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Chitosan has been widely used as a nature-derived polymeric biomaterial due to its high biocompatibility and abundance. However, poor solubility in aqueous solutions of neutral pH and multiple fabrication steps for the molding process limit its application to microneedle technology as a drug delivery carrier. Here, we present a facile method to prepare water-soluble chitosan and its application for sustained transdermal drug delivery. The water-soluble chitosan was prepared by acid hydrolysis using trifluoroacetic acid followed by dialysis in 0.1 M NaCl solutions. We successfully fabricated bullet-shaped microneedle (MN) arrays by the single molding process with neutral aqueous chitosan solutions (pH 6.0). The chitosan MN showed sufficient mechanical properties for skin insertion and, interestingly, exhibited slow dissolving behavior in wet conditions, possibly resulting from a physical crosslinking of chitosan chains. Chitosan MN patches loading rhodamine B, a model hydrophilic drug, showed prolonged release kinetics in the course of the dissolving process for more than 72 h and they were found to be biocompatible to use. Since the water-soluble chitosan can be used for MN fabrication in the mild conditions (neutral pH and 25 °C) required for the loading of bioactive agents such as proteins and achieve a prolonged release, this biocompatible chitosan MN would be suitable for sustained transdermal drug delivery of a diverse range of drugs.

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Intravenous (IV) medication administration processes have been considered as high-risk steps, because accidents during IV administration can lead to serious adverse effects, which can deteriorate the therapeutic effect or threaten the patient’s life. In this study, we propose a multi-modal infusion pump (IP) monitoring technique, which can detect mismatches between the IP setting and actual infusion state and between the IP setting and doctor’s prescription in real time using a thin membrane potentiometer and convolutional-neural-network-based deep learning technique. During performance evaluation, the percentage errors between the reference infusion rate (IR) and average estimated IR were in the range of 0.50–2.55%, while those between the average actual IR and average estimated IR were in the range of 0.22–2.90%. In addition, the training, validation, and test accuracies of the implemented deep learning model after training were 98.3%, 97.7%, and 98.5%, respectively. The training and validation losses were 0.33 and 0.36, respectively. According to these experimental results, the proposed technique could provide improved protection functions to IV-administration patients.

Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disease characterized by selective death of motor neurons. Mutations in Cu, Zn-superoxide dismutase (SOD1) causing the gain of its toxic property are the major culprit of familial ALS (fALS). The abnormal SOD1 aggregation in the motor neurons has been suggested as the major pathological hallmark of ALS patients. However, the development of pharmacological interventions against SOD1 still needs further investigation. In this study, using ELISA-based chemical screening with wild and mutant SOD1 proteins, we screened a new small molecule, PRG-A01, which could block the misfolding/aggregation of SOD1 or TDP-43. The drug rescued the cell death induced by mutant SOD1 in human neuroblastoma cell line. Administration of PRG-A01 into the ALS model mouse resulted in significant improvement of muscle strength, motor neuron viability and mobility with extended lifespan. These results suggest that SOD1 misfolding/aggregation is a potent therapeutic target for SOD1 related ALS.