The purpose of this study was to examine the changes in the ventilatory (VT) and lactate (LT) thresholds and VO₂max following prolonged aerobic exercise. Six well-trained distance runners (T:age=25.2 yrs, Ht=170.0 cm, Wt=65.0 kg, VO₂max=59.6 ml •kg⁻¹•min⁻¹) and six untrained (UT:age=25.3 yrs, Ht=180.3 cm, Wt=79.2 kg, VO₂max=46.8 ml•kg⁻¹•min⁻¹) males were studied on two occasions seven days apart. The initial evaluation involved a continuous horizontal treadmill test with a starting velocity of 2.22 m•s⁻¹, which was increased by 0.22 m•s⁻¹ each minute until fatigue. Expired gases were continuously sampled and analyzed by a Beckman Metabolic Measurement Cart. Measurements were processed by a data acquisition system (HP 3052A), which determined respiratory gas exchange variables every 15 seconds. Blood lactate measurements were taken via an indwelling catheter during the last 10 sec of each minute of work. VT and LT were determined by visual inspection of the excess CO₂ elimination and lactate curves, respectively. Seven days later the subjects repeated the treadmill test preceded by a 60 minute treadmill run at a heart rate corresponding to their LT. The physiological measurements recorded during the first session were repeated. There were significant (p<0.10) reductions in VO₂max, LT, VT, and total treadmill time on the VO₂max test (TTT) in the T group (59.6 to 56.9 ml •kg⁻¹•min⁻¹, 9.6 to 9.3 mph , 8.9 to 8.2 mph , and 925.0 to 882.5 sec, respectively). VO₂max, LT, VT, and TTT were reduced in the UT group (46.8 to 45.0 ml•kg⁻¹•min⁻¹ 7.7 to 7.6 mph, 8.0 to 7.2 mph, and 730.0 to 652.5 sec, respectively), however, only VT and TTT were reduced significantly (p<0.10). Although the groups were significantly different (p<0.05) in the initial physiological measures due to training status, there was no change in the rate of decline in VO₂max, LT, VT, or TTT when the UT group was compared to T. As LT and VT are affected by prolonged aerobic exercise it is questionable whether these thresholds can be used with confidence to predict endurance performance in events up to 60 min duration for well-trained and recreational athletes. / Education, Faculty of / Curriculum and Pedagogy (EDCP), Department of / Graduate

Effective blood glucose (BG) control is essential for patients with diabetes. This calls for an immediate need to closely keep track of patients' BG level all the time. However, sometimes individual patients may not be able to monitor their BG level regularly due to all kinds of real-life interference. To address this issue, in this paper we propose machine-learning based prediction models that can automatically predict patients BG level based on their historical data and known current status. We take two approaches, one for predicting BG level only using individual's data and second is to use a population data. Our experimental results illustrate the effectiveness of the proposed model.

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Memory performance was measured in 48 persons between the ages of 40 - 65 with Type II diabetes. Correlations between performance on the California Verbal Learning Test, tests of Working Memory, Priming Memory, and Prospective Memory and several predictor variables were examined. These variables included the Slosson Intelligence Test Scores, demographic variables, presence of diabetic complications, finger-stick and HbA1c measures. Subjects performed worse than the normative sample on the California Verbal Learning Test. Higher chronic and acute blood glucose tended to be associated with worse performance on the CVLT, Priming, and Working Memory. However, after the effects of intelligence, education, and sex were statistically controlled, glycemic status predicted performance on just a few memory measures. These were short-delay recall compared with recall on List A trial 5, and List B on the CVLT, and recall accuracy on digit forward of the Working Memory Test. Glucose status was unrelated to performance on a prospective memory test. Several other demographic and diabetic complication factors predicted performance beyond the contribution of intelligence. These results contrast with previous studies which found strong effects of glycemic control, but did not statistically control for the contribution of intelligence. Differential effects of diabetic status on different aspects of memory were discussed.

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Chu Lut-Hey. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 137-140). / Abstracts in English and Chinese. / Abstract --- p.II / Acknowledgement --- p.V / List of Tables --- p.IX / List of Figures --- p.X / List of Abbreviations --- p.XIII / Chapter 1 --- Introduction / Chapter 1.1 --- Objectives.l --- p.1 / Chapter 1.2 --- What is diabetes mellitus?. --- p.1 / Chapter 1.3 --- "Review of ""Technique for blood glucose measurement""" --- p.3 / Chapter 1.3.1 --- Conventional blood glucose meter --- p.4 / Chapter 1.3.2 --- Semi-invasive blood glucose meter --- p.5 / Chapter 1.3.3 --- Surfacelasmon resonance (SPR) method --- p.6 / Chapter 1.3.4 --- Non-invasive blood glucose meters --- p.7 / Chapter 1.4 --- Research directions --- p.8 / Chapter 1.5 --- Data analysis methods --- p.9 / Chapter 1.6 --- Organization of the thesis --- p.10 / Chapter 2 --- Spectral Data Analysis Methods / Chapter 2.1 --- What is spectral data? --- p.12 / Chapter 2.2 --- Nature of spectral data --- p.12 / Chapter 2.3 --- How to interpret the spectral data? --- p.15 / Chapter 2.3.1 --- Linear regression --- p.15 / Chapter 2.3.2 --- Multiple linear regression (MLR) --- p.17 / Chapter 2.3.2.1 --- Data reduction --- p.19 / Chapter 2.3.3 --- Bilinear modeling (BLM) --- p.21 / Chapter 2.3.3.1 --- artial least square regression (PLSR) --- p.23 / Chapter 2.3.3.2 --- Model reliability --- p.24 / Chapter 2.3.3.3 --- LS segmentation analysis --- p.25 / Chapter 2.4 --- Session discussion and conclusion --- p.27 / Chapter 3 --- Surfacelasmon Resonance (SPR) Experiments and Analysis / Chapter 3.1 --- Intrcduction --- p.29 / Chapter 3.2 --- Theory --- p.30 / Chapter 3.2.1 --- Maxwell's equations --- p.30 / Chapter 3.2.2 --- olarization of EM wave --- p.32 / Chapter 3.2.3 --- Boundary condition --- p.33 / Chapter 3.2.4 --- Total internal reflection --- p.37 / Chapter 3.2.5 --- hase shift of total internal reflection wave --- p.38 / Chapter 3.2.6 --- Kretschmannrism configuration --- p.38 / Chapter 3.2.7 --- Conditions affecting SPR --- p.41 / Chapter 3.3 --- Different SPR configuration systems --- p.44 / Chapter 3.3.1 --- rinciple of angular-based interrogation SPR setup --- p.44 / Chapter 3.3.2 --- rinciple of intensity-based interrogation SPR setup --- p.45 / Chapter 3.3.3 --- rinciple of wavelength-based interrogation SPR setup --- p.45 / Chapter 3.3.4 --- hase-based interrogation SPR setup --- p.47 / Chapter 3.3.4.1 --- Michelson Interferometer --- p.48 / Chapter 3.3.4.2 --- Thehase-based setup --- p.49 / Chapter 3.3.5 --- Comparison and discussion of different configurations --- p.51 / Chapter 3.4 --- SPR experiment adopts wavelengths-based interrogation setup --- p.54 / Chapter 3.4.1 --- The experiment setup --- p.54 / Chapter 3.4.1.1 --- Samplereparation --- p.58 / Chapter 3.4.1.2 --- Experiment Result using 928 nm and 1120 nm initial resonant wavelength --- p.58 / Chapter 3.4.2 --- Experimental data analysis --- p.60 / Chapter 3.4.2.1 --- Conventional SPR data analysis --- p.61 / Chapter 3.4.2.1.1 --- Data analysis using minimum hunting witholynomial fit --- p.62 / Chapter 3.4.3 --- LS regression analysis for SPR data --- p.67 / Chapter 3.5 --- Session discussion and conclusion --- p.71 / Chapter 4 --- Near-Infrared Spectroscopy Experiment and Analysis / Chapter 4.1 --- Overview of near-infrared spectroscopy --- p.74 / Chapter 4.1.1 --- Electromagnetic spectrum --- p.75 / Chapter 4.1.2 --- Molecules vibration --- p.76 / Chapter 4.1.3 --- Law of absorption: Lambert-Beer Law --- p.77 / Chapter 4.2 --- Near Infrared Spectrometer --- p.79 / Chapter 4.2.1 --- Dispersive NIR spectrometer --- p.79 / Chapter 4.2.2 --- Fourier-Transformed NIR spectrometer --- p.83 / Chapter 4.2.3 --- Comparison between dispersive NIR and FT-NIR spectrometers --- p.85 / Chapter 4.2.4 --- Self-established monochromator based spectrometer --- p.87 / Chapter 4.2.4.1 --- Choose of detector andhotonic signal conversion unit --- p.90 / Chapter 4.3 --- Experimental result of NIR-spectrum by self-established spectrometer --- p.95 / Chapter 4.3.1 --- New approach for improving RMSEP without normalization of water spectrum as background information --- p.105 / Chapter 4.4 --- Experimental results of NIR-spectrum by commercial spectrometers --- p.109 / Chapter 4.5 --- Instrumentation limitation of NIR spectrometer --- p.117 / Chapter 4.6 --- Session Discussion and conclusion --- p.119 / Chapter 5 --- Conclusions and Suggestions for the Future Work / Chapter 5.1 --- Research aim and contributions --- p.126 / Chapter 5.2 --- Discussion on experiments --- p.128 / Chapter 5.3 --- Future work --- p.131 / Chapter 5.4 --- Conclusion --- p.134 / Reference: --- p.136 / Appendixes: --- p.141 / Appendix A Different approaches for non-invasive glucose measurement --- p.142 / Appendix B Calibration model of the SPR experimental result with 1120 nm as initial resonance wavelength --- p.145 / Appendix C Matlabrogram written for data analysis and simulation --- p.146 / Appendix D Detail specification of the monochromator using in our laboratory --- p.150 / Appendix E Monochromator Controlrogram --- p.156 / Appendix F The throughput example copied from the handbook of ORIEL Instruments --- p.165 / Appendix G RMSEPlot of the data obtained by self-established spectrometer with and without datare-treatments --- p.166 / "Appendix H RMSEPlot of the raw data obtained from the three different commercial machines (Vector-22N/C, NIRSystem 6500 and NIRSystem XDS) with and without datare-treatment" --- p.176

Bioanalytics involves quantitative measurements of complex biological samples that contain metabolites, DNA, RNA, and proteins. Efficient sample preparation for downstream analysis and sensitive detection of analytes can be achieved via bioanalytical microdevices. Fully realizing the potential of these devices requires tool characterization and bioprocess optimization, in addition to understanding device physics. Therefore, this thesis introduces multiphysical modeling and experimentation of microdevices, with applications to diabetes care and single-cell analysis. To understand the physics of viscometric glucose microsensors, this thesis presents a model of the sensor, which couples the fluid flow with vibrating diaphragms. The model is used to predict the sensor response to glucose via theory of squeeze-film damping and vibrations of pre-stressed plate. A first-principle-based model resulting from the theory can be evaluated from the device's geometric and material properties, and quantitatively determines the device response to vibrational excitations at varying glucose concentrations. Next, this thesis introduces a theoretical model for viscometric glucose microsensors that employ harmonic microcantilever oscillation in the sensing liquid. The presented model associates the unsteady Stokes equation with the motion of a bounded viscous liquid to understand the hydrodynamic impact on the cantilever. With a proper consideration of the viscosity and bounded geometry of liquid media, the model relaxes the thin-film assumption required for the diaphragm-based model, enabling an accurate representation of fluid-structure interactions based on fundamental structural vibration and fluid flow equations. Next, this thesis presents an experimental exploration of a hydrogel-based affinity microsensor for glucose monitoring via dielectric measurements. The microsensor incorporates a synthetic hydrogel that is attached to the device surface via in situ polymerization, which eliminates mechanical moving parts required in the viscometric glucose sensors. Changes in the dielectric properties of the hydrogel when binding reversibly with glucose molecules have been measured using a MEMS capacitive transducer to determine the glucose concentration. Experimental results demonstrate that in a glucose concentration range of 0–500 mg/dL and with a resolution of 0.35 mg/dL or better, the microsensor exhibits a repeatable and reversible response, and can potentially be useful for continuous glucose monitoring in diabetes care. Additionally, this thesis presents a microfluidic preprocessing method that integrates single-cell picking, lysing, reverse transcription and digital polymerase chain reaction to enable the isolation, tracking and gene expression analysis at single-cell level for individual cells. The approach utilizes a photocleavable bead-based microfluidic device to synthesize and deliver stable complementary DNA for downstream gene expression analysis, thereby allowing chip-based integration of multiple reactions and facilitating the minimization of sample loss or contamination. Finally, this thesis ends with concluding remarks and directions of future work towards continuous glucose monitoring and high-throughput single-cell genetic analysis.

Thesis (M.S.)--Baylor University, 2005. / Includes bibliographical references (p. 53-56).

The increasing prevalence causes great burden to global health. Although there is not yet an agreement on the effect of SMBG for non-insulin-treating type 2 DM patients in comprehensive management, some guidelines recommended all diabetes patients should conduct SMBG. This literature review of 5 meta-analyses and 13 randomized controlled clinical trials assessed the effectiveness of SMBG in glucose control (HbA1c), detection of hypoglycemia, non-glycemic outcomes and potential influence factors(duration of diabetes, baseline HaB1c level, SMBG frequency, SMBG duration, co-interventions) of SMBG efficacy on type 2 diabetes patients not using insulin. The method of this literature review is through a comprehensive electronic literature search of Ovid MEDLINE, EMBASE, the Cochrane Library and China Journals Full-text Database. Both English and Chinese language literatures were reviewed. All meta-analysis and randomized controlled trials of type 2 diabetes non-insulin-using patients taking SMBG to improve the glycemic control and other outcomes were included. In these studies, absolute HbA1c reduction, recognized episodes of hypoglycemia, wellbeing, QALY, DALY, complication morbidity, mortality were used as outcome measures if available. A score list based on the PRISMA Statement was used to evaluate the quality of meta-analyses. 5 meta-analysis all reported a statistical significant but clinical modest-moderate difference in HbA1c reduction between SMBG and non-SMBG group, a new published randomized controlled trial with small cohort enrolled in none of the meta- analyses did not support this conclusion. Evidence showed frequency of SMBG did not influence the efficacy of SMBG, co-interventions as education/consultation, regimen change played a positive roll on SMBG efficacy. Whether baseline HbA1c, duration of diabetes or SMBG itself have an effect on SMBG efficacy was still unknown. There is inadequate evidence of SMBG efficacy of detection of hypoglycemia of patient-oriented outcomes. No eligible Chinese article was defined to enroll in this review. This review did not support to suggest all type 2 diabetes patients not using insulin to conduct SMBG at the frequency the guidelines recommended. Carefully designed and longer-term trials are needed to obtain evidence that is more robust. Further investigation would provide more evidence of the characteristics of potential influence factors, which may help to define the specific population or optimal mode that guarantee the greatest efficacy of SMBG. / published_or_final_version / Public Health / Master / Master of Public Health