Lactococcus lactis: LAB model organism for bacteria-mediated therapeutic strategies

Lactococcus lactis is a well-characterized, food-grade lactic acid bacterium (LAB) with generally recognized as safe (GRAS) status. Better understanding of this bacterium at a molecular level has led to the development of unprecedented genetic tools that enable the expression of heterologous proteins. Subsequently, the ability of L. lactis to express and deliver these proteins to eukaryotic hosts presents a promising approach to achieve potent treatments for various diseases. Here, we have reviewed the characteristics of L. lactis and the expression systems established for this LAB model organism. We also described the experimental applications of L. lactis in disease therapy, especially its role as a vector in vaccination strategies

En'light'ening journey

The saying ‘seeing is believing’ describes the curious nature of man to see things far smaller than can be perceived with the naked eye. It has been known for over 2000 years that glass bends light. Since then, many innovations have been made to manipulate light bending through different shapes of clear glass. These innovations paved the way for the development of the optical lens

Assessing the in vitro cytotoxicity of synthesized chitosan nanoparticles against different organic and inorganic nanomaterials in human kidney cancer cells

One of the biggest concerns regarding the use of nanomaterials for biological and medical applications is its toxicity. A simple way to evaluate nanomaterials’ toxicity is by conducting in vitro cytoxicity assays. In this study, the synthesis of chitosan nanoparticles (CNP) and the colorimetric MTT assay of CNP along with various organic and inorganic nanomaterials were explored. CNP were synthesised via ionic gelation routes and particle size distribution were analysed using dynamic light scattering (DLS) supplemented with field-emission scanning electron microscopy (FE-SEM) imaging. The 786-O human kidney cancer cell lines were established and treated with various concentrations of CNP, carbon nanotubes (CNT), layered double hydroxides (LDH), solid lipid nanoparticles (SLN), and iron oxide nanoparticles for MTT assay. The morphologies of cells treated with each nanomaterial were also observed. DLS analysis showed that nanoparticles with average size of 67.70 nm were obtained using a formulation of 600 μl of 0.5 mg/ml chitosan solution and 250 μl of 0.7 mg/ml tripolyphosphate (TPP) solution (CNP-F3) and was further supported by FE- SEM results. Results from MTT assay showed that cells treated with 1 mg/ml SLN and CNP-F3 gave the highest cell viability of 49.38% and 39.72%, respectively. Cells treated with 1 mg/ml CNT gave the lowest cell viability of 31.54%. These results were consistent with the observations made on cell morphologies, implying that both organic CNP and SLN were the least toxic. The inorganic nanomaterials tend to be more toxic, with CNT being the most toxic

System identification and predictive functional control for electro-hydraulic actuator system

Nowadays, hydraulic systems are widely applied in industries due to its. However it suffers nonlinearities problem causing inaccurate position control. This study presents the modeling and development of a predictive functional control (PFC) algorithm for position control of electro-hydraulic actuator (EHA). System identification (SI) approach is used to obtain the linear transfer function of the system in discrete form. PFC is proposed based on its ability to predict the future outputs of the actual plant over the predictive horizon and computes the control effort over the control horizon at every sampling instance. Numerical simulation and real-time experiment are conducted to study the PFC performance with respect to optimized PID controller tuned by particle swarm optimization (PID-PSO) for several position tracking inputs. Result shows that the PFC algorithm has better performance in term of overshoot and integral absolute error (IAE) as compared to the optimized PID

System identification and PID-PSO force control of thin soft actuator

This paper presents the System Identification (SI) method for modeling of thin soft actuator and its force control using PID-PSO. System identification is used to obtain the mathematical model (transfer function) from the measured experimental data using the SI procedure. Auto Regressive with Exogenous Input (ARX) model is chosen as model structure of the system. The result from SI model shows linear discrete model to obtain a discrete transfer function for the soft actuator system. Next, PSO-PID controller was proposed for the force control of the actuator. Validation was made between simulation and experimental data for the force control. Results show that the developed model represents the actual system by giving same characteristics in the force control analysis