ABSTRACT
In this study,a fluorescent(FL)aptasensor was developed for on-site detection of live Salmonella typhimurium(S.T.)and Vibrio parahaemolyticus(V.P.).Complementary DNA(cDNA)of aptamer(Apt)-functionalized multicolor polyhedral oligomeric silsesquioxane-perovskite quantum dots(cDNA-POSS-PQDs)were used as encoded probes and combined with dual-stirring-bar-assisted signal amplification for pathogen quantification.In this system,bar 1 was labeled with the S.T.and V.P.Apts,and then bar 2 was functionalized with cDNA-POSS-PQDs.When S.T.and V.P.were introduced,pathogen-Apt complexes would form and be released into the supernatant from bar 1.Under agitation,the two complexes reached bar 2 and subsequently reacted with cDNA-POSS-PQDs,which were immobilized on MXene.Then,the encoded probes would be detached from bar 2 to generate FL signals in the supernatant.Notably,the pathogens can resume their free state and initiate next cycle.They swim between the two bars,and the FL signals can be gradually enhanced to maximum after several cycles.The FL signals from released encoded probes can be used to detect the analytes.In particular,live pathogens can be distinguished from dead ones by using an assay.The detection limits and linear range for S.T.and V.P.were 30 and 10 CFU/mL and 102-106 CFU/mL,respectively.Therefore,this assay has broad application potential for simultaneous on-site detection of various live pathogenic bacteria in water.
ABSTRACT
Protein–protein interactions (PPIs) are important for the study of protein functions and pathways involved in differentbiological processes, as well as for understanding the cause and progression of diseases. Several high-throughput experimental techniques have been employed for the identification of PPIs in a few model organisms, but still, there is a huge gapin identifying all possible binary PPIs in an organism. Therefore, PPI prediction using machine-learning algorithms hasbeen used in conjunction with experimental methods for discovery of novel protein interactions. The two most popularsupervised machine-learning techniques used in the prediction of PPIs are support vector machines and random forestclassifiers. Bayesian-probabilistic inference has also been used but mainly for the scoring of high-throughput PPI datasetconfidence measures. Recently, deep-learning algorithms have been used for sequence-based prediction of PPIs. Severalclustering methods such as hierarchical and k-means are useful as unsupervised machine-learning algorithms for theprediction of interacting protein pairs without explicit data labelling. In summary, machine-learning techniques have beenwidely used for the prediction of PPIs thus allowing experimental researchers to study cellular PPI networks.
ABSTRACT
Background: Aptamers are single-stranded nucleic acids, so-called ‘artificial antibodies’, identified from the randomized combinatorial library against the target by the process called ‘SELEX’ (Systematic Evolution of Ligands by EXponential enrichment). Target can have any sizes from small molecules to the whole cell, attests the versatility of aptamers to bind a wide range of targets. Aptamers have several advantages over antibodies, such as they are easy to prepare, cheaper, have no batch variations, are easy to modify, stable and most importantly, non-immunogenic. Because of these positive characteristics, aptamers are incorporated in different fields, and most attractive in the applications involving therapeutics and diagnoses (theranostics). With either aptamers alone or complementing with antibodies, several high sensitive, portable sensors have been demonstrated for use in ‘bedside analysis’. Moreover, aptamers are more amenable to chemical modifications, making them capable of utilization with the most developed aptasensors (aptamerbased sensors). Significance: The development of more sensitive aptasensors could be useful and important for medical diagnosis, identification of pathogens for the quality control of consumable items, and surveillance of emerging diseases. In fact, aptasensors have already shown their efficacy in the detection of life threatening diseases caused by early stage of viral infections. In this review, role of aptasensors in detecting pathogenic viruses are overviewed. Keywords: Anti-virus; aptamer; aptasensor; bedside analysis; SELEX
ABSTRACT
Aptamers are oligonucleotides which can combine targets with high affinity and specificity.Graphene oxide is a kind of new material with many unique physical and chemical properties.Recently, graphene oxide is gradually applied to the field of aptamers and has made a series of progress.This review focused on the application progress of graphene oxide and aptamers in the detection of different targets including small molecules and metal ion, biomacromolecules and cells in order to provide references for the mass application of graphene oxide and aptamers in the field of detection .
ABSTRACT
An electrochemical aptasensor for Pb2+ was constructed based on the layer-by-layer assembly of graphene (GR) and anthraquinone -2-sulfonic acid sodium salt (AQMS) on the surface of Pb2+ aptamer. The mercapto-modified Pb2+ aptamer was first anchored on a gold electrode. Then the highly conductive material of GR was adsorbed on apt through the unique π-π stacking interaction, which was further used for the assembly and signal amplification of the electroactive AQMS. Upon interaction with Pb2+ ions, the apt on the aptasensor undergone conformational switch from a single-stranded form to the G-quadruplex structure, causing the GR assembled with AQMS releasing from the electrode surface into solution. As a result, the electrochemical signal of AQMS on the aptasensor was substantially reduced. Base on this concept, a useful platform for detection of Pb2+ ions was constructed. Under the optimal experimental conditions, the attenuation of peak currents (△Ipa ) showed linear relationship with the logarithm of Pb2+ concentrations (lgCPb2+) over the range from 5. 0×10-10 to 5. 0×10-8 mol/ L. The detection limit was estimated to be 6. 0×10-11 mol/ L.
ABSTRACT
A new method for O-ethyl S-[2-( diisopropylamino) ethyl] methylphosphonothiolate ( VX) , sarin detection and its kinetic analysis based on piezoresistive microcantilever aptasensor was developed, where VX, sarin aptamers were immobilized on the microcantilever surface by biotin-avidin binding system. A linear relationship between the response voltage and the concentration of VX in the range of 2-60μg/L was obtained. The linear regression equation was △Ue=0. 886C-1. 039 (n=5, R=0. 984, p<0. 001) and the detection limit was 2μg/L ( S/N≥3 ) . A linear relationship between the response voltage and the concentration of sarin in the range of 10-60 μg/L was obtained, the linear regression equation was △Ue=0. 716C-2. 304 ( n=5, R=0 . 996 , p<0 . 001 ) and the detection limit was 10 μg/L ( S/N≥3 ) . The sensor showed no response for O-butyl methylphosphonochloridate, a structural analog of VX and sarin, which indicated high specificity and good anti-interference ability. On this basis, a reaction kinetic model based on receptor-ligand binding and the relationship with output voltage change was established. Response voltage (△Ue ) and response time( t0 ) were obtained from the fitting equation on different concentrations of VX, sarin fitted well with the measured values.
ABSTRACT
Pd nanoparticles (Pd NPs) were electrodeposited on the glassy carbon electrode (GCE). Then, Pd NPs/GCE was further inserted into H2 SO4 solution to polarize for 5 min to absorb a certain amount of active hydrogen. Then, the electrode was quickly inserted to HAuCl4 solution for 15 min. AuNPs were deposited spontaneously on the surface of Pd NPs due to the reduction of active hydrogen. As a result, Pd-AuNPs were modified on the surface of GCE. Next, aptamer I of thrombin was immobilized on Pd-Au nanoparticles. In the presence of thrombin, it bound with the aptamer immobilized on Pd-Au nanoparticles and thus the formed complex obstructed the catalysis of Pd-Au nanoparticles to H2 O2 . Hence, the reduction current of H2 O2 decreased with the increase of thrombin concentration. The aptamer sensor had a good linear relationship with the concentration of thrombin in the range of 2. 98-297 nmol/L with a detection limit of 0. 98 nmol/L.