DETECTION OF BIOAGENTS USING A SHEAR HORIZONTAL SURFACE ACOUSTIC WAVE BIOSENSOR

    公开(公告)号:US20190242888A1

    公开(公告)日:2019-08-08

    申请号:US16277618

    申请日:2019-02-15

    Abstract: Viruses and other bioagents are of high medical and biodefense concern and their detection at concentrations well below the threshold necessary to cause health hazards continues to be a challenge with respect to sensitivity, specificity, and selectivity. Ideally, assays for accurate and real time detection of viral agents and other bioagents would not necessitate any pre-processing of the analyte, which would make them applicable for example to bodily fluids (blood, sputum) and man-made as well as naturally occurring bodies of water (pools, rivers). We describe herein a robust biosensor that combines the sensitivity of surface acoustic waves (SAW) generated at a frequency of 325 MHz with the specificity provided by antibodies and other ligands for the detection of viral agents. In preferred embodiments, a lithium tantalate based SAW transducer with silicon dioxide waveguide sensor platform featuring three test and one reference delay lines was used to adsorb antibodies directed against Coxsackie virus B4 or the negative-stranded category A bioagent Sin Nombre virus (SNV), a member of the genus Hantavirus, family Bunyaviridae, negative-stranded RNA viruses. Rapid detection (within seconds) of increasing concentrations of viral particles was linear over a range of order of magnitude for both viruses, although the sensor was approximately 50×104-fold more sensitive for the detection of SNV. For both pathogens, the sensor's selectivity for its target was not compromised by the presence of confounding Herpes Simplex virus type 1. The biosensor was able to detect SNV at doses lower than the load of virus typically found in a human patient suffering from hantavirus cardiopulmonary syndrome (HCPS). Further, in a proof-of-principle real world application, the SAW biosensor was capable of selectively detecting SNV agents in complex solutions, such as naturally occurring bodies of water (river, sewage effluent) without analyte pre-processing.

    System and methods for detecting ribosome inhibition

    公开(公告)号:US10365269B2

    公开(公告)日:2019-07-30

    申请号:US15577809

    申请日:2016-06-02

    Abstract: This disclosure describes a cell genetically modified to detect ribosome inhibition in the cell and methods involving such a cell. Generally, the genetically-modified cell includes an aminoglycoside-sensitive orthogonal 16S rRNA (O-16S) coding region bearing a mutated anti-Shine-Dalgarno (O-ASD) sequence, a repressor/operator system, and a polynucleotide encoding a detectable reporter under transcriptional control of the repressor/operator system. The repressor/operator system includes a coding region that encodes a transcriptional regulator and having an orthogonal SD (O-SD) sequence complementary to the 16S rRNA O-ASD sequence. The operator sequence, which is repressable by the transcriptional regulator, is operably linked to the polynucleotide encoding a detectable reporter.

    Methods and systems for detecting cancer

    公开(公告)号:US10278649B2

    公开(公告)日:2019-05-07

    申请号:US15531135

    申请日:2015-11-24

    Applicant: STC.UNM

    Abstract: A technique for classifying lesions as malignant or benign is disclosed. The technique can include: cooling an area of skin including a lesion of a patient to initiate a warm-up process; receiving a temporal sequence of thermal images of the area of skin representing a thermal recovery of the area of skin, the temporal sequence of thermal images generated by an infrared camera; generating a temporal profile of the thermal recovery based on the temporal sequence of thermal images; analyzing temporal statistical properties of the temporal profile; determining a malignancy probability that the lesion is malignant based on an analysis of the temporal profile, wherein the determining includes extracting one or more statistical features based on continuous-time stochastic signals in the sequence of thermal images; and classifying the lesion based on the malignancy probability.

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