Biomedical optical analytical techniques to analyse the medicinal drugs and optical properties of tissues
09 Sep 2013-International Journal of Biomedical Engineering and Technology (Inderscience Publishers Ltd)-Vol. 12, Iss: 1, pp 38
TL;DR: In this paper, the biomedical optical analytical techniques such as ultra-violet spectroscopy, Fourier transform infrared spectrograms, and Fourier Transform-Raman spectrographs are used to characterise and quantise the medicinal drugs.
Abstract: In this research paper, the biomedical optical analytical techniques such as ultra-violet spectroscopy, Fourier transform infrared spectroscopy and Fourier transform-Raman spectroscopy are used to characterise and quantise the medicinal drugs. The hydroxyl group and methyl group was assigned to the wave numbers 3230.67 cm–1 and 2928.50 cm–1. The functional groups are also identified –N=C=O, –N=C=S, –N=C=N–, –N3 at 4.67 µm. The CH, CH2, and CH3 three bands with strong intensity are assigned to the wave numbers 2985 cm–1 to 2850 cm–1. The FT-Raman spectra is obtained for the blue sample, the functional group C≡C is assigned to the wave number 2129.84 to 2191.41 cm–1. The wave numbers 2558.71–2620.73 cm–1 are obtained for the functional bond –S–H. The wave number 1610.95 cm–1 is obtained for the C=N functional group. In this paper one sample namely Fibril-SF is taken by a patient and his Saliva sample is collected for Fourier Transform Infrared spectroscopy test to analyse the reaction of drugs with human body tissues.
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TL;DR: The spectral bio-diagnosis of normal human body saliva sample shows the following functional compounds and it is related to various proteins and enzymes as mentioned in this paper : the hydroxyl group is observed in the form of O-H at 3,305 cm−1, because of the presence of lipids, the functional group C-H is obtained from 2,928 to 2,856 cm− 1, due to the presence amide-I in the forms of C=N and C=C obtained at 1,658 cm−2, the proteins are exhibited.
Abstract: The spectral bio-diagnosis of normal human body saliva sample shows the following functional compounds and it is related to various proteins and enzymes. Because of the presence of water in the saliva sample, the hydroxyl group is observed in the form of O–H at 3,305 cm−1, because of the presence of lipids, the functional group C–H is obtained from 2,928 to 2,856 cm−1, due to the presence of amide-I in the form of C=N and C=C obtained at 1,658 cm−1, the proteins are exhibited. Due to the presence of aliphatic CH2, the Lipids, Adenine, Cytosine, Collagen are observed at 1,455 cm−1, because of the presence of Carbohydrates, Phospholipids, Nucleic acids, the functional groups C=O and P=O from 1,159 to 1,064 cm−1 are exhibited. Due to the presence of Phenylalanine, Tyrosine, Cystine and Hydroxyapatite C–C twist, C–C stretch, C–S stretch and PO42− are observed at 748 and 483 cm−1. Silver nanoparticle has attracted considerable interest due to their extensive applicability in various areas such as electronics, catalysis, chemistry, energy and medicine. To study the opto-electronics properties of the samples, it was mixed with silver nanoparticles and characterized.
5 citations
TL;DR: This study was the first-step in developing a deflection feedback controlled surgical instrument in the needle-assisted percutaneous operation and provided understanding on the mechanics of needle insertion.
Abstract: Needle insertion for minimally-invasive surgery is a technique explored and studied for percutaneous procedure, diagnosis, localised therapeutic drug-delivery, and biopsy. While the instruments and techniques determine the success of every surgical procedure, minimal attention was given to the medium, interaction between tissue and needle, development tools and surgical techniques. This paper addresses the interaction by studying the needle deflection during insertion into porcine back tissue and simulated flesh-like tissue (gelatine). A customised testing set-up measures and quantifies these interactions. Needle deflection magnitude and insertion forces were measured and correlated to define the bio-mechanical properties of back abdomen tissue. Needle deflections were measured for gelatine analogues developed to model consistency of the tissues in the back lumbar region. Mathematical two-dimensional (2D) force-model was developed to provide understanding on the mechanics of needle insertion. This study was the first-step in developing a deflection feedback controlled surgical instrument in the needle-assisted percutaneous operation.
1 citations
References
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TL;DR: Raman spectroscopy shows promise as a method for objectively grading prostate cancer, and was able to correctly identify each pathological group studied with an overall accuracy of 89%.
Abstract: Raman spectroscopy is an optical technique, which provides a measure of the molecular composition of tissue. Raman spectra were recorded in vitro from both benign and malignant prostate biopsies, and used to construct a diagnostic algorithm. The algorithm was able to correctly identify each pathological group studied with an overall accuracy of 89%. The technique shows promise as a method for objectively grading prostate cancer.
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TL;DR: This review of OCT imaging applied within dermatology covers the application of OCT to normal skin, and reports on a large number of applications in the fields of non‐melanoma skin cancer, malignant melanomas, psoriasis and dermatitis, infestations, bullous skin diseases, tattoos, nails, haemangiomas, and other skin diseases.
Abstract: Optical coherence tomography (OCT) provides clinicians and researchers with micrometer-resolution, in vivo, cross-sectional images of human skin up to several millimeter depth. This review of OCT imaging applied within dermatology covers the application of OCT to normal skin, and reports on a large number of applications in the fields of non-melanoma skin cancer, malignant melanomas, psoriasis and dermatitis, infestations, bullous skin diseases, tattoos, nails, haemangiomas, and other skin diseases.
176 citations
"Biomedical optical analytical techn..." refers background in this paper
...Later researchers described standard measurement technique together with normal values for transillumination (Mogensen et al., 2009)....
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TL;DR: Successful mHu and μHu systems for drug development and systems biology will require low-volume microdevices that support chemical signaling, microfabricated pumps, valves and microformulators, automated optical microscopy, electrochemical sensors for rapid metabolic assessment, ion mobility-mass spectrometry for real-time molecular analysis, advanced bioinformatics, and machine learning algorithms for automated model inference and integrated electronic control.
Abstract: The sophistication and success of recently reported microfabricated organs-on-chips and human organ constructs have made it possible to design scaled and interconnected organ systems that may significantly augment the current drug development pipeline and lead to advances in systems biology. Physiologically realistic live microHuman (μHu) and milliHuman (mHu) systems operating for weeks to months present exciting and important engineering challenges such as determining the appropriate size for each organ to ensure appropriate relative organ functional activity, achieving appropriate cell density, providing the requisite universal perfusion media, sensing the breadth of physiological responses, and maintaining stable control of the entire system, while maintaining fluid scaling that consists of ~5 mL for the mHu and ~5 μL for the μHu. We believe that successful mHu and μHu systems for drug development and systems biology will require low-volume microdevices that support chemical signaling, microfabricated pumps, valves and microformulators, automated optical microscopy, electrochemical sensors for rapid metabolic assessment, ion mobility-mass spectrometry for real-time molecular analysis, advanced bioinformatics, and machine learning algorithms for automated model inference and integrated electronic control. Toward this goal, we are building functional prototype components and are working toward top-down system integration.
157 citations
11 Nov 2010
TL;DR: A simple and iterative method is developed that can be used to automate the preprocessing of data to identify segments with such noise for exclusion and this method is also suitable for real time applications.
Abstract: Functional Near-Infrared Spectroscopy (fNIR) is an optical brain monitoring technology that tracks changes in hemodynamic responses within the cortex. fNIR uses specific wavelengths of light, introduced at the scalp, to enable the noninvasive measurement of changes in the relative ratios of deoxygenated hemoglobin (deoxy-Hb) and oxygenated hemoglobin (oxy-Hb) during brain activity. This technology allows the design of portable, safe, affordable, noninvasive, and minimally intrusive monitoring systems that can be used to measure brain activity in natural environments, ambulatory and field conditions. However, for such applications fNIR signals can get prone to noise due to motion of the head. Improving signal quality and reducing noise, can be especially challenging for real time applications. Here, we study motion artifact related noise especially due to poor and changing sensor coupling. We have developed a simple and iterative method that can be used to automate the preprocessing of data to identify segments with such noise for exclusion and this method is also suitable for real time applications.
152 citations
Additional excerpts
...(Ayaz et al., 2010)....
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TL;DR: This paper addresses the denoising problem associated with magnetic resonance spectroscopic imaging (MRSI), where signal-to-noise ratio (SNR) has been a critical problem, and proposes a new scheme that exploits two low-rank structures that exist in MRSI data, one due to partial separability and the other due to linear predictability.
Abstract: This paper addresses the denoising problem associated with magnetic resonance spectroscopic imaging (MRSI), where signal-to-noise ratio (SNR) has been a critical problem. A new scheme is proposed, which exploits two low-rank structures that exist in MRSI data, one due to partial separability and the other due to linear predictability. Denoising is performed by arranging the measured data in appropriate matrix forms (i.e., Casorati and Hankel) and applying low-rank approximations by singular value decomposition (SVD). The proposed method has been validated using simulated and experimental data, producing encouraging results. Specifically, the method can effectively denoise MRSI data in a wide range of SNR values while preserving spatial-spectral features. The method could prove useful for denoising MRSI data and other spatial-spectral and spatial-temporal imaging data as well.
148 citations