Comparison of Abbott COVID-19 testing Platforms for timely results- CMCVAMC experience

Introduction/Objective COVID-19 is a new disease, caused by the SARS-CoV-2 coronavirus capable of causing severe disease and death. The Alinity-m has a high throughput and random-access features that are not on the Abbott m2000, both of which had been validated and brought into clinical use for high throughput SARS-CoV-2 testing. The additional features of Alinity-m would be expected to improve turnaround time; however, there are no published reports in the English literature comparing the turnaround time between the Abbott m2000 and Alinity-m. Methods/Case Report A retrospective quality assurance search for all SARS-CoV-2 tests performed on the Abbott m2000 and Alinity-m (both Chicago IL) between February 1st 2021 to March 1st, 2021, to capture the turnaround time differences for the Abbott m2000 versus the Alinity-m for the month after the Alinity-m was brought into clinical service after validation. Results (if a Case Study enter NA) There was a total of 318 tests performed on the Abbott m2000 and 1329 tests performed on the Alinity-m during this time period. The average turnaround time on the Alinity was 6 hours, compared with 11 hours on the Abbott m2000. This difference was statistically significant by the t-test (p-value = <0.01). Both the optimized throughput and random-access features of the Alinity-m contributed significantly to this improvement. The Alinity-m is capable of producing results within 115 minutes for the first specimen and then 3 minutes for each sequential specimen. On the other hand, the Abbott m2000 must be batched in limited 8-12 hour runs without random access capability. All the results were reported and communicated to the clinical teams, so the timely patient management can be administrated and surveillance of the same can be done in real time. Conclusion Alinity M has a significant advantage for a random access as well as improved TAT for detection of SARS-CoV-2, leading to prompt patient care and management.

Shortening the Time of the Identification and Antimicrobial Susceptibility Testing on Positive Blood Cultures with MALDI-TOF MS

The current processes used in clinical microbiology laboratories take ~24 h for incubation to identify the bacteria after the blood culture has been confirmed as positive and fa further ~24 h to report the results of antimicrobial susceptibility tests (ASTs). Patients with suspected bloodstream infection are treated with empiric broad-spectrum antibiotics but delayed targeted antimicrobial therapy. This study aimed to develop a method with a significantly shortened turnaround time for clinical application by identifying the optimal incubation period of a subculture. A total of 188 positive blood culture samples obtained from Nov. 2019 to Aug. 2020 were included. Compared to the conventional 24-h incubation for bacterial identification, our approach achieved 96.1% and 97.4% identification accuracy after shortening the incubation time to 4.5 and 3.5 h for gram-positive (GP) and gram-negative (GN) bacterial samples, respectively. Samples from short-term incubation without any intermediate step or process were directly subjected to analysis with the Phoenix M50 AST. Compared to the conventional disk diffusion AST, the category agreements for GP (excluding Streptococcus spp.), Streptococcus spp., and GN bacterial samples were 91.8%, 97.5%, and 92.7%, respectively. Our approach significantly reduced the average turnaround time from 48 h to 28 h for reporting bacterial identity and decreased average AST from 72 h to 50.3 h compared to the conventional methods. Accordingly, this approach allows a physician to prescribe the appropriate antibiotic(s) ~21.7 h earlier, thereby improving patient outcomes.

Comparative Study for Different Provisioning Policies for Load Balancing in Cloudsim

Distributing application requests across applications located in different datacenters with in cloud equally must be provided by cloud load balancing. In this paper, we compare different provisioning policies within cloud for virtual machines and workloads, where we are focusing on how to distribute the processing power between virtual machines and how to distribute workload among virtual machines. Cloudsim is the simulation plate form used to test the different distributions scenarios to check the performance on makespan, average turnaround time, bandwidth utilization and CPU utilization. Result showed the difference in performance between the three tested provisioning schemes, where the space-shared gives better readings for the selected performance metrics.

QoS Enabled Task Scheduling Approach in Cloud Computing as Per Service Level Agreement

Due to constraints along with profit margins in background, service provider’s sometime neglect to feed essential services to their respective clients. Such compulsion raises the demand for efficient task scheduling that can meet multiple objectives. But without any prior agreement, again makes a casual approach. So this dispute can be addressed when competent scheduling executes right over the Service Level Agreement. It acts as hotspots to define set of rules to assure quality of service. At this time, there is a huge demand of SLA opted scheduling that can produce profitable results from provider’s and client’s as well. This article presents a fundamental approach that can be applied to existing scheduling techniques on the fly. Result shows drastic improvement in terms of average waiting time, average turnaround time without comprising provider’s cost margin at all along with fairness.

A scalable saliva-based, extraction-free rt-lamp protocol for sars-cov-2 diagnosis

I.ABSTRACTScalable, cost-effective screening methods are an essential tool to control SARS-CoV-2 spread. We have developed a straight saliva-based, RNA extraction-free, RT-LAMP test that is comparable to current nasopharyngeal swab RT-PCR tests in both sensitivity and specificity. Using a 2-step readout of fluorescence and melting-point curve analysis, the test is scalable to more than 30,000 tests per day with average turnaround time of less than 3 hours. The test was validated using samples from 244 symptomatic patients, and showed sensitivity of 78.9% (vs. 85.5% for nasopharyngeal swabs RT-PCR) and specificity of 100% (vs. 100% for nasopharyngeal swabs RT-PCR). Our method is therefore accurate, robust, time and cost effective and therefore can be used for screening of SARS-CoV-2.

Mounting a Regional Response to the COVID-19 Pandemic: Another Reason to “Keep” Your Lab

Context.— Declining reimbursement shifts hospital laboratories from system assets to cost centers. This has resulted in increased outsourcing of laboratory services, which can jeopardize a hospital systems' ability to respond to a health care crisis. Objectives.— To demonstrate that investment in a core laboratory serving an academic medical center equipped a regional health system to respond to the Coronavirus disease 2019 (COVID-19) pandemic. Design.— COVID-19 diagnostic testing data were analyzed. Volumes were evaluated by result date (March 16, 2020–May 6, 2020), and the average of received-to-verified turnaround time was calculated and compared for in-house and send-out testing, and different in-house testing methodologies. Results.— Daily viral diagnostic testing capacity increased by greater than 3000% (from 21 tests per day to 658 tests per day). Total viral diagnostic testing reported by the core laboratory increased by 128 times during 22 days of test method validation and 826 times during the analysis period, while average turnaround time per day for send-out testing increased from 3.7 days to 21 days. Decreased overall average turnaround time was observed at the core laboratory (0.45 days) versus send-out testing (7.63 days) (P < .001). Conclusions.— Investment in a core laboratory provided the health system with the necessary expertise and resources to mount a robust response to the pandemic. Local access to testing allowed rapid triage of patients and conservation of scarce personal protective equipment (PPE). In addition, the core laboratory was able to support regional health departments and several hospitals outside of the system.

Comparative performance analysis of optimized round robin scheduling(ORR) using dynamic time quantum with round robin scheduling using static time quantum in Real Time System

One of major component of operating system is task scheduling for the optimum utilization of the resources. Round Robin had been an effective task scheduling method so far, but it has certain limitations. It uses static time quantum which sometimes leads to starvation. The proposed Optimised Round Robin is a modified version of the existing Round Robin scheduling which results in better average time and average turnaround time and overall increase in the performance. The comparative analysis is being done that indicates ORR gives improvement in the system performance.

Cloud Computing Modeling and Simulation using Cloud Sim Environment

Cloud computing is a major technology in the development of internet services, and it delivers software, infrastructure and platform. It enables the client to offer-based services in a pay-per-use concept. So, it offers a less expensive and easy way of managing things. In this paper, we introduce a formal definition of CloudSim simulator, including its architecture, reasons for adopting for modeling and simulation, pros and cons. Moreover, CloudSim versions and how to implement the cloud environment using CloudSim. Further, we demonstrate that cloudlet scheduler policy TimeShared, SpaceShared and DynamicWorkload approach for VM scheduler TimeShared policy are compared on the bases of some performance parameters in term of average turnaround time, throughput, total execution time and total simulation time. These parameters outperform in DynamicWorkload cloudlet scheduler policy than TimeShared and SpaceShared approach for TimeShared VM scheduler policy. This work is anticipated to the beginner of the field to choose CloudSim simulation platform and suitable approaches for cloud computing

Performance Evaluation of Hybrid Round Robin Algorithm and Modified Round Robin Algorithm in Cloud Computing

The scheduling Round Robin (RR) is an impartial algorithm that schedules cloud resources by giving static time quantum to all processes. Time quantum selection is very crucial as it determines performance of algorithms. This research paper suggests an approach to improve RR scheduling algorithm in cloud computing by considering the quantum to be equal to burst time of start request, which dynamically vary after each execution of a request. And also, if the remaining burst time of CPU for currently executing process is lesser than time quantum, then the CPU will be allocated again to the executing process for rest of CPU burst time. MatLAb was used to implement the planned algorithm and benchmarked against MRRA available in literature. In comparison with the planned algorithm, Average Turnaround Time (ATAT) and minimal Average Waiting Time (AWT) was recorded. Based on the obtained simulated outcome, the planned algorithm should be preferred over modified round robin algorithm as it significantly improves the system efficiency. Keywords: Cloud Computing, throughput, Cloud Services, Response Time, Turnaround Time.