Impact of Azithromycin and 8-Hydroxychloroquine in 2019 Novel Coronavirus (COVID-19) Pandemic: A Systematic Review

As we know novel coronavirus is an emergent nuisance in this stipulated period. Corona virus is a group of enveloped viruses, with non-segmented, single stranded & positive sense RNA genomes. Human Corona virus is mainly subdivided into four categories such as 229E, NL63, OC43, HKU1. Epidemiologically it has a greater prevalence in the modern era. The features encountered in the clinical course of the disease are multifarious spanning from cough, sneezing, fever, breathlessness. It may take 2-14 days for a person to notice symptoms after infection. Azithromycin and 8 Hydroxychloroquine both plays an instrumental role for management of COVID-19. Azithromycin is a macrolide antibiotic and it binds with a 50s ribosome then inhibits bacterial protein synthesis. On the other hand 8-Hydroxychloroquine was approved by United State in the year of 1955 .Basically it is used as a antimalarial drugs . Briefly, in inflammatory conditions it binds with toll like receptor & blocks them. 8- hydroxychloroquine increases lysosomal pH in antigen presenting cells . In inflammatory conditions it blocks toll like receptors on plasmacytoid dendritic cells. In our review we focused on the role of Azithromycin, and 8-hydroxychloroquine in Covid-19 .Copyright © 2021 International Journal of Pharma and Bio Sciences. All rights reserved.

A Novel SARS-COV-2 Variant Omicron Disseminating Evaluation

Omicron is a relatively new form of COVID-19 that has created an unavoidable and life-threatening situation to the entire world since late 2021. Absence of appropriate vaccination, medication, the epidemiological cycle has become more complex. This study primarily concentrates on the analysis of genome sequence for COVID-19 variants. To conduct such analysis, two datasets are collected from Kaggle and GISAID. Using these datasets, the globally existing genome sequences are identified and insights regarding the countries that are carrying significantly higher genome sequence count are provided. This investigation analyzes the worldwide virus variants and further identifies that the United States and United Kingdom are the countries where proper inspection should be provided because of the genome sequence count. An adequate idea regarding the mutations of the Omicron virus is also considered in this study. To address this issue, recent genome sequence data ranging from February, 2022 to 10th March, 2022 is analyzed to understand how the latest arrival, Omicron, is perturbing the world. This study emphasizes on the constant surveillance of genome sequences among all the countries which in turn will benefit the health care professionals and frontline healthcare workers as well as the Governments can take necessary policies and precautions to combat such pandemic. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Curbing Pandemic Through Evolutionary Algorithm-Based Priority Aware Mobility Scheduling

COVID-19 is a global pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus 2. While swift vaccine development and distribution have arrested the infection spread rate, it is necessary to design public policies that inform human mobility to curb outbreaks from future strains of the virus. While existing non-pharmaceutical approaches employing network science and machine learning offer promising travel policy solutions, they are guided by epidemiological and economic considerations alone and not human itineraries. We introduce an evolutionary algorithm (EA) based mobility scheduler that incorporates the personalized itineraries of individuals to determine the ideal timing of their mobility. We mathematically analyze the computational efficiency versus the optimality trade-off of the mobility scheduler. Through extensive simulations, we demonstrate that the EA-based mobility scheduler can balance the trade-off between (1) optimality and computational cost and (2) fair and preferential human mobility while reducing contagion under lockdown and no-lockdown as well as even and uneven human mobility traffic scenarios. We show that for two human mobility models, the scheduler exhibits lower infection numbers than a baseline trip-planning approach that directs human traffic along the least congested route to minimize contagion. We discuss that the EA scheduler lends itself to intricate mobility schedules of multiple destination choices with varying priorities and socioeconomic and demographic considerations.

Combined Ibrutinib and Venetoclax for First-Line Treatment of Patients with Chronic Lymphocytic Leukemia (CLL): Focus on Long-Term MRD Results

Background: Ibrutinib (IBR) and venetoclax (VEN) combination is a highly effective therapy for patients (pts) with CLL (Jain, NEJM 2019;Wierda, ASH 2020;Kater, EHA 2021). We previously reported results of the first-line cohort of a phase II trial of combined IBR and VEN for high-risk pts with CLL (Jain, NEJM 2019;Jain, JAMA Oncology 2021). Here we report updated data for these pts with focus on MRD. Methods: Pts with previously untreated CLL meeting IWCLL treatment criteria were enrolled. All pts had at least one high-risk feature: del(17p), mutated TP53, del(11q), unmutated IGHV, or age ≥65 years (yrs). Pts received IBR 420 mg daily for 3 cycles followed by addition of VEN (weekly dose-escalation to 400mg daily). Combined therapy was given for 24 cycles (28 days/cycle). Pts with bone marrow (BM) undetectable MRD (U-MRD) (flow cytometry;sensitivity 10 -4) at 24 cycles of combined therapy discontinued both VEN and IBR;MRD+ pts continued IBR. A trial amendment allowed an additional 12 cycles of combined VEN and ibrutinib for pts who remained BM MRD+ after Cycle 24. Response assessments were performed using BM and CT imaging studies (2008 IWCLL criteria). U-MRD was defined as <0.01%;low MRD+ 0.01% to <1%;high MRD+ ≥1%. Progression-free survival (PFS) was assessed as the time from the start of study drug to CLL progression, Richter transformation, or death from any cause. Blood MRD was monitored every 6 months in pts off treatment or on ibrutinib monotherapy beyond 24 cycles of combined treatment. Results: A total of 80 pts were enrolled. Baseline characteristics are shown in Table 1. The median follow-up was 44.1 months. Five pts came off study during 1 st 3 cycles of IBR monotherapy;75 pts initiated VEN. We previously reported that after 12 cycles of the combination, 45/80 (56%) achieved BM U-MRD remission;24/80 (30%) were BM MRD-positive (low MRD+, n=19;high MRD+, n=5). After 24 cycles of the combination, 53/80 (66%) achieved BM U-MRD remission;14/80 (17%) were BM MRD+ (low MRD+, n=13;high MRD+, n=1). Overall, 60/80 (75%) achieved BM U-MRD as the best response. Updated PFS is provided in Figure 1. Of the 53 pts who were BM U-MRD at the end of cycle 24 of the combination, 52 pts had a subsequent blood MRD assessment done in follow-up (1 missed due to COVID-19);51/53 discontinued all therapy, 2 pts continued IBR per treatment physician discretion. With a median time of 18.4 months post Cycle 24, 8 pts had recurrence of blood MRD (defined as MRD ≥ 0.01% in 2 consecutive visits) in follow-up with 1 pt with CLL progression. The sole pt with CLL progression had mutated IGHV with del(11q) and NOTCH1 mutation. The pt had delayed achievement of BM U-MRD with the pt achieving U-MRD for the first time at the end of Cycle 24 of combined therapy. She was noted to have disease progression 22 months off therapy;BTK or PLCG2 mutation were not detected and the patient is currently in clinical remission on acalabrutinib. The time to MRD conversion for these 53 pts is shown in Figure 2. There were 14 pts who were BM MRD+ at the end of cycle 24 of the combination (low MRD+, n=13;high MRD+, n=1). The only pt with high-MRD+ at end of cycle 24 was noted to have Richter transformation at that time. The remaining 13 pts (all low MRD+ in BM, range 0.01-0.56%) continued IBR monotherapy. With a recent trial amendment, MRD+ pts after Cycle 24 could get 12 additional cycles of venetoclax;9/13 pts have resumed VEN. 6/9 pts have achieved U-MRD remission. 2 pts had Richter transformation and 3 pts have died (Jain, JAMA Oncology 2021). Conclusions: We report long term follow-up of combined IBR and VEN in first-line CLL. Remissions were durable with some pts having recurrence of blood MRD in follow-up, which may be an early indicator of relapse. In a small subset of the pts with BM MRD+ disease at 24 cycles of combined therapy, additional VEN appears to lead to U-MRD remission in majority of the pts. Whether this will lead to improved long-term PFS remains to be determined. [Formula presented] Disclosures: Jain: TG Therapeutics: Honoraria;Beigene: Honoraria;Janssen: Honoraria;Fate Therapeutics: Research Funding;Aprea Therapeutics: Research Funding;Precision Biosciences: Honoraria, Research Funding;Incyte: Research Funding;Adaptive Biotechnologies: Honoraria, Research Funding;Cellectis: Honoraria, Research Funding;ADC Therapeutics: Honoraria, Research Funding;Servier: Honoraria, Research Funding;Pfizer: Research Funding;Bristol Myers Squibb: Honoraria, Research Funding;AstraZeneca: Honoraria, Research Funding;Genentech: Honoraria, Research Funding;AbbVie: Honoraria, Research Funding;Pharmacyclics: Research Funding. Thompson: AbbVie: Other: Institution: Advisory/Consultancy, Honoraria, Research Grant/Funding;Amgen: Other: Institution: Honoraria, Research Grant/Funding;Genentech: Other: Institution: Advisory/Consultancy, Honoraria, Research Grant/Funding;Adaptive Biotechnologies: Other: Institution: Advisory/Consultancy, Honoraria, Research Grant/Funding, Expert Testimony;Pharmacyclics: Other: Institution: Advisory/Consultancy, Honoraria, Research Grant/Funding;Janssen: Consultancy, Honoraria;Gilead: Other: Institution: Advisory/Consultancy, Honoraria. Ferrajoli: BeiGene: Other: Advisory Board, Research Funding;Janssen: Other: Advisory Board;AstraZeneca: Other: Advisory Board, Research Funding. Burger: Novartis: Other: Travel/Accommodations/Expenses, Speakers Bureau;TG Therapeutics: Other: Travel/Accommodations/Expenses, Research Funding, Speakers Bureau;Janssen: Consultancy, Other: Travel/Accommodations/Expenses, Speakers Bureau;Beigene: Research Funding, Speakers Bureau;Pharmacyclics LLC: Consultancy, Other: Travel/Accommodations/Expenses, Research Funding, Speakers Bureau;Gilead: Consultancy, Other: Travel/Accommodations/Expenses, Research Funding, Speakers Bureau;AstraZeneca: Consultancy. Borthakur: GSK: Consultancy;ArgenX: Membership on an entity's Board of Directors or advisory committees;University of Texas MD Anderson Cancer Center: Current Employment;Protagonist: Consultancy;Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees;Astex: Research Funding;Ryvu: Research Funding;Takeda: Membership on an entity's Board of Directors or advisory committees. Takahashi: Symbio Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees;Novartis: Consultancy;Celgene/BMS: Consultancy;GSK: Consultancy. Sasaki: Daiichi-Sankyo: Membership on an entity's Board of Directors or advisory committees;Pfizer: Membership on an entity's Board of Directors or advisory committees;Novartis: Consultancy, Research Funding. Kadia: Cellonkos: Other;Aglos: Consultancy;Dalichi Sankyo: Consultancy;AbbVie: Consultancy, Other: Grant/research support;BMS: Other: Grant/research support;Amgen: Other: Grant/research support;Cure: Speakers Bureau;Jazz: Consultancy;Genentech: Consultancy, Other: Grant/research support;Liberum: Consultancy;Novartis: Consultancy;Pfizer: Consultancy, Other;Pulmotech: Other;Sanofi-Aventis: Consultancy;AstraZeneca: Other;Astellas: Other;Genfleet: Other;Ascentage: Other. Konopleva: Sanofi: Other: grant support, Research Funding;Cellectis: Other: grant support;Calithera: Other: grant support, Research Funding;KisoJi: Research Funding;Agios: Other: grant support, Research Funding;Ascentage: Other: grant support, Research Funding;AbbVie: Consultancy, Honoraria, Other: Grant Support, Research Funding;Ablynx: Other: grant support, Research Funding;Stemline Therapeutics: Research Funding;Eli Lilly: Patents & Royalties: intellectual property rights, Research Funding;AstraZeneca: Other: grant support, Research Funding;Rafael Pharmaceuticals: Other: grant support, Research Funding;Genentech: Consultancy, Honoraria, Other: grant support, Research Funding;F. Hoffmann-La Roche: Consultancy, Honoraria, Other: grant support;Forty Seven: Other: grant support, Research Funding;Novartis: Other: research funding pending, Patents & Royalties: intellectual property rights;Reata Pharmaceuticals: Current holder of stock options in a privately-held company, Patents & Royalties: intellectual property rights. Alvarado: BerGenBio: Research Funding;Jazz Pharmaceuticals: Research Funding;Astex Pharmaceuticals: Research Funding;Sun Pharma: Consultancy, Research Funding;MEI Pharma: Research Funding;FibroGen: Research Funding;Daiichi-Sankyo: Research Funding;CytomX Therapeutics: Consultancy. Yilmaz: Pfizer: Research Funding;Daiichi-Sankyo: Research Funding. DiNardo: Notable Labs: Current holder of stock options in a privately-held company, Membership on an entity's Board of Directors or advisory committees;Novartis: Honoraria;Takeda: Honoraria;Celgene, a Bristol Myers Squibb company: Honoraria, Research Funding;Forma: Honoraria, Research Funding;AbbVie: Consultancy, Research Funding;GlaxoSmithKline: Membership on an entity's Board of Directors or advisory committees;Bristol Myers Squibb: Honoraria, Research Funding;ImmuneOnc: Honoraria, Research Funding;Agios/Servier: Consultancy, Honoraria, Research Funding;Foghorn: Honoraria, Research Funding. Bose: Kartos Therapeutics: Honoraria, Research Funding;Sierra Oncology: Honoraria;Novartis: Honoraria;Constellation Pharmaceuticals: Research Funding;NS Pharma: Research Funding;Celgene Corporation: Honoraria, Research Funding;Blueprint Medicines: Honoraria, Research Funding;Pfizer: Research Funding;Promedior: Research Funding;Astellas: Research Funding;Incyte Corporation: Honoraria, Research Funding;BMS: Honoraria, Research Funding;CTI BioPharma: Honoraria, Research Funding. Pemmaraju: Blueprint Medicines: Consultancy;LFB Biotechnologies: Consultancy;Novartis Pharmaceuticals: Consultancy, Other: Research Support, Research Funding;ASCO Leukemia Advisory Panel: Membership on an entity's Board of Directors or advisory committees;Dan's House of Hope: Membership on an entity's Board of Directors or advisorycommittees;Roche Diagnostics: Consultancy;MustangBio: Consultancy, Other;Affymetrix: Consultancy, Research Funding;Samus: Other, Research Funding;ImmunoGen, Inc: Consultancy;ASH Communications Committee: Membership on an entity's Board of Directors or advisory committees;Aptitude Health: Consultancy;Plexxicon: Other, Research Funding;Springer Science + Business Media: Other;Protagonist Therapeutics, Inc.: Consultancy;HemOnc Times/Oncology Times: Membership on an entity's Board of Directors or advisory committees;Clearview Healthcare Partners: Consultancy;Abbvie Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding;CareDx, Inc.: Consultancy;Sager Strong Foundation: Other;Daiichi Sankyo, Inc.: Other, Research Funding;Incyte: Consultancy;Stemline Therapeutics, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding;Bristol-Myers Squibb Co.: Consultancy;DAVA Oncology: Consultancy;Pacylex Pharmaceuticals: Consultancy;Celgene Corporation: Consultancy;Cellectis S.A. ADR: Other, Research Funding. Jabbour: Amgen, AbbVie, Spectrum, BMS, Takeda, Pfizer, Adaptive, Genentech: Research Funding. Wang: Stemline Therapeutics: Honoraria. Kantarjian: Taiho Pharmaceutical Canada: Honoraria;Precision Biosciences: Honoraria;Immunogen: Research Funding;Daiichi-Sankyo: Research Funding;Jazz: Research Funding;BMS: Research Funding;AbbVie: Honoraria, Research Funding;Pfizer: Honoraria, Research Funding;Novartis: Honoraria, Research Funding;NOVA Research: Honoraria;KAHR Medical Ltd: Honoraria;Ipsen Pharmaceuticals: Honoraria;Astra Zeneca: Honoraria;Astellas Health: Honoraria;Aptitude Health: Honoraria;Amgen: Honoraria, Research Funding;Ascentage: Research Funding. Wierda: Juno Therapeutics: Research Funding;AstraZeneca: Research Funding;Xencor: Research Funding;Janssen: Research Funding;Loxo Oncology, Inc.: Research Funding;Cyclacel: Research Funding;Oncternal Therapeutics, Inc.: Research Funding;Miragen: Research Funding;KITE Pharma: Research Funding;Sunesis: Research Funding;Gilead Sciences: Research Funding;Acerta Pharma Inc.: Rese rch Funding;Pharmacyclics LLC, an AbbVie Company: Research Funding;Karyopharm: Research Funding;Genentech: Research Funding;GSK/Novartis: Research Funding;Genzyme Corporation: Consultancy;AbbVie: Research Funding. OffLabel Disclosure: The combination of ibrutinib and venetoclax is not FDA approved

Pharmacy Impact for Distinguishing Normal Face from Abnormal Face Due to COVID-19

In today's world face detection is the most important task. Due to the chromosomes disorder sometimes a human face suffers from different abnormalities. In the recent scenario, the entire globe is facing enormous health risks occurred due to Covid-19. To fight against this deadly disease, consumption of drugs is essential. Consumption of drugs may provide some abnormalities to human face. For example, one eye is bigger than the other, cliff face, different chin-length, variation of nose length, length or width of lips are different, etc. To assess these human face abnormalities, the application of computer vision is favoured in this study. This work analyses an input image of human's frontal face and performs a segregation method to separate the abnormal faces. In this research work, a method has been proposed that can detect normal or abnormal faces from a frontal input image due to COVID-19. This method has used Fast Fourier Transformation (FFT) and Discrete Cosine Transformation offrequency domain and spatialdomain analysis to detect those faces.

Overall and cause specific changes in mortality of patients with myelofibrosis over the last 3 decades

Background: Patients with myelofibrosis (MF) have decreased overall survival with a median of 5 years. Leading causes of death are believed to be directly or indirectly related to MF progression or complications, including transformation to acute leukemia (AML) and infections. Aims: We aimed to compare survival, mortality rate and causes of death of MF patients referred to our institution before (≤) and after the years of 2010. Methods: This retrospective study included patients with MF diagnosed within one year prior to their presentation to our institution (between January 1990 and February 2020, 4% presented < year 2000). Patients with unknown cause of death or loss to follow-up were excluded (final cohort, n = 995). Causes of death were divided into groups: i) MF progression;ii) AML (≥ 20% blasts);iii) sudden multi-organ failure or vascular event (possibly related to MF, Vasc/MOF);iii) infections (MF had to be controlled or in remission);iv) other (unrelated medical conditions). We calculated crude mortality rate per 1000 person - years of observation (comparison by Poisson's regression) and overall survival (by Kaplan Meier method with log rank test). To account for variations in age groups (< 49;50-59;60-69;and 70+ years), we assessed age-adjusted mortality rates per 1000 person-years standardized for the entire cohort. Results: The study included 378 and 617 patients ≤ and > 2010, respectively, 59% of which were males. Sixty-five percent of patients were ≥60 years. Distribution of age groups between periods was comparable, except for higher proportion of 70+ year old patients > 2010, counterbalanced by lower proportion of < 60 years. Over the observation time of 1942 and 1608 person-years for ≤ and > 2010, 206 (55%) and 100 (16%) of patients died, respectively (p < 0.001, RR 0.54, CI 0.48-0.63). Crude, age-adjusted and cause-specific mortality per 1000 person-years in both periods is outlined in Table. Overall mortality, both crude and age-adjusted, was significantly higher ≤ 2010 irrespectively of gender and age. As expected, crude mortality steadily increased with age in both periods. For each of the respective age groups, more patients died ≤ 2010 than > 2010. In both periods, males COVID-19 (n= 30 PV, n= 30 ET, n= 30 PMF) (45 men, 45 women, mean age 50 years, range 30-60). All patients gave written informed consent for study enrollment. The mean duration of disease was 12 years. All patients were on ASA 100 mg once daily. Concerning presentation and therapy, our MPNs patients with COVID-19 and without COVID-19 had not comorbidities and drug treatment was consistent with therapeutic standards (hydroxyurea, interferon, anagrelide, ruxolitinib). All patients were on ASA 100 mg once daily. IL-6 was measured by multiplex bead array (Millipore Sigma), TF and DD, and TAT by ELISA and Fib by Clauss method. PF4 was determined by ELISA. Whole blood viscoelastic analysis including clotting time (CT), and clot formation time (CFT) were measured by thomboelastometry method. Results: MPNs with COVID-19 had high IL-6 and TF (50±12pg/ml and 1950±500 pg/ml) compared with MPNs without COVID-19 (3±2pg/ml and 19±2 pg/ml), as well as DD, TAT and Fib (549±100 □g/l, 69±10 □g/l and 590±20mg/dl) compared with MPNs without COVID-19 (59±5□g/l, 2±1□g/l and 149±10 mg/l). PF4 was elevated (150.1□62.7 IU/ml) in MPNs with COVID-19 compared with MPNs without COVID-19 (2□1 UI/ml). A positive correlation was found between inflammatory, endothelial and coagulation mediators. A p-value of <.05 was considered statistically significant. Shortened CT (CT, unit: s. n.v. 100-240 s) (40±20 s) and shortened CFT (CFT, unit: s, n.v. 30-160 s (14±10 s) there were in MPNs with COVID-19 compared with MPNs without COVID-19 (CT 99±10 s and CFT 39±5 s). Summary/Conclusion: These data suggest that COVID-19 infection in MPNs patients may increase the thrombotic risk and get worse prognosis. In our opinion, this study can serve as a baseline study of COVID-19 thrombotic risk in MPNs and it is worthy of dissemina ion amongst patients and clinician communities.

Rapid and cost effective covid-19 diagnostic test kit

COVID-19 or Corona Virus disease spreading since December 2019 starting from Wuhan China has now become a pandemic throughout the globe. For early detection of the infected persons prior to the onset of the disease has become necessary to stop the spreading of this communicable disease. The process of testing of respiratory COVID-19 and the associated SARS-COV-2 virus is proved to be possible only by the detection of antibody produced due to response to the infection. This process of antibody detection can be used for both diagnosis and population surveillance. The testing of generated antibodies due to SARS-COV-2 are antibody isotopes that are to be detected are IgG and IgM . Due to having novel antigens the first responding antigen antibody interactions was IgM. Antibodies that show higher affinity for more specifically binding capable antigens leading to proper immune response is IgG. Due to the infectious reactions IgG antibodies are produced. Positiveness of a sample is considered if both IgG and IgM are present. Whole blood, serum or plasma specimens are used for membrane based immunoassay in a qualitative IgG and IgM test kit. The principle for the testing is same that of a HCG pregnancy test where human glycoprotein is assayed in a rapid chromatographic way. The mentioned process is very useful for mass detection of the COVID-19 infected population in a minimum time and is also safe for the laboratory technicians as it can easily be done without getting infected if proper physical barrier are taken personally. © 2021, Universitatea de Vest Vasile Goldis din Arad. All rights reserved.

Opportunities of Adopting AI-Powered Robotics to Tackle COVID-19

The coronavirus disease (COVID-19) pandemic has made a dire requirement for traditional and disruptive technologies to react to the flare-up across health and wellbeing areas, and technologies such as AI and robotics have been recognized as promising ways to tackle the current challenges. The COVID-19 pandemic has exhibited the solid capability of different advanced technologies that have been tried during the emergency. However, acceptability and adoptability of the latest technologies may face serious challenges due to potential conflicts with users' cultural, moral, and religious backgrounds. This paper discusses the current opportunities and challenges with respect to artificial intelligence (AI) powered robots to battle COVID-19. To diminish the danger of contamination and infection, the opportunities must be utilized during this pandemic for a better future. More deliberate measures ought to be executed to guarantee that future robotic health initiatives will have a greater impact on the pandemic and meet the most key needs to facilitate the life of individuals who are at the forefront of the crisis. © 2021 IEEE.

Automatic Health Machine for COVID-19 and Other Emergencies

The novel Corona Virus (COVID-19) is a pandemic of unimaginable proportion and magnitude that is posing a great challenge worldwide to the medical industry in the 21st century. It has completely changed the texture of life to a greater extent. The increasing number of victims succumbing to the disease has created an indelible fear in the minds of the people who are afraid to access even the basic healthcare facilities. This paper deals with the Automatic Health Machine (AHM) which uses IoT and Artificial Intelligence technologies to help users access medical facilities during a pandemic and medical emergency mostly in rural and urban areas. The AHM provides complete virtual health checkup, connects with the doctor or specialist online and books appointments for the swab test or ambulance in case of emergency based on the patient's condition, dispenses the swab test or emergency medicines and electronic prescription to patients for later reference. The services of the AHM are accessible to all individuals using 'Smart Health Card'. According to the Sustainable Development Goals (SDG3) proposed by the United Nations, the AHM ensures the wellbeing of all ages in society and increases the survival rate during unprecedented times like a pandemic/epidemic. We collaborated with industries and hospitals to understand healthcare/patients' requirements considering pandemic and post-pandemic. We conducted virtual workshops with the COVID-recovered patients and frontline nurses and doctors. As an overall outcome, the healthcare professionals feel the system can be adopted in an area where medical facility is not available immediately. Thus, our work has led to a patent being published in India and USA. © 2021 IEEE.

AutoImpilo: Smart Automated Health Machine using IoT to Improve Telemedicine and Telehealth

This paper deals with the modalities of the Smart Automated Health Machine (AHM) using Internet of Things (IoT), a user-friendly health machine with an interactive graphical user interface for medical necessities. It is a virtual health check-up/self-screening/test system, aimed at being the first point of contact for patient screening, to monitor heart rate, blood pressure, ECG, oxygen saturation, and visual acuity of patients. In case of emergency, a doctor will be available online through a video call, based on the severity of patients' conditions, a call can be placed by a doctor to book an ambulance van or ambulance bike (based on the conditions). If a patient is unconscious, a direct call can be placed by any bystander or patient's carer to book an ambulance. In non-emergency cases, medical appointments can be booked - the system will also dispense medicines based on the health conditions and/or doctor's electronic prescription which can be selected among the list of available medicines. Each individual will be provided with a "smart health card", which will keep a record of patients' personal details, health conditions, prescriptions, and amount of tablets consumed. The AHM will provide all the necessary information, keep a record of different medical problems in different regions and different necessities required to be adopted. The system can also be called as "Self Healthcare Service (SHS)"or "Self Healthcare Units (SHU)"-the initiatives are part of sustainable development goals (SDG-3) proposed by United Nations.We conducted a pilot evaluation with the patients, nurses and doctors. As an overall outcome, the practitioners feel the system can be adopted in an area where medical facility is not available immediately. Adopting this system in such regions not only help in medical emergencies/epidemic/pandemic such as COVID, it also increases the percentage of survival. The system can also be adopted in hospitals, pharmacies, schools, public places like bus/train stations, airports, markets or any commercial areas, zones prone to accidents in rural and urban areas and other places that can be quite useful and helpful to the public. © 2020 IEEE.