MODELING DRUG ELUTING STENTS FOR CORONARY ARTERY BIFURCATION CONSIDERING NON-NEWTONIAN EFFECTS

ABSTRACT 1 Drug Eluting Stents (DES) are commonly used for the treatment of stenotic arteries. Restenosis can be treated by delivering anti-thrombotic and anti-proliferative drugs to the arterial wall. The main mechanism of the drug eluting stent is to allow diffusion of the drug from the coating on the stent, into the arterial wall over a prolonged period of time. Investigation of blood flow hemodynamics and shear stress are of great importance in understanding the transport of drugs through the circulatory systems and predicting the performance of drug eluting stents. While drug eluting stent effectively reduces restenosis rate, the conventional drug eluting stent should be optimized to be used in the bifurcation stenting. Various flow patterns due to specific designs of drug eluting stent influence drug delivery. Numerical simulation techniques are appropriate approaches to study such phenomena which can be used to optimize the design of drug eluting stents for bifurcations. In this paper, the complexity of drug eluting stent function in the bifurcation is presented by employing computational fluid dynamics analysis for various stent strut designs. Drug transportation through the lumen and determination of local drug concentrations in arterial wall is carried out for both Newtonian and non-Newtonian flow * Corresponding author conditions. It is, to the author"s best knowledge, the first investigation of drug dispersion in arterial bifurcation considering the effects of both the blood rheological properties and stent strut design. NOMENCLATUR

Antagonists of growth hormone-releasing hormone (GH-RH) inhibit IGF-II production and growth of HT-29 human colon cancers

Insulin-like growth factors (IGFs) I and II are implicated in progression of various tumours including colorectal carcinomas. To interfere with the production of IGFs, we treated male nude mice bearing xenografts of HT-29 human colon cancer with various potent growth hormone-releasing hormone (GH-RH) antagonists. Twice daily injections of antagonist MZ-4-71, 10 μg intraperitoneally or 5 μg subcutaneously (s.c.) resulted in a significant 43–45% inhibition of tumour growth. Longer acting GH-RH antagonists, MZ-5-156 and JV-1-36 given once daily at doses of 20 μg s.c. produced a 43–58% decrease in volume and weight of cancers. Histological analyses of HT-29 cancers demonstrated that both a decreased cell proliferation and an increased apoptosis contributed to tumour inhibition. GH-RH antagonists did not change serum IGF-I or IGF-II levels, but significantly decreased IGF-II concentration and reduced mRNA expression for IGF-II in tumours. In vitro studies showed that HT-29 cells produced and secreted IGF-II into the medium, and addition of MZ-5-156 dose-dependently decreased IGF-II production by about 40% as well as proliferation of HT-29 cells. Our studies demonstrate that GH-RH antagonists inhibit growth of HT-29 human colon cancers in vivo and in vitro. The effect of GH-RH antagonists may be mediated through a reduced production and secretion of IGF-II by cancer cells. © 2000 Cancer Research Campaig

Psychedelics Promote Structural and Functional Neural Plasticity.

Atrophy of neurons in the prefrontal cortex (PFC) plays a key role in the pathophysiology of depression and related disorders. The ability to promote both structural and functional plasticity in the PFC has been hypothesized to underlie the fast-acting antidepressant properties of the dissociative anesthetic ketamine. Here, we report that, like ketamine, serotonergic psychedelics are capable of robustly increasing neuritogenesis and/or spinogenesis both in vitro and in vivo. These changes in neuronal structure are accompanied by increased synapse number and function, as measured by fluorescence microscopy and electrophysiology. The structural changes induced by psychedelics appear to result from stimulation of the TrkB, mTOR, and 5-HT2A signaling pathways and could possibly explain the clinical effectiveness of these compounds. Our results underscore the therapeutic potential of psychedelics and, importantly, identify several lead scaffolds for medicinal chemistry efforts focused on developing plasticity-promoting compounds as safe, effective, and fast-acting treatments for depression and related disorders

Knocking down gene expression for growth hormone-releasing hormone inhibits proliferation of human cancer cell lines

Splice Variant 1 (SV-1) of growth hormone-releasing hormone (GHRH) receptor, found in a wide range of human cancers and established human cancer cell lines, is a functional receptor with ligand-dependent and independent activity. In the present study, we demonstrated by western blots the presence of the SV1 of GHRH receptor and the production of GHRH in MDA-MB-468, MDA-MB-435S and T47D human breast cancer cell lines, LNCaP prostate cancer cell line as well as in NCI H838 non-small cell lung carcinoma. We have also shown that GHRH produced in the conditioned media of these cell lines is biologically active. We then inhibited the intrinsic production of GHRH in these cancer cell lines using si-RNA, specially designed for human GHRH. The knocking down of the GHRH gene expression suppressed the proliferation of T47D, MDA-MB-435S, MDA-MB-468 breast cancer, LNCaP prostate cancer and NCI H838 non-SCLC cell lines in vitro. However, the replacement of the knocked down GHRH expression by exogenous GHRH (1–29)NH2 re-established the proliferation of the silenced cancer cell lines. Furthermore, the proliferation rate of untransfected cancer cell lines could be stimulated by GHRH (1–29)NH2 and inhibited by GHRH antagonists MZ-5-156, MZ-4-71 and JMR-132. These results extend previous findings on the critical function of GHRH in tumorigenesis and support the role of GHRH as a tumour growth factor

A Genetic Tuning to Improve the Performance of Fuzzy Rule-Based Classification Systems with Interval-Valued Fuzzy Sets: Degree of Ignorance and Lateral Position

Fuzzy Rule-Based Systems are appropriate tools to deal with classification problems due to their good properties. However, they can suffer a lack of system accuracy as a result of the uncertainty inherent in the definition of the membership functions and the limitation of the homogeneous distribution of the linguistic labels. The aim of the paper is to improve the performance of Fuzzy Rule-Based Classification Systems by means of the Theory of Interval-Valued Fuzzy Sets and a post-processing genetic tuning step. In order to build the Interval-Valued Fuzzy Sets we define a new function called weak ignorance for modeling the uncertainty associated with the definition of the membership functions. Next, we adapt the fuzzy partitions to the problem in an optimal way through a cooperative evolutionary tuning in which we handle both the degree of ignorance and the lateral position (based on the 2-tuples fuzzy linguistic representation) of the linguistic labels. The experimental study is carried out over a large collection of data-sets and it is supported by a statistical analysis. Our results show empirically that the use of our methodology outperforms the initial Fuzzy Rule-Based Classification System. The application of our cooperative tuning enhances the results provided by the use of the isolated tuning approaches and also improves the behavior of the genetic tuning based on the 3-tuples fuzzy linguistic representation.Spanish Government TIN2008-06681-C06-01 TIN2010-1505

Intuitionistic Fuzzy Time Series Functions Approach for Time Series Forecasting

Fuzzy inference systems have been commonly used for time series forecasting in the literature. Adaptive network fuzzy inference system, fuzzy time series approaches and fuzzy regression functions approaches are popular among fuzzy inference systems. In recent years, intuitionistic fuzzy sets have been preferred in the fuzzy modeling and new fuzzy inference systems have been proposed based on intuitionistic fuzzy sets. In this paper, a new intuitionistic fuzzy regression functions approach is proposed based on intuitionistic fuzzy sets for forecasting purpose. This new inference system is called an intuitionistic fuzzy time series functions approach. The contribution of the paper is proposing a new intuitionistic fuzzy inference system. To evaluate the performance of intuitionistic fuzzy time series functions, twenty-three real-world time series data sets are analyzed. The results obtained from the intuitionistic fuzzy time series functions approach are compared with some other methods according to a root mean square error and mean absolute percentage error criteria. The proposed method has superior forecasting performance among all methods

Determination of Flow Conditions in Coronary Bifurcation Lesions in the Context of the Medina Classification

Coronary artery bifurcation lesions are complex and several classifications are presented to describe them. Recently, the Medina classification has been proposed. This classification uses binary values for characterization of stenosis. Flow conditions according to Medina classification have not been described. In this paper, bifurcation lesions corresponding to anatomical Medina lesion classification are compared on the basis of flow and Wall Shear Stress (WSS). Computational models of healthy and stenosed coronary artery bifurcations ((1, 1, 1), (0, 1, 1) and (1, 0, 1)) with moderate and severe stenoses of 50% and 75% diameter were analyzed. The results showed that, flow conditions vary in bifurcation lesion types according to the clinically-oriented Medina classification. The flow in SB of bifurcation was dependent of the Medina lesion type and was more affected in lesion type (1, 0, 1). The magnitudes of WSS on the inner and outer walls of SB of bifurcation lesion (1, 0, 1) in post-stenotic region and along the arterial wall were smaller than bifurcations lesions (0, 1, 1) and (1, 1, 1) respectively. Our results suggest that SB of bifurcation lesion (1, 0, 1) is more prone to atherosclerosis progression compared to types (0, 1, 1) and (1, 1, 1)

Simulation and design of individual neutron dosimeter and optimization of energy response using an array of semiconductor sensors

Many researches have been done to develop and improve the performance of personal (individual) dosimeter response to cover a wide of neutron energy range (from thermal to fast). Depending on the individual category of the dosimeter, the semiconductor sensor has been used to simplify and lightweight. In this plan, it’s very important to have a fairly accurate counting of doses rate in different energies. With a general design and single-sensor simulations, all optimal thicknesses have been extracted. The performance of the simulation scheme has been compared with the commercial and laboratory samples in the world. Due to the deviation of all dosimeters with a flat energy response, in this paper, has been used an idea of one semi-conductor sensor to have the flat energy-response in the entire neutron energy range. Finally, by analyzing of the sensors data as arrays for the first time, we have reached a nearly flat and acceptable energy-response. Also a comparison has been made between Lucite-PMMA (H5C5O2) and polyethylene–PE (CH2) as a radiator and B4C has been studied as absorbent. Moreover, in this paper, the effect of gamma dose in the dosimeter has been investigated and shown around the standard has not been exceeded. Keywords: Neutron dosimeter, Semiconductor, Sensors, Energy response, MCNPX cod