas modern medical science has different approach of disease diagnosis. It utilizes different tools and testing to diagnose a disease effectively. Saliva analysis is already accepted in modern medical as an important bio-substance, as we see in COVID-19, but not in ayurveda. This paper shows how salivary analysis can act as an evidential proof for diagnosing a disease, in the ayurvedic way. The salivary contents can be analyzed use various biosensors. One of these is Surface Enhanced Raman Spectroscopy (SERS) platform. It allows molecular detection in bio fluids like saliva, sweat, urine, etc. The saliva analysis using SERS technique will help to detect various trace level molecules which is likely to assist the Ayurvedic diagnosis more accurately and dependency on subjective parameters will reduce to evaluate patient's condition. © 2023 IEEE.
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The epidemic caused by a new mutation of the coronavirus family called Covid-19 has created a global crisis involving all the world's countries. This disease has become a severe danger to everyone due to its unknown nature, high spread, and inability to detect the infected. In this regard, one of the important issues facing patients with Covid-19 is the prescription of Drugs according to the severity of the disease and considering the records of underlying diseases in people. In recent years, recommender systems have been developed significantly along with the advancement in information technology and artificial intelligence, which is one of its applications in various fields of medical sciences. Among them, we can refer to recommending systems for the prevention, control, and treatment of diseases. In this research, using the collaborative filtering approach as one of the types of recommender systems as well as the K-means clustering algorithm, a Drug recommendation system for patients with Covid-19 in the treatment stage of the disease is presented. The results of this research show that this recommender system has an acceptable performance based on the evaluation criteria of precision, recall, and F1-score compared to the opinions of experts in this field. © 2023 IEEE.
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The COVID-19 epidemic is running at a high level globally, affecting all aspects of society, and medical education is no exception. With the rapid development of medical science, continuing medical education is an important way for medical workers to receive lifelong education. Meanwhile, attending continuing medical education is an inevitable requirement to ensure clinical ability. Under the background of normalization of epidemic prevention and control and the new situation of medical development, the management of continuing medical education in hospitals must follow the current situation and keep pace with the times. Therefore, the Internet support system to continuing education has emerged. This study used PDSA method to explore the construction of the regional center of continuing medical education through Internet under the background of normalization of epidemic prevention and control, aiming to promote the integration of medical education resources under the new situation, expand the learning channels of medical staff, and improve the level of medical education and teaching. © 2022 IEEE.
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In chemistry and medical sciences, it is essential to study the chemical, biological, clinical, and therapeutic aspects of pharmaceuticals. To save time and money, mathematical chemistry focuses on topological indices used in quantitative structure-property relationship (QSPR) models to predict the properties of chemical structures. The COVID-19 pandemic is widely recognized as the greatest life-threatening crisis facing modern medicine. Scientists have tested various antiviral drugs available to treat COVID-19 disease, and some have found that they help get rid of this viral infection. Antiviral drugs such as Arbidol, chloroquine, hydroxychloroquine, lopinavir, remdesivir, ritonavir, thalidomide, and theaflavin are used to treat COVID-19. In this paper, reformulated leap Zagreb indices are introduced. Then, the reformulated leap Zagreb indices, leap eccentric connectivity indices, and reformulated Zagreb connectivity indices of these antiviral drugs are calculated. Curvilinear and multilinear regression models predicting the physicochemical properties of these antiviral drugs in terms of proposed indices are obtained and analyzed. The findings and models of this study will shed light on new drug discoveries for the treatment of COVID-19.
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Due to the rise of severe and acute infections called Coronavirus 19, contact tracing has become a critical subject in medical science. A system for automatically detecting diseases aids medical professionals in disease diagnosis to lessen the death rate of patients. To automatically diagnose COVID-19 from contact tracing, this research seeks to offer a deep learning technique based on integrating a Bayesian Network and K-Anonymity. In this system, data classification is done using the Bayesian Network Model. For privacy concerns, the K-Anonymity algorithm is utilized to prevent malicious users from accessing patients' personal information. The dataset for this system consisted of 114 patients. The researchers proposed methods such as the K-Anonymity model to remove personal information. The age group and occupations were replaced with more extensive categories such as age range and numbers of employed and unemployed. Further, the accuracy score for the Bayesian Network with k-Anonymity is 97.058%, which is an exceptional accuracy score. On the other hand, the Bayesian Network without k-Anonymity has an accuracy score of 97.1429%. These two have a minimal percent difference, indicating that they are both excellent and accurate models. The system produced the desired results on the currently available dataset. The researchers can experiment with other approaches to address the problem statements in the future by utilizing other algorithms besides the Bayesian one, observing how they perform on the dataset, and testing the algorithm with undersampled data to evaluate how it performs. In addition, researchers should also gather more information from various sources to improve the sample size distribution and make the model sufficiently fair to generate accurate predictions. © 2022 IEEE.
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Due to the increase in world population, a lot of research is being done in the medical sciences. Pandemics and epidemics have multiple outbreaks in many regions of the world. In order to solve the issue, creative probing is being used. Most of the illnesses in the group are obstructive and may result in a loss of life. Heart and lung conditions make up a large portion of the obstructive illnesses in this group. More than 5 lakh people die each year from lung illnesses, generally known as pulmonary disorders, with an equal proportion of men and women affected. Each disease has unique symptoms that are connected to it in the fields of medicine and healthcare. There are several new tests that are being developed to identify each of the dangerous diseases that are on the rise. This results from the necessity for quick illness prediction. This paper examines numerous studies and experiments carried out over a variety of timelines and approaches selected by various experiments, carefully examining the benefits and drawbacks of the approaches in order to construct an appropriate model for the cause. It focuses on the study of diagnosing pulmonary disorders and making the user's task easy in understanding the scanned images obtained. © 2023 IEEE.
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The pandemic is changing the clinical needs and potential for AI-driven computer-assisted diagnoses (CDS). Since the beginning, rapid identification of COVID-19 patients has been a significant difficulty, especially in areas with limited diagnostic testing capacity. Intelligent Information System (IIS) represents the knowledge progression of available data. It has been directed by recent technological integration, data processing, and distribution in multiple computational environments. Intelligent Information Systems are aimed to work like an advanced human brain, where, as per the requirement of changing circumstances, the optimal decision can be evolved. IIS tools are expected to be adaptive, which may vary according to their processing data. As a result, the goal of this study was to provide a complete analysis of various technologies for combating COVID-19, with a focus on their features, problems, and domiciliation nation. Our findings demonstrate the performance of developing technologies. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Sensor technology has become an integral part of the diagnosis, monitoring, therapeutic and surgical areas of medical science. Various sensors like glucose biosensors for diagnosis of diabetes mellitus or fluorescent sensors for gene expression and protein localization have become a common part of the biomedical field. Due to their widespread applications, various advances and improvements have taken place in medical sensor technology which has led to an increase in the ease and accuracy of diagnosis as well as treatment of diseases. This review article aims at studying various novel and innovative developments in biosensors, fibre optic sensors, sensors used for microelectromechanical systems, flexible sensors and wearable sensors. This article also explores new sensing methodologies and techniques in different medical domains like dentistry, robotic surgery and diagnosis of severe life-threatening diseases like cancer and diabetes. Various sensors and systems used for rapid detection of the SARS-CoV-2 virus which is responsible for the COVID-19 pandemic have also been discussed in this article. Comparison of novel sensor-based systems for detection of various medical parameters with traditional techniques is included. Further research is necessary to develop low cost, highly accurate and easy-to-use medical devices with the help of these innovative sensor technologies. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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The field of statistics has a long and rich history of contributions to all sectors of the global economy and society. Our field is a cornerstone of the data and digital revolution we are witnessing today. Consider the role of statisticians in medical science and public health. Statisticians are key to developing new cures for diseases, ensuring patients receive safe and effective medicines, and supporting the delivery of care efficiently and effectively. The contributions of our profession were central to moving the pandemic to its endemic phase, safeguarding public health, and informing our understanding of the day-to-day impact of the pandemic on our populations. Our science and expertise helped with the rapid development, approval, and deployment of vaccines, educating the public and public health officials on the evolution and spatial-temporal extent of the virus, and collecting and communicating people's perspectives as they managed through the pandemic.
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Supramolecular architectures decorated with various conjugated building blocks give rise to numerous luminescent frameworks with interesting chemical and photophysical properties. The luminescence properties of these MOFs help global researchers achieve success in the field of recognition applications of MOFs for the detection of various targeted toxic analytes. In this regard, different MOF-based materials, along with their different host-guest recognition strategies, have been developed, emphasising selective and sensitive natures towards a particular analyte, which indeed helps in protecting our environment. The present review article discusses state-of-the art progress based on (i) advancement of electrochemical MOF-based sensors, (ii) detection of various waterborne pollutants & VOCs, and (iii) recent progress of MOFs in biomedical sciences, with regard to cancer & SARS-CoV-2, along with the advantages and current challenges to combat SARS-CoV-2 for the clinical purposes. Herein, detection of particular analytes along with their interactive mechanisms have been precisely described;however, it needs to be noted that detailed host-guest mechanistic revelations is not the topic of discussion in the present exploration. In this review, we have covered almost the last 14 years (2008-2022) of research on MOFs in the various sensing platforms. In a nutshell, the luminescent MOFs, along with their extraordinary applicability in the domains of chemical, biomedical and environmental arenas as welfare tools, have been studied in the present review article. © 2023 The Royal Society of Chemistry.
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The process of making changes to software after it has been delivered to the client is known as maintainability. Maintainability deals with new or changed client requirements. Service-oriented architecture (SOA) is a method for developing applications that helps services work on different environments. SOA works on patterns of distributed systems that help different applications communicate with each other using different protocols. To assess the maintainability of service-oriented architecture, different factors are required. Some of these factors are analyzability, changeability, stability, and testability. Modification is the process of upgrading the software functionality. After modification of service-oriented architecture, the module will go to the testing phase. The evaluation and verification of whether a software product or application performs as intended is known as testing. The testing phase is a combination of various stages, such as individual module testing and testing after collaborations between them. This testing stage is time-consuming in the maintenance process. The term "outlier" refers to a module in software systems that deviates significantly from the rest of the module. It represents the collection of data, variables, and methods. For instance, the program might have been coded mistakenly or an investigation might not have been run accurately. To detect the outlier module, test cases are needed. A methodology is proposed to reduce the predefined test cases. K-means clustering is the best approach to calculate the number of test cases, but the outlier is not automatically determined. In this paper, a hybrid clustering approach is applied to detect the outlier. This clustering method is used in software testing to count the number of comments in various software and in medical science to diagnose the disease of Covid patients. The experimental outcomes show that our strategy achieves better results.
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Catering to the widespread COVID-19 pandemic, the authors aim to develop a system based on machine learning combined with the knowledge of medical science. Considering the prevailing situation, it becomes necessary to diagnose the COVID-19 at initial stages. The idea behind the described designed model is to identify the spread of infection in patients as fast as possible. The paper sketches two different approaches: K-fold cross-validation and deep network designer which are based on deep learning technology for the prediction of COVID-19 in the initial stages by using the chest X-rays. The performance evaluation of the cross-fold validation process is compared with the designed application in the deep network designer to find an effective and efficient methodology for classification which attained better accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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BACKGROUND: The Journal of Korean Medical Science (JKMS) is a weekly periodical published by the Korean Academy of Medical Sciences. JKMS invites global researchers to submit articles covering various areas in general medicine. The present article's aim was to analyze citations of JKMS articles in 2011-2020 for updating editorial policies. METHODS: Citation records of JKMS articles were tracked in Web of Science (WoS), Clarivate® from August 2021 to June 2022. RESULTS: In 2011-2020, JKMS published 2,880 articles, including 2,757 (96.0%) ever cited. All reviews (57/57) and 96% of original research reports (2,184 out of 2,264) received at least one citation. Brief communications, opinions, and images were least cited items. Of 36 subject categories covered by JKMS, only biomedical engineering was significantly less advantageous citation-wise. Five articles published in 2012-2017 attracted more than 100 citations. Most other articles were cited less than 50 times. Article categories of nationwide epidemiology, disease or patient registries, clinical trials, and infectious diseases were distinguished as well cited. Of 378 articles published in 2020, 10 were cited at least 100 times; all these ten items were related to severe acute respiratory syndrome coronavirus 2 and coronavirus disease 2019. In the past 5 years, studies on health care laws, management, and some specific topics in clinical specialties were not cited. The citation trends in WoS, Crossref, and Scopus were similar while PubMed Central records were roughly twice less. CONCLUSION: Most of JKMS articles are cited during 5 years post publication, with 1.4% non-citation rate. The obtained results suggest that inviting review articles in clinical sciences, research reports on hot medical topics, and nationwide database analyses may attract more author interest and related citations.
Subject(s)
COVID-19 , Medicine , Humans , COVID-19/epidemiology , Bibliometrics , Editorial Policies , Republic of KoreaABSTRACT
The article analyzes unpublished sources stored in the Russian State Archive of Economics (RGAE), the State Archive of the Russian Federation (GARF), and the Russian State Archive in Samara (RGA v Samare) to determine the representativeness of these documents in reconstruction of the scientific biography of the Soviet microbiologist Zinaida Vissarionovna Ermolieva (1898-1974). The coronavirus pandemic, which has engulfed all the humanity, has changed the vector of biomedical research subjects. In this regard, one of the important tasks of humanitarian researchers is to update historical knowledge about extreme periods. The task of studying the life and work of Soviet scientists who made a significant contribution to studying epidemic diseases and fighting them seems significant. Scar.ity of publications devoted to the activities of Z. V. Ermolyeva means that an extensive layer of unpublished documents remains out of view of researchers. Source analysis, archival heuristics, and historical-comparative method permit to conduct research and to assess the informative value of different types of documents for comprehensive reconstruction of Z. V. Ermolyeva's scientific fate. In the RGAE, a great number of documents is concentrated in the scientist's personal fond, its analysis shows that official, scientific, and personal documents are stored there. In addition to text documents, there are photos of Zinaida Vissarionovna with her colleagues in various scientific institutions which are of great interest. Some official documents also provide personal information. In the GARF, documents on this topic are dispersed in different fonds. The fond of the All-Union Institute of Experimental Medicine, where Z. V. Ermolyeva worked, contains information on preparation of first Soviet penicillin and launch of its production. The fond of the People's Commissariat of Health of the USSR permits to follow the discussion of her scientific works at its Scientific Medical Council meetings. The documents of the Committee for the Assistance of Scientists under the Council of People's Commissars of the USSR help to reconstruct elements of material support provided to Ermolyeva in the 1930s. Among the sources stored in the RGA in Samara, of interest are application documents for inventions, in which Zinaida Vissarionovna participated. They show the scientist as the author of inventions and scientific discoveries. The analysis demonstrates that conjunctive use of documents from federal archives showcase multifaceted activities of the well-known microbiologist.
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WATERLIT : Results from a randomised study reported no benefit of daily oral melatonin (2 mg QD) for 12 weeks compared to placebo as prophylaxis against SARS-CoV-2 in 314 Spanish health workers
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WATERLIT : The rapid development of vaccines against COVID-19 led many people to question whether similar investment could have achieved an effective HIV vaccine