Abstract: The disclosure provides for a system and method for detecting a threat agent. A sample is illuminated to produce photons Raman scattered and emitted by the sample. The Raman scattered photons are collected using time-gated detection without collecting the emitted photons. A Raman spectroscopic data set is generated from said Raman scattered photons wherein said Raman spectroscopic data comprises at least one of a Raman spectrum and a Raman chemical image. The Raman spectroscopic data is assessed to thereby determine the presence or absence of a threat agent in the sample. The sample may be in a target area. The sample may be illuminated using a pulsed laser or an intensity modulated laser. The illumination source may be synchronized with a gating element that enables time-gated detection.

Abstract: A system and method for determining at least one of: a disease state, a metabolic state, a clinical outcome, and a disease progression of a test renal or prostate sample. A test Raman data set is obtained from the sample wherein said test Raman data set may comprise at least one of a plurality of Raman spectra and a plurality of spatially accurate wavelength resolved Raman images. The test Raman data set is compared to a plurality of reference Raman data sets using a chemometric technique. For analysis of renal samples, each of these reference Raman data sets may have an associated known renal sample and an associated known metabolic state, clinical outcome, and/or disease progression. For analysis of prostate samples, each of these reference Raman data sets may have an associated known prostate sample and an associated known disease state, metabolic state, clinical outcome, and/or disease progression.

Abstract: A method for generating an image of a sample that is informative of the disease state of a cell in the sample. A sample including the cell is irradiated with monochromatic light. The Raman scattered light is assessed. A digital brightfield image of the Raman scattered light is generated and combined with the Raman scattered light emitted by the cell whereby the Raman scattered light is informative of the disease state of the cell in the sample. The method can also be used to determine the metabolic activity of the cell, the inflammatory status of the cell and/or the infected status of the cell in the sample.

Abstract: A system and method to predict the progression of disease of a test sample. A group of known biological samples is provided. Each known biological sample has an associated known outcome including a non-diseased sample or a diseased sample. A Raman data set is obtained for each known biological sample. Each Raman data set is analyzed to identify a diseased or non-diseased reference Raman data set depending on whether respective biological sample is the non-diseased sample or the diseased sample. A first database is generated where the first database contains reference Raman data sets for all diseased samples. A second database is generated where the second database contains reference Raman data sets for all non-diseased samples. A test Raman data set of a test biological sample is received, where the test biological sample has an unknown disease status. A diagnostic is provided as to whether the test sample is a non-diseased sample or a diseased sample.

Abstract: System and method for determining a disease state of a sample. A sample is positioned in a field of view and a first spectroscopic data set is obtained. The positional information is stored and the sample is treated with a contrast enhancing agent. The sample is repositioned in the field of view and a digital image is obtained. The spectroscopic data is linked with the digital image and a database comprising representative spectroscopic data sets is searched to classify the disease state of the sample. The disclosure also provides for the step of obtaining a processed derivative image and searching a database comprising representative processed derivative images to classify a disease state of the sample.

Abstract: A spectroscopic method and system to identify a biofilm of a microorganism. A sample containing a sample microorganism is irradiated with substantially monochromatic radiation. A Raman data set is obtained based on radiation scattered from the irradiated sample. A database is searched in accordance with the Raman data set in order to identify a known Raman data set from the database. The database contains a plurality of known Raman data sets where each known Raman data set is associated with a known sessile form of a corresponding known microorganism. A sessile form of the sample microorganism is identified based on the known Raman data set identified by the searching.

Abstract: A system and method to provide a diagnosis of the renal disease state of a test renal sample. A database containing a plurality of reference Raman data sets is provided where each reference Raman data set has an associated known renal sample and an associated known renal disease state. A test renal sample is irradiated with substantially monochromatic light to generate scattered photons resulting in a test Raman data set. The test Raman data set is compared to the plurality of reference Raman data sets using a chemometric technique. Based on the comparison, a diagnosis of a renal disease state of the test renal sample is provided. The renal disease state includes renal oncocytoma or chromophobe renal carcinoma disease state.

Abstract: A system and method of correlating Raman measurements with digital images of a sample so as to classify the sample's disease state. A spectroscopic data set is obtained for the sample positioned in the field of view of a spectroscopic device. With the sample removed from the field of view, the sample is treated with a contrast enhancing agent. The treated sample is repositioned in the spectroscopic device's field of view and a digital image of the treated sample is obtained. The spectroscopic data set is linked with the digital image by defining a transformation to map the image spatial coordinates of the digital image to the spectral spatial coordinates of the spectroscopic data. For the spectroscopic data set of the sample, the database is searched to identify a spectroscopic data set, of a known sample having well characterized pathology, which matches the sample's spectroscopic data set.

Abstract: A system and method to distinguish normal cells from cells having undergone a biochemical change. A pre-determined vector space is selected where the vector space mathematically describes a first plurality of reference spectral data sets for normal cells and a second plurality of reference spectral data sets for cells having undergone a biochemical change. A sample is irradiated to generate a target spectral data set based on photons absorbed, reflected, emitted, or scattered by the sample. The target spectral data set is transformed into a pre-determined vector space. A distribution of transformed data is analyzed in the pre-determined vector space. Based on the analysis, the sample is classified as containing normal cells, cells having undergone a biochemical change, and combinations thereof. The method includes treating the sample with a pharmaceutical agent prior to irradiating the sample and using the classification to assess the efficiency of the pharmaceutical agent.

Abstract: A system and method of determining an attribute of a biological tissue sample or a drug delivery device. A sample is illuminated with substantially monochromatic light to thereby generate Raman scattered photons. The Raman scattered photons are assessed to thereby generate a spectroscopic data set wherein said spectroscopic data set comprises at least one of: a Raman spectra and a spatially accurate wavelength resolved image. Tile spectroscopic data set is evaluated to determine at least one of: an attribute of a biological tissue sample and a drug delivery device. In one embodiment, the biological tissue comprises arterial tissue. In another embodiment, the drug delivery device is a drug-eluting stent. In another embodiment, Raman chemical imaging can be used to evaluate a sample and identify at least one of: the tissue, a drug, a drug delivery device, and a matrix associated with a drug delivery device.

Abstract: A system and method to distinguish normal cells from apoptotic cells. A pre-determined vector space is selected where the vector space mathematically describes a first plurality of reference Raman data sets for normal cells and a second plurality of reference Raman data sets for apoptotic cells. A sample is irradiated with substantially monochromatic light generating a target Raman data set based on scattered photons. The target Raman data set is transformed into a vector space defined by the pre-determined vector space. A distribution of transformed data is analyzed in the pre-determined vector space. Based on the analysis, the sample is classified as containing normal cells, apoptotic cells, and a combination of normal and apoptotic cells. The sample includes the step of treating the sample with a pharmaceutical agent prior to irradiating the sample. Based on the classification, the therapeutic efficiency of the pharmaceutical agent is assessed.

Abstract: A system and method to automatically obtain spectra for samples. The method involves a two phase process including a photobleaching phase and a spectral acquisition phase. In the photobleaching phase, a series of spectral data sets of a sample are collected. A relative difference is determined between the background of subsequent spectral data sets is determined and compared to a predetermined threshold value. If threshold difference is less than the relative difference between the background of subsequent spectral data sets, the steps of collecting a series of spectra data sets is automatically repeated. In the spectrum acquisition phase, a series of Raman data sets of the sample are collected until a target SNR is obtained.

Abstract: A method to diagnosis a disease state of an unknown sample. A test Raman data set for an unknown sample is generated. A reference Raman database is provided where the database contains a plurality of reference Raman data sets and a plurality of reference Raman difference data sets. The reference Raman difference data set is generated by determining a difference between a first reference Raman data set and a second reference Raman data set. A first reference Raman data set is associated with first known sample and associated with one or more of: a first known disease state and a first known clinical outcome. A second reference Raman data set is associated with a second known sample and associated with one or more of: a second known disease state and a second known clinical outcome.

Abstract: A spectroscopic method and system to identify a biofilm of a microorganism. A sample containing a sample microorganism is irradiated with substantially monochromatic radiation. A Raman data set is obtained based on radiation scattered from the irradiated sample. A database is searched in accordance with the Raman data set in order to identify a known Raman data set from the database. The database contains a plurality of known Raman data sets where each known Raman data set is associated with a known sessile form of a corresponding known microorganism. A sessile form of the sample microorganism is identified based on the known Raman data set identified by the searching.

Abstract: An ink delivery device for depositing ink onto a media includes an ink reservoir, a reaction chamber situated near the reservoir, and a valve situated between the reservoir and the reaction chamber. A method of depositing ink onto a media is also disclosed.

Abstract: A system and method to provide a diagnosis of the renal disease state of a test renal sample. A database containing a plurality of reference Raman data sets is provided where each reference Raman data set has an associated known renal sample and an associated known renal disease state. A test renal sample is irradiated with substantially monochromatic light to generate scattered photons resulting in a test Raman data set. The test Raman data set is compared to the plurality of reference Raman data sets using a chemometric technique. Based on the comparison, a diagnosis of a renal disease state of the test renal sample is provided. The renal disease state includes renal oncocytoma or chromophobe renal carcinoma disease state.

Abstract: A system and method to provide a diagnosis of the breast disease state of a test breast sample. A database containing a plurality of reference Raman data sets is provided where each reference Raman data set has an associated known breast sample and an associated known breast disease state. A test breast sample is irradiated with substantially monochromatic light to generate scattered photons resulting in a test Raman data set. The test Raman data set is compared to the plurality of reference Raman data sets using a chemometric technique. Based on the comparison, a diagnosis of a breast disease state of the test breast sample is provided. The breast disease state includes invasive ductal carcinoma or invasive lobular carcinoma disease state.

Abstract: A system and method of correlating Raman measurements with digital images of a treated sample to classify the disease state of the sample. A spectroscopic data set is obtained for the sample positioned in the field of view of a spectroscopic device. Information about the field of view is stored. The sample is removed from the field of view and treated. The treated sample is repositioned in the field of view using the stored information. A digital image of the treated sample is obtained and the spectroscopic data set is linked with the digital image. A database is provided having a plurality of spectroscopic data sets. Each data set is linked to a corresponding digital image, and associated with the known sample. Each corresponding digital image is associated with the known treated samples. The database is searched to identify and match a data set of a known sample and the sample.

Abstract: A spectroscopic method and system to identify a biofilm of a microorganism. A sample containing a sample microorganism is irradiated with substantially monochromatic radiation. A Raman data set is obtained based on radiation scattered from the irradiated sample. A database is searched in accordance with the Raman data set in order to identify a known Raman data set from the database. The database contains a plurality of known Raman data sets where each known Raman data set is associated with a known sessile form of a corresponding known microorganism. A sessile form of the sample microorganism is identified based on the known Raman data set identified by the searching.

Abstract: A system and method to predict the progression of disease of a test sample. A group of known biological samples is provided. Each known biological sample has an associated known outcome including a non-diseased sample or a diseased sample. A Raman data set is obtained for each known biological sample. Each Raman data set is analyzed to identify a diseased or non-diseased reference Raman data set depending on whether respective biological sample is the non-diseased sample or the diseased sample. A first database is generated where the first database contains reference Raman data sets for all diseased samples. A second database is generated where the second database contains reference Raman data sets for all non-diseased samples. A test Raman data set of a test biological sample is received, where the test biological sample has an unknown disease status. A diagnostic is provided as to whether the test sample is a non-diseased sample or a diseased sample.