Human serum albumin (HSA) nanoparticles have been widely used as versatile drug delivery systems for improving the efficiency and pharmaceutical properties of drugs. The present study aimed to design HSA nanoparticle encapsulated with the hydrophobic anticancer pyridine derivative (2-((2-([1,1'-biphenyl]-4-yl)imidazo[1,2-a]pyrimidin-3-yl)methylene)hydrazine-1-carbothioamide (BIPHC)). The synthesis of HSA-BIPHC nanoparticles was achieved using a desolvation process. Atomic force microscopy (AFM) analysis showed the average size of HSA-BIPHC nanoparticles was 80.21 nm. The percentages of entrapment efficacy, loading capacity and production yield were 98.11%, 9.77% and 91.29%, respectively. An In vitro release study revealed that HSA-BIPHC nan

Invasomes are newly developed types of nanovesicles. A vesicular drug delivery system is considered one of the approaches for transdermal delivery to enhance permeation and improve drug bioavailability. Ondansetron is a serotonin receptor antagonist used for treating vomiting associated with different clinical cases. The study aimed to prepare invasomal dispersions for improving permeation of ondansetron across the skin with a controlled release pattern. Twenty-seven formulas of ondansetron-loaded invasomes were prepared by a modified mechanical dispersion method. These formulas were optimized by studying the effect of variables on entrapment efficiency. Vesicle size, polydispersity, zeta potential, in-vitro release and ex-vivo perm

         Recently, emulgel has emerged as one of the most interesting topical preparations in the field of pharmaceutics. In this research clotrimazole was formulated as topically applied emulgel ; different formulas were prepared. The prepared emulgels were evaluated for their physical appearance , rheological behaviour , and in vitro drug release . The influence of the type of gelling agent (carbopol 934 and methyl cellulose), the concentration of both the emulsifying agent (2% and 4% w/w of mixture of span 20 and tween 20) and the oil phase (5% and 7.5% w/w of liquid paraffin) and the type of oil phase (liquid paraffin and cetyl alcohol), on the drug release from the prepared emulgels was invest

Blood lipids are important mediators of host defense during the acute phase of innate immunity. Parasites may induce significant changes in lipid parameters, as has been shown in vitro study where substitution of serum by lipid/cholesterol in medium and in experimental models (in vivo). Thus changes in lipid profile occur in patients that having active infections with most of the parasites. Toxoplasma cannot synthesize cholesterol and depends upon acquisition of low density lipoprotein (LDL)-derived from the host cell, via endocytosis mediated by the LDL receptor or the LDL receptor-related protein.The present study is conducted to evaluate the changes in lipid profile in T. gondii infected women.A total of patients included 87 aborted wom

Machine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity (1520 healthy, 331 KCN1, 1319 KCN2, 1699 KCN3 a

In this work , a hybrid scheme tor Arabic speech for the recognition

of  the speaker  verification  is presented  . The scheme is hybrid as utilizes the traditional digi tal signal processi ng and neural network . Kohonen neural  network has been used as a recognizer  tor speaker verification after extract spectral  features from an acoustic signal  by Fast Fourier Transformation Algorithm(FFT) .

The system was im plemented using a PENTIUM  processor , I000

MHZ compatible and MS-dos 6.2 .

Monaural source separation is a challenging issue due to the fact that there is only a single channel available; however, there is an unlimited range of possible solutions. In this paper, a monaural source separation model based hybrid deep learning model, which consists of convolution neural network (CNN), dense neural network (DNN) and recurrent neural network (RNN), will be presented. A trial and error method will be used to optimize the number of layers in the proposed model. Moreover, the effects of the learning rate, optimization algorithms, and the number of epochs on the separation performance will be explored. Our model was evaluated using the MIR-1K dataset for singing voice separation. Moreover, the proposed approach achi

Abstract 

For sparse system identification,recent suggested algorithms are  -norm Least Mean Square (  -LMS), Zero-Attracting LMS (ZA-LMS), Reweighted Zero-Attracting LMS (RZA-LMS), and p-norm LMS (p-LMS) algorithms, that have modified the cost function of the conventional LMS algorithm by adding a constraint of coefficients sparsity. And so, the proposed algorithms are named  -ZA-LMS,