Fluoxetine (FX) is an antidepressant drug administered only orally in humans. Despite the wide use of FX, until now, there is only limited literature concerning the pharmacokinetics (PK) of FX and the effect of food on its PK. Thus, the objective of this investigation was to study the PK of FX in Arabic healthy male adult volunteers under fasting and fed conditions. In the fasting study, FX 20 mg capsules (Prozac®, Eli Lilly, Canada) were administered to 41 volunteers after overnight fasting of 12 hours, followed by blood sampling from each volunteer immediately before dosing (zero time) and then at 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 24, 36, 48, 60, 72, 96, 120, and eventually at 144 hours after FX dosing. The fed study was conducted after 90 days wash-out period following the completion of the fasting study. The same subjects who received FX in the fasting study were administered the drug directly after a fatty breakfast (fed study), followed by blood sampling intervals similar to the schedule mentioned above for the fasting study. The current investigation demonstrated no statistical differences in the FX pharmacokinetic parameters Cmax, AUC0–t, AUC0–∞, Kel, T1/2, MRT, Cl/F, and Vd/F after fasting compared to the fed conditions, whereas there was statistically significant elongation in the Tmax values after food intake. Therefore, this study concludes the absence of food effect on the PK of FX (except Tmax) in the Arabic population and confirms the method of administration mentioned in the product information but also concludes high interindividual variation in FX exposure (AUC), which suggest that therapeutic drug monitoring (TDM) might be advisable when feasible.
Industrial development has recently increased, including that of plastic industries. Since plastic has a very long analytical life, it will cause environmental pollution, so studies have resorted to reusing recycled waste plastic (sustainable plastic) to produce environmentally friendly concrete (green concrete). In this research, producing environmentally friendly load-bearing concrete masonry units (blocks) was considered where five concrete mixtures were compressed at the blocks producing machine. The cement content reduced from 400 kg/m3 (B-400) to 300 kg/m3 (B-300) then to 200 kg/m3 (B-200). While (B-380) was produced using 380 kg/m3 cement and 20 kg/m3 nano-sil
... Show MoreIn this study, several ionanofluids (INFs) were prepared in order to study their efficiency as a cooling medium at 25 °C. The two-step technique is used to prepare ionanofluid (INF) by dispersing multi-walled carbon nanotubes (MWCNTs) in two concentrations 0.5 and 1 wt% in ionic liquid (IL). Two types of ionic liquids (ILs) were used: hydrophilic represented by 1-ethyl-3-methylimidazolium tetrafluoroborate [EMIM][BF4] and hydrophobic represented by 1-hexyl-3-methylimidazolium hexafluorophosphate [HMIM][PF6]. The thermophysical properties of the prepared INFs including thermal conductivity (TC), density and viscosity were measured experimental
We aimed to obtain magnesium/iron (Mg/Fe)-layered double hydroxides (LDHs) nanoparticles-immobilized on waste foundry sand-a byproduct of the metal casting industry. XRD and FT-IR tests were applied to characterize the prepared sorbent. The results revealed that a new peak reflected LDHs nanoparticles. In addition, SEM-EDS mapping confirmed that the coating process was appropriate. Sorption tests for the interaction of this sorbent with an aqueous solution contaminated with Congo red dye revealed the efficacy of this material where the maximum adsorption capacity reached approximately 9127.08 mg/g. The pseudo-first-order and pseudo-second-order kinetic models helped to describe the sorption measure
Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of ne
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