Production of drug delivery systems using fused filament fabrication : a systematic review

Fused filament fabrication (FFF) 3D printing technology is widely used in many fields. For almost a decade, medical researchers have been exploring the potential use of this technology for improving the healthcare sector. Advances in personalized medicine have been more achievable due to the applicability of producing drug delivery devices, which are explicitly designed based on patients’ needs. For the production of these devices, a filament—which is the feedstock for the FFF 3D printer—consists of a carrier polymer (or polymers) and a loaded active pharmaceutical ingredient (API). This systematic review of the literature investigates the most widely used approaches for producing drug-loaded filaments. It also focusses on several factors, such as the polymeric carrier and the drug, loading capacity and homogeneity, processing conditions, and the intended applications. This review concludes that the filament preparation method has a significant effect on both the drug homogeneity within the polymeric carrier and drug loading efficiency

3D-Printed Drug Delivery Systems: The Effects of Drug Incorporation Methods on Their Release and Antibacterial Efficiency

Additive manufacturing technologies have been widely used in the medical field. More specifically, fused filament fabrication (FFF) 3D-printing technology has been thoroughly investigated to produce drug delivery systems. Recently, few researchers have explored the possibility of directly 3D printing such systems without the need for producing a filament which is usually the feedstock material for the printer. This was possible via direct feeding of a mixture consisting of the carrier polymer and the required drug. However, as this direct feeding approach shows limited homogenizing abilities, it is vital to investigate the effect of the pre-mixing step on the quality of the 3D printed products. Our study investigates the two commonly used mixing approaches—solvent casting and powder mixing. For this purpose, polycaprolactone (PCL) was used as the main polymer under investigation and gentamicin sulfate (GS) was selected as a reference. The produced systems’ efficacy was investigated for bacterial and biofilm prevention. Our data show that the solvent casting approach offers improved drug distribution within the polymeric matrix, as was observed from micro-computed topography and scanning electron microscopy visualization. Moreover, this approach shows a higher drug release rate and thus improved antibacterial efficacy. However, there were no differences among the tested approaches in terms of thermal and mechanical properties

3D-Printed Drug Delivery Systems: The Effects of Drug Incorporation Methods on Their Release and Antibacterial Efficiency

Additive manufacturing technologies have been widely used in the medical field. More specifically, fused filament fabrication (FFF) 3D-printing technology has been thoroughly investigated to produce drug delivery systems. Recently, few researchers have explored the possibility of directly 3D printing such systems without the need for producing a filament which is usually the feedstock material for the printer. This was possible via direct feeding of a mixture consisting of the carrier polymer and the required drug. However, as this direct feeding approach shows limited homogenizing abilities, it is vital to investigate the effect of the pre-mixing step on the quality of the 3D printed products. Our study investigates the two commonly used mixing approaches—solvent casting and powder mixing. For this purpose, polycaprolactone (PCL) was used as the main polymer under investigation and gentamicin sulfate (GS) was selected as a reference. The produced systems’ efficacy was investigated for bacterial and biofilm prevention. Our data show that the solvent casting approach offers improved drug distribution within the polymeric matrix, as was observed from micro-computed topography and scanning electron microscopy visualization. Moreover, this approach shows a higher drug release rate and thus improved antibacterial efficacy. However, there were no differences among the tested approaches in terms of thermal and mechanical properties

Spatial monitoring of groundwater drawdown and rebound associated with quarry dewatering using automated time-lapse electrical resistivity tomography and distribution guided clustering

Dewatering systems used for mining and quarrying operations often result in highly artificial and complex groundwater conditions, which can be difficult to characterise and monitor using borehole point sampling approaches. Here automated time-lapse electrical resistivity tomography (ALERT) is considered as a means of monitoring subsurface groundwater dynamics associated with changes in the dewatering regime in an operational sand and gravel quarry. We considered two scenarios: the first was unplanned interruption to dewatering due to a pump failure for a period of several days, which involved comparing ALERT monitoring results before and after groundwater rebound; the second involved a planned interruption to pumping over a period of 6 h, for which near-continuous ALERT monitoring of groundwater rebound and drawdown was undertaken. The results of the second test were analysed using distribution guided clustering (DGC) to provide a more quantitative and objective assessment of changes in the subsurface over time. ALERT successfully identified groundwater level changes during both monitoring scenarios. It provided a more useful indication of the rate of water level rise and maximum water levels than piezometer monitoring results. This was due to the piezometers rapidly responding to pressure changes at depth, whilst ALERT/DGC provided information of slower changes associated with the storage and delayed drainage of water within the sediment. By applying DGC we were able to automatically and quantitatively define changes in the resistivity sections, which correlated well with the direct observations of groundwater at site. For ERT monitoring applications that generate numerous time series, the use of DGC could significantly enhance the efficiency of data interpretation, and provide a means of automating groundwater monitoring through assigning alarm thresholds associated with rapid changes in groundwater conditions

Effects of groundwater level changes on the engineering properties of desert sands in Kuwait

SIGLEAvailable from British Library Document Supply Centre- DSC:D90733 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Systematic Review on Deep Reinforcement Learning-Based Energy Management for Different Building Types

Owing to the high energy demand of buildings, which accounted for 36% of the global share in 2020, they are one of the core targets for energy-efficiency research and regulations. Hence, coupled with the increasing complexity of decentralized power grids and high renewable energy penetration, the inception of smart buildings is becoming increasingly urgent. Data-driven building energy management systems (BEMS) based on deep reinforcement learning (DRL) have attracted significant research interest, particularly in recent years, primarily owing to their ability to overcome many of the challenges faced by conventional control methods related to real-time building modelling, multi-objective optimization, and the generalization of BEMS for efficient wide deployment. A PRISMA-based systematic assessment of a large database of 470 papers was conducted to review recent advancements in DRL-based BEMS for different building types, their research directions, and knowledge gaps. Five building types were identified: residential, offices, educational, data centres, and other commercial buildings. Their comparative analysis was conducted based on the types of appliances and systems controlled by the BEMS, renewable energy integration, DR, and unique system objectives other than energy, such as cost, and comfort. Moreover, it is worth considering that only approximately 11% of the recent research considers real system implementations

Day-Ahead Residential Electricity Demand Response Model Based on Deep Neural Networks for Peak Demand Reduction in the Jordanian Power Sector

修士（工学