No abstract

No abstract

The effect of the integration between MCM-48 and some biopolymers (starch, chitosan, and β-cyclodextrin) on enhancing the pharmaceutical properties of MCM-48 as advanced carriers for the 5-fluorouracil drug was studied considering the loading capacities and the release profiles. The prepared carriers are MCM-48/chitosan (MCM/CH), MCM-48/starch composite (MCM/ST), and MCM-48/β-Cyclodextrin (MCM/CD). They emphasized excellent 5-Fu loading capacities of 141.2 mg/g (MCM-48), 156.6 mg/g (MCM/ST), 191 mg/g (MCM/CH), and 170 mg/g (MCM/CD), reflecting significant enhancement in the loading capacities. The kinetic and equilibrium investigation suggested physisorption loading of 5-Fu drug in a monolayer form for MCM-48, MCM/ST, and MCM/CH (Langmuir) and in a multilayer form for MCM/CD (Freundlich). This was supported by the estimated adsorption energies (0.23 kJ/mol (MCM-48), 0.26 kJ/mol (MCM/ST), 0.3 kJ/mol (MCM/CH), and 0.75 kJ/mol (MCM/CD)) and the thermodynamic parameters of free energy and enthalpy. The obtained release profiles for 80 h reflected significant controlling for the releasing behavior of MCM/48 on integrating its structure by adjusting the type of the selected polymer and its ratio. The pharmacokinetic modeling and the diffusion exponent from the Korsmeyer–Peppas model suggested non-Fickian transport behavior (a combination of erosion and diffusion releasing mechanism) for MCM/ST, MCM/CH, and MCM/CD and Fickian diffusion behavior (diffusion releasing mechanism) for MCM-48.

Chest diseases can be dangerous and deadly. They include many chest infections such as pneumonia, asthma, edema, and, lately, COVID-19. COVID-19 has many similar symptoms compared to pneumonia, such as breathing hardness and chest burden. However, it is a challenging task to differentiate COVID-19 from other chest diseases. Several related studies proposed a computer-aided COVID-19 detection system for the single-class COVID-19 detection, which may be misleading due to similar symptoms of other chest diseases. This paper proposes a framework for the detection of 15 types of chest diseases, including the COVID-19 disease, via a chest X-ray modality. Two-way classification is performed in proposed Framework. First, a deep learning-based convolutional neural network (CNN) architecture with a soft-max classifier is proposed. Second, transfer learning is applied using fully-connected layer of proposed CNN that extracted deep features. The deep features are fed to the classical Machine Learning (ML) classification methods. However, the proposed framework improves the accuracy for COVID-19 detection and increases the predictability rates for other chest diseases. The experimental results show that the proposed framework, when compared to other state-of-the-art models for diagnosing COVID-19 and other chest diseases, is more robust, and the results are promising.

No abstract