Polymeric drug delivery systems for biological antimicrobial agents

The objective of this work was to develop suitable delivery systems for biological agents that have antimicrobial activities using biocompatible polymers, aiming to reduce their toxicity when administered. Two biological agents, colistin as an antibacterial agent and nystatin (Nys) as an antifungal agent, are the focus of this thesis as they are potent treatments for current pathogen infections, especially to the multidrug-resistant (MDR) bacteria/fungi, but have potential toxicity to human. Polymeric drug delivery systems, including prodrug, hydrogel and micelle formulations, have been developed and discussed for their potential as topical and systemic regimes. The majority of the work was focused on the effect of the covalently attachment of synthetic polymers onto the biological agents upon their antimicrobial activities and the toxicity. The conjugation between colistin and polymers was achieved successfully through either irreversible or releasable linkages. Although irreversible polymer modifications on colistin showed no antimicrobial activity (chapter 2), an acceptable antibacterial activity was observed from the polymer-colistin conjugates with a releasable linkage through either ‘grafting-to’ (chapter 3) or ‘grafting-from’ (chapter 4) approaches. On the other hand, even though the pure polymer-Nys conjugate with a releasable imine linkage cannot be obtained due to the nature of the labile imine bond, the crude conjugate showed an excellent antifungal activity and a reduced toxicity compared to the native Nys (chapter 6). Other polymeric delivery systems were also discussed in this thesis. The incorporation of colistin within a developed hydrogel delivery system as an antibacterial patch for burn infections was investigated through in vitro and in vivo studies, showing a similar antibacterial activity as the native colistin solution against MDR Gram-negative bacteria with no systemic toxicity (chapter 5). Finally, an amphiphilic polymer containing boronic acid groups on the side chains was synthesised and used to target the hydroxyl groups on Nys, expecting to build up an environmental responsive micelle through dynamic boronate ester bond (chapter 7). Although more work is still needed, this system showed a potential to improve Nys solubility

Synthesis of well-defined catechol polymers for surface functionalization of magnetic nanoparticles

In order to obtain dual-modal fluorescent magnetic nanoparticles, well-defined fluorescent functional polymers with terminal catechol groups were synthesized by single electron transfer living radical polymerization (SET-LRP) under aqueous conditions for “grafting to” modification of iron oxide nanoparticles. Acrylamide, N-isopropylacrylamide, poly(ethylene glycol) methyl ether acrylate, 2-hydroxyethyl acrylate, glycomonomer and rhodamine B piperazine acrylamide were homo-polymerized or block-copolymerized directly from an unprotected dopamine-functionalized initiator in an ice-water bath. The Cu-LRP tolerated the presence of catechol groups leading to polymers with narrow molecular weight distributions (Mw/Mn < 1.2) and high or full conversion obtained in a few minutes. Subsequent immobilization of dopamine-terminal copolymers on an iron oxide surface were successful as demonstrated by Fourier transform infrared spectroscopy (FTIR), dynamic light scattering (DLS), transition electron microscopy (TEM) and thermogravimetric analysis (TGA), generating stable polymer-coated fluorescent magnetic nanoparticles. The nanoparticles coated with hydrophilic polymers showed no significant cytotoxicity when compared with unmodified particles and the cellular-uptake of fluorescent nanoparticles by A549 cells was very efficient, which also indicated the potential application of these advanced nano materials for bio-imaging

A hydrogel based localized release of colistin for antimicrobial treatment of burn wound infection

There is an urgent unmet medical need for new treatments for wound and burn infections caused by multidrug-resistant (MDR) Gram-negative ‘superbugs’, especially the problematic Pseudomonas aeruginosa. In this work we report the incorporation of colistin, a potent lipopeptide into a self-healable hydrogel (via dynamic imine bond formation) following the chemical reaction between the amine groups present in glycol chitosan and an aldehyde modified poly (ethylene glycol) (PEG). The storage module (G’) of the colistin-loaded hydrogel ranged from 1.3 kPa to 5.3 kPa by varying the amount of the cross-linker and colistin loading providing different options for topical wound healing. The majority of the colistin is released from the hydrogel within 24 h and remains active as demonstrated by both antibacterial in vitro disk diffusion and time-kill assays. Moreover and pleasingly, the colistin-loaded hydrogel performed almost equally well as native colistin against both the colistin-sensitive and also colistin-resistant P. aeruginosa strain in the in vivo animal 'burn' infection model despite exhibiting a slower killing profile in vitro. Based on this antibiotic performance along with the biodegradability of the product, we believe the colistin-loaded hydrogel to be a potential localized wound-healing formulation to treat burn wounds against microbial infection

Machine-learning Classifier to Detect Atrial Fibrillation based on Facial Action Units

Facial action units (AUs) are numerical representations of indicative facial features drawn by facial landmark localization. Tiny muscular movements on the face can be reflected through facial AUs. In this project, using AUs to diagnose atrial fibrillation (AFib) in real-time is investigated for the first time, and AUs are shown to have a connection with AFib occurrence. Unlike ECG/PPG/VPG, the machine-learning-enabled AFib detector proposed in this project can realize real-time contactless monitoring of AFib continuously in home settings while maintaining the accuracy of AFib detection. ECG data and tablet image snapshots are synchronized to form the dataset that will be used. AUs extracted from the images serve as predictors, and the ECG data corresponds to a binary AFib label at a time instant. Due to limited data, the original dataset is balanced and augmented. AUs are engineered based on a feature selection method. AFib data are partitioned into a training/validation set and a test set, and modern machine-learning classifiers are used to fit the data. Upon five-fold cross-validation, random forest (RF) stands out and is further fine-tuned, achieving the best performance curve and the highest area under the curve (AUC). After RF has been fine-tuned, an F2-score of 0.811, recall of 0.782, and accuracy of 0.969 can be attained. For new subjects that have not been trained before in the test set, RF is also proved to be superior to a random classifier. As more training data are available, the RF classifier to detect AFib is promising for future real-life applications

Chitosan-Based Self-Healing Hydrogel: From Fabrication to Biomedical Application

Biocompatible self-healing hydrogels are new-generation smart soft materials that hold great promise in biomedical fields. Chitosan-based self-healing hydrogels, mainly prepared via dynamic imine bonds, have attracted broad attention due to their mild preparation conditions, excellent biocompatibility, and self-recovery ability under a physiological environment. In this review, we present a comprehensive overview of the design and fabrication of chitosan-based self-healing hydrogels, and summarize their biomedical applications in tissue regeneration, customized drug delivery, smart biosensors, and three/four dimensional (3D/4D) printing. Finally, we will discuss the challenges and future perspectives for the development of chitosan-based self-healing hydrogels in the biomedical field

Effects of Antimony Stress on Photosynthesis and Growth of Acorus calamus

This study was aimed to explore that effects of Sb on physiological parameters of Acorus calamus and the possibility of using A. calamus as a remediation plant. A. calamus potted experiments were conducted using different concentrations (0, 250, 500, 1000, and 2000 mg/kg) of antimony potassium tartrate (Sb3+) (marked as CK, T1, T2, T3, and T4, respectively) and potassium pyroantimonate (Sb5+) (marked as CK, T′1, T′2, T′3, and T′4, respectively). The effects of Sb stress (Sb3+ and Sb5+) on leaf photosynthetic pigments, biomass, photosynthetic characteristics and chlorophyll fluorescence parameters of potted A. calamus were studied. With the rise of Sb3+ concentration from T1 to T4, the leaf pigment contents (chlorophyll a, b, carotenoid), plant height, dry weight, net photosynthetic rate (Pn), stomatal conductance (Gs), evaporation rate (E), PSII maximum photochemical efficiency (Fv/Fm), and PSII electron transfer quantum yield rate (ΦPSII) of A. calamus all reduced, while intercellular CO2 concentration (Ci) significantly increased. The reduction of Pn was mainly induced by non-stomatal limitation. Chlorophyll a/b ratio increased significantly versus the control, while carotenoid/chlorophyll ratio (Car/Chl) first decreased and then increased. The leaf Chl a, Chl b, Car, plant height, dry weight, Pn, Gs, E, Fv/Fm, and ΦPSII all maximized in T′1 (250 mg/kg), but were not significantly different from the control. As the Sb5+ concentration increased from T′2 to T′4, the above indices all decreased and were significantly different from the control. Moreover, intercellular CO2 concentration (Ci) decreased significantly. The reduction of Pn was caused by non-stomatal limitation, indicating the mesophyll cells were damaged. The Car/Chl ratio was stable within 0–500 mg/kg Sb, but decreased in T3 and T4, and rose in T′3 and T′4. After Sb3+ and Sb5+ treatments, translocation factor varied 19.44–27.8 and 19.44–24.86%, respectively. In conclusion, different form Sb3+ treatment, Sb5+ treatment showed a Hormesi effect, as low-concentration treatment promoted A. calamus growth, but high-concentration treatment inhibited its growth. The two forms of Sb both caused unfavorable effects on A. calamus, but the seedlings did not die and were modestly adaptive and Sb-accumulative. A. calamus, which is easily maintained and cultivated, can serve as a good candidate for phytoremediation of water contaminated with Sb

Numerical Analysis for Wetting Behaviors of an Oil Jet Lubricated Spur Gear

As it is widely employed in the aeronautical transmission system, a better understanding of the oil jet lubrication behavior is vital to determine the total system energy consumption. Firstly, this study presents related theoretical models such as the sum of oil jet resistance torque, impingement depth, and wetted area of the oil film for calibrating the physical characteristics of the impact of the oil jet on the gear flank. Then, in terms of the flow phenomenology of the liquid column for the oil jet impact on an isolated spur gear, a detailed transient and spatial flow field analysis becomes available, benefiting from an overset mesh method integrating with a volume-of-fluid (VOF) method. Furthermore, not only the oil jet resistance torque, but also the impingement depth as well as the spatial and temporal evolution of wetted surface by the oil film on the gear tooth given by numerical investigations were compared well with the theoretical calculations

A Fairness-Enhanced Federated Learning Scheduling Mechanism for UAV-Assisted Emergency Communication

As the frequency of natural disasters increases, the study of emergency communication becomes increasingly important. The use of federated learning (FL) in this scenario can facilitate communication collaboration between devices while protecting privacy, greatly improving system performance. Considering the complex geographic environment, the flexible mobility and large communication radius of unmanned aerial vehicles (UAVs) make them ideal auxiliary devices for wireless communication. Using the UAV as a mobile base station can better provide stable communication signals. However, the number of ground-based IoT terminals is large and closely distributed, so if all of them transmit data to the UAV, the UAV will not be able to take on all of the computation and communication tasks because of its limited energy. In addition, there is competition for spectrum resources among many terrestrial devices, and all devices transmitting data will bring about an extreme shortage of resources, which will lead to the degradation of model performance. This will bring indelible damage to the rescue of the disaster area and greatly threaten the life safety of the vulnerable and injured. Therefore, we use user scheduling to select some terrestrial devices to participate in the FL process. In order to avoid the resource waste generated by the terrestrial device resource prediction, we use the multi-armed bandit (MAB) algorithm for equipment evaluation. Considering the fairness issue of selection, we try to replace the single criterion with multiple criteria, using model freshness and energy consumption weighting as reward functions. The state of the art of our approach is demonstrated by simulations on the datasets