Collagen/gelatin and polysaccharide complexes enhance gastric retention and mucoadhesive properties.

This article has focused on collagen-gelatin, the gelation process, as well as blend interaction between collagen/gelatin with various polysaccharides to boost mucoadhesion and gastric retention. The interaction between mucoadhesive materials and mucin layers is of significant interest in the development of drug delivery systems and biomedical applications for effective targeting and prolonged time in the gastrointestinal tract. This paper reviews the current advancement and mucoadhesive properties of collagen/gelatin and different polysaccharide complexes concerning the mucin layer and interactions are briefly highlighted. Collagen/gelatin and polysaccharide blends biocompatible and biodegradable, the complex biomolecules have shown encouraging mucoadhesive properties due to their cationic nature and ability to form hydrogen bonds with mucin glycoproteins. The mucoadhesion mechanism was attributed to the electrostatic interactions between the positively charged amino (NH2) groups of blend biopolymers and the negatively charged sialic acid residues present in mucin glycoprotein. At the end of this article, the encouraging prospect of collagen/polysaccharide complex and mucin glycoprotein is highlighted.

Adaptive machine learning method for photoacoustic computed tomography based on sparse array sensor data.

BACKGROUND AND OBJECTIVE: Photoacoustic computed tomography (PACT) is a non-invasive biomedical imaging technology that has developed rapidly in recent decades, especially has shown potential for small animal studies and early diagnosis of human diseases. To obtain high-quality images, the photoacoustic imaging system needs a high-element-density detector array. However, in practical applications, due to the cost limitation, manufacturing technology, and the system requirement in miniaturization and robustness, it is challenging to achieve sufficient elements and high-quality reconstructed images, which may even suffer from artifacts. Different from the latest machine learning methods based on removing distortions and artifacts to recover high-quality images, this paper proposes an adaptive machine learning method to firstly predict and complement the photoacoustic sensor channel data from sparse array sampling and then reconstruct images through conventional reconstruction algorithms. METHODS: We develop an adaptive machine learning method to predict and complement the photoacoustic sensor channel data. The model consists of XGBoost and a neural network named SS-net. To handle data sets of different sizes and improve the generalization, a tunable parameter is used to control the weights of XGBoost and SS-net outputs. RESULTS: The proposed method achieved superior performance as demonstrated by simulation, phantom experiments, and in vivo experiment results. Compared with linear interpolation, XGBoost, CAE, and U-net, the simulation results show that the SSIM value is increased by 12.83%, 6.78%, 21.46%, and 12.33%. Moreover, the median R2 is increased by 34.4%, 8.1%, 28.6%, and 84.1% with the in vivo data. CONCLUSIONS: This model provides a framework to predict the missed photoacoustic sensor data on a sparse ring-shaped array for PACT imaging and has achieved considerable improvements in reconstructing the objects. Compared with linear interpolation and other deep learning methods qualitatively and quantitatively, our proposed methods can effectively suppress artifacts and improve image quality. The advantage of our methods is that there is no need for preparing a large number of images as the training dataset, and the data for training is directly from the sensors. It has the potential to be applied to a wide range of photoacoustic imaging detector arrays for low-cost and user-friendly clinical applications.

Nurses' knowledge, attitude, and practice regarding the use of physical restraints in children in the intensive care setting in China: A cross-sectional multicentre study.

OBJECTIVE: The objectives of this study were to investigate paediatric nurses' knowledge, attitude, and practice (KAP) regarding the use of physical restraints and to explore the factors related to the use of physical restraints. Findings will provide a reference to develop standard procedures and training. BACKGROUND: Nurses' KAP regarding the use of physical restraints affect the use of physical restraints in the paediatric intensive care unit and neonatal intensive care unit. Understanding nurses' decision-making processes should inform strategies and methods for effectively reducing and regulating the use of physical restraints in paediatric patients in the intensive care unit (ICU) in China. METHODS: We conducted a cross-sectional survey of 823 registered ICU nurses from 12 children's hospitals in China between April and June, 2020. ICU nurses' KAP regarding the use of physical restraints in children were evaluated using a structured self-administered questionnaire that was distributed through an online platform. Descriptive and multiple linear regression analyses were used to examine the factors that influenced ICU nurses' KAP regarding the use of physical restraints in children. RESULTS: Overall, 49.8% of respondents were paediatric intensive care unit nurses, 25.0% of respondents were neonatal intensive care unit nurses, and 25.2% of respondents were other ICU nurses; 58.44% of nurses had received some training on the use of physical restraints in children. Mean total scores on the items addressing ICU nurses' knowledge (range, 0 [lowest level of knowledge] -11 [highest level of knowledge]), attitude (range, 11 [least likely to use physical restraint] - 55 [most likely to use physical restraint]), and practice (range, 14 [few skills] - 42 [good skills]) regarding the use of physical restraints in children were 8.00 ± 1.46, 30.67 ± 5.31, and 37.61 ± 3.46, respectively. Multiple linear regression analysis showed a higher level of education and less work experience (years) were related to higher knowledge scores; prior training in the use of physical restraint was related to lower attitude scores; and female, prior training in the use of physical restraints, and a higher level of education were related to higher practice scores. CONCLUSIONS: Nurses would like to use physical restraints without physician approval in an emergency or when they could not pay close attention to a child. There are a few standardised training and lack of clinical guidelines for paediatric nurses. We recommend establishing a standard of care for physical restraints in paediatric patients.