ABSTRACT
The health sector is focusing on the wellness of the society, is advancing in the phases of diagnosis and treatment. Biosensors based devices are used to diagnose a variety of human diseases. Recently, there was a sudden hike in the human mortality rate by chronic diseases caused by mutants of SARS-COV-2, on global scale. It is important to detect these kinds of diseases on an early stage to reduce the risk of spreading. For the analysis of Covid-19 influenza, tests such as Rapid Antigen Test (RAT), True NAT, CBNAAT and the commonly done RPT PCR were utilised. This proposal describes a non-invasive, quick and practical method for sensing the at-risk or infected persons with SARS-COV-2, aiming at controlling the epidemic. The proposed method employs a breath sensing device consisting of a Graphene Field Effect Transistor biosensor which can identify disease-specific biomarkers from exhaled sniff, hence allowing speedy and precise detection. This test aids screening of large populations as it is simple and quick and emerges as a promising candidate for SARS-COV-2 tests due to a high sensitivity. This work justifies the accurate diagnosis of Severe Acute Respiratory Syndrome COV 2 from aerosol particles by GFET Biosensor.
ABSTRACT
Previous studies of genome sequencing (GS) in critically ill childrenhave made use of either modified hardware or working procedureswhich would be difficult, if not impossible, to integrate into existingclinical workflows1. Our lab’s transition from exome sequencing (ES) to GS offered an opportunity to implement in-house rapid genomesequencing (rGS) in critically ill children in a manner which couldintegrate with existing clinical workflows. We conducted a feasibilityand implementation pilot by offering rGS to child-parent triosconcurrently undergoing clinical rapid ES (rES) via a reference lab.The purpose of this study was to identify and address operationalbarriers to implementation of a rGS program capable of communicatinga preliminary result within 7 days of consent. We consideredthis time span to be more reflective of clinical realities than lab-quotedturnaround times (TAT) which typically start at sample receipt andthus do not account for challenges in sample acquisition and pre-testcounseling in a critical care setting, nor the impact of shipping times.Here we present data on TAT and lessons learned from the first 27subjects enrolled.Using rapid cycle improvement methodologies, we identified fourdistinct but inter-related workflows requiring optimization:1. Pre-analytic: patient identification through acquisition ofsamples2. Wet-lab: extraction through sequencing3. Bioinformatics: secondary and tertiary analysis as well as rapididentification of causal variants4. Return of resultsFigure 1 summarizes TAT across cases, demonstrating the markedimprovements in TAT with our programmatic approach to improvement.We used our first 9 cases to determine a baseline TAT for theentire process and to delineate the 4 main workflows (above). Atbaseline, excluding cases delayed by COVID-19 restrictions, mean TATwas 17.12 days (3 sequential deviant range: 7.05–27.19 days).Following deployment of our programmatic approach to rGS, meanTAT fell to 6.19 days (3 sequential deviant range: 0.51–11.87 days).Table 1 summarizes the observations and insights, by workflow, whichimpacted upon TAT and/or implementation. The single biggest impacton TAT was optimization of bioinformatics by removing all manualsteps between starting sequencing and producing human interpretable,filtered, annotated output of high-priority variants for interpretation.The second biggest source of improvement was optimization ofthe sequencing itself as well as prioritizing sample processing for andaccess to sequencing runs. While variant ranking is helpful in identifying causal variants, in 9/10 cases with a diagnostic findingthe causal variant(s)were obvious to the study teamwithin minutes ofviewing the annotated variant list, regardless of variant rank. (Figure Presented) As time required for sequencing and analytic workflows fell, therelative contribution of other workflows to overall TAT shifted and itbecame more obvious that early identification and utilization of thisapproach is very important in lowering overall time to diagnosis(Figure 2). In 6/10 cases with a diagnostic finding, the initial approachof the clinical team was NOT rES (and thus patients were not eligiblefor rGS on a research basis). Had rGS been the initial diagnosticmodality chosen, a diagnosis could have been reached in a median 12days sooner (range 2–28 days). There were also several cases wheresequencing was delayed when one or both parents did not present tothe lab to provide a blood sample in a timely manner. Optimization ofsequencing or analytic workflows cannot meaningfully improveoutcomes either of these situations.Our findings suggest some important considerations for institutionsdeveloping or seeking to improve rapid sequencing programs for acuteand critically ill children: (Table Presented) • Optimization of computational resource utilization and phenotypecuration saves more time than improved variant filtering orprioritization.• Obtaining samples from parents is non-trivial.• Even trained geneticists may fail to recognize appropriatecandidates for rGS.