The incidence of chronic wound healing is promoted by the growing trend of elderly population, obesity, and type II diabetes. Although numerous wound dressings have been studied over the years, it is still challenging for many wound dressings to perfectly adapt to the healing process due to the dynamic and complicated wound microenvironment. Aiming at an optimal reproduction of the physiological environment, multifunctional electrospinning nanofibrous membranes (ENMs) have emerged as a promising platform for the wound treatment owing to their resemblance to extracellular matrix (ECM), adjustable preparation processes, porousness, and good conformability to the wound site. Moreover, profiting from the booming development of human-machine interaction and artificial intelligence, a next generation of intelligent electrospinning nanofibrous membranes (iENMs) based wound dressing substrates that could realize the real-time monitoring of wound proceeding and individual-based wound therapy has evoked a surge of interest. In this regard, general wound-related biomarkers and process are overviewed firstly and representative iENMs stimuli-responsive materials are briefly summarized. Subsequently, the emergent applications of iENMs for the wound healing are highlighted. Finally, the opportunities and challenges for the development of next-generation iENMs as well as translating iENMs into clinical practice are evaluated.

Dental caries occur through a multifactorial process that may influence all tooth surfaces throughout an individual’s life. The application of decision analytical modeling (DAM) has gained an increasing level of attention in long-term outcome assessment and economic evaluation of interventions on caries in recent years. The objective of this study was to systematically review the application of DAM and assess their methodological quality in the context of dental caries. A systematic review of the literature published to 31^st December 2018 was conducted in Medline, EMBASE, NHSEED, and Web of Science electronic databases. The main information and model characteristics of studies was extracted with the methodological quality of included studies reviewed and assessed using the Philips’ checklist. Twenty five studies from different settings were included. Modeling techniques mainly comprised main type of modeling including Markov models (n = 12), Markov/microsimulation mixed model (n = 7), systematic dynamic models (n = 3), microsimulation models (n = 2) and decision tree (n = 1). The mean number of criteria fulfilled was 25.1 out of 60 items, which varied between year of study and research groups. The percentage of criteria fulfilled for data dimension was lower than for the structure and consistency dimension. Critical issues were data quality, incorporation of utility values, and uncertainty analysis in modeling. The current review revealed that the methodological quality of DAM in dental caries economic evaluations is unsatisfied. Future modeling studies should adhere more closely to good practice guidelines, especially with respect to data quality evaluation, utility values incorporation, and uncertainty analysis in DAM based studies.

Objectives This study aims to investigate factors with a significant influence on deceased organ donation rates in Organisation for Economic Co-operation and Development (OECD) countries and determine their relative importance. It seeks to provide the necessary data to facilitate the development of more efficient strategies for improving deceased organ donation rates. Design Retrospective study. Setting Publicly available secondary annual data. Participants The study includes 36 OECD countries as panel members for data analysis. Outcome measures Multivariable panel data regression analysis was employed, encompassing data from 2010 to 2018 for all investigated variables in the included countries. Results The following variables had a significant influence on deceased organ donation rates: ‘opt-in’ system (β=−4.734, p<0.001, ref: ‘opt-out’ system), only donation after brain death (DBD) donors allowed (β=−4.049, p=0.002, ref: both DBD and donation after circulatory death (DCD) donors allowed), number of hospital beds per million population (pmp) (β=0.002, p<0.001), total healthcare employment pmp (β=−0.00012, p=0.012), World Giving Index (β=0.124, p=0.008), total tax revenue as a percentage of gross domestic product (β=0.312, p=0.009) and percentage of population aged ≥65 years (β=0.801, p<0.001) as well as high education population in percentage (β=0.118, p=0.017). Conclusions Compared with the promotion of socioeconomic factors with a positive significant impact on deceased organ donation rates, the following policies have been shown to significantly increase rates of deceased organ donation, which could be further actively promoted: the adoption of an ‘opt-out’ system with presumed consent for deceased organ donation and the legal authorisation of both DBD and DCD for transplantation.

As the gap between a shortage of organs and the immense demand for liver grafts persists, every available donor liver needs to be optimized for utility, urgency and equity. To overcome this challenge, decision modelling might allow us to gather evidence from previous studies as well as compare the costs and consequences of alternative options. For public health policy and clinical intervention assessment, it is a potentially powerful tool. The most commonly used types of decision analytical models include decision trees, the Markov model, microsimulation, discrete event simulation and the system dynamic model. Analytic models could support decision makers in the field of liver transplantation when facing specific problems by synthesizing evidence, comprising all relevant options, generalizing to other contexts, extending the time horizon and exploring the uncertainty. For modeling studies of economic evaluation for transplantation, understanding the current nature of the disease is crucial, as well as the selection of appropriate modelling techniques. The quality and availability of data is another key element for the selection and development of decision analytical models. In addition, good practice guidelines should be complied, which is important for standardization and comparability between economic outputs.