Three series of monomers, polymers and thioester cyclic compounds containing 4H-1,2,4-triazol-3-thiol moiety were synthesized and examined for their liquid crystalline properties. All monomers, polymers and thioester compounds were characterized by elemental analysis and FTIR, 1 H-NMR and mass spectroscopy. The phase transition and mesomorphic properties were investigated by polarized optical microscope (POM) and differential scanning calorimetry (DSC). The monomer with terminal phenyl substituent display dimorphism nematic and smectic A (SmA) mesophases. The corresponding polymers derived from acrylic and phenyl acrylic acid monomers show nematic mesophase. The only thioester cyclic compound derived from terephtaloyl chloride show nemati

Background: Anemia is a common finding in patients with renal failure disease and it is, primarily, due to reduced production of iron and erythropoietin. Therefore, the aim of current study was to evaluate the relationship between serum level of hepcidin 25, on the one hand, and levels of serum iron, total iron-binding capacity, serum erythropoietin and transferrin saturation% in Iraqi male patients undrgoning haemodialysis.. Methods: Serum from75 males in the end stage of hemodialysis, as group one, and 25 Iraqi healthy malecontrols, as group two, were recruited in this study. Results: Data from current study showed significant increase in hepcidin 25 and significant decrease in serum iron, total iron-binding capacity, transferrin saturati

In this paper, we used two monomers, 3,3',4,4'-benzophenone tetracarboxylic dianhydride (BTDA) and m,m'-diaminobenzophenone (m, m’-DABP), to produce polyamide acid and then converted it to polyimide (PI). The effects of phosphoric acid (H₃PO₄) molarity (1, 2, and 3 M) on the structural, thermal, mechanical, and electrical characteristics of the polyimides/polyaniline (PI/PANI) nanocomposites were studied. Two sharp reflection peaks were developed by the addition of PANI to PI. When 3 M H₃PO₄ is added, the crystalline sharp peak loses some of its intensity. The complex formation of PI/PANI-H₃PO₄ was confi

In the present study, five derivatives have been designed to be synthesized as possible mutual prodrugs for 5-Fluorouracil (5-FU) and non steroidal anti-inflammatory drugs (NSAIDs) to selectively deliver the drugs into the cancer cells. The synthesis of the target compounds were accomplished following multistep reaction procedures, the chemical reaction followed up and the purity of the products were checked by TLC. The structure of the final compounds and their intermediates were confirmed by their melting points, infrared spectroscopy and elemental microanalysis, the hydrolysis of compound III was studied using HPLC technique. According to the results mentioned above, compounds (I−V) can be good candidates as possible mutual prod

This study includes design and synthesis of new non-steroidal anti-inflammatory agents (NSAIDs) with expected cyclooxygenase-2 (COX-2) selective inhibition to achieve better activity and low gastric side effects. Two series of compounds have been designed and synthesized as potential NSAIDs,these  are:     Salicylamide derivatives (compounds 3,4,5 ) and Diflunisal derivatives (compounds 10&11). In vivo acute anti-inflammatory effect of one of the synthesized agents (compound 3)  was evaluated in the rat using egg-white induced paw edema model of inflammation. Preliminary pharmacological study revealed that compound 3 exhibited less anti-inflammatory effect  compared to that of aspirin after

Intrusion Detection Systems (IDS) is the main defense mechanism deployed by the current networks to prevent cyber threats. Recurrent Neural Network (RNN) are also a novel IDS structure that replaces the conventional training and testing mechanism. The strategy encodes network traffic data as biological sequences using amino acid codons in such a fashion that the RNN is capable of effectively analyzing temporal and sequence data patterns. RNN architecture design adopts embedding layers to handle codon representations and Long Short-Term Memory (LSTM) layers to perform sequential data learning, which is followed by a fully connected network to perform classification functions, which preserve high feature extraction and classification