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4407results about "Raman scattering" patented technology

Detection of nucleic acids and nucleic acid units

PCT No. PCT/GB96/01830 Sec. 371 Date Apr. 21, 1998 Sec. 102(e) Date Apr. 21, 1998 PCT Filed Jul. 25, 1996 PCT Pub. No. WO97/05280 PCT Pub. Date Feb. 13, 1997The invention relates to the detection of target nucleic acids or nucleic acid units in a sample, by obtaining a SER(R)S spectrum for a SER(R)S-active complex containing, or derived directly from, the target. The complex includes at least a SER(R)S-active label, and optionally a target binding species containing a nucleic acid or nucleic acid unit. In this detection method, the concentration of the target present in the SER(R)S-active complex, or of the nucleic acid or unit contained in the target binding species in the SER(R)S-active complex, is no higher than 10-10 moles per liter. Additionally or alternatively, one or more of the following features may be used with the method: i) the introduction of a polyamine; ii) modification of the target, and/or of the nucleic acid or nucleic acid unit contained in the target binding species, in a manner that promotes or facilitates its chemi-sorption onto a SER(R)S-active surface; iii) inclusion of a chemi-sorptive functional group in the SER(R)S-active label. The invention also provides SER(R)S-active complexes for use in such a method, a kit for use in carrying out the method or preparing the complexes and a method for sequencing a nucleic acid which comprises the use of the detection method to detect at least one target nucleotide or sequence of nucleotides within the acid.
Owner:RENISHAW DIAGNOSTICS

Assessing blood brain barrier dynamics or identifying or measuring selected substances or toxins in a subject by analyzing Raman spectrum signals of selected regions in the eye

InactiveUS6574501B2Reduced energy/density exposure ratingImproved margin of safetyRaman scatteringDiagnostic recording/measuringConjunctivaNon invasive
A non-invasive method for analyzing the blood-brain barrier includes obtaining a Raman spectrum of a selected portion of the eye and monitoring the Raman spectrum to ascertain a change to the dynamics of the blood brain barrier. Also, non-invasive methods for determining the brain or blood level of an analyte of interest, such as glucose, drugs, alcohol, poisons, and the like, comprises: generating an excitation laser beam (e.g., at a wavelength of 600 to 900 nanometers); focusing the excitation laser beam into the anterior chamber of an eye of the subject so that aqueous humor, vitreous humor, or one or more conjunctiva vessels in the eye is illuminated; detecting (preferably confocally detecting) a Raman spectrum from the illuminated portion of the eye; and then determining the blood level or brain level (intracranial or cerebral spinal fluid level) of an analyte of interest for the subject from the Raman spectrum. In certain embodiments, the detecting step may be followed by the step of subtracting a confounding fluorescence spectrum from the Raman spectrum to produce a difference spectrum; and determining the blood level and/or brain level of the analyte of interest for the subject from that difference spectrum, preferably using linear or nonlinear multivariate analysis such as partial least squares analysis. Apparatus for carrying out the foregoing methods are also disclosed.
Owner:CHILDRENS HOSPITAL OF LOS ANGELES +1

Assessing blood brain barrier dynamics or identifying or measuring selected substances, including ethanol or toxins, in a subject by analyzing Raman spectrum signals

InactiveUS7398119B2Fast “ triage ” assessmentReliable and faster treatment decisionRadiation pyrometrySpectrum investigationNon invasivePhysics
A non-invasive method for analyzing the blood-brain barrier includes obtaining a Raman spectrum of a selected portion of the eye and monitoring the Raman spectrum to ascertain a change to the dynamics of the blood brain barrier.
Also, non-invasive methods for determining the brain or blood level of an analyte of interest, such as glucose, drugs, alcohol, poisons, and the like, comprises: generating an excitation laser beam at a selected wavelength (e.g., at a wavelength of about 400 to 900 nanometers); focusing the excitation laser beam into the anterior chamber of an eye of the subject so that aqueous humor, vitreous humor, or one or more conjunctiva vessels in the eye is illuminated; detecting (preferably confocally detecting) a Raman spectrum from the illuminated portion of the eye; and then determining the blood level or brain level (intracranial or cerebral spinal fluid level) of an analyte of interest for the subject from the Raman spectrum. In certain embodiments, the detecting step may be followed by the step of subtracting a confounding fluorescence spectrum from the Raman spectrum to produce a difference spectrum; and determining the blood level and/or brain level of the analyte of interest for the subject from that difference spectrum, preferably using linear or nonlinear multivariate analysis such as partial least squares analysis. Apparatus for carrying out the foregoing methods are also disclosed.
Owner:CALIFORNIA INST OF TECH +1

Augmented classical least squares multivariate spectral analysis

A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.
Owner:NAT TECH & ENG SOLUTIONS OF SANDIA LLC
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