Correlation between Metabolic Syndrome and Psoriasis

Background: Psoriasis is a chronic inflammatory disease that is mediated by the immune system. It was thought to be a specific skin condition, but numerous studies have shown that it is a systemic disorder, as well as psychological difficulties like shyness, low self-esteem, and anxiety are related with it. Resistin is considered to be an important modulator of chronic inflammation.Objective: To determine serum level of resistin and C-reactive protien (CRP) in psoriasis vulgaris patients with metabolic syndrome (MetS).Patients and Methods: Clinical examinations were performed on 40 patients ranging in age from 28 to 53, and venous blood samples were collected. Tests were performed on the blood samples to identify levels of, resistin, and C-reactive protein (CRP). Results: levels of resistin were elevated with increased severity of psoriasis as measured by Psoriasis Area/Severity Index (PASI) score with statistically significant relation; p value (0.04), PASI score also was positively associated with elevated CRP levels; p value (0.001) .Conclusion: We conclude that resistin levels provide important value to optimize medical treatment and improve clinical outcomes in patients with psoriasis

Moving the needle—what does RSAP look for and what does it aim to do?

Peer reviewe

New hybrid ensemble method for anomaly detection in data science

Anomaly detection is a significant research area in data science. Anomaly detection is used to find unusual points or uncommon events in data streams. It is gaining popularity not only in the business world but also in different of other fields, such as cyber security, fraud detection for financial systems, and healthcare. Detecting anomalies could be useful to find new knowledge in the data. This study aims to build an effective model to protect the data from these anomalies. We propose a new hyper ensemble machine learning method that combines the predictions from two methodologies the outcomes of isolation forest-k-means and random forest using a voting majority. Several available datasets, including KDD Cup-99, Credit Card, Wisconsin Prognosis Breast Cancer (WPBC), Forest Cover, and Pima, were used to evaluate the proposed method. The experimental results exhibit that our proposed model gives the highest realization in terms of receiver operating characteristic performance, accuracy, precision, and recall. Our approach is more efficient in detecting anomalies than other approaches. The highest accuracy rate achieved is 99.9%, compared to accuracy without a voting method, which achieves 97%

The Effect of Some Animal and Plant Proteins on uric acid index in Rats with Acute Renal Failure

Kidney diseases are a public health problem all over the world. From recent studies it has been found that a low-protein diet as part of diet therapy has beneficial effects that slow the progression of chronic kidney disease. So, this study was carried out to investigate the uric acid index of some animal protein (Beef, eggs, kidney) and plant protein (mushroom, yellow lentils, lupine) in rats with induced-acute renal failure (ARF). Forty rats were divided into 8 groups (n=5) in each group. The first group of rats was fed on basal diet. The other rats were injected with one dose of 50% glycerol (10 ml/kg B.Wt.) in their hind limbs to induce ARF, these rats were divided into 7 subgroups, as follows: Subgroup (1): Rats with ARF were fed on basal diet supplemented with 150 gm/kg casein as positive control group (+Ve). From subgroups (2 : 7) rats were fed on the basal diet supplemented with 150 gm/kg from dried beef, eggs, kidney, mushroom, yellow lentils, lupine, respectively for 4 weeks. The treated groups with either animal or plant proteins had a significant decrease (P<0.05) in the level of kidney functions as well as lowering the mean values of phosphorous, sodium and potassium. The level of serum albumin and total protein were significantly (P<0.05) increased as compared to the +ve control group. It could be concluded that a diet containing animal protein (beef, eggs, kidney) or plant protein (mushroom, yellow lentils, lupine) may be used as a part of diet therapy to slow the progression of kidney disease and improve the kidney functions

Improving irrigation water delivery performance of a large-scale rice irrigation scheme

The availability of irrigation water and its equitable allocation in a large-scale rice irrigation scheme of Malaysia have been modeled. The model reliably estimates available water for irrigation at the intake of the main canal and simulates the recommended irrigation deliveries for 120 tertiary canals. Different water allocation and management scenarios were evaluated based on the sensitivity of the growth stages of rice to water, varying field-water demand, and perceived water shortages. The model provides a quantitative assessment not only of water allocation for irrigation but also of day-to-day or periodic irrigation delivery performances for a large-scale rice irrigation system. It provides 86% adequacy and 87% equity of irrigation delivery in the main season (August-December). The corresponding performance indicators provided by the model are 74 and 89% in the off-season (February-June). The dependability of water supply is higher in the off-season than in the main season, while the relative water supply (RWS) is the converse. RWS often becomes >1.0 in the main season, while such a RWS is rarely obtained in the off-season. The model augments the water delivery performance of the scheme and hence would serve as a useful tool for irrigation managers in decision making

Cost Model for end-milling of AISI D2 tool steel

In this research paper, user-friendly and accurate mathematical model for estimating the cost of end-milling of AISI D2 tool steel using Polycrystalline Cubic Boron Nitride (PCBN) cutting tool inserts is developed. Initially, the different components of machining cost were identified, followed by establishment of equations to determine their values. Then, the required experimental and non-experimental data were collected and the bottom-up approach was adopted for evaluating the cost of machining corresponding to each of fifteen experimental runs. The Response Surface Methodology (RSM) was used to develop the model in which the cost of machining is given as a function of the machining parameters; cutting speed, feed per tooth, and depth of cut, and expressed in Ringgit Malaysia per cubic cm (RM per cm)3. Analysis of Variance (ANOVA) was utilized to check the adequacy of the developed model. The developed model was found to be statistically adequate