Liu, Haijing. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 112-116). / Abstracts in English and Chinese. / Acknowledgements --- p.i / Abstract --- p.iii / 摘要 --- p.v / Table of contents --- p.vi / List of figures --- p.xii / List of tables --- p.xv / List of abbreviations --- p.xvi / Chapter / Chapter 1 --- Introduction / Chapter 1.1 --- Fermentation --- p.1 / Chapter 1.1.1 --- Biopharmaceutical production --- p.1 / Chapter 1.1.2 --- History of fermentation --- p.1 / Chapter 1.1.3 --- Fermentation Technology --- p.2 / Chapter 1.1.3.1 --- Fermentation process --- p.2 / Chapter 1.1.3.2 --- Fermenter --- p.4 / Chapter "1,1.3.3" --- Mode of fermentation process --- p.5 / Chapter 1.1.3.4 --- Bacterial growth --- p.6 / Chapter 1.1.4 --- High cell density fermentation for plasmid DNA production --- p.7 / Chapter 1.1.4.1 --- Porcine growth hormone releasing factor (pGRF) --- p.8 / Chapter 1.1.4.2 --- H5N1 avian influenza viruses DNA vaccine --- p.9 / Chapter 1.1.4.3 --- Fed-batch high-cell-density fermentation --- p.9 / Chapter 1.1.5 --- Fermentation process monitoring --- p.11 / Chapter 1.2 --- Near-infrared spectroscopy (NIRS) --- p.12 / Chapter 1.2.1 --- Basic near-infrared spectroscopy --- p.13 / Chapter 1.2.1.1 --- Rationale of near-infrared spectroscopy --- p.13 / Chapter 1.2.1.2 --- NIR spectra acquisition --- p.16 / Chapter 1.2.1.3 --- Interpretation of NIR spectra --- p.18 / Chapter 1.2.2 --- Multivariate calibration --- p.20 / Chapter 1.2.2.1 --- Why multivariate calibration --- p.22 / Chapter 1.2.2.2 --- The problem of collinearity --- p.25 / Chapter 1.2.2.3 --- Spectral range selection --- p.26 / Chapter 1.2.2.4 --- Signal optimization --- p.26 / Chapter 1.2.2.5 --- Spectral pretreatment --- p.27 / Chapter 1.2.2.6 --- Parameter selection --- p.28 / Chapter 1.2.3 --- Applications of NIRS in high cell density E. coli fermentation --- p.31 / Chapter 1.2.4 --- Adaptive calibration strategy --- p.33 / Chapter 1.3 --- Aims of study --- p.34 / Chapter 2 --- Materials and methods / Chapter 2.1 --- High cell density fermentation of plasmid DNA --- p.35 / Chapter 2.1.1 --- Fermentation system --- p.35 / Chapter 2.1.2 --- pGRF fermentation --- p.38 / Chapter 2.1.2.1 --- Prepare seed flask --- p.38 / Chapter 2.1.2.2 --- Reagents for bacterial culture by fermenter --- p.38 / Chapter 2.1.2.2.1 --- LB based complex fermentation medium --- p.38 / Chapter 2.1.2.2.2 --- Batch fermentation medium --- p.39 / Chapter 2.1.2.2.3 --- Feeding medium-1 --- p.41 / Chapter 2.1.2.2.4 --- Base feed --- p.41 / Chapter 2.1.2.3 --- Fermentation methods --- p.42 / Chapter 2.1.2.3.1 --- Fermenter set-up --- p.42 / Chapter 2.1.2.3.2 --- Inoculate seed culture into the fermenter --- p.43 / Chapter 2.1.2.3.3 --- Transfer the feeding medium --- p.44 / Chapter 2.1.2.3.4 --- Heat induction --- p.44 / Chapter 2.1.2.3.5 --- Fermentation harvest --- p.44 / Chapter 2.1.2.4 --- Scale-up fermentation and large scale production 45 / Chapter 2.1.3 --- H5N1 fermentation --- p.45 / Chapter 2.1.4 --- Reference analytical testing --- p.45 / Chapter 2.1.4.1 --- Optical density --- p.45 / Chapter 2.1.4.2 --- Cell dry weight --- p.45 / Chapter 2.1.4.3 --- Specific plasmid DNA yield --- p.46 / Chapter 2.1.4.3.1 --- Plasmid DNA isolation --- p.46 / Chapter 2.1.4.3.2 --- Plasmid DNA measurement --- p.46 / Chapter 2.1.4.3.3 --- Plasmid DNA identification --- p.46 / Chapter 2.1.4.4 --- Analysis of glycerol and acetate --- p.46 / Chapter 2.1.4.4.1 --- Standard samples --- p.46 / Chapter 2.1.4.4.2 --- Enzymatic test kits --- p.47 / Chapter 2.1.4.4.3 --- Automatic biochemistry analyzer --- p.47 / Chapter 2.1.5 --- Summary of all fermentation batches --- p.48 / Chapter 2.2 --- Full factorial design and semi-synthetic samples --- p.49 / Chapter 2.2.1 --- Adaptive calibration samples --- p.49 / Chapter 2.2.1.1 --- Matrix 1 calibration samples --- p.49 / Chapter 2.2.1.2 --- Matrix 2 calibration samples --- p.50 / Chapter 2.2.1.3 --- Matrix 3 calibration samples --- p.50 / Chapter 2.2.2 --- Summary of all samples --- p.51 / Chapter 2.3 --- NIR sample presentation and spectral acquisition --- p.52 / Chapter 2.3.1 --- NIR spectrophotometers --- p.52 / Chapter 2.3.2 --- Cuvettes for transmittance spectral acquisition --- p.53 / Chapter 2.3.3 --- Bottles for reflectance spectral acquisition --- p.54 / Chapter 2.3.4 --- Spectral acquisition --- p.55 / Chapter 2.3.4.1 --- Transmittance --- p.55 / Chapter 2.3.4.2 --- Reflectance --- p.55 / Chapter 2.4 --- Multivariate calibration and validation --- p.56 / Chapter 2.4.1 --- Spectral preprocessing --- p.56 / Chapter 2.4.2 --- Multivariate calibration --- p.57 / Chapter 2.4.3 --- Model validation --- p.57 / Chapter 3 --- Results and discussion / Chapter 3.1 --- Sample presentation and NIR spectrum --- p.59 / Chapter 3.1.1 --- Transmission measurement --- p.60 / Chapter 3.1.2 --- Reflectance measurement --- p.63 / Chapter 3.1.3 --- Spectral responses and pre-treatment method --- p.64 / Chapter 3.1.4 --- Design of experiments for calibration sample preparation --- p.67 / Chapter 3.1.5 --- Summary --- p.68 / Chapter 3.2 --- Adaptive calibration --- p.69 / Chapter 3.2.1 --- Selection of Multivariate calibration model --- p.70 / Chapter 3.2.1.1 --- Matrix 1 calibration models --- p.70 / Chapter 3.2.1.2 --- Matrix 2 calibration models --- p.74 / Chapter 3.2.1.3 --- Matrix 3 calibration models --- p.76 / Chapter 3.2.1.4 --- Summary --- p.78 / Chapter 3.2.2 --- Model validation --- p.79 / Chapter 3.2.2.1 --- Performance of Reference analytical methods --- p.79 / Chapter 3.2.2.1.1 --- Enzymatic test kits --- p.79 / Chapter 3.2.2.1.2 --- Automatic biochemistry anaylyzer (Bioprofile) --- p.81 / Chapter 3.2.2.1.3 --- Summary --- p.83 / Chapter 3.2.2.2 --- Model validation using external test samples --- p.83 / Chapter 3.2.2.2.1 --- Matrix 1 models --- p.83 / Chapter 3.2.2.2.2 --- Matrix 2 models --- p.87 / Chapter 3.2.2.2.3 --- Matrix 3 models --- p.89 / Chapter 3.2.2.2.4 --- Overall NIR measurement errors --- p.90 / Chapter 3.2.2.2.5 --- Summary --- p.91 / Chapter 3.3 --- Use of calibrated NIRS in at-line monitoring and control of fermentation --- p.93 / Chapter 3.3.1 --- Analysis of small-scale fermentation --- p.94 / Chapter 3.3.1.1 --- pGRF plasmid DNA production --- p.94 / Chapter 3.3.1.2 --- H5N1 plasmid DNA production --- p.95 / Chapter 3.3.1.3 --- Summary --- p.97 / Chapter 3.3.2 --- Analysis of large scale fermentation --- p.97 / Chapter 3.3.2.1 --- 30 L clinical production of H5N1 plasmid DNA --- p.97 / Chapter 3.3.2.2 --- 80 L scale-up production of H5N1 plasmid DNA --- p.99 / Chapter 3.3.2.3 --- Summary --- p.100 / Chapter 3.3.3 --- Effective control of fermentation production using at-line NIR analysis --- p.101 / Chapter 3.3.3.1 --- At-line monitoring of Batch 11 --- p.101 / Chapter 3.3.3.2 --- At-line monitoring of Batch 12 X --- p.102 / Chapter 3.3.3.3 --- Summary --- p.104 / Chapter 3.4 --- General discussion and conclusion --- p.105 / Chapter 3.5 --- Future prospects --- p.108 / References --- p.112 / Appendix 1 --- p.117 / Appendix 2 --- p.118 / Appendix 3 --- p.123 / Appendix 4 --- p.131 / Appendix 5 --- p.134

The invasive nature and undesirable side-effects related to conventional cancer therapy, such as surgery and chemotherapy, have led to the development of novel drug delivery systems (DDS). A minimally invasive DDS using near-infrared (NIR) light as a trigger for drug release is investigated to reduce the adverse side-effects triggered by systemic delivery of chemotherapeutic drugs. The low tissue absorbance in the NIR region, λ = 650–2500 nm, allows the irradiation to penetrate through tissues to release cisplatin from a NIR-sensitive nanocomposite of Au-Au₂S. Our laboratory has recently shown that cisplatin can be effectively released from Au-Au₂S upon NIR irradiation. Cisplatin was loaded onto Au-Au₂S through its adsorption on COOH-functionalized alkanethiols coated on Au-Au₂S. The current work focuses on the development of methods to control the release of cisplatin. Drug release is controlled by either the irradiation parameters or the type of coatings. The effect of different coatings on NIR sensitivity and drug release is investigated. Molecular layers of HS-(CH₂)n-COOH and HS-CH₂-COO-CH₂(CH₂CH₂O)xCH₂-COOH have been successfully coated onto Au-Au₂S. The effect of different surface layers on drug adsorption is being examined. In addition, a mathematical model has been developed to describe the thermal effects of different irradiation parameters on soft tissues. / Singapore-MIT Alliance (SMA)

Near-infrared spectroscopy (NIRS) in an imaging technique that has gained much attention in brain-computer interfaces (BCIs). Previous NIRS-BCI studies have primarily employed temporal features, derived from the time course of hemodynamic activity, despite potential value contained in the spatial attributes of a response. In an initial offline study, we investigated the value of using joint spatial-temporal pattern classification with dynamic NIR topograms to differentiate intentional cortical activation from rest. With the inclusion of spatiotemporal features, we demonstrated a significant increase in achievable classification accuracies from those obtained using temporal features alone (p < 10-4). In a second study, we evaluated the feasibility of implementing joint spatial-temporal pattern classification in an online system. We developed an online system-paced NIRS-BCI, and were able to differentiate two cortical states with high accuracy (77.4±10.5%). Collectively, these findings demonstrate the value of including spatiotemporal features in the classification of functional NIRS data for BCI applications.

Near-infrared spectroscopy (NIRS) in an imaging technique that has gained much attention in brain-computer interfaces (BCIs). Previous NIRS-BCI studies have primarily employed temporal features, derived from the time course of hemodynamic activity, despite potential value contained in the spatial attributes of a response. In an initial offline study, we investigated the value of using joint spatial-temporal pattern classification with dynamic NIR topograms to differentiate intentional cortical activation from rest. With the inclusion of spatiotemporal features, we demonstrated a significant increase in achievable classification accuracies from those obtained using temporal features alone (p < 10-4). In a second study, we evaluated the feasibility of implementing joint spatial-temporal pattern classification in an online system. We developed an online system-paced NIRS-BCI, and were able to differentiate two cortical states with high accuracy (77.4±10.5%). Collectively, these findings demonstrate the value of including spatiotemporal features in the classification of functional NIRS data for BCI applications.

Near-infrared (NIR) fluorescence imaging has emerged as an attractive non-invasive approach for direct visualization of diseases which depends on the development of stable, highly specific and sensitive optical probes. The NIR region of the electronic spectrum offers a reduction in the background autofluorescence and an increase in the tissue penetration depth. Cyanine dyes have often been considered promising contrast optic agents owing to their photophysical properties. Herein the synthesis of various penta- and heptamethine benzothiazole cyanine dyes has been described and their in vivo imaging efficacy was determined. Varying functionalities on the benzothiazole aromatic ring and changing substituents on the benzothiazolium nitrogen atom reflected subsequent changes in the imaging pattern and have resulted in the development of promising brain targeting agents.

This thesis begins with a brief review about the role and importance of the small molecules containing fluorine atoms in medicine and imaging. Then, the first part of the thesis will discuss the synthesis, purification and characterization of pentamethine cyanine dyes. The structure identification of the final dyes is done by using 1H NMR, 13C NMR, 19F NMR, and mass spectrometry. The studies performed after full characterization were the determination of optical and physicochemical properties. After these properties were performed, the fluorophores were evaluated to be good candidates for in vivo testing.

Indocyanine green (ICG) is a fluorescent dye used as an indicator in medicine and surgery. The maximum absorption wavelength of ICG is at 785 nm, while the maximum emission is around 820 nm. ICG is nontoxic and is rapidly excreted into the bile. Near infrared (NIR) fluorescence imaging or spectroscopy offer new settings for seeing the blood vessels, and also in oncological applications for finding sentinel lymph nodes (SLN) to investigate if the cancer has spread from the tumor to the lymphatic system. Given the aforementioned applications, the aim of this thesis was to develop a hardware control and a user interface in LabVIEW, and to evaluate the software, as well as the instrumentation using phantom measurements.The system consisted of a spectrometer, a laser (785 ± 5 nm) for ICG excitation, optical filters, and a fiber optical probe containing five fibers for light excitation, and one for light collection. The basic LabVIEW program designed for the spectrometer was used, and additional features were added such as the recording functions, online measurements, opening of the recorded files, saving comments, and a loop was created for the laser control. Optical phantoms were prepared to model tissue for measurements using 20 % intralipid that gave μs = 298 mm−¹ at the excitation wavelength. Agar 1% w/v and ICG were added to the phantoms using different fluorophore concentrations of 2 μg/mL, 10 μg/mL, 20 μg/mL, 25 μg/mL, and 40 μg/mL. The objective was to perform controlled measurements of steady state ICG fluorescence, the dynamics of photobleaching at different concentrations, and to find the optimal ICG concentration for obtaining the maximum fluorescence intensity. The light to excite ICG fluorescence emission was provided by using a laser output power of 10.4 mW and 200 ms of integration time in the spectrometer for optimal measurements.Measurements using the different gel phantoms showed maximum fluorescence ICG concentration to be between 16 μg/mL and 20 μg/mL. Moreover, photobleaching measurements showed to be ICG concentration-dependent, where those concentrations higher than the optimal one incrementally photobleached with time after being exposed to light. Higher concentrations presented an incremental photobleaching where they first reached a maximum peak and then the intensity decayed with time. Additionally, laser reflection at 782 nm showed that the reflection increased with time ranging from 130% – 460% as the ICG photobleached to 50% of its initial value. Normalization of ICG by the laser reflection signal was investigated to compensate for the intensity variations due to the measurement parameters including the distance from the light source to the target, and the angle of inclination of the probe. The lowest ICG concentration detectable by the system was 0.05 μg/mL.In conclusion, a LabVIEW hardware control and user interface was developed for controlling the spectrometer and the laser. Several measurements were made using the different phantoms, where the optimal concentration of ICG was estimated. It was shown that ICG fluorescence intensity and photobleaching behavior were dependent on the concentration. The results gave suggestions for future experimental design. / NIRF

Introduction: Currently, there are no objective techniques to assess burn depth. An early assessment of burn depth would enable accurate management decisions, which would improve patient outcomes. Near infrared (NIR) technology has shown promise as a non-invasive monitor of oxygenation and perfusion, and its potential to assess the depth of burn injuries has been investigated clinically over the past five years. The purpose of the thesis was to determine the capacity of NIR technology to differentiate acute thermal injuries. Methods: Burn sites (n=5) and control sites (n=5) were created on the dorsum of sixteen animals with brass rods held at constant pressure and heated to 100°C and 37.5°C respectively. NIR data was collected from the burns and control sites pre-burn, immediately post-burn, and 1, 12, 24, 36, 48 and 96 hours after the burn injury. Biopsies of the burn and control sites were acquired at each time point and used to confirm the depth of injury. NIR data was processed for the content of water, oxy-, deoxy- and methemoglobin. Results: Oxyhemoglobin and total hemoglobin decreased as burn depth increased. The proportion of oxy- and deoxyhemoglobin to total hemoglobin showed that the ratio of oxy- to deoxyhemoglobin decreased as burn injury increased. Methemoglobin levels as a ratio of total hemoglobin also showed that as the severity of injury increased the proportion of methemoglobin also increased. Finally, superficial partial thickness injuries (3 s and 12 s) showed early peak levels of water, which rapidly declined towards baseline. The deep partial thickness injuries (20 s and 30 s) do not experience peak levels and retain water over the course of the experiment. The full thickness injuries water levels remain close or below baseline levels throughout the experiment. Conclusion: NIR spectroscopy could distinguish burn depth using water, oxy-, met- and total hemoglobin as separate entities. The presence of methemoglobin in the burn wounds is a novel finding that has not been described previously in burn literature.

Diabetes Mellitus is a major health problem which affects about 200 million people worldwide. Diabetics require their blood glucose levels to be kept within the normal range in order to prevent diabetes-related complications from occurring. Blood glucose measurement is therefore of vital importance. The current glucose measurement techniques are, however, painful, inconvenient and episodic. This document provides an investigation into the use of near-infrared spectroscopy for continuous, non-invasive measurement of blood glucose. Artificial neural networks are used for the development of multivariate calibration models which predict glucose concentrations based on the near-infrared spectral data. Simulations have been performed which make use of simulated spectral data generated from the characteristic spectra of many of the major components of human blood. The simulations show that artificial neural networks are capable of predicting the glucose concentrations of complex aqueous solutions with clinically relevant accuracy. The effect of interference, such as temperature changes, pathlength variations, measurement noise and absorption due other analytes, has been investigated and modelled. The artificial neural network calibration models are capable of providing acceptably accurate predictions in the presence of multiple forms of interference. It was found that the performance of the measurement technique can be improved through careful selection of the optical pathlength and wavelength range for the spectroscopic measurements, and by using preprocessing techniques to reduce the effect of interference. Although the simulations suggest that near-infrared spectroscopy is a promising method of blood glucose measurement, which could greatly improve the quality of life of diabetics, many further issues must be resolved before the long-term goal of developing a continuous non-invasive home glucose monitor can be achieved.

A madeira é uma excelente matéria-prima renovável, sendo empregada como fonte de celulose e na construção civil, e sendo usada ainda como combustível. Contudo, as propriedades da madeira variam muito entre as espécies. Logo, saber a qual espécie pertence a madeira, assim como algumas de suas propriedades é essencial para um planejamento adequado de sua aplicação, evitando desperdícios e melhorando a qualidade dos produtos derivados do material. No presente estudo, analisou-se a aplicação da espectroscopia de infravermelho-próximo (NIR) na identificação e predição de propriedades físicas de madeiras nativas ou plantadas no Brasil, as quais apresentam grande variação entre suas propriedades e características anatômicas. Primeiramente, foi verificada a relação do espectro NIR com a densidade básica e algumas das características anatômicas importantes para identificação de madeiras, em especial aquelas ligadas aos raios parênquimáticos. Em seguida, com a espectroscopia NIR, foram desenvolvidos modelos para predição da densidade básica em madeiras de várias espécies brasileiras. Por fim, aplicou-se a espectroscopia NIR para discriminação entre as madeiras de Dalbergia nigra e D. spruceana, as quais são muito semelhantes em aspecto e características anatômicas. Os resultados sugerem que a espectroscopia de infravermelho-próximo é uma potencial ferramenta para classificação das diferentes madeiras brasileiras de acordo com suas propriedades físicas. Os resultados sugerem ainda uma metodologia auxiliar no processo de identificação pela anatomia do lenho. / Wood is an excellent renewable raw material, used as fuel, pulp and as building material. However, the properties of wood vary widely among species. Therefore, knowing the wood species as well as some of its properties is essential for proper planning of its application, avoiding waste and improving the quality of wood based products. In this study it was analysed the application of the near infrared spectroscopy (NIR) to identification and prediction of physical properties of native or planted timbers from Brazil, which show great variation on their properties and anatomical features. First, it was verified the relationship between the NIR spectrum and the basic density as well as some important anatomical features for wood identification, especially those related to ray parenchyma. Then, using NIR spectroscopy, models were developed to predict the wood basic density from various Brazilian species. It was also applied NIR spectroscopy to separate the woods from Dalbergia nigra and D. spruceana, two woods that are very similar in appearance and anatomical features. The results suggest that near-infrared spectroscopy is a potential tool for classification of various Brazilian woods based on their physical properties, as well as an auxiliary method in wood anatomy identification.