In Vivo Evaluation of Praziquantel-Loaded Solid Lipid Nanoparticles against S. mansoni Infection in Preclinical Murine Models

This study aimed to develop and assess the long-term stability of drug-loaded solid lipid nanoparticles (SLNs). The SLNs were designed to extend the release profile, overcome the problems of bioavailability and solubility, investigate toxicity, and improve the antischistosomal efficacy of praziquantel. The aim was pursued using solvent injection co-homogenization techniques to fabricate SLNs in which Compritol ATO 888 and lecithin were used as lipids, and Pluronic F127 (PF127) was used as a stabilizer. The long-term stability effect of the PF127 as a stabilizer on the SLNs was evaluated. Dynamic light scattering (DLS) was used to determine the particle size, stability, and polydispersity. The morphology of the SLNs was examined through the use of transmission electron microscopy (TEM) and scanning electron microscopy (SEM). The chemical properties, as well as the mechanical, thermal, and crystal behaviours of SLNs were evaluated using FTIR, ElastoSens Bio2, XRPD, DSC, and TGA, respectively. SLNs with PF127 depicted an encapsulation efficiency of 71.63% and a drug loading capacity of 11.46%. The in vitro drug release study for SLNs with PF127 showed a cumulative release of 48.08% for the PZQ within 24 h, with a similar release profile for SLNs’ suspension after 120 days. DLS, ELS, and optical characterization and stability profiling data indicate that the addition of PF127 as the surfactants provided long-term stability for SLNs. In vitro cell viability and in vivo toxicity evaluation signify the safety of SLNs stabilized with PF127. In conclusion, the parasitological data showed that in S. mansoni-infected mice, a single (250 mg/kg) oral dosage of CLPF-SLNs greatly improved PZQ antischistosomal efficacy both two and four weeks post-infection. Thus, the fabricated CLPF-SLNs demonstrated significant efficiency inthe delivery of PZQ, and hence are a promising therapeutic strategy against schistosomiasis.     

Ameliorative Effect of Dabigatran on CFA-Induced Rheumatoid Arthritis via Modulating Kallikrein-Kinin System in Rats

Rheumatoid arthritis is an autoimmune disease that affects joints, leading to swelling, inflammation, and dysfunction in the joints. Recently, research efforts have been focused on finding novel curative approaches for rheumatoid arthritis, as current therapies are associated with adverse effects. Here, we examined the effectiveness of dabigatran, the antithrombotic agent, in treating complete Freund’s adjuvant (CFA)-induced arthritis in rats. Subcutaneous injection of a single 0.3 mL dosage of CFA into the rat’s hind leg planter surface resulted in articular surface deformities, reduced cartilage thickness, loss of intercellular matrix, and inflammatory cell infiltration. There were also increased levels of the Anti-cyclic citrullinated peptide antibody (ACPA), oxidative stress, and tissue Receptor activator of nuclear factor–kappa B ligand (RANKL). Proteins of the kallikrein-kinin system (KKS) were also elevated. The inhibitory effects of dabigatran on thrombin led to a subsequent inhibition of KKS and reduced Toll-like receptor 4 (TLR4) expression. These effects also decreased RANKL levels and showed anti-inflammatory and antioxidant effects. Therefore, dabigatran could be a novel therapeutic strategy for arthritis.     

Characterization of cobalt ferrite-supported activated carbon for removal of chromium and lead ions from tannery wastewater via adsorption equilibrium

In this experiment, cobalt ferrite-supported activated carbon (CF-AC) was developed and characterized via the wet impregnation method for the removal of Cr and Pb(II) ions from tannery wastewater. Batch adsorption was carried out to evaluate the effect of experimental operating conditions (pH of solution, contact time, adsorbent dose, and temperature), and the removal efficiencies of Cr and Pb(II) ions by the developed adsorbents were calculated and recorded for all experimental conditions. These variables were estimated and reported as removal efficiencies of 98.2% for Cr and 96.4% for Pb(II) ions at the optimal conditions of 5, 0.8 g, 80 min, and 333 K for pH, adsorbent dose, contact time, and temperature, respectively. The equilibrium for the sorption of Cr and Pb(II) ions was studied using four widely used isotherm models (the Langmuir, Freundlich, Dubinin-Radushkevich, and Temkin isotherm models). It was found that the Freundlich isotherm model fit better with the coefficient of determination (R²) of 0.948 4 and a small sum of square error of 0.000 6. The maximum adsorption capacities (Q_m) of Pb(II) and Cr adsorbed onto CF-AC were determined to be 6.27 and 23.6 mg/g, respectively. The adsorption process conformed well to pseudo-second order kinetics as revealed by the high R² values obtained for both metals. The thermodynamic parameters showed that adsorption of Cr and Pb(II) ions onto CF-AC was spontaneous, feasible, and endothermic under the studied conditions. The mean adsorption energy (E) values revealed that the adsorption mechanism of Cr and Pb(II) by CF-AC is physical in nature. The results of the study showed that adsorbent developed from CF-AC can be efficiently used as an environmentally friendly alternative adsorbent, for removal of Cr and Pb(II) ions in tannery wastewater.     

Analysing Various Control Technics for Manipulator Robotic System (Robogymnast)

The Robogymnast is a highly complex, three-link system based on the triple-inverted pendulum and is modelled on the human example of a gymnast suspended by their hands from the high bar and executing larger and larger upswings to eventually rotate fully. The links of the Robogymnast correspond respectively to the arms, trunk, and lower limbs of the gymnast, and from its three joints, one is under passive operation, while the remaining two are powered. The passive top joint poses severe challenges in attaining the smooth movement control needed to operate the Robogymnast effectively. This study assesses four types of controllers used for systems operation and identifies how far response stabilisation is achieved with each. The system is simulated using MATLAB Simulink, with findings generated regarding rising and settling time, as well as overshoot. The research primarily seeks to examine the application of a linear quadratic regulator controller, proportionalintegral-derivative controller, fuzzy linear quadratic regulator controller and linear quadratic regulator- proportional-integral-derivative controller for this type of system and comparisons between the different controllers to demonstrate successful performance, which highlights the claimed advantages of the proposed system.     

Simultaneous effects of viscous and Darcy dissipation on mixed convection flow in an annulus partially filled with porous material: Analytical approach

The effect of thermal dissipation on a steady, fully developed, mixed convection viscous, incompressible fluid in an annulus partially filled with porous materials has been thoroughly examined in this work. Fluid flow begins within the annulus when a pressure gradient is applied abruptly in the flow direction. The fluid flow in the porous zone is characterized by the Brinkmann-extended Darcy model. The fluid is divided into transparent and porous parts by a minimal interface. By matching their velocities and considering the shear stress jump conditions at the interface, the clear fluid and the porous region are connected. Additionally, the viscous dissipation effect is considered while determining the energy equation in the clear fluid zone. However, in the porous area, the Darcy dissipation effects are considered in addition to the viscous dissipation influence. In the model, the results of various fluid parameters in the problem were addressed using line graphs and the homotopy perturbation method. The study found that when the porous region's thickness grows, heat transmission on the annular surface enclosing the clear fluid region increases while it decreases on the border surface close to the porous region. In addition, a thicker porous region requires a greater pressure gradient to propel the flow.     

Mechanically Robust Shape-Memory Organohydrogels Based on Silk Fibroin with Organogel Microinclusions of Various Sizes

Organohydrogels (OHGs) are soft materials with antagonistic hydrophilic and hydrophobic domains that have great interest for many different applications. This study presents the preparation of mechanically strong OHGs with shape-memory function by incorporating semicrystalline organo-microgels within the pores of silk fibroin (SF) scaffolds. In the first step, SF cryogels with various pore diameters between 26 ± 8 and 17 ± 4 µm are synthesized by cryogelation of aqueous SF solutions at concentrations between 5 and 20 w/v%. In the second step, the pores of SF scaffolds are filled with an organogel precursor solution containing n-octadecyl acrylate (C18A), acrylic acid, N,N'-methylene(bis)acrylamide, and an initiator. Once the free-radical polymerization took place inside the pores, OHGs containing organo-microgels of various sizes are obtained. The incorporation of the organogel component in the cryogels generates crystalline areas due to the side-by-side packed C18 side chains. OHGs' melting temperature and crystallinity level can be varied from 42 to 54 °C and from 2 to 16%, respectively. The stiffness of OHGs increases from 5.9 ± 0.5 to 18 ± 1 MPa with increasing SF concentration from 5 to 20 w/v%, which is attributed to the decreasing pore size of the cryogels and increasing thickness of the pore walls.     

Cyberattack Detection Framework Using Machine Learning and User Behavior Analytics

This paper proposes a novel framework to detect cyber-attacks using Machine Learning coupled with User Behavior Analytics. The framework models the user behavior as sequences of events representing the user activities at such a network. The represented sequences are then fitted into a recurrent neural network model to extract features that draw distinctive behavior for individual users. Thus, the model can recognize frequencies of regular behavior to profile the user manner in the network. The subsequent procedure is that the recurrent neural network would detect abnormal behavior by classifying unknown behavior to either regular or irregular behavior. The importance of the proposed framework is due to the increase of cyber-attacks especially when the attack is triggered from such sources inside the network. Typically detecting inside attacks are much more challenging in that the security protocols can barely recognize attacks from trustful resources at the network, including users. Therefore, the user behavior can be extracted and ultimately learned to recognize insightful patterns in which the regular patterns reflect a normal network workflow. In contrast, the irregular patterns can trigger an alert for a potential cyber-attack. The framework has been fully described where the evaluation metrics have also been introduced. The experimental results show that the approach performed better compared to other approaches and AUC 0.97 was achieved using RNN-LSTM 1. The paper has been concluded with providing the potential directions for future improvements.