Synthesis of amino acid-derived ligands and investigation of their metal binding properties and biological applications

In this thesis, two different potential fluorescent probes for metal ions and four potential anticancer drugs have been designed, synthesised, characterised and studied for their metal binding properties and biological applications.This report aims to design and synthesise amino acid-derived ligands as potential metal ion sensors in biological and aqueous systems. Metal ions play many pivotal roles in biological systems such as catalysing biochemical reactions and creating action potentials allowing for the movement of organisms. Dysfunction of such metals can contribute to the development of diseases such as Alzheimer’s disease (AD) and Wilson’s disease. As a result, the biological roles of metal ions are of great intrigue to many scientists in multiple fields. These researchers depend upon the development of more ideal fluorescent sensors for metal ions to conduct their investigations. Unfortunately, current fluorescent probes for metal ions have limitations such as poor selectivity to their analyte, weak fluorescence intensity, and are often unsuitable for biological application due to poor water solubility, and high cytotoxicity. Therefore, the development of new probes is important to enhance the knowledge of metal ions in biology and medicine.The fluorescent sensors synthesised in this thesis report that undergo cell viability assays with low effective concentration (EC) values are declared inappropriate for biological metal sensing. Their high cytotoxicity is therefore explored as potential anticancer agents when bound to lanthanides. Cancer constitutes to the top three of the total world’s deaths. Platinum-based anticancer drugs have been the focal point of many chemotherapies used to treat cancer-related illnesses, however, are limited with intrinsic resistance and have negative side-effects due to a lack of cell selectivity. Other metal-based therapies, such as lanthanides-based therapies, have come of intrigue as potential substitutes for platinum-based drugs due to their unique properties. Despite the potential of lanthanide complexes as alternative anticancer therapies, this area of research has not received much attention

Interfacing Quantitative NDE with Computer Algorithms for Automated Statistical Process Control

In the Factory of the Future (FOF), production will be unified under a system of Computer Integrated Manufacturing (CIM). Automatic computer-integrated control of processes, detection of errors, and determination of corrective action will be necessary because of the proposed level of manpower in the FOFL[1,2]. Under these circumstances, a process which might go out of control and remain that way would be highly detrimental. Very rapid Statistical Process Control (SPC) will provide definitive warnings of out-of-control conditions

UA12/2/1 Western Normal Letter, Vol. I, No. 9

Western Normal Student as a Factor in Rural Life by A.J. Kinnaman in recruitment newsletter sent out by WKU

UA3/1/5/3 Airplane View College Heights / An Interpretation

Broadside printed as fund raiser for construction of the Kentucky Building

Scope and Arbitration in Machine Learning Clinical EEG Classification

A key task in clinical EEG interpretation is to classify a recording or session as normal or abnormal. In machine learning approaches to this task, recordings are typically divided into shorter windows for practical reasons, and these windows inherit the label of their parent recording. We hypothesised that window labels derived in this manner can be misleading for example, windows without evident abnormalities can be labelled `abnormal' disrupting the learning process and degrading performance. We explored two separable approaches to mitigate this problem: increasing the window length and introducing a second-stage model to arbitrate between the window-specific predictions within a recording. Evaluating these methods on the Temple University Hospital Abnormal EEG Corpus, we significantly improved state-of-the-art average accuracy from 89.8 percent to 93.3 percent. This result defies previous estimates of the upper limit for performance on this dataset and represents a major step towards clinical translation of machine learning approaches to this problem.Comment: 10 pages, 6 figure

Palliation of Abdominal Aortic Aneurysms in the Endovascular Era

AbstractObjectivesTo establish outcome of patients with abdominal aortic aneurysm (AAA) deemed unfit for repair.DesignRetrospective non-randomised study.Materials and methodsIdentification of males with >5.5 cm or females with >5.0 cm AAA turned down for elective repair between 01/01/2006–24/07/2009 from a prospective database. Comorbidities, reasons for non-intervention, aneurysm size, survival, use of CPEX (cardio-pulmonary exercise) testing and cause of death were analysed. Although well-established at the time, patients unfit for open operation were not considered for endovascular repair.ResultsSeventy two patients were unsuitable for AAA repair. Aneurysm size ranged from 5.3 cm to 12 cm. Functional status, comorbidity and patient preference determined decision to palliate. Sixty percent of patients were alive at study close. Aneurysm rupture was cause of death in 46%. CPEX testing was performed in 54%, whose mortality was 28%, vs. 54% in the non-CPEX group (P < 0.05).Median survival of patients with 5.1–6.0 cm AAA was 44 months and 11% died of rupture. Between 6.1 and 7.0 cm median survival was 26 months and 20% died of rupture. However, with >7 cm aneurysms, survival was 6 months and 43% ruptured.ConclusionUnder half the deaths in our comorbid cohort were due to rupture. However, decision to palliate may be revisited as risk-benefit ratio changes with aneurysm expansion

Employment, Unemployment and the Health of Pregnant Women

Much of what little we know about the impact of unemployment or health is based upon data or studies of predominantly unemployed men. These studies, though weak in methodology, imply that unemployment may lead to excess morbidity and mortality. This paper reports a study of 4,000 pregnant women in Brisbane. Unemployment amongst women is associated with high-risk health behaviour, which in turn may lead to low birthweight births. Further, unemployed women are more anxious and depressed than are employed women. The mental health of the mother appears to be more closely related to the employment status of her spouse than to her own employment status

Statistical analysis of student performance in redesigned developmental mathematics courses

Colleges and universities are focusing their efforts on improving the instruction in developmental mathematics courses. In 2013, community colleges in North Carolina were in the process of implementing the redesigned approach to teaching developmental mathematics with the goal of improving student graduation rates. The purpose of this study is to investigate the differences in academic improvements of developmental mathematics students in order to evaluate the effectiveness of the redesigned MyMathLab (MML) courses. This study investigates the following variables: College Placement Test (CPT) scores in Algebra, Arithmetic, Reading Comprehension, and Sentence Structure, gender, and instructional method. Multiple regression analyses were performed using the statistical computing software, R. In Phase I of this study, two linear regression models were developed to predict student academic improvement in MML and Educo developmental mathematics courses using the standardized CPT scores, gender, and methodological indicator as potential predictors. In Phase II, three linear models were analyzed to predict student academic performance in redesigned MML classes. Using the data from Phase II, three additional regression models were developed with the MML post-test as the response variable and the CPT scores, gender, and the MML pre-test as the set of possible predictors to identify “at-risk” students. An out-of-sample prediction method was used to evaluate the misclassification rate in identifying “at-risk” students. The results of this study suggest that Algebra and Arithmetic CPT scores are significant predictors of student academic improvement. However, for each model, less than 50% of the variability in student improvement is explained by the linear relationship between the variables. Based on the results of this study, students enrolled in module 050 MML classes at Southwestern Community College (SCC) showed greater improvement than Educo students. Furthermore, the predictive models that include CPT scores and gender as the only predictors of learning can be employed to identify “at-risk” students at the beginning of each school semester. In the conclusion of the thesis, the limitations and implications of this study are discussed