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BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

5.1. Detection Formats 475 5.2. Food Quality and Safety Analysis 477 5.2.1. Pathogens 477 5.2.2. Toxins 479 5.2.3. Veterinary Drugs 479 5.2.4. Vitamins 480 5.2.5. Hormones 480 5.2.6. Diagnostic Antibodies 480 5.2.7. Allergens 481 5.2.8. Proteins 481 5.2.9. Chemical Contaminants 481 5.3. Medical Diagnostics 481 5.3.1. Cancer Markers 481 5.3.2. Antibodies against Viral Pathogens 482 5.3.3. Drugs and Drug-Induced Antibodies 483 5.3.4. Hormones 483 5.3.5. Allergy Markers 483 5.3.6. Heart Attack Markers 484 5.3.7. Other Molecular Biomarkers 484 5.4. Environmental Monitoring 484 5.4.1. Pesticides 484 5.4.2. 2,4,6-Trinitrotoluene (TNT) 485 5.4.3. Aromatic Hydrocarbons 485 5.4.4. Heavy Metals 485 5.4.5. Phenols 485 5.4.6. Polychlorinated Biphenyls 487 5.4.7. Dioxins 487 5.5. Summary 488 6. Conclusions 489 7. Abbreviations 489 8. Acknowledgment 489 9. References 489

Surface Plasmon Resonance Sensors for Detection of Chemical and Biological Species

Infrared spectroscopy of proteins.

Sensitive optical biosensors for unlabeled targets: a review.

Near infrared Raman spectra of human brain lipids.

Infrared (IR) and Raman spectroscopy are emerging biophotonic tools to recognize various diseases. The current review gives an overview of the experimental techniques, data-classification algorithms and applications to assess soft tissues, hard tissues and body fluids. The methodology section presents the principles to combine vibrational spectroscopy with microscopy, lateral information and fiber-optic probes. A crucial step is the classification of spectral data by a variety of algorithms. We discuss unsupervised algorithms such as cluster analysis or principal component analysis and supervised algorithms such as linear discriminant analysis, soft independent modeling of class analogies, artificial neural networks support vector machines, Bayesian classification, partial least-squares regression and ensemble methods. The selected topics include tumors of epithelial tissue, brain tumors, prion diseases, bone diseases, atherosclerosis, kidney stones and gallstones, skin tumors, diabetes and osteoarthritis.

Disease recognition by infrared and Raman spectroscopy.

Preface PART I: Basic Methodology INFRARED AND RAMAN INSTRUMENTATION FOR MAPPING AND IMAGING Introduction to Mapping and Imaging Mid-Infrared Microspectroscopy and Mapping Raman Microspectroscopy and Mapping Near-Infrared Hyperspectral Imaging Raman Hyperspectral Imaging Mid-Infrared Hyperspectral Imaging Mapping with Pulsed Terahertz Radiation Summary CHEMOMETRIC TOOLS FOR IMAGE ANALYSIS Introduction Hyperspectral Images: The Measurement Image Preprocessing Exploratory Image Analysis Quantitative Image Information: Multivariate Image Regression (MIR) Image Segmentation Image Resolution Future Trends PART II: Biomedical Applications VIBRATIONAL SPECTROSCOPIC IMAGING OF SOFT TISSUE Introduction Preparation of Soft Tissue for Vibrational Spectroscopic Imaging Applications to Soft Tissue Conclusions VIBRATIONAL SPECTROSCOPIC ANALYSIS OF HARD TISSUES Introduction Importance of Tissue Age versus Specimen Age FT-IR Spectroscopy Raman Spectroscopy Clinical Applications of Raman Spectroscopy MEDICAL APPLICATIONS OF INFRARED SPECTRAL IMAGING OF INDIVIDUAL CELLS Introduction Methods Results and Discussion Future Potential of SCP/Conclusions PART III: Agriculture, Plants, and Food INFRARED AND RAMAN SPECTROSCOPIC MAPPING AND IMAGING OF PLANT MATERIALS Introduction, Background, and Perspective Application of Mapping and Imaging to Horticultural Crops Application of Mapping and Imaging to Agricultural Crops Mapping and Imaging of Wild Plants and Trees Application of Mapping and Imaging to Algae Interaction Between Plant Tissue and Plant Pathogens NIR HYPERSPECTRAL IMAGING FOR FOOD AND AGRICULTURAL PRODUCTS Introduction HSI as a "Super" NIR Analyzer NIR HS Imager as a "Super" Vision System Conclusion PART IV: Polymers and Pharmaceuticals FT-IR AND NIR SPECTROSCOPIC IMAGING: PRINCIPLES, PRACTICAL ASPECTS, AND APPLICATIONS IN MATERIAL AND PHARMACEUTICAL SCIENCE Introduction Instrumentation for NIR and FT-IR Imaging Applications of FT-IR and FT-NIR Imaging for Polymer Characterization NIR Imaging Spectroscopy for Quality Control of Pharmaceutical Drug Formulations FT-IR Spectroscopic Imaging of Inorganic Materials FT-IR IMAGING IN ATR AND TRANSMISSION MODES: PRACTCAL CONSIDERATIONS AND EMERGING APPLICATIONS FT-IR Imaging: Introduction FT-IR Imaging: Technical Considerations Practical Applications Conclusion and Outlook TERAHERTZ IMAGING OF DRUG PRODUCTS Introduction Low Wavenumber Region in the Infrared Spectrum THz-TDS Technology and Applications THz Imaging in the Pharmaceutical Industry Going Forward Competition versus Cost: A Challenge for the Future Conclusion PART V: Imaging Beyond the Diffraction Limit SPECTROSCOPIC IMAGING OF BIOLOGICAL SAMPLES USING NEAR-FIELD METHODS Tip-Enhanced Raman Scattering (TERS) Detection of Biomolecules Biopolymers Membranes, Viruses, and Bacteria Conclusion INFRARED MAPPING BELOW THE DIFFRACTION LIMIT Introduction and Description of Early Work Near-Field Microscopy by Elastic Scattering from a Tip PART VI: Developments in Methodology SUBSURFACE RAMAN SPECTROSCOPY IN TURBID MEDIA Introduction Techniques for Deep Noninvasive Raman Spectroscopy Examples of Application Areas Conclusions NONLINEAR VIBRATIONAL SPECTROSCOPIC MICROSCOPY OF CELLS AND TISSUE Introduction Principles of Nonlinear Optical Imaging Instrumentation for Multimodal Nonlinear Microscopy Applications WIDEFIELD FT-IR 2D AND 3D IMAGING AT THE MICROSCALE USING SYNCHROTRON RADIATION Introduction Optical Evaluation Mathematical Evaluation of Hyperspectral Cubes Widefield versus Raster Scanning Geometries Examples Conclusions Index

Infrared and Raman spectroscopic imaging

We report for the first time a proof-of-concept experiment employing Raman spectroscopy to detect intracerebral tumors in vivo by brain surface mapping. Raman spectroscopy is a non-destructive biophotonic method which probes molecular vibrations. It provides a specific fingerprint of the biochemical composition and structure of tissue without using any labels. Here, the Raman system was coupled to a fiber-optic probe. Metastatic brain tumors were induced by injection of murine melanoma cells into the carotid artery of mice, which led to subcortical and cortical tumor growth within 14 days. Before data acquisition, the cortex was exposed by creating a bony window covered by a calcium fluoride window. Spectral contributions were assigned to proteins, lipids, blood, water, bone, and melanin. Based on the spectral information, Raman images enabled the localization of cortical and subcortical tumor cell aggregates with accuracy of roughly 250 μm. This study demonstrates the prospects of Raman spectroscopy as an intravital tool to detect cerebral pathologies and opens the field for biophotonic imaging of the living brain. Future investigations aim to reduce the exposure time from minutes to seconds and improve the lateral resolution.

Raman spectroscopic imaging for in vivo detection of cerebral brain metastases

This study assessed the diagnostic potential of Raman spectroscopic mapping by evaluating its ability to distinguish between normal brain tissue and the human intracranial tumors gliomas and meningeomas. Seven Raman maps of native specimens were collected ex vivo by a Raman spectrometer with 785 nm excitation coupled to a microscope with a motorized stage. Variations within each Raman map were analyzed by cluster analysis. The dependence of tissue composition on the tissue type in cluster averaged Raman spectra was shown by linear combinations of reference spectra. Normal brain tissue was found to contain higher levels of lipids, intracranial tumors have more hemoglobin and lower lipid to protein ratios, meningeomas contain more collagen with maximum collagen content in normal meninges. One sample was studied without freezing. Whereas tumor regions did not change significantly, spectral changes were observed in the hemoglobin component after snap freezing and thawing to room temperature. The results constitute a basis for subsequent Raman studies to develop classification models for diagnosis of brain tissue.

Reiner Salzer

Papers

Near infrared Raman spectra of human brain lipids.

Disease recognition by infrared and Raman spectroscopy.

Infrared and Raman spectroscopic imaging

Raman spectroscopic imaging for in vivo detection of cerebral brain metastases

Near infrared Raman spectroscopic mapping of native brain tissue and intracranial tumors