On the basis of known coumarin-based prodrug system, a novel coumarin-based mutual prodrug of 5-fluorouracil and dichloroacetic acid was designed, synthesized and evaluated as a promising oral chemotherapeutic agent basing on in vitro stability study in HCl buffer (pH 1.2) and in phosphate buffer (pH 7.4), as well as in vitro release study in human serum. The chemical structure of prodrug was confirmed by analyzing its FTIR, ¹H NMR, ¹³C NMR and MS-ESI spectra. The results of in vitro kinetic study indicated that the prodrug was significantly stable in HCl and in phosphate buffers, and was hydrolyzed in human serum followed pseudo first order kinetics.

Keywords: Coumarin-bas

Tooth restoration one of the most common procedures in dental practice. The replacement of the entire restoration leads to loss of tooth structure and increase risk of pulp injury; replacement is also time consuming and costly. According to the minimally invasive approach when minimal defects, repair is the better choice than the total replacement of the restoration. This study aims to evaluate repair rating versus replacement treatment procedure for defective composite fillings among Iraqi dentists. Material and methodology: A questionnaire survey were designed and distributed to 184 post-graduate dentists in Iraq. The inquiry pertained general information; including their clinical experience in years, their preference in terms of direct c

Abstract

     In this investigation, Al₂O₃ nano material of 50nm particles size were added to the 6061 Al aluminium alloy by using the stir casting technique to fabricate the nanocomposite of 10wt% Al₂O₃. The experimental results observed that the addition of 10wt% Al₂O₃ improved the fatigue life and strength of constant and cumulative fatigue. Comparison between the S-N curves behaviour of metal matrix (AA6061) and the nanocomposite 10wt% Al₂O₃ has been made. The comparison revealed that 12.8% enhancement in fatigue strength at 10⁷cycles due to 10wt% nano reinforcement. Also cumulative fatigue l

CD-nanosponges were prepared by crosslinking B-CD with diphenylcarbonate (DPC) using ultrasound assisted technique. 5-FU was incorporated with NS by freeze drying, and the phase solubility study, complexation efficiency (CE) entrapment efficiency were performed. Also, the particle morphology was studied using SEM and AFM. The in-vitro release of 5-FU from the prepared nanosponges was carried out in 0.1N HCl.

5-FU nanosponges particle size was in the nano size. The optimum formula showed a particle size of (405.46±30) nm, with a polydispersity index (PDI) (0.328±0.002) and a negative zeta potential (-18.75±1.8). Also the drug entrapment efficiency varied with the CD: DPC molar ratio from 15.6 % to 30%. The SEM an

Traumatic spinal cord injury is a serious neurological disorder. Patients experience a plethora of symptoms that can be attributed to the nerve fiber tracts that are compromised. This includes limb weakness, sensory impairment, and truncal instability, as well as a variety of autonomic abnormalities. This article will discuss how machine learning classification can be used to characterize the initial impairment and subsequent recovery of electromyography signals in an non-human primate model of traumatic spinal cord injury. The ultimate objective is to identify potential treatments for traumatic spinal cord injury. This work focuses specifically on finding a suitable classifier that differentiates between two distinct experimental

Heart disease is a significant and impactful health condition that ranks as the leading cause of death in many countries. In order to aid physicians in diagnosing cardiovascular diseases, clinical datasets are available for reference. However, with the rise of big data and medical datasets, it has become increasingly challenging for medical practitioners to accurately predict heart disease due to the abundance of unrelated and redundant features that hinder computational complexity and accuracy. As such, this study aims to identify the most discriminative features within high-dimensional datasets while minimizing complexity and improving accuracy through an Extra Tree feature selection based technique. The work study assesses the efficac