Genoprotective effects of green tea (Camellia sinensis) in human subjects : results of a controlled supplementation trial

2010-2011 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

A HIV-1 heterosexual transmission chain in Guangzhou, China: a molecular epidemiological study

<p>Abstract</p> <p>Background</p> <p>We conducted molecular analyses to confirm four clustering HIV-1 infections (Patient A, B, C & D) in Guangzhou, China. These cases were identified by epidemiological investigation and suspected to acquire the infection through a common heterosexual transmission chain.</p> <p>Methods</p> <p><it>Env C2V3V4 </it>region, <it>gag p17/p24 </it>junction and partial <it>pol </it>gene of HIV-1 genome from serum specimens of these infected cases were amplified by reverse transcription polymerase chain reaction (RT-PCR) and nucleotide sequenced.</p> <p>Results</p> <p>Phylogenetic analyses indicated that their viral nucleotide sequences were significantly clustered together (bootstrap value is 99%, 98% and 100% in <it>env</it>, <it>gag </it>and <it>pol </it>tree respectively). Evolutionary distance analysis indicated that their genetic diversities of <it>env</it>, <it>gag </it>and <it>pol </it>genes were significantly lower than non-clustered controls, as measured by unpaired <it>t</it>-test (<it>env </it>gene comparison: <it>p </it>< 0.005; <it>gag </it>gene comparison: <it>p </it>< 0.005; <it>pol </it>gene comparison: <it>p </it>< 0.005).</p> <p>Conclusion</p> <p>Epidemiological results and molecular analyses consistently illustrated these four cases represented a transmission chain which dispersed in the locality through heterosexual contact involving commercial sex worker.</p

Introduction of an agent-based multi-scale modular architecture for dynamic knowledge representation of acute inflammation

<p>Abstract</p> <p>Background</p> <p>One of the greatest challenges facing biomedical research is the integration and sharing of vast amounts of information, not only for individual researchers, but also for the community at large. Agent Based Modeling (ABM) can provide a means of addressing this challenge via a unifying translational architecture for dynamic knowledge representation. This paper presents a series of linked ABMs representing multiple levels of biological organization. They are intended to translate the knowledge derived from in vitro models of acute inflammation to clinically relevant phenomenon such as multiple organ failure.</p> <p>Results and Discussion</p> <p>ABM development followed a sequence starting with relatively direct translation from in-vitro derived rules into a cell-as-agent level ABM, leading on to concatenated ABMs into multi-tissue models, eventually resulting in topologically linked aggregate multi-tissue ABMs modeling organ-organ crosstalk. As an underlying design principle organs were considered to be functionally composed of an epithelial surface, which determined organ integrity, and an endothelial/blood interface, representing the reaction surface for the initiation and propagation of inflammation. The development of the epithelial ABM derived from an in-vitro model of gut epithelial permeability is described. Next, the epithelial ABM was concatenated with the endothelial/inflammatory cell ABM to produce an organ model of the gut. This model was validated against in-vivo models of the inflammatory response of the gut to ischemia. Finally, the gut ABM was linked to a similarly constructed pulmonary ABM to simulate the gut-pulmonary axis in the pathogenesis of multiple organ failure. The behavior of this model was validated against in-vivo and clinical observations on the cross-talk between these two organ systems</p> <p>Conclusion</p> <p>A series of ABMs are presented extending from the level of intracellular mechanism to clinically observed behavior in the intensive care setting. The ABMs all utilize cell-level agents that encapsulate specific mechanistic knowledge extracted from in vitro experiments. The execution of the ABMs results in a dynamic representation of the multi-scale conceptual models derived from those experiments. These models represent a qualitative means of integrating basic scientific information on acute inflammation in a multi-scale, modular architecture as a means of conceptual model verification that can potentially be used to concatenate, communicate and advance community-wide knowledge.</p

Sign Language Recognition Using Sub-units

This chapter discusses sign language recognition using linguistic sub-units. It presents three types of sub-units for consideration; those learnt from appearance data as well as those inferred from both 2D or 3D tracking data. These sub-units are then combined using a sign level classifier; here, two options are presented. The first uses Markov Models to encode the temporal changes between sub-units. The second makes use of Sequential Pattern Boosting to apply discriminative feature selection at the same time as encoding temporal information. This approach is more robust to noise and performs well in signer independent tests, improving results from the 54% achieved by the Markov Chains to 76%

Overlap-based undersampling method for classification of imbalanced medical datasets.

Early diagnosis of some life-threatening diseases such as cancers and heart is crucial for effective treatments. Supervised machine learning has proved to be a very useful tool to serve this purpose. Historical data of patients including clinical and demographic information is used for training learning algorithms. This builds predictive models that provide initial diagnoses. However, in the medical domain, it is common to have the positive class under-represented in a dataset. In such a scenario, a typical learning algorithm tends to be biased towards the negative class, which is the majority class, and misclassify positive cases. This is known as the class imbalance problem. In this paper, a framework for predictive diagnostics of diseases with imbalanced records is presented. To reduce the classification bias, we propose the usage of an overlap-based undersampling method to improve the visibility of minority class samples in the region where the two classes overlap. This is achieved by detecting and removing negative class instances from the overlapping region. This will improve class separability in the data space. Experimental results show achievement of high accuracy in the positive class, which is highly preferable in the medical domain, while good trade-offs between sensitivity and specificity were obtained. Results also show that the method often outperformed other state-of-the-art and well-established techniques

XRCC1 Arg194Trp polymorphism, risk of nonmelanoma skin cancer and extramammary Paget’s disease in a Japanese population

The X-ray repair cross-complementing groups 1 gene plays an important role in base excision repair. At least three common single nucleotide polymorphisms frequently occur in this gene (Arg399Gln, Arg194Trp and Arg280His). Recent studies reported that these polymorphisms were associated with not only risk of visceral malignancy but also that of skin cancer such as basal cell carcinoma and squamous cell carcinoma, whereas the results of previous study vary among races. In this case–control study, we investigated whether these single nucleotide polymorphisms were associated with the risk of skin cancer in a Japanese population. The study population was composed of 197 patients with skin cancer (27 actinic keratoses, 47 basal cell carcinomas, 27 squamous cell carcinomas, 29 Bowen’s diseases, 46 malignant melanomas and 21 extramammary Paget’s diseases) and 93 control subjects. We genotyped two single nucleotide polymorphisms (Arg194Trp and Arg399Gln) using polymerase chain reaction-restriction fragments length polymorphism analysis. We found a significantly increased risk for basal cell carcinoma, squamous cell carcinoma and extramammary Paget’s disease associated with Arg194Trp [adjusted odds ratio (AOR) = 2.347, 3.587, 3.741, 95 % confidence interval (CI) 1.02–5.39, 1.19–10.8, 1.15–12.2, respectively]. We also found a significantly decreased risk for basal cell carcinoma associated with Gln399Gln (AOR = 0.259, 95 % CI 0.07–0.96). Our data suggest that the Arg194Trp polymorphism could be associated with nonmelanoma skin cancer and extramammary Paget’s disease risk in a Japanese population

A Classification Method Based on Principal Components of SELDI Spectra to Diagnose of Lung Adenocarcinoma

Lung cancer is the leading cause of cancer death worldwide, but techniques for effective early diagnosis are still lacking. Proteomics technology has been applied extensively to the study of the proteins involved in carcinogenesis. In this paper, a classification method was developed based on principal components of surface-enhanced laser desorption/ionization (SELDI) spectral data. This method was applied to SELDI spectral data from 71 lung adenocarcinoma patients and 24 healthy individuals. Unlike other peak-selection-based methods, this method takes each spectrum as a unity. The aim of this paper was to demonstrate that this unity-based classification method is more robust and powerful as a method of diagnosis than peak-selection-based methods.The results showed that this classification method, which is based on principal components, has outstanding performance with respect to distinguishing lung adenocarcinoma patients from normal individuals. Through leaving-one-out, 19-fold, 5-fold and 2-fold cross-validation studies, we found that this classification method based on principal components completely outperforms peak-selection-based methods, such as decision tree, classification and regression tree, support vector machine, and linear discriminant analysis.The classification method based on principal components of SELDI spectral data is a robust and powerful means of diagnosing lung adenocarcinoma. We assert that the high efficiency of this classification method renders it feasible for large-scale clinical use

Telomere disruption results in non-random formation of de novo dicentric chromosomes involving acrocentric human chromosomes

Copyright: © 2010 Stimpson et al.Genome rearrangement often produces chromosomes with two centromeres (dicentrics) that are inherently unstable because of bridge formation and breakage during cell division. However, mammalian dicentrics, and particularly those in humans, can be quite stable, usually because one centromere is functionally silenced. Molecular mechanisms of centromere inactivation are poorly understood since there are few systems to experimentally create dicentric human chromosomes. Here, we describe a human cell culture model that enriches for de novo dicentrics. We demonstrate that transient disruption of human telomere structure non-randomly produces dicentric fusions involving acrocentric chromosomes. The induced dicentrics vary in structure near fusion breakpoints and like naturally-occurring dicentrics, exhibit various inter-centromeric distances. Many functional dicentrics persist for months after formation. Even those with distantly spaced centromeres remain functionally dicentric for 20 cell generations. Other dicentrics within the population reflect centromere inactivation. In some cases, centromere inactivation occurs by an apparently epigenetic mechanism. In other dicentrics, the size of the alpha-satellite DNA array associated with CENP-A is reduced compared to the same array before dicentric formation. Extrachromosomal fragments that contained CENP-A often appear in the same cells as dicentrics. Some of these fragments are derived from the same alpha-satellite DNA array as inactivated centromeres. Our results indicate that dicentric human chromosomes undergo alternative fates after formation. Many retain two active centromeres and are stable through multiple cell divisions. Others undergo centromere inactivation. This event occurs within a broad temporal window and can involve deletion of chromatin that marks the locus as a site for CENP-A maintenance/replenishment.This work was supported by the Tumorzentrum Heidelberg/Mannheim grant (D.10026941)and by March of Dimes Research Foundation grant #1-FY06-377 and NIH R01 GM069514