Predictive modelling for health and health-care utilisation : an observational study for Australians aged 45 and up

The burden of chronic disease is growing at a fast pace, leading to poor quality of life and high healthcare expenditures in a large portion of the Australian population. Much of the burden is borne by hospitals, and therefore there is an ever-increasing interest in preventative interventions that can keep people out of hospitals and healthier for longer periods. There is a wide range of potential interventions that may be able to achieve this goal, and policy makers need to decide which one should be funded and implemented. This task is difficult for two reasons: first it is often not clear what is the short-term effectiveness of an intervention, and how it varies in specific sub-populations, and second it is also not clear what the long-term intended and unintended consequences might be. In this thesis I make contributions to address both these difficulties. On the short-term side I focus on the use of physical activity to prevent the development of chronic disease and to reduce hospital costs. Increasing physical activity has been long heralded as a way to achieve these goals but evidence of its effectiveness has been elusive. In this thesis I provide data driven evidence to justify policies that encourage higher levels of physical activity (PA) in middle age and older Australian population. I use data from the “45 and up” and the Social, Economic and Environmental Factors (SEEF) study, linked with the Admitted Patient Data Collection (APDC), to identify and study the cost and health trajectories of individuals with different levels of physical activity. The results show a clear statistically significant association between PA and lower hospitalisation cost, as well as between PA and reduced risk of heart disease, diabetes and stroke. On the long-term side of the analysis, I placed this thesis in the context of a larger program of work performed at Western Sydney University that aims to build a microsimulation model for the analysis of health policy interventions. In this framework I studied predictive models that use survey and/or administrative data to predict hospital costs and resource utilisation. I placed particular emphasis on the application of methods borrowed from Natural Language Processing to understand how to use the thousands of diagnosis and procedure codes found in administrative data as input to predictive models. The methods developed in this thesis go beyond the application to hospital data and can be used in any predictive model that relies on complex coding of healthcare information

The necessity of extraction of ligand binding data from literature

After identification and validation of targets in a drug development process, some independent lead compounds should be selected and optimized for their activities. Then, safety assessments and clinical trials decide whether the drug is proper to enter the market. Different stages of this process (and especially the identification of lead compounds) are extremely expensive and time-consuming. Rational drug development methods try to reduce the costs by optimizing the pace of drug discovery and reducing the number of products abandoned during development. For decades, many investigators have studied the ligand-protein interactions, but very few structured databases are devoted to such information. Herein, development of such databases is proposed, since it is obvious that our prior knowledge about the chemico-biological interactions can help us choosing appropriate lead compounds without further experimental and computational investigations, which are usually based on searching in gigantic combinatorial databases of chemical compounds

Impact of residue accessible surface area on the prediction of protein secondary structures

<p>Abstract</p> <p>Background</p> <p>The problem of accurate prediction of protein secondary structure continues to be one of the challenging problems in Bioinformatics. It has been previously suggested that amino acid relative solvent accessibility (RSA) might be an effective factor for increasing the accuracy of protein secondary structure prediction. Previous studies have either used a single constant threshold to classify residues into discrete classes (buries vs. exposed), or used the real-value predicted RSAs in their prediction method.</p> <p>Results</p> <p>We studied the effect of applying different RSA threshold types (namely, fixed thresholds vs. residue-dependent thresholds) on a variety of secondary structure prediction methods. With the consideration of DSSP-assigned RSA values we realized that improvement in the accuracy of prediction strictly depends on the selected threshold(s). Furthermore, we showed that choosing a single threshold for all amino acids is not the best possible parameter. We therefore used residue-dependent thresholds and most of residues showed improvement in prediction. Next, we tried to consider predicted RSA values, since in the real-world problem, protein sequence is the only available information. We first predicted the RSA classes by RVP-net program and then used these data in our method. Using this approach, improvement in prediction was also obtained.</p> <p>Conclusion</p> <p>The success of applying the RSA information on different secondary structure prediction methods suggest that prediction accuracy can be improved independent of prediction approaches. Thus, solvent accessibility can be considered as a rich source of information to help the improvement of these methods.</p

The Architectural Formation of Stadiums in Different Periods of Time

Stadiums are places that can bring thousands of people together and create a very sensational architectural atmosphere. Unfortunately, they are seen as monumental objects in big cities and it is as if they were used as sculptures but it has to mention that they are durable volumes but have remained unknown in architectural studies. Looking at stadiums shows that their interior and exterior spaces should be interlocked and makes harmony as the exterior walls can create a city façade and the interior can make balance in people’s emotion, providing that the same regulations and codes should be applied to stadiums in order to increase the coherence with the city

A method for determining states in the course of protein unfolding

A simple model is presented for analyzing a set of spectra obtained from spectrophotometric study of protein titration. With this model one can determine the states of (probable) intermediates in the course of protein unfolding. The model is developed based on abundance of native state, intermediate(s) and denatured state, and their contributions to differential absorbance at selected wavelengths. The model is tested for the two-state unfolding of ribonuclease A by urea in formate buffer, and also for the three-state unfolding of a-lactalbumin by guanidine hydrochloride (GdnHCl) in phosphate buffer. It was demonstrated that unfolding of ribonuclease A is matched acceptably with the two-state model, while a-lactalbumin unfolding starts with a two-state mechanism (when [GdnHCl]>=1.6M) followed by a three-state pathway

Impact of RNA structure on the prediction of donor and acceptor splice sites

BACKGROUND: gene identification in genomic DNA sequences by computational methods has become an important task in bioinformatics and computational gene prediction tools are now essential components of every genome sequencing project. Prediction of splice sites is a key step of all gene structural prediction algorithms. RESULTS: we sought the role of mRNA secondary structures and their information contents for five vertebrate and plant splice site datasets. We selected 900-nucleotide sequences centered at each (real or decoy) donor and acceptor sites, and predicted their corresponding RNA structures by Vienna software. Then, based on whether the nucleotide is in a stem or not, the conventional four-letter nucleotide alphabet was translated into an eight-letter alphabet. Zero-, first- and second-order Markov models were selected as the signal detection methods. It is shown that applying the eight-letter alphabet compared to the four-letter alphabet considerably increases the accuracy of both donor and acceptor site predictions in case of higher order Markov models. CONCLUSION: Our results imply that RNA structure contains important data and future gene prediction programs can take advantage of such information

Mitochondrial DNA might be influenced in calprotectin-induced cell death

It is generally believed that calprotectin acts via exclusion of extracellular zinc, and/or binding of calprotectin to a cell membrane receptor, and consequently, activation of a signaling pathway for apoptosis. Recently, we suggested that calprotectin may have an “internal target” within cells. Here, using target theory, we provided evidence that this internal target is DNA. Trypan blue (TB) and dimythylthiazol diphenyl tetrazolium bromide (MTT) assays were used to estimate survival of calprotectin-treated cells. TB assay relies on the viability of cell membrane, while MTT assay relies on the functionality of mitochondria. We demonstrated that MTT-based survival values fit to the “single-hit, single-target” model, while TB-based survival values are best matched to the “single-hit, multi-target” model with N=2. Assumption of DNA as the target of calprotectin is fully consistent with the models, since each mitochondrion contains one chromosome and each “cell” is diploid and contains two chromosome sets. To the best of our knowledge this is the first report that suggests mitochondrial DNA is affected during calprotectin-induced cell death. Furthermore, our results explain why toxicity measures (like LD50), when estimated based on TB assay, are sometimes significantly greater than toxicity measures based on MTT assay