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This study aims to evaluate the therapeutic potential of cationic liposomal neostigmine bromide (NB), a novel drug delivery system, for the treatment of detrusor underactivity. By comparing the characteristics of NB‐liposomes (NLP), NB‐β‐cyclodextrin inclusion complex liposomes (NCLP), and NB‐mesoporous silica nanoparticle@CaCO3 liposomes (NMCLP), NMCLP is selected as the main research subject. It has an average particle size and zeta potential of 100 nm and +50 mV, and its encapsulation efficiency and loading capacity of NB are 14.75% and 12.8%, respectively. Most importantly, NMCLP shows the best in vitro release performance among the three liposomes, demonstrating its ability in sustained release of NB. During cell and animal assays, efficient cellular uptake of liposomes through liposome‐specific pathways is observed, facilitating targeted drug delivery, and in vivo experiments demonstrate the efficacy of NMCLP in improving bladder function in mice. Urodynamic measurements show increased bladder capacity and reduced voiding pressure, indicating enhanced bladder muscle activity. Histological analysis reveals the distribution and deep penetration of NMCLP within bladder tissues, supporting its localized drug effect. Therefore, NMCLP holds promise as a targeted and effective therapeutic strategy for detrusor underactivity. [ABSTRACT FROM AUTHOR]

The accumulative freeze-thaw cycles in cold regions could cause deteriorations to concrete pavements. The measurement of air-void system inside hardened concrete is important for estimating the freeze-thawing performance of a concrete pavement. The segmentation of air voids from the concrete surface image is one of the most essential and challenging steps for automated air-void measurement. Conventional automated air-voids segmentation methods rely on a color enhancement step to manually create a contrast between air voids and non-air-voids. This study aims to investigate the use of a photometric stereo method for automated segmentation of air voids in hardened concrete surface without a need for contrast enhancement. In the study, the photometric stereo system with a Charge-Coupled Device (CCD) camera and six illumination lights was designed to collect images of polished concrete specimen surfaces under various lighting directions. Two air-void segmentation approaches were developed in the study. One approach interpreted the air voids by the surface depth. The other approach interpreted the air voids by the surface normal. Consequently, the segmentation results were refined by a set of image processing procedures. The results showed that the proposed automated air-void segmentation procedures could detect air voids in concrete surface with a satisfactory accuracy. [ABSTRACT FROM AUTHOR]

• Automated segmentation of air voids from hardened concrete surface. • Three-dimensional image reconstruction method. • Deep learning strategies and process. The automated air-void detection methods specified in the ASTM C457 require the aid of contrast enhancement which is time consuming and labor intensive. This study investigated the utilization of three-dimensional (3D) reconstruction and Deep Convolution Neural Network (DCNN) methods to detect the air voids in hardened concrete surfaces without the use of contrast enhancement. The experimental results showed that the DCNN could accurately distinguish air voids from hardened concrete images with the detection accuracy of over 0.9 in only less than a minute. The accuracy rates for air content, specific surface, and spacing factor were 0.92, 0.91, and 0.89, respectively. [ABSTRACT FROM AUTHOR]

Currently, clinical intravesical instillation chemotherapy has been greatly compromised by the toxicological and physiological factors. New formulations that can specifically and efficiently kill bladder cancer cells are in urgent need to overcome the low residence efficiency and dose limiting toxicity of current ones. The combination of mucoadhesive nanocarriers and cancer cell selective prodrugs can to great extent address these limitations. However, the insignificant endogenous stimulus difference between cancer cells and normal cells in most cases and the high local drug concentration make it essential to develop new drugs with broader selectivity-window. Herein, based on the statistically different NQO1 expression between cancerous and normal bladder tissues, the reactive oxygen species (ROS) activatable epirubicin prodrug and highly potent NQO1 substrate, KP372–1, was co-delivered using a GSH-responsive mucoadhesive nanocarrier. After endocytosis, epirubicin could be promptly activated by the NQO1-dependent ROS production caused by KP372–1, thus specifically inhibiting the proliferation of bladder cancer cells. Since KP372–1 is much more potent than some commonly used NQO1 substrates, for example, β-lapachone, the cascade drug activation could occur under much lower drug concentration, thus greatly lowering the toxicity in normal cells and broadening the selectivity-window during intravesical bladder cancer chemotherapy. [Display omitted] • Mucoadhesive nanoparticles enhanced the adhesion and penetration of drugs in bladder; • KP372–1 activated epirubicin prodrug in cancer cells through NQO1-dependent way; • Dual-drug loaded nanoparticles selectively killed cancer cells and exempted normal cells; • Prodrug nanoparticles caused significant tumor inhibition and negligible side effect. [ABSTRACT FROM AUTHOR]

Data quality assurance (QA) procedures have been developed and implemented to evaluate the quality of automated pavement distress data. Most State Departments of Transportation (DOTs) use fixed acceptance thresholds to evaluate data quality at the network level and project level. However, errors of automated measurements can be affected by many factors such as pavement types and pavement conditions. It is necessary to take those factors into account while developing the QA acceptance thresholds. The objective of this research is to propose thresholding methods that could effectively utilise these factors to improve QA of the automated data. In this study, the potential problems associated with the use of fixed acceptance thresholds for QA were first discussed. Then, one conventional prediction interval method and one bootstrapping prediction interval method were proposed for QA procedures. The proposed prediction interval based methods can be used to determine variable thresholds that are sensitive to measurement errors. Lastly, QA results from the proposed methods and a fixed thresholding method were compared and analysed. The experimental results showed that prediction interval methods were superior to the fixed threshold methods in identifying pavement sections with data quality issues and evaluating the overall data quality of the network. [ABSTRACT FROM AUTHOR]

Data quality assurance (QA) procedures have been developed and implemented to evaluate the quality of automated pavement distress data. Most State Departments of Transportation (DOTs) use fixed acceptance thresholds to evaluate data quality at the network level and project level. However, errors of automated measurements can be affected by many factors such as pavement types and pavement conditions. It is necessary to take those factors into account while developing the QA acceptance thresholds. The objective of this research is to propose thresholding methods that could effectively utilise these factors to improve QA of the automated data. In this study, the potential problems associated with the use of fixed acceptance thresholds for QA were first discussed. Then, one conventional prediction interval method and one bootstrapping prediction interval method were proposed for QA procedures. The proposed prediction interval based methods can be used to determine variable thresholds that are sensitive to measurement errors. Lastly, QA results from the proposed methods and a fixed thresholding method were compared and analysed. The experimental results showed that prediction interval methods were superior to the fixed threshold methods in identifying pavement sections with data quality issues and evaluating the overall data quality of the network. [ABSTRACT FROM AUTHOR]