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Human carbonic anhydrase 1 (CA1) has been suggested as a biomarker for identification of several diseases including cancers, pancreatitis, diabetes and Sjogren's syndrome. However, the lack of a rapid, cheap, accurate and easy‐to‐use quantification technique has prevented widespread utilization of CA1 for practical clinical applications. To this end, we present a label‐free electronic biosensor for detection of CA1 utilizing highly sensitive graphene field effect transistors (G‐FETs) as a transducer and specific RNA aptamers as a probe. The binding of CA1 with aptamers resulted in a positive shift in Dirac voltageV_Dof the G‐FETs, the magnitude of which depended on target concentration. These aptameric G‐FET biosensors showed the binding affinity (K_D) of ~2.3 ng/ml (70 pM), which is four orders lower than that reported using a gel shift assay. This lower value ofK_Denabled us to achieve a detection range (10 pg/ml –100 ng/ml) which is well in line with the clinically relevant range. These highly sensitive devices allowed us to further prove their clinical relevance by successfully detecting the presence of CA1 in human saliva samples. Utilization of this label‐free biosensor could facilitate the early‐stage identification of various diseases associated with changes in concentration of CAs.

 

Abstract Many measurements at the LHC require efficient identification of heavy-flavour jets, i.e. jets originating from bottom (b) or charm (c) quarks. An overview of the algorithms used to identify c jets is described and a novel method to calibrate them is presented. This new method adjusts the entire distributions of the outputs obtained when the algorithms are applied to jets of different flavours. It is based on an iterative approach exploiting three distinct control regions that are enriched with either b jets, c jets, or light-flavour and gluon jets. Results are presented in the form of correction factors evaluated using proton-proton collision data with an integrated luminosity of 41.5 fb -1 at  √s = 13 TeV, collected by the CMS experiment in 2017. The closure of the method is tested by applying the measured correction factors on simulated data sets and checking the agreement between the adjusted simulation and collision data. Furthermore, a validation is performed by testing the method on pseudodata, which emulate various mismodelling conditions. The calibrated results enable the use of the full distributions of heavy-flavour identification algorithm outputs, e.g. as inputs to machine-learning models. Thus, they are expected to increase the sensitivity of future physics analyses.