Deploying the Minimum Number of Rechargeable UAVs for a Quarantine Barrier

To control the rapid spread of COVID-19, we consider deploying a set of Unmanned Aerial Vehicles (UAVs) to form a quarantine barrier such that anyone crossing the barrier can be detected. We use a charging pile to recharge UAVs. The problem is scheduling UAVs to cover the barrier, and, for any scheduling strategy, estimating theminimum number of UAVs needed to cover the barrier forever. We propose breaking the barrier into subsegments so that each subsegment can be monitored by a single UAV. We then analyze two scheduling strategies, where the first one is simple to implement and the second one requires fewer UAVs. The first strategy divides UAVs into groups with each group covering a subsegment. For this strategy, we derive a closed-form formula for the minimum number of UAVs. In the case of insufficient UAVs, we give a recursive function to compute the exact coverage time and give a dynamic-programming algorithm to allocate UAVs to subsegments to maximize the overall coverage time. The second strategy schedules all UAVs dynamically. We prove a lower and an upper bound on the minimum number of UAVs. We implement a prototype system to verify the proposed coverage model and perform simulations to investigate the performance.

A case series of diabetic ketoacidosis associated with SGLT2 inhibitors

Sodium-glucose cotransporter 2 inhibitors (SGLT2i) are increasingly initiated as treatment for type 2 diabetes due to favourable cardiorenal characteristics. However, studies have identified an increased risk of diabetic ketoacidosis (DKA). We carried out a retrospective, case-based study at East and North Herts NHS Trust between February 2018 and December 2020. Fifteen cases of SGLT2i associated DKA were identified in people with presumed type 2 diabetes;33.3% were classed as euglycaemic DKA with a blood glucose of <11mmol/L. All cases were associated with a significant precipitating factor including diarrhoea, vomiting, reduced oral intake and sepsis. One case was related to COVID-19. Two people were subsequently found to have raised islet autoantibodies suggesting type 1 diabetes or latent autoimmune diabetes in adults. It is important that awareness of SGLT2i associated DKA is raised among users and health care practitioners, including the recognition of euglycaemic DKA. Sick day rules should be emphasised and reiterated at clinical encounters. Non-specialists in primary care, oncology and in perioperative settings should be empowered to advocate for temporary withdrawal and there should be readier access to blood ketone monitoring when required. When SGLT2i associated DKA occurs, due consideration should be given to evaluate the diabetes classification and investigate the circumstances of the event. Copyright © 2023 John Wiley & Sons.Copyright © 2023 John Wiley & Sons, Ltd.

Current Healthcare Policy and Environmental Status of Digital Health Medical Devices in South Korea

Objectives: COVID 19 and increasing unmet needs of health technology had accelerated an adoption of digital health globally and the major categories are mobile-health, health information technology, telemedicine. Digital health interventions have various benefit on clinical efficacy, quality of care and reducing healthcare costs. The objective of the study is to identify new reimbursement policy trend of digital health medical devices in South Korea. Method(s): Official announcements published in national bodies and supplementary secondary research were used to capture policies, frameworks and currently approved products since 2019. Result(s): With policy development, several digital health devices and AI software have been introduced as non-reimbursement by utilizing new Health Technology Assessment (nHTA) pathway including grace period of nHTA and innovative medical devices integrated assessment pathway. AI based cardiac arrest risk management software (DeepCARS) and electroceutical device for major depressive disorders (MINDD STIM) have been approved as non-reimbursement use for about 3 years. Two digital therapeutics for insomnia and AI software for diagnosis of cerebral infarction were approved as the first innovative medical devices under new integrated assessment system, and they could be treated in the market. In addition, there is remote patient monitoring (RPM) reimbursement service fee. Continuous glucose monitoring devices have been reimbursed for type 1 diabetes patients by the National Health Insurance Service (NHIS) since January 2019. Homecare RPM service for peritoneal dialysis patients with cloud platform (Sharesource) has been reimbursed since December 2019, and long-term continuous ECG monitoring service fee for wearable ECG monitoring devices (ATpatch, MEMO) became reimbursement since January 2022. Conclusion(s): Although Korean government has been developed guidelines for digital health actively, only few products had been reimbursed. To introduce new technologies for improved patient centric treatment, novel value-based assessment and new pricing guideline of digital health medical devices are quite required.Copyright © 2023

Water quality analysis in a municipal outdoor swimming pool complex before and during the COVID-19 pandemic

Like all public utility, swimming pools had been treated with special procedures during the COVID19 pandemic. In addition to the basic rules (social distance + hand disinfection + masks), applicable to all citizens, the managers of swimming pools were obliged to reduce the number of swimmers and to increase the effects of water treatment. Monitoring, control and rapid response to unfavourable changes in the quality of swimming pool water are the basis for minimizing the risk of disease transmission or exposure of bathers to pathogens. The main purpose of this work is to analyse and compare the quality of swimming pool water in a municipal outdoor swimming pool complex, before (2018-2019) and during the COVID-19 pandemic (2020-2021). Water samples taken from a paddling pool for children (CP), a recreational pool (RP), and a sports pool (SP) were analysed. The results of the research, based on real case studies, were compared with the documents on water quality in swimming pools in force at the time. An analysis was carried out to determine the relationships between swimming pool water quality before and during the COVID-19 pandemic. The tested parameters determining the quality of water were physico-chemical parameters (temperature, pH, redox, and bacteriological parameters (colony forming units CFU of Pseudomonas aeruginosa, Escherichia coli, Legionella sp.). Based on the results of the analysis of the parameters mentioned-above, the validation of the procedures applied during the COVID-19 hazard and their impact on the quality of swimming pool water were evaluated. The results of the pool water quality tests were discussed with particular emphasis on disinfection by-products (THM and combined chlorine). Detailed analysis showed better water quality in the first year of the pandemic (2020) compared to 2018-2019 (before COVID-19) and 2021 (the second year of COVID-19 pandemic). The following parameters were found to be significantly different: THM (before 0.069 mg/L and during 0.034 mg/L), free chlorine (before 0.86 mg/L and during 0.66 mg/L), and redox potential (before 667 and during 713 mV).

Impact on glycaemic outcomes of transitioning from in-person to virtual FreeStyle Libre onboarding during the Covid-19 pandemic

Background and aims: Onboarding of the FreeStyle Libre, an intermittently scanned continuous glucose monitoring (isCGM) device, was pre-dominantly conducted in-person prior to the Covid-19 pandemic. However, onboarding rapidly become virtual due to enforced social distancing restrictions. This audit aimed to determine if onboarding method impacted on glycaemic outcomes and engagement statistics in people living with diabetes (pwD). Method(s): PwD who started FreeStyle Libre between January 2019 and March 2022, had their mode of onboarding recorded and had >=70% data were identified and included within the audit. Glycaemic indices and engagement statistics (previous 90 day averages) were obtained from LibreView (Abbott, USA) three months after the last person was onboarded, and compared using linear models, adjusting for FreeStyle Libre duration, %active (where appropriate), age and sex. Result(s): From 1007 eligible participants (in-person 44% [n = 445];virtual 56% [n = 562]), FreeStyle Libre usage duration was greater for those onboarded in-person vs. virtually (974[891,1101) vs. 420[280,564] days [p < 0.001]). There were no significant differences in glycaemic or engagement indices between in-person and virtual onboarding methods: average glucose (10[9,11]) vs. 10[9,11])mmol/l), %time very-low (<3.0mmol/l, 0[0,1]) vs. 0[0,1]%), %time low (3.0-3.8mmol/ l, 2[1,4] vs. 2[1,4]), %time in range (3.9-10.0mmol/ l, 54[+/-17] vs. 53[+/-19]%), %time high (10.1-13.9mmol/ l, 27[21,31]) vs. 26[21,31]%), %time very-high (>13.9mmol/l, 14[6,24] vs. 15[7,26]%), %active (96[90,100] vs. 94[87,99]%) or scans/day (11[8,15] vs. 10[7,14]). Conclusion(s): There were no differences in glycaemic outcomes or engagement indices between pwD between onboarding methods. Virtual onboarding using online videos for isCGM is as equally effective as face to face.

Improving Clinical Decisions Via Remote Blood Sampling

3 An important component for the provision of patient-centric healthcare is the ability to collect blood samples remotely in a manner that delivers samples of a quality comparable to that of current standard phlebotomy collection, enabling routine clinical testing, monitoring disease progression and testing drug concentrations.2-4-г, This blood sampling could be performed at the patient's home, at a nearby pharmacy, or at a local clinic, rather than a centralized facility. Examples of commercially available devices include the Neoteryx Mitra,13 Tasso-M20, SST and Tasso+,14 SeventhSense TAP and TAP II,15 Trajan hemaPEN,16 Labcorp Pixel,17 Capitainer qDBS,1" HemaXis DB10 and DX,19 and Drawbridge OneDraw.20 These devices are capable of collecting from as little as 20 microliters (approximately half a drop) to several hundred microliters of blood and have been widely used to determine clinical parameters,21 drug concentrations,5'22 therapeutic drug monitoring,23 and, more recently, COVID antibody levels.24,25 Some of these devices enable the collection of a fixed volume of blood, collected as dried blood, which can then be shipped and handled at room temperatures-avoiding the need for freezers and dry ice for storing and shipping samples-enabling its adoption even in remote areas with limited infrastructure. Patient-centric blood sampling techniques have been gaining popularity for use in pharmaceutical drug development;however, to date they have not been broadly accessible to the general public.26 This can be partially attributed to the "cliniccentric" healthcare model, where reimbursement is dependent on in-person visits and sample collection. [...]the status quo remains and anyone who needs a blood test is required to go to the doctor's office or clinic. [...]studies have demonstrated that the overall cost to society will be lower, by improving health outcomes and allowing broader access and patient convenience.27 The availability and adoption of patient-centric approaches can provide access and treatment options to clinical trial participants not geographically co-located with the investigative sites and improving access in rural or lesser developed communities, globally, potentially improving the health of the general population.

Diagnosing gestational diabetes during the Covid-19 pandemic using HbA1c rather than oral glucose tolerance test (OGTT)

The Royal College of Obstetrics and Gynaecology advocated replacing OGTT with HbA1c for gestational diabetes (GDM) screening for women with risk factors during the Covid-19 pandemic. HbA1c >=48mmol/mol/random plasma glucose (RPG) >=11.1mmol/l at booking indicated diabetes, and 41-47mmol/ mol/9-11mmol/ l prediabetes or possible GDM. Testing was repeated at 26 weeks if normal previously, with HbA1c >=39mmol/mol, fasting PG >=5.6mmol/l, or RPG >=9mmol/l diagnostic for GDM. A) At her clinic booking visit at 10 weeks gestation, 36 year-old South Asian female had HbA1c 55mmol/mol/RPG 9.5mmol/l suggesting undiagnosed type 2 diabetes. Initially managed with dietary advice and home blood glucose monitoring, metformin was added when self-monitored glucose above pregnancy targets (fasting and pre-meal <5.3mmol/l or 1 h post meal <7.8mmol/l) but insulin was required later. Metformin and insulin were stopped after delivery at 38 weeks with HbA1c 50mmol/mol three months postpartum, supporting the earlier diagnosis of type 2 diabetes. B) 32 year-old White Caucasian female was screened for GDM on booking at 11 weeks as BMI 38 kg/m2. HbA1c 44mmol/mol and RPG 6.9mmol/l confirmed GDM which was managed by dietary/lifestyle changes with glucose and pregnancy targets achieved until 28 weeks when metformin added. Normal delivery at 40 weeks with HbA1c 40mmol/mol three months postpartum triggered advice on long-term dietary/lifestyle changes and annual HbA1c checks. HbA1c was useful during the pandemic but most centres reverted to OGTT for GDM screening due to a significant fall in diagnoses using HbA1c >=39mmol/mol at 26 weeks. But, HbA1c testing was advantageous at booking to diagnose type 2 diabetes earlier.

Smart Watch to Avoid Face Touching During Pandemic Condition

The most dangerous Coronavirus, COVID-19, is the source of this pandemic illness. This illness was initially identified in Wuhan, China, in December 2019, and currently sweeping the globe. The virus spreads quickly because it is so simple to transmit from one person to another. Fever is one of the obvious signs of COVID-19 and is one of its prevalent symptoms. The mucosal areas, such as the nose, eyes, and mouth, are among the most significant ways to catch this virus. In order to prevent and track the corona virus infection, this research suggests a face-touching detection and self-health report monitoring system. Their hygiene will immediately improve thanks to this system. In this pandemic circumstance, people use their hands in dirty environments like buses, trains, and other surfaces, where the virus can remain active for a very long time. With an accelerometer and a pulse oximeter sensor, this system alerts the user when they are carrying their hands close to their faces. © 2023 IEEE.

Smart Healthcare: Future Applications & Challenges

After the outbreak of corona virus, all counties are paying special attention to their healthcare infrastructure. During second phase of covid-19, entire world has seen health care crisis. Large number of people died globally. Entire world was affected mentally or physically. There is a great need to strengthen the healthcare infrastructure, to vaccinate the population against covid virus infection and to take proper precaution to avoid spread of the virus, so that the world will not see such deadly days again. This paper discusses how technologies like Internet of Things (IoT), Artificial Intelligence (AI), Drones etc can help in remote monitoring of patients, judicious hospital admission, conscious distribution of lifesaving drugs etc. Investment in technology with not only help in the reduction of spread of the virus but will also help in fighting with all other future pandemics. All the countries must have to invest more on latest technologies in their healthcare to make themselves ready for such future pandemics. When the things will improve, the new normal will be very much different from the life that was before pandemic. IoT, AI and other technologies will become the non-separatable part of our life. © 2023 Bharati Vidyapeeth, New Delhi.

AI and ICU Monitoring on the Wake of the COVID-19 Pandemic

The Intensive Care Unit (ICU) is a paradigmatic example of the potential reach of data-centred knowledge discovery. This is because the contemporary ICU heavily depends on medical devices for patient monitoring through electronic data acquisition. This poses a unique opportunity for multivariate data analysis to support evidence-based medicine (EBM), particularly in the form of Artificial Intelligence (AI) approaches. The COVID-19 pandemic has tested the limits of critical care management, often overwhelming ICUs. In this brief chapter, we sketch the role of AI, especially in the form of Machine Learning (ML), at the ICU and discuss what can it offer to address COVID-19 disruption in this environment. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Efficient Centroid Based Distance Monitoring System Using Deep Learning

COVID-19 virus spread very rapidly if we come in contact to the other person who is infected, this was treated as acute pandemic. As per the data available at WHO more than 663 million infected cases reported and 6.7 million deaths are confirmed worldwide till Dec, 2022. On the basis of this big reported number, we can say that ignorance can cause harm to the people worldwide. Most of the people are vaccinated now but as per standard guideline of WHO social distancing is best practiced to avoid spreading of COVID-19 variants. This is difficult to monitor manually by analyzing the persons live cameras feed. Therefore, there is a need to develop an automated Artificial Intelligence based System that detects and track humans for monitoring. To accomplish this task, many deep learning models have been proposed to calculate distance among each pair of human objects detected in each frame. This paper presents an efficient deep learning monitoring system by considering distance as well as velocity of the object detected to avoid each frame processing to improve the computation complexity in term of frames/second. The detected human object closer to some allowed limit (1m) marked by red color and all other object marked with green color. The comparison of with and without direction consideration is presented and average efficiency found 20.08 FPS (frame/Second) and 22.98 FPS respectively, which is 14.44% faster as well as preserve the accuracy of detection. © 2023 IEEE.

Cost Evaluation of COVID-19 Remote Home Monitoring Services' Patients in England during Wave 2 from October 2020-April 2021

Objectives: During the COVID-19 pandemic, the NHS delivered a community-based remote home monitoring service for COVID-19 patients. The service came in two models characterised based on the referral method: home-based care to ensure the right people were admitted at hospital at the right time (named COVID Oximetry @ home (CO@h)) and facilitation of patients' transition when discharged home (named Covid-19 Virtual Ward (CVW)). Patients were provided with pulse oximeters and asked to regularly record and submit oxygen levels and other symptoms to a team of administrators and clinicians via digital means (tech-enable and analogue mode) and/or over the phone (analogue). Our aim was to evaluate the costs of implementing these services in England during wave 2 of the pandemic (October 2020-April 2021). Method(s): We used a top-down approach to describe the costs of setting-up and running the service. 26 sites reported the number of patients and staff members involved in the service, and other used resources. Descriptive statistics and multivariate regression analysis were used appropriately. Result(s): The mean cost per patient monitored was lower in the CO@h service compared to the CVW (527.5 vs. 599.1). The corresponding cost was lower for sites using tech-enabled and analogue data submission mode compared to sites using analogue-only mode for both CO@h (515 vs. 561) and CVW (584 vs. 612) services. The number of patients enrolled in the service and the service type significantly affected the mean cost per patient (b=0.62, p= 0.001;b=-457.99, p=0.05 correspondingly). Conclusion(s): Our analysis offers a model for future research since it covers sites of various sizes and raises questions about different practices within the overall remote monitoring services.Copyright © 2023

Managing diabetes remotely: Evaluation of the uptake and efficacy of the DBm app

Objectives: Insulin optimisation requires review of glucose monitoring;Covid-19 posed challenges to this. We evaluated DBm -a remote monitoring platform utilising a glucometer and smartphone app. Method(s): Evaluation was from January to November 2021. Inclusion criteria was insulin treated diabetes with HbA1c greater than 68mmol/mol. HbA1c, demographics, frequency of CBG uploads and interactions with clinicians were collected. Result(s): 97 patients were offered DBm. 48.5% used the app. There were no statistically significant differences in gender (p = 0.05), age (p = 0.36), type of diabetes (p = 0.13) or deprivation index (p = 0.96) between users and non-users. Patients of white ethnicity were less likely to use the platform (p = 0.01). Amongst users, 70% had a reduction of HbA1c of at least 5mmol/mol over six months, with a mean reduction of 25.6mmol/mol (p = 0.01). There was no difference in age (p = 0.64), gender (p = 0.4), and type of diabetes (p = 0.23) between responders and non-responders. There was also no difference in number of call back requests generated by patients (p = 0.32) or number of CBG uploads (p = 0.899) between responders and non-responders. Conclusion(s): Uptake of the remote monitoring solution was just under 50%, with no evidence of digital exclusion, although the finding that white ethnicity patients were less likely to use the system needs further exploration. Most users had improved glucose control, but there was no association with numbers of tests or call back requests. This study demonstrates that insulin optimisation can effectively be delivered using a remote glucose monitoring system. Future work will explore patient experience and patient satisfaction.

Face Recognition Attendance System

The conventional procedure used in all of India's major regions is attendance monitoring on paper with pens. Although the final data is computerized, it takes a long time to get from a classroom to a database. The effectiveness of the classes is directly impacted by the number of absences. The attendance takes up almost half of the lecture's allotted time. The alternative method that is being used involves using fingerprints, but even this approach is ineffective since it takes so long. Due to the illnesses (COVID-19) spreading over the world, however, the situation as it stands right now does not make this the best course of action. Therefore, it will be advisable to develop a contactless and more efficient. © 2023 IEEE.

Management of cardiovascular complications of BTKis: Are we complying with the good practice guidelines?

Bruton tyrosine kinase inhibitors (BTKis) were approved for use at the end of 2013 and have since been used for indications including chronic lymphocytic leukaemia (CLL), Waldenstrom's macroglobulinaemia and mantle cell lymphoma. The use of BTKis has increased significantly in the UK since they achieved NICE (National Institute for Health and Care Excellence) approval for frontline treatment of CLL in 2021. However, they are associated with significant adverse cardiovascular events. In September 2021 the British Journal of Haematology published good practice guidelines for the management of cardiovascular complications of BTKis. Our aim was to see whether these guidelines had been adhered to for patients taking BTKis. Method(s): Data was collected for all patients being prescribed BTKis (ibrutinib and acalabrutinib) in the South Tees NHS Trust in July 2022. Patients' medical records were used to assess whether their management adhered to the good practice guidelines. Data was collated for 67 patients in total. Result(s): The data showed that although all patients were consented for the risk of atrial fibrillation only 6% were consented for hypertension and only 1.5% for ventricular arrhythmias and sudden cardiac death. The guidelines recommend a baseline ECG (electrocardiogram) on commencement of treatment;however, only 7% had this completed and 0% had the minimum monitoring recommendation of 6-monthly ECGs. Thirty patients (45%) had an indication for a baseline echocardiogram;however, only one had this completed. For patients reporting symptoms of syncope, dizziness or palpitations only 50% had an ECG completed. Three patients developed worsening heart failure. The recommendations suggest referral to a cardio-oncologist;however, due to lack of availability of this service the referrals were instead made to the usual cardiologist. Conclusion(s): Although there was a lack of compliance with guideline recommendations, it should be considered that most usual checks were affected by COVID-19 outbreaks and a drop in face-to- face clinics, which were replaced by phone clinics and home delivery of medications. However, the premade consent forms for BTKis need to be updated to include consent for ventricular arrhythmias and sudden cardiac death. There also needs to be routine procedures in place to ensure that regular blood pressure testing and ECG monitoring occurs and that there is prompt recognition of cardiovascular complications. Action and implementation: To ensure improved compliance with these guidelines we plan to update our consent forms and create a proforma for clinic use to ensure that clinicians are aware of the various monitoring criteria required.

Strategies and Challenges to Teaching Students with Special Needs during a Pandemic

The COVID-19 pandemic has led to immediate school closures resulting in online learning. Online learning under such circumstances has presented challenges for teachers of students with mild to extensive support needs. The current study interviewed twenty special education teachers about the strategies they used when teaching online and the challenges they faced. Teachers discussed several strategies including the use of direct instruction, providing active engagement, caregiver involvement, and their frequent use of informal assessments. Challenges included time constraints and student inattention. Findings of this study can be used to inform strategies to use when teaching students with special needs online and in future online emergency teaching environments.

Predictors of severity in COVID-19

Background: In the context of home monitoring of severe acute respiratory syndrome coronavirus-2 disease (COVID-19) patients, it is imperative to evaluate the accuracy of finger pulse oximetry oxygen saturation (SpO2) in the assessment of hypoxia. Methods: Retrospective data analysis was performed on (n = 132) hospitalised COVID-19 patients with various levels of severity, in whom SpO2, haematological, biochemical and arterial blood gas (ABG) parameters were measured within 48 h after admission. Discrepancy between SpO2 and arterial blood oxygen saturation SaO2 was compared between mild, moderate and severe COVID-19 to assess the accuracy of finger pulse oximetry. Results: We found that total white blood cell count, neutrophil %, neutrophil-lymphocyte ratio, platelet-lymphocyte ratio, ferritin, C-reactive protein and lactate dehydrogenase (LDH) were significantly increased in severe COVID-19, while lymphocyte % was significantly less when compared to mild and moderate cases. Multivariable analysis suggested that red cell distribution width (RDW) and LDH together account for significant variance in the severity of disease. The SpO2 and SaO2 were significantly less in the severe group. The difference between SpO2 and SaO2 has a clinically meaningful albeit statistically nonsignificant trend with the discrepancy greater in severe COVID-19 cases when compared to mild and moderate cases. Conclusions: Finger pulse oximetry has the potential to underestimate the severity of hypoxia in severe COVID-19 and this has implications in the decision to start oxygen therapy. RDW and LDH constitute the best parsimonious set of variables to predict severity.