Aceclofenac (AC) is an orally active phenyl acetic acid derivative, non-steroidal anti-inflammatory drug with exceptional anti-inflammatory, analgesic and antipyretic properties. It has low aqueous solubility, leading to slow dissolution, low permeability and inadequate bioavailability. The aim of the current study was to prepare and characterize AC-NS-based gel to enhance the dissolution rate and then percutaneous permeability. NS.s were prepared using solvent/antisovent precipitation method at different drug to polymer ratios (1:1, 1:2, and 1:3) using different polymers such as poly vinyl pyrrolidone (PVP-K25), hydroxy propyl methyl cellulose (HPMC-E5) and poloxamer® (388) as stabilizer

Lasmiditan (LAS) was formulated as a nanoemulsion based in situ gel (NEIG)with the aim of improving its oral bioavailability via application intranasally. The solubility of LAS in oils, emulsifiers, and co-emulsifiers was determined to identify nanoemulsion (NE)components. Phase diagrams were constructed to identify the area of nanoemulsification. LAS NE was formulated using the spontaneous nanoemulsification method. Four NEs (F19, F24, F31, and F34) containing 7-15 % oleic acid (OA) as an oily phase, 40-55% labrasol (LR), and transcutol (TC) as emulsifier mixture at (1:1), (2:1), (3:1), and (1:2) ratio with 30-53 % (w/w) aqueous phase, having suitable optical transparency of 95–98%, globule size of 104-140 nm and polydisper

In recent years, the world witnessed a rapid growth in attacks on the internet which resulted in deficiencies in networks performances. The growth was in both quantity and versatility of the attacks. To cope with this, new detection techniques are required especially the ones that use Artificial Intelligence techniques such as machine learning based intrusion detection and prevention systems. Many machine learning models are used to deal with intrusion detection and each has its own pros and cons and this is where this paper falls in, performance analysis of different Machine Learning Models for Intrusion Detection Systems based on supervised machine learning algorithms. Using Python Scikit-Learn library KNN, Support Ve

Background: Manuka honey (MH) is a mono-floral honey derived from the Manuka tree (Leptospermum scoparium). MH is a highly recognized for its non-peroxide antibacterial activities, which are mostly related to its unique methylglyoxal content (MGO) in MH. The beneficial phytochemicals in MH is directly related to their favorable health effects, which include wound healing, anticancer, antioxidant, and anti-inflammatory properties. Aims: The purpose of this study was to evaluate the effect of MH on pro-inflammatory cytokines (IL-8 and TNF-α) in patients with gingivitis and compare it with chlorhexidine (CHX) and distilled water (DW). Materials and Methods: This study was a randomized, double blinded, and parallel clinical trial. Forty-fiv

Three new hydrazone derivatives of Etodolac were synthesized and evaluated for their anti-inflammatory activity by using egg white induced paw edema method. All the synthesized target compounds were characterized by CHN- microanalysis, FT-IR spectroscopy, and ¹HNMR analysis. The synthesis of the target (P1-P3) compounds was accomplished following multistep reaction procedures. The synthesized target compounds were found to be active in reducing paw edema thickness and their anti-inflammatory effect was comparable to that of the standard (Etodolac).

The  thermal  and  electrical  performance  of  different  designs  of  air  based  hybrid photovoltaic/thermal collectors is investigated experimentally and theoretically. The circulating air is used to cool PV panels and to collect the absorbed energy to improve their performance. Four different collectors have been designed, manufactured and instrumented namely; double PV panels without cooling (model I), single duct double pass collector (model II), double duct single pass (model III), and single duct single pass (model IV) . Each collector consists of: channel duct, glass cover, axial fan to circulate air and two PV panel in parallel connection. The temperature of the upper and

Thermal conductivity measurement was done for specimens of Polystyrene/ titanium dioxide, Polycarbonate/ titanium dioxide and Polymethylmetha acrylate/ titanium dioxide composites for weight ratio of 1.9/ 0.1 and 1.8/ 0.2 wt% for different thickness of the samples. The experimental results show that the thermal conductivity is increased with the increasing of thickness of layers and with the weight ratio of TiO2