Real-Time Tracking of Health Parameters Using IOT and Data Logger: Application to COVID-19

There is an alarming upward trend in COVID-19 cases, and the existing healthcare system is unable to cater the day-to-day requirements from testing followed by appropriate patient care. The use of monitoring devices has found the importance of detecting the presence of the virus early. In order to expand the exciting monitoring system, every technocrat needed to come forward and donate his money to this. Authors as a responsible persons deemed it proper to work on the development design of multipurpose all in one reliable device to help the front like warriors in identifying persons with COVID-19 symptoms like temperature, heartbeat, humidity and positioning real time. After putting in an extra effort, author has succeeded in translating my idea into action. A device that is so well-designed and will increase the medical brotherhood effort to continue monitoring patient health parameters using a variety of sensors connected to the Arduino board. The generated data is then transmitted via Wi-Fi module to IOT platform, i.e., ThingSpeak and which can be monitored on devices like desktop, laptop or smartphone. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Performance of micro and small foodpreneurs in Manipur during COVID-19 pandemic

To critically assess the performance of micro and small foodpreneurs during COVID-19 pandemic in Manipur state of India, an online survey was conducted during the year 2020. The survey data was subjected to non-parametric statistical analysis using PROC NPAR1WAY and Dwass, Steel, Critchlow-Ffigner multiple comparison analysis. The findings suggested that during COVID-19 pandemic, the micro and small foodpreneurs faced multifaceted challenges with respect to the number of employees, working hours, supply of raw material, selling mode, production, sale, turnover and the extent of loss, etc. The performance of different categories of foodpreneurs was compared based on the change in various performance indicators between pre-pandemic and pandemic period. The study also highlights the major problems faced by the foodpreneurs and remedial measures. This is the first report on the impact of COVID-19 pandemic on foodpreneurs from North East India.

Myeloid-derived suppressor cells as a potential biomarker and therapeutic target in rhino-orbital mucormycosis patients

Background: Mucormycosis is a deadly fungal infection that emerges in patients affected with COVID-19. All fungal illnesses are caused by dysregulated adaptive immunity, but Myeloid-derived suppressor cells (MDSC) have added a new di-mension to the chronic inflammatory response. Objective(s): We attempted to enumerate the MDSC immune response in rhino-orbital mucormycosis patients before and after treatment and compared the data with healthy control. Method(s): A total of 3 ml of blood samples were taken in an EDTA vial from 20 patients with mucormycosis and 20 age-matched healthy control. A second blood sample was collected to examine the immune system post three months of treatment. Mycological identification was performed on nasal crust retrieved aftersurgery using KOH/culture.The expression of the MDSC marker was analyzed by immunostaining with the antibodies against CD14, HLA-DR, CD11b, CD33, CD66 (Biolegend). Flu-orescence profiles were recorded by Flow Cytometer (BD FACSAria TM III) and analyzed by Flow Jo s oftware (BD Biosciences). The percentage of positive cells is used to express the results.The GraphPad Prism (version 8, GraphPad s oftware, LaJolla, CA, USA) was used to analyze the data. All of the results were considered significant when P <.05. Result(s): All of the patients tested positive for Rhizopus arrhizus, which was confirmed by the culture. The percentages of Monocytic-MDSC (mMDSC: CD14 + HLA-DR-/low) cells were significantly high in patients compared to healthy control. In post-3-month treatment, the percentages of mMDSC were found significantly low and comparable with healthy control. Granulocytic MDSC (gMDSC: HLA-DR-/low CD33 + CD11b + CD66 +) cell population was higher in patients compared with healthy control and patients with post-3-month treatment. Conclusion(s): MDSC regulates T cells and other immune cells with a different mode of action. The findings in this study imminently indicatethe mechanism of immunedysregulation involvingMDSCpathways inmucormycosis andprovide evidence that restoration of immune balance causes reduction of MDSCcells may be considered a therapeutic option for long-term benefit.

A Bibliometric Analysis of Literature on Covid-19 and Mental Health

Purpose: The present study analyzed the existing literature related to COVID 19 and Mental Health by using the technique of bibliometric analysis which may serve as guide-map for future researchers and policy makers. Method: Bibliometric analysis is conducted in the present study by using various techniques like citation analysis, co-citation analysis, co-occurrence of keywords, thematic mapping by using visualization of similarities (VOS) viewer open-source software and R-based bibliometrix. Results: The study highlighted the most significant journals, authors, co-cited authors, institutions, keywords co-occurrence, and most cited articles in the area of COVID-19 and Mental Health on the basis of bibliometric analysis of 149 studies taken from the database of Scopus for the past three years (2020– first quarter of 2022). Also, authors identified few relevant themes such as Economic Effects of COVID-19, COVID-19 and its Impact on Healthcare workers, COVID-19 and its Impact on Patients and General Population as important emerging key areas for further research. Conclusion: We have highlighted significant citations, co-citations and keywords co-occurrence to summarize the literature. The present bibliometric study convincingly confirms the effect of COVID 19 pandemic on mental health and provides enough evidence to advocate formulation of strategies to tackle mental health issues. © 2022, MedSci Publications. All rights reserved.

AI-based diagnosis techniques for cardiac disease analysis and predictions

Artificial intelligence (AI) has developed speedily since the late 1980s. Enhancement of medical datasets and outcomes in the last twenty years has resulted in unprecedented improvement in AI-based journals. In addition, with the introduction of unparalleled computational efficiency, the accessibility of AI tools has improved. There are two fundamental tools in AI. The first is machine learning (ML), where organized information like electrophysiology (EP), images, and genetic information are broken down and examined. The second is natural language processing (NLP), where unorganized information is scrutinized. These two AI tools have enhanced strategies, calculations, and applications. Different endeavors and new techniques of AI have been utilized for ailments like cardiovascular disease (CVD), neural disorders, and cancer, among others. Presently, a sophisticated deep learning (DL) technique has instigated exceptional growth of AI in clinical imaging diagnostic frameworks. Thus, this chapter presents pivotal and specialized information about AI-based techniques for predicting, diagnosing, and analyzing cardiac diseases. © 2021 Elsevier Inc. All rights reserved.

Diagnosis of hypercritical chronic pulmonary disorders using dense convolutional network through chest radiography.

Lung-related ailments are prevalent all over the world which majorly includes asthma, chronic obstructive pulmonary disease (COPD), tuberculosis, pneumonia, fibrosis, etc. and now COVID-19 is added to this list. Infection of COVID-19 poses respirational complications with other indications like cough, high fever, and pneumonia. WHO had identified cancer in the lungs as a fatal cancer type amongst others and thus, the timely detection of such cancer is pivotal for an individual's health. Since the elementary convolutional neural networks have not performed fairly well in identifying atypical image types hence, we recommend a novel and completely automated framework with a deep learning approach for the recognition and classification of chronic pulmonary disorders (CPD) and COVID-pneumonia using Thoracic or Chest X-Ray (CXR) images. A novel three-step, completely automated, approach is presented that first extracts the region of interest from CXR images for preprocessing, and they are then used to detects infected lungs X-rays from the Normal ones. Thereafter, the infected lung images are further classified into COVID-pneumonia, pneumonia, and other chronic pulmonary disorders (OCPD), which might be utilized in the current scenario to help the radiologist in substantiating their diagnosis and in starting well in time treatment of these deadly lung diseases. And finally, highlight the regions in the CXR which are indicative of severe chronic pulmonary disorders like COVID-19 and pneumonia. A detailed investigation of various pivotal parameters based on several experimental outcomes are made here. This paper presents an approach that detects the Normal lung X-rays from infected ones and the infected lung images are further classified into COVID-pneumonia, pneumonia, and other chronic pulmonary disorders with an utmost accuracy of 96.8%. Several other collective performance measurements validate the superiority of the presented model. The proposed framework shows effective results in classifying lung images into Normal, COVID-pneumonia, pneumonia, and other chronic pulmonary disorders (OCPD). This framework can be effectively utilized in this current pandemic scenario to help the radiologist in substantiating their diagnosis and in starting well in time treatment of these deadly lung diseases.

REPORT- Varying pattern of blood gases, ferritin, procalcitonin, C-Reactive protein, interleukin 6 and lactate dehydrogenase in Covid-19 patients suffering from diverse Co-Morbid, suspected cytokine storm and periodic effects of antiviral and steroidal treatments

This study depicted varying pattern of inflammatory markers and blood gases of selected SARS Covid-19 patients with triggered cytokine storm, during their stay in ICUs, HDUs, on ventilators for 21 days. All were treated with Antiviral (remdesivir), steroid (dexamethason) and antipyretic (paracetamol) medications. Procalcitonin, PCT, C-reactive protein CRP, Interleukin 6 (IL6) and Lactate dehydrogenase (LDH) blood gases pressure (pO2, pCO2), coagulation (D-Dimer DD) and Iron storage proteins (Ferritin Ft) were analyzed by fully automated analyzers. All biomarkers of each patient category was statistically compared with days 1st, 4th, 7th versus 10th, 14th and 17th days and reported as significant where p<0.05, to assess progression, worsening or recovery status. IL6 (P<0.0224, P< 0.0228) and CRP (P<0.0277) exhibited none or mild statistical significance difference, with the exception of Ferritin (P<0.0185;P<0.0088) and D Dimer (P<0.0086), demonstrating slow recovery, revealing stronger cytokine storming assault. LDH, pCO2 and pO2 exhibited variable significance difference when data of earlier days were compared with recovery phase, thus advocating blended treatment or progressing of disease. Analysis confirms overwhelming pathogenesis of SARS Covid-19 distinctive cytokine storm, which needed to be cautiously monitored as infection progressed using pro-inflammatory biomarkers as indicators of recovery or worsening of the disease.

Profiling of inflammatory biomarkers in mild to critically ill severe acute respiratory syndrome corona virus-19 (SARS Covid-19) patients from Karachi, Pakistan. (Special Issue.)

SARS-Covid-19 infection got spread in many countries and WHO declared it as a serious global Pandemic. Pro-inflammatory cytokines storm generated by Covid-19 infection hyper-activates inflammatory response in host body, resulting in elevated release of inflammatory biomarkers. Present article describes the characteristic profile of these inflammatory and related biomarkers in a total of 48 critically ill Covid-19 patients, (Male = 38, F = 10), with mildly ill to severe, critically ill status and thus grouped accordingly. Inflammatory Biomarkers, Ferritin, ProCalcitonin, CReactive Protein, coagulation marker-D-Dimer, chemical analytes, Protein, Albumin, BUN, Bilirubin, Creatinine, and enzymes, Lactate Dehydrogenase, P-Glutamyl transpeptidases, Alkaline phosphatase were routine analyzed by standard methods described earlier. D-dimer, Ferritin, CRP and Procalcitonin exhibited variable alterations (P<0.05 to P<0.001), more markedly in critically ill patients than in the mild and severe. Biochemical analytes and enzymatic parameters showed elevated levels (P<0.05 to P<0.01) mostly in critically ill category of patients when compared with mild or severe, except total protein and albumin, which remained non-significant. It is concluded that cytokine, chemokines and pro-inflammatory markers, which released in abnormally high concentrations in Covid-19 patients of variable syndrome intensity, are significant indicators of disease severity, progression and success of treatments. As the pharmacological options may vary with the different stages of the disease therefore identifying the correct stage of the disease may be very useful in selecting the best option.