Knowledge, attitude and application towards fast track surgery among operating room paramedics: a cross-sectional study.

BACKGROUND: Fast track surgery has shown its effectiveness to accelerate recovery and gained acceptance in many operations. However, data for paramedics using fast track surgery are limited in China. The aims of our study are to evaluate the knowledge, attitude and application status of fast track surgery in paramedics and to provide suggestions for the better application of fast track surgery. METHODS: Two Hundred Ninety-one operating room paramedics were investigated by simple random sampling from October 20 to December 20, 2019 time. A self-reported questionnaire was used to collect data with five dimensions: demographic data, cognitive level, knowledge, attitude and application of fast track surgery. Data were analyzed using qualitative and quantitative methods. RESULTS: 19.93% of participants never heard fast track surgery and only 3.32% of participants were very familiar with it. Gender (0.702, 95% CI 0.109-1.294), technical title (0.342, 95% CI 0.126-0.558) and awareness of the concept of fast track surgery (0.471, 95% CI 0.165-0.776) had a correlation with the knowledge level of paramedics towards fast track surgery. In terms of attitude towards fast track surgery, gender (- 1.944, 95% CI -3.830- -0.058), age (0.303, 95% CI 0.021-0.585) and knowledge score of fast track surgery (0.426, 95% CI 0.014-0.838) are related. Half of the paramedics believe the most difficult problem in the application of fast track surgery was the lack of multi-team communication and cooperation. CONCLUSION: The knowledge of fast track surgery among paramedics in Wuhan is poor, and some paramedics have a negative attitude towards it. As the attitude is positively correlated with the knowledge, it is necessary to improve the knowledge level of fast track surgery among paramedics in Wuhan.

Mesenchymal Stem Cell-Loaded Hydrogel Improves Surgical Treatment for Chronic Cerebral Ischemia.

Chronic cerebral ischemia (CCI) results in a prolonged insufficient blood supply to the brain tissue, leading to impaired neuronal function and subsequent impairment of cognitive and motor abilities. Our previous research showed that in mice with bilateral carotid artery stenosis, the collateral neovascularization post Encephalo-myo-synangiosis (EMS) treatment could be facilitated by bone marrow mesenchymal stem cells (MSCs) transplantation. Considering the advantages of biomaterials, we synthesized and modified a gelatin hydrogel for MSCs encapsulation. We then applied this hydrogel on the brain surface during EMS operation in rats with CCI, and evaluated its impact on cognitive performance and collateral circulation. Consequently, MSCs encapsulated in hydrogel significantly augment the therapeutic effects of EMS, potentially by promoting neovascularization, facilitating neuronal differentiation, and suppressing neuroinflammation. Furthermore, taking advantage of multi-RNA-sequencing and in silico analysis, we revealed that MSCs loaded in hydrogel regulate PDCD4 and CASP2 through the overexpression of miR-183-5p and miR-96-5p, thereby downregulating the expression of apoptosis-related proteins and inhibiting early apoptosis. In conclusion, a gelatin hydrogel to enhance the functionality of MSCs has been developed, and its combination with EMS treatment can improve the therapeutic effect in rats with CCI, suggesting its potential clinical benefit.

When Machine Learning Meets 2D Materials: A Review.

The availability of an ever-expanding portfolio of 2D materials with rich internal degrees of freedom (spin, excitonic, valley, sublattice, and layer pseudospin) together with the unique ability to tailor heterostructures made layer by layer in a precisely chosen stacking sequence and relative crystallographic alignments, offers an unprecedented platform for realizing materials by design. However, the breadth of multi-dimensional parameter space and massive data sets involved is emblematic of complex, resource-intensive experimentation, which not only challenges the current state of the art but also renders exhaustive sampling untenable. To this end, machine learning, a very powerful data-driven approach and subset of artificial intelligence, is a potential game-changer, enabling a cheaper - yet more efficient - alternative to traditional computational strategies. It is also a new paradigm for autonomous experimentation for accelerated discovery and machine-assisted design of functional 2D materials and heterostructures. Here, the study reviews the recent progress and challenges of such endeavors, and highlight various emerging opportunities in this frontier research area.