Rapid Scalability Research Articles

Sulfonamide-Sulfonimide Copolymers as Novel, Fluorine-Lean Type of Proton Exchange Membranes for Fuel Cell Application.

In this work, a novel type of fluorine-lean proton exchange membranes is presented, using sulfonamide-sulfonimide functional groups for ion conduction. These groups are constructed on a polystyrene backbone for simple and cost-efficient usage as well as rapid scalability. The polymer is further tailored by adjusting the sulfonamide functionality with various end-groups, namely pentafluorophenyl, 4-fluorophenyl, butyl and octyl groups. These groups affect the pKa, leading to pKa values of 5.7 for the pentafluorophenyl substitution and pKa 10.5 for the alkyl chain. The glass transition temperature of the sulfonamide homopolymers can be reduced from Tg=151 °C (Pentafluorophenyl) to 49 °C (Octyl), making the ionomer more flexible at room temperature. The combination of the non-swelling sulfonamide further mitigates the high water uptake of the sulfonimide while maintaining the nominal ion exchange capacity. This combination leads to extremely high proton conductivities with up to σ=283 mS cm-1 at room temperature, which is clearly outperforming Nafion and approaches values for acid doped systems. This approach can pave the way to a novel type of ion conducting class in proton exchange membrane fuel cells.

The Transformative Potential of mRNA Vaccines in Revolutionizing Vaccine Development And Therapeutic Applications

Messenger RNA (mRNA) technology has emerged as a transformative force in the field of vaccine development and therapeutic applications, revolutionizing the landscape of modern medicine. This cutting-edge technology enables the rapid and flexible design of vaccines and therapeutics, unleashing a new era of personalized and targeted treatments. During the COVID-19 pandemic, mRNA vaccines demonstrated their immense potential by facilitating the swift development and deployment of highly effective vaccines against SARS-CoV-2, contributing significantly to the global response against the virus. Beyond infectious diseases, mRNA technology has opened new frontiers in cancer immunotherapy, offering personalized and therapeutic cancer vaccines that stimulate robust immune responses against tumors. Furthermore, mRNA technology is being explored for the treatment of genetic disorders, autoimmune diseases, and allergies, demonstrating its versatility and broad therapeutic potential. The rapid scalability and production of mRNA vaccines and therapeutics also present opportunities to address global health inequities by ensuring wider accessibility and distribution, particularly in low- and middle-income countries. Ongoing research efforts aim to further stabilize mRNA formulations, enhance delivery systems, and expand the range of target diseases, including neurological disorders and zoonotic infections. As innovation and collaboration continue to drive advancements in mRNA technology, its impact on global health and personalized medicine is poised to expand, promising more effective, targeted, and personalized medical solutions. Ultimately, mRNA technology represents a groundbreaking advancement in biomedical science, with the potential to transform healthcare delivery and improve outcomes across diverse medical fields

Advancing vaccine development: Evaluation of a mannose-modified lipid nanoparticle-based candidate for African swine fever p30 mRNA vaccine eliciting robust immune response in mice

The African swine fever virus (ASFV) continues to pose significant economic and pandemic risks. Consequently, discovering new, efficient vaccines is crucial. Messenger RNA (mRNA) vaccines have emerged as promising candidates, providing minimal risk of insertional mutagenesis, high safety profiles, effectiveness, rapid scalability in production, and cost-effectiveness. In this study, we have developed an ASF p30 mRNA vaccine candidate (mRNA/Man-LNP) employing mannose-modified lipid nanoparticles (LNPs). The mRNA/Man-LNP exhibited effective antigen presentation and facilitated dendritic cells (DCs) maturation. Notably, it elicited strong IgG titers and activated CD4+ and CD8+ T-cells in immunized mice, all while adhering to stringent biosafety standards. This investigation demonstrates that mRNA/Man-LNP can trigger both humoral and cellular immune responses, suggesting its potential as a potent and promising vaccine candidate for controlling African swine fever (ASF).

Exploring FSO link performance in varied atmospheric conditions to optimize 5G communication with a polarized quasi-diffuse transmitter

Abstract Free space optical (FSO) communication is an innovative technology that holds immense promise for numerous applications thanks to its high data transmission rates, rapid scalability, cost-effectiveness, superior security, and comprehensive capacity access techniques for Gbps data transmission. FSO communication relies on the Earth’s atmosphere as the medium, which introduces atmospheric disturbances that have driven the development of spatial diversity techniques aimed at enhancing the technology’s performance. Our proposed system employs a polarized quasi-diffused system with a fork as a spatial diversity scheme, which eliminates the need for multiple transmitters. We have developed a model that integrates both simulation designs to achieve optimal results. The proposed model has demonstrated excellent performance under various atmospheric turbulences, including rain, fog, and haze, exhibiting a very high maximum quality factor, improved received power, and better bit error rate (BER). Finally, we have obtained, analyzed, and extensively discussed the simulation results to provide a comprehensive understanding of the proposed model’s potential benefits. Our simulations show a maximum quality factor of 23 compared to existing models in much better, a 34 % increase in received power, and a 31 % decrease in BER compared to existing models. These results highlight the potential benefits of the proposed model for FSO communication systems.

A Method for the Production of Recombinant VSVs with Confirmation of Biological Activity.

The design of new effective cancer treatment methods is a promising and important research field in translational medicine. Oncolytic viruses can induce immunogenic cell death by activating the body's immune system to recognize tumor cells. This work presents the results for optimizing the production of recombinant vesicular stomatitis viruses (rVSVs). To ensure the assembly of viral particles, we developed the HEK293TN-T7 cell line, which stably expresses DNA-dependent RNA polymerase 7 for viral genome transcription, and obtained helper plasmids encoding viral genes under the control of the CAG promoter. The oncolytic activity of the purified virus preparation was assessed in a murine model of B16F10Red melanoma cells expressing a red fluorescent protein. The presented method makes it possible to obtain purified viral preparations with a high titer and oncolytic activity. The amplification of viral particles in a HEK293 suspension culture allows for rapid scalability. Therefore, the developed approach can be used to obtain other recombinant VSV-based oncolytic viruses for tumor immunotherapy.

Artificial Intelligence and Healthcare Simulation: The Shifting Landscape of Medical Education.

The impact of artificial intelligence (AI) will be felt not only in the arenaof patient care and deliverable therapies but will also be uniquely disruptive in medical education and healthcare simulation (HCS), in particular. As HCS is intertwined with computer technology, it offers opportunities for rapid scalability with AI and, therefore, will be the most practical place to test new AI applications. This will ensure the acquisition of AI literacy for graduates from the country's various healthcare professional schools. Artificial intelligence has proven to be a useful adjunct in developing interprofessional education and team and leadership skills assessments. Outcome-driven medical simulation has been extensively used to train students in image-centric disciplines such as radiology, ultrasound, echocardiography, and pathology. Allowing students and trainees in healthcare to first apply diagnostic decision support systems (DDSS) under simulated conditions leads to improved diagnostic accuracy, enhanced communication with patients, safer triage decisions, and improved outcomes from rapid response teams. However, the issue of bias, hallucinations, and the uncertainty of emergent properties may undermine the faith of healthcare professionals as they see AI systems deployed in the clinical setting and participating in diagnostic judgments. Also, the demands of ensuring AI literacy in our healthcare professional curricula will place burdens on simulation assets and faculty to adapt to a rapidly changing technological landscape. Nevertheless, the introduction of AI will place increased emphasis on virtual reality platforms, thereby improving the availability of self-directed learning and making it available 24/7, along with uniquely personalized evaluations and customized coaching. Yet, caution must be exercised concerning AI, especially as society's earlier, delayed, and muted responses to the inherent dangers of social media raise serious questions about whether the American government and its citizenry can anticipate the security and privacy guardrails that need to be in place to protect our healthcare practitioners, medical students, and patients.

Enhanced binding and inhibition of SARS-CoV-2 by a plant-derived ACE2 protein containing a fused mu tailpiece.

Infectious diseases such as Coronavirus disease 2019 (COVID-19) and Middle East respiratory syndrome (MERS) present an increasingly persistent crisis in many parts of the world. COVID-19 is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The angiotensin-converting enzyme 2 (ACE2) is a crucial cellular receptor for SARS-CoV-2 infection. Inhibition of the interaction between SARS-CoV-2 and ACE2 has been proposed as a target for the prevention and treatment of COVID-19. We produced four recombinant plant-derived ACE2 isoforms with or without the mu tailpiece (μ-tp) of immunoglobulin M (IgM) and the KDEL endoplasmic reticulum retention motif in a plant expression system. The plant-derived ACE2 isoforms bound whole SARS-CoV-2 virus and the isolated receptor binding domains of SARS-CoV-2 Alpha, Beta, Gamma, Delta, and Omicron variants. Fusion of μ-tp and KDEL to the ACE2 protein (ACE2 μK) had enhanced binding activity with SARS-CoV-2 in comparison with unmodified ACE2 protein derived from CHO cells. Furthermore, the plant-derived ACE2 μK protein exhibited no cytotoxic effects on Vero E6 cells and effectively inhibited SARS-CoV-2 infection. The efficient and rapid scalability of plant-derived ACE2 μK protein offers potential for the development of preventive and therapeutic agents in the early response to future viral outbreaks.

Optimization of Task-Scheduling Strategy in Edge Kubernetes Clusters Based on Deep Reinforcement Learning

Kubernetes, known for its versatility in infrastructure management, rapid scalability, and ease of deployment, makes it an excellent platform for edge computing. However, its native scheduling algorithm struggles with load balancing, especially during peak task deployment in edge environments characterized by resource limitations and low latency demands. To address this issue, a proximal policy optimization with the least response time (PPO-LRT) algorithm was proposed in this paper. This deep reinforcement learning approach learns the pod-scheduling process, which can adaptively schedule edge tasks to the most suitable worker nodes with the shortest response time according to the current cluster load and pod state. To evaluate the effectiveness of the proposed algorithm, multiple virtual machines were created, and we built a heterogeneous node cluster. Additionally, we deployed k3s, a Kubernetes distribution suited for edge environments, on the cluster. The load balancing, high load resilience, and average response time during peak task deployment were tested by initiating numerous tasks within a limited time frame. The results validate that the PPO-LRT-based scheduler shows superior performance in cluster load balancing compared to the Kube scheduler. After the deployment of 500 random tasks, several cluster nodes become overwhelmed by using the Kube scheduler, whereas the PPO-LRT-based scheduler evenly allocates the workload across the cluster, reducing the average response time by approximately 31%.

UIPATH BOT FRAMEWORK: ACCELERATING RPA DEVELOPMENT AND INNOVATION

This research paper explores the significance of the “UiPath Bot Framework” and its pivotal role in streamlining UiPath bot development processes. In an era where businesses are constantly seeking ways to enhance efficiency and productivity, UiPath has emerged as a powerful tool for automating repetitive tasks and driving digital transformation. 
 UiPath's intuitive interface, AI-powered intelligence, and extensive capabilities have made it a preferred choice for organizations worldwide. Its ability to automate tasks, boost efficiency, cut costs, integrate with existing systems, and facilitate rapid scalability has led to high demand in the industry. As UiPath's adoption continues to grow, the need for structured and systematic approaches to bot development becomes increasingly apparent.
 The central idea of this research paper revolves around the introduction and exploration of the "UiPath Bot Framework." This framework serves as a standardized foundation that guides developers in building bots, ensuring consistency, scalability, and success. It is particularly valuable when collaborating with citizen developers and establishing Centers of Excellence (COEs) for RPA within organizations.
 The paper delves into the fundamental elements of the UiPath Bot Framework, emphasizing key components such as context ID creation, working folder setup, log management, configuration file reading, error handling, standardized communication, and secure bot execution environments. These components collectively contribute to a structured and resilient approach to bot development.
 Furthermore, the UiPath Bot Framework significantly accelerates the development lifecycle by providing developers with a well-defined starting point. It allows developers to focus on crafting functionalities specific to their use cases without reinventing common practices for each bot. This streamlined approach not only saves time and resources but also empowers developers to innovate and create value-added features.
 To illustrate the practical application of the UiPath Bot Framework, the paper presents two comprehensive case studies: "Data Synchronization" and "HR Onboarding." These scenarios demonstrate how the framework's standardized components, streamlined practices, and reliable error management accelerate the development of distinct automation use cases.
 In conclusion, the UiPath Bot Framework represents a paradigm shift in UiPath bot development, emphasizing the importance of consistency, resilience, design patterns, and development acceleration. This research paper offers insights into the benefits of adopting the UiPath Bot Framework and its potential to revolutionize UiPath bot development practices, ultimately contributing to the realization of more efficient and effective automation solutions.

Technoeconomics and carbon footprint of hydrogen production

Decarbonization of the global economy needs not only carbon-free electricity but also a fuel that is free of greenhouse gas emissions. Hydrogen is a prime candidate, with colors such as gray, blue and green production methods receiving attention. Generally missing from this discourse, however, is a quantitative comparison on the cost, scale, carbon footprint and infrastructure requirements for each of these technologies. Here we present a detailed technoeconomic comparison of five hydrogen production technologies: thermochemical water splitting, methane pyrolysis, water electrolysis, steam methane reforming, and chemical looping hydrogen production. Steam or autothermal methane reforming with carbon capture (SMR + CC) at large scale sets the benchmark for costs, but at small scales, electrochemical technologies appear more suitable. Potentially competitive with and sometimes more affordable than SMR + CC are chemical looping hydrogen production and methane pyrolysis. The impacts of the CO2 footprint of the electricity grid and a price on CO2 are also quantitatively evaluated. A qualitative consideration of the ability to leverage existing infrastructure is also offered with a view towards rapid scalability. Finally, the key research needs are identified for each technology.

Expression of plant-produced anti-PD-L1 antibody with anoikis sensitizing activity in human lung cancer cells via., suppression on epithelial-mesenchymal transition.

Immune checkpoint antibodies in cancer treatment are receptor-ligand pairs that modulate cancer immunity. PD-1/PD-L1 pathway has emerged as one of the major targets in cancer immunotherapy. Atezolizumab, the first anti-PD-L1 antibody approved for the treatment of metastatic urothelial, non-small cell lung, small cell lung and triple-negative breast cancers, is produced in Chinese Hamster Ovary (CHO) cells with several limitations i.e., high-production costs, low-capacity yields, and contamination risks. Due to the rapid scalability and low production costs, the transient expression in Nicotiana benthamiana leaves was investigated by co-infiltration of Agrobacterium tumefaciens GV3101 cultures harboring the nucleic acid sequences encoding for Atezolizumab heavy chain and light chain in this study. The transient expression of Atezolizumab in transformed N. benthamiana accumulated up to 86.76 μg/g fresh leaf weight after 6 days of agroinfiltration (OD 600 nm: 0.4) with 1:1 ratio of heavy chain to light chain. The structural and functional characteristics of plant-produced Atezolizumab was compared with commercially available Tecentriq® from CHO cells with similar binding efficacies to PD-L1 receptor. The direct anti-cancer effect of plant-produced anti-PD-L1 was further performed in human lung metastatic cancer cells H460 cultured under detachment condition, demonstrating the activity of anti-PD-L1-antibody on sensitizing anoikis as well as the suppression on anti-apoptosis proteins (Bcl-2 and Mcl-1) and modulation of epithelial to mesenchymal regulating proteins (E-cadherin, N-cadherin, Snail and Slug). In conclusion, this study manifests plants as an alternative cost-effective platform for the production of functional monoclonal antibodies for use in cancer therapy.

Validation Study of a Direct Real-Time PCR Protocol for Detection of Monkeypox Virus

Monkeypox has recently been described as a public health emergency of international concern by the World Health Organization and a public health emergency by the United States. If the outbreak continues to grow, rapid scalability of laboratory testing will be imperative. During the early days of the coronavirus disease 2019 (COVID-19) pandemic, laboratories improved the scalability of testing by using a direct-to-PCR approach. To improve the scalability of monkeypox testing, a direct real-time PCR protocol for the detection of monkeypox virus was validated. The assay retains the sensitivity and accuracy of the indirect assay while eliminating the need for nucleic acid extraction kits, reducing laboratory technologist time per sample and decreasing exposure to an infectious agent. The direct method will make it easier for laboratories across the world to rapidly develop, validate, and scale testing for monkeypox virus.

Home Hospital for Orthopaedic Surgery: Opportunities and Challenges of a New Delivery Model.

Home Hospital (HH) is a clinical service involving the delivery of acute inpatient care in the home setting. Acute care services provided via HH include continuous telemonitoring, intravenous fluids and medications, nursing care, point-of-care imaging and laboratory tests, and in-person and virtual clinician visits. Despite offering an inpatient level of care, HH has lower fixed costs and less overhead than conventional hospital settings and offers rapid scalability. Originally implemented for acute medical conditions, HH has proven to be a safe and value-based care-delivery model for a variety of medical conditions, ranging from heart failure to chronic obstructive pulmonary disease exacerbations. For surgical conditions, HH represents an opportunity to reduce adverse hospital-acquired conditions, improve patient and caregiver satisfaction, and decrease cost. The patient profile of orthopaedic surgery inpatients matches that of patients who are most likely to benefit from HH-namely, those who are prone to functional decline, delirium, and nosocomial infections. A focus on surgeon leadership, quality and safety, and digital health with collection of patient-reported outcome measures (PROMs) will ensure that the potential of HH is realized as implementation and widespread rollout proceed.

Opening the Conversation: The Development of a Faculty-Facing Module on Teaching Students With Dyslexia

Introduction: Accessibility training frequently relies upon checklist-based approaches to convey important standards and guidance. Such approaches limit scalability and acknowledgement of the user experience. About the case: A certificate program for faculty and graduate teaching assistants who are instructors of record at one university provided an opportunity to develop a learning module about dyslexia. Situating the case: Dyslexic students frequently require alterations to both teaching approaches and document design; however, studies show that some faculty do not see or feel a need to offer any accommodations for this dis/ability. Research indicates that train-the-trainer approaches to accessibility training offer improved scalability and efficacy when it comes to engaging and acknowledging the needs of dis/abled communities. Amplifying voices from the dis/abled community in training also personalizes the need to make content accessible. Approach: This article covers the module's creation and implementation via the iterative Analysis, Design, Development, Implementation, and Evaluation (ADDIE) method. In doing so, it provides a framework for creating similar modules that industry practitioners and faculty can implement within their own organizations. Results/discussion: The COVID-19 pandemic led to fewer participants in the module than anticipated; however, preliminary feedback suggests the approach's efficacy. Conclusion: A train-the-trainer approach that integrates testimonials from the dis/abled community offers a user-focused means of disseminating accessibility guidelines that organizations should consider if looking for rapid scalability for new accessibility initiatives.

Lean Principles to Improve Quality in High-Throughput COVID-19 Testing Using SwabSeq: A Barcoded Sequencing-Based Testing Platform.

ObjectiveTo describe and quantify the effect of quality control (QC) metrics to increase testing efficiency in a high-complexity, Clinical Laboratory Improvement Amendments–certified laboratory that uses amplicon-based, next generation sequencing for the clinical detection of SARS-CoV-2. To enable rapid scalability to several thousands of specimens per day without fully automated platforms, we developed internal QC methods to ensure high-accuracy testing.MethodsWe implemented procedures to increase efficiency by applying the Lean Six Sigma model into our sequencing-based COVID-19 detection.ResultsThe application of the Lean Six Sigma model increased laboratory efficiency by reducing errors, allowing for a higher testing volume to be met with minimal staffing. Furthermore, these improvements resulted in an improved turnaround time.ConclusionLean Six Sigma model execution has increased laboratory efficiency by decreasing critical testing errors and has prepared the laboratory for future scaling up to 50,000 tests per day.

Potential for Developing Plant-Derived Candidate Vaccines and Biologics against Emerging Coronavirus Infections.

The emerging human coronavirus infections in the 21st century remain a major public health crisis causing worldwide impact and challenging the global health care system. The virus is circulating in several zoonotic hosts and continuously evolving, causing occasional outbreaks due to spill-over events occurring between animals and humans. Hence, the development of effective vaccines or therapeutic interventions is the current global priority in order to reduce disease severity, frequent outbreaks, and to prevent future infections. Vaccine development for newly emerging pathogens takes a long time, which hinders rapid immunization programs. The concept of plant-based pharmaceuticals can be readily applied to meet the recombinant protein demand by means of transient expression. Plants are evolved as an expression platform, and they bring a combination of unique interests in terms of rapid scalability, flexibility, and economy for industrial-scale production of effective vaccines, diagnostic reagents, and other biopharmaceuticals. Plants offer safe biologics to fulfill emergency demands, especially during pandemic situations or outbreaks caused by emerging strains. This review highlights the features of a plant expression platform for producing recombinant biopharmaceuticals to combat coronavirus infections with emphasis on COVID-19 vaccine and biologics development.

Encrypted Control for Networked Systems: An Illustrative Introduction and Current Challenges

Cloud computing and distributed computing are becoming ubiquitous in many modern control areas such as smart grids, building automation, robot swarms, and intelligent transportation systems. Compared to “isolated” control systems, the main advantages of cloud-based and distributed control systems are resource pooling and outsourcing, rapid scalability, and high performance. However, these capabilities do not come without risks. In fact, the involved communication and processing of sensitive data via public networks and on third-party platforms promote (among other cyberthreats) eavesdropping and the manipulation of data (see “Summary”). That these threats are relevant to real-world applications is apparent from an increasing number of cyberattacks explicitly addressing industrial control systems [68]. Prominent examples are the malwares Stuxnet, Duqu, Industroyer, and Triton [14] as well as inference attacks arising from smart meters used as surveillance devices [30, 46].

Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform.

The MACCS (Medical Assistant for Chronic Care Service) platform enables secure sharing of key medical information between patients after kidney transplantation and physicians. Patients provide information such as vital signs, well-being, and medication intake via smartphone apps. The information is transferred directly into a database and electronic health record at the kidney transplant center, which is used for routine patient care and research. Physicians can send an updated medication plan and laboratory data directly to the patient app via this secure platform. Other features of the app are medical messages and video consultations. Consequently, the patient is better-informed, and self-management is facilitated. In addition, the transplant center and the patient's local nephrologist automatically exchange notes, medical reports, laboratory values, and medication data via the platform. A telemedicine team reviews all incoming data on a dashboard and takes action, if necessary. Tools to identify patients at risk for complications are under development. The platform exchanges data via a standardized secure interface (Health Level 7 (HL7), Fast Healthcare Interoperability Resources (FHIR)). The standardized data exchange based on HL7 FHIR guarantees interoperability with other eHealth solutions and allows rapid scalability to other chronic diseases. The underlying data protection concept is in concordance with the latest European General Data Protection Regulation. Enrollment started in February 2020, and 131 kidney transplant recipients are actively participating as of July 2020. Two large German health insurance companies are currently funding the telemedicine services of the project. The deployment for other chronic kidney diseases and solid organ transplant recipients is planned. In conclusion, the platform is designed to enable home monitoring and automatic data exchange, empower patients, reduce hospitalizations, and improve adherence, and outcomes after kidney transplantation.

GPU-aware resource management in heterogeneous cloud data centers

The power of rapid scalability and easy maintainability of cloud services is driving many high-performance computing applications from company server racks into cloud data centers. With the evolution of Graphics Processing Units, composing of an extensive array of parallel computing single-instruction-multiple-data processors are being considered as a platform for high-performance computing because of their high throughput. Many cloud providers have begun offering GPU-enabled services for the users where GPUs are essential (for high computational power) to meet the desired Quality-of-service. Virtual machine placement and load balancing the GPUs in the virtualized environments like the cloud is still an evolving area of research and it is of prime importance to achieve higher resource efficiency and also to save energy. The current VM placement techniques do not consider the impact of VM workload type and GPU memory status on the VM placement decisions. This paper discusses the current issues with the First Fit policy of virtual machine placement used in VMWare Horizon and proposes a GPU-aware VM placement technique for GPU-enabled virtualized environments like cloud data centers. The experiments conducted using the synthetic workloads indicate reduction in the energy consumption, reduction in search space of physical hosts, and the makespan of the system. It also presents a summary of the current challenges for GPU resource management in virtualized environments and specific issues in developing cloud applications targeting GPUs under the virtualization layer.

Evidence of a Rapid Shift in Outpatient Practice During the COVID-19 Pandemic Using Telemedicine.

Introduction: During the current pandemic, measures for preventing SARS-CoV-2 virus exposure has severely impacted the delivery of outpatient clinical care to patients with a chronic neurological condition. Telemedicine has emerged as an obvious choice to counter these impediments. However, its potential for maintaining outpatient care at pre-pandemic levels during these rapidly changing times is untested. Therefore, we analyzed our experience in a tertiary care epilepsy center. Methods: We divided the study period from March 1, 2020 to April 15, 2020 into a baseline, transition (to telemedicine), and current phase. We divided outpatient encounters into clinic, virtual (using Cleveland Clinic Express Care Online platform), and telephone (including commercial video conferencing platforms). Results: Completed outpatient visits during baseline and current period were 595 and 590, respectively. Nearly 1 out of 4 patients missed outpatient visits during the transition period. The virtual visits accounted for 19.7% of completed visits during baseline and increased to 66.8% during the current period. There were no telephone visits during the baseline phase but accounted for 26.1% of completed visits during the current phase. Less than 1 percent of completed visits in the current phase were in the clinic. Conclusion: We provide evidence that telemedicine's robust and rapid scalability can help maintain a seamless transition of outpatient care during the pandemic.