Cell membrane-camouflaged PLGA biomimetic system for diverse biomedical application.

The emerging cell membrane (CM)-camouflaged poly(lactide-co-glycolide) (PLGA) nanoparticles (NPs) (CM@PLGA NPs) have witnessed tremendous developments since coming to the limelight. Donning a novel membrane coat on traditional PLGA carriers enables combining the strengths of PLGA with cell-like behavior, including inherently interacting with the surrounding environment. Thereby, the in vivo defects of PLGA (such as drug leakage and poor specific distribution) can be overcome, its therapeutic potential can be amplified, and additional novel functions beyond drug delivery can be conferred. To elucidate the development and promote the clinical transformation of CM@PLGA NPs, the commonly used anucleate and eukaryotic CMs have been described first. Then, CM engineering strategies, such as genetic and nongenetic engineering methods and hybrid membrane technology, have been discussed. The reviewed CM engineering technologies are expected to enrich the functions of CM@PLGA for diverse therapeutic purposes. Third, this article highlights the therapeutic and diagnostic applications and action mechanisms of PLGA biomimetic systems for cancer, cardiovascular diseases, virus infection, and eye diseases. Finally, future expectations and challenges are spotlighted in the concept of translational medicine.

Developing Machine Learning and Statistical Tools to Evaluate the Accessibility of Public Health Advice on Infectious Diseases among Vulnerable People.

BACKGROUND: From Ebola, Zika, to the latest COVID-19 pandemic, outbreaks of highly infectious diseases continue to reveal severe consequences of social and health inequalities. People from low socioeconomic and educational backgrounds as well as low health literacy tend to be affected by the uncertainty, complexity, volatility, and progressiveness of public health crises and emergencies. A key lesson that governments have taken from the ongoing coronavirus pandemic is the importance of developing and disseminating highly accessible, actionable, inclusive, coherent public health advice, which represent a critical tool to help people with diverse cultural, educational backgrounds and varying abilities to effectively implement health policies at the grassroots level. OBJECTIVE: We aimed to translate the best practices of accessible, inclusive public health advice (purposefully designed for people with low socioeconomic and educational background, health literacy levels, limited English proficiency, and cognitive/functional impairments) on COVID-19 from health authorities in English-speaking multicultural countries (USA, Australia, and UK) to adaptive tools for the evaluation of the accessibility of public health advice in other languages. METHODS: We developed an optimised Bayesian classifier to produce probabilistic prediction of the accessibility of official health advice among vulnerable people including migrants and foreigners living in China. We developed an adaptive statistical formula for the rapid evaluation of the accessibility of health advice among vulnerable people in China. RESULTS: Our study provides needed research tools to fill in a persistent gap in Chinese public health research on accessible, inclusive communication of infectious diseases' prevention and management. For the probabilistic prediction, using the optimised Bayesian machine learning classifier (GNB), the largest positive likelihood ratio (LR+) 16.685 (95% confidence interval: 4.35, 64.04) was identified when the probability threshold was set at 0.2 (sensitivity: 0.98; specificity: 0.94). CONCLUSION: Effective communication of health risks through accessible, inclusive, actionable public advice represents a powerful tool to reduce health inequalities amidst health crises and emergencies. Our study translated the best-practice public health advice developed during the pandemic into intuitive machine learning classifiers for health authorities to develop evidence-based guidelines of accessible health advice. In addition, we developed adaptive statistical tools for frontline health professionals to assess accessibility of public health advice for people from non-English speaking backgrounds.