Phospholipid-Anchored Ligand Conjugation on Extracellular Vesicles for Enhanced Cancer Targeting.

Extracellular vesicles (EVs) are recognized as potential candidates for next-generation drug delivery systems. However, the inherent cancer-targeting efficiency is unsatisfactory, necessitating surface modification to attach cell-binding ligands. By utilizing phospholipase D from Streptomyces in combination with maleimide-containing primary alcohol, the authors successfully anchored ligands onto milk-derived EVs (mEVs), overcoming the issues of ligand leakage or functional alteration seen in traditional methods. Quantitative nano-flow cytometry demonstrated that over 90% of mEVs are effectively modified with hundreds to thousands of ligands. The resulting mEV formulations exhibited remarkable long-term stability in conjugation proportion, ligand number, size distribution, and particle concentration, even after months of storage. It is further shown that conjugating transferrin onto mEVs significantly enhanced cellular uptake and induced pronounced cytotoxic effects when loaded with paclitaxel. Overall, this study presents a highly efficient, stable, cost-effective, and scalable ligand conjugation approach, offering a promising strategy for targeted drug delivery of EVs.

Life Cycle Assessment-Based Carbon Footprint Accounting Model and Analysis for Integrated Energy Stations in China.

To achieve its carbon neutrality goal, China has invested broadly in energy infrastructure and the emerging integrated energy stations (IESs) projects will bring enormous opportunities. Accurate carbon emission accounting for IESs is challenging in view of the complexity of the manufacturing process and uncertainty in construction and operation processes. To overcome these challenges, this paper develops a novel quantitative carbon footprint analysis model for IESs from a lifecycle perspective, with production and materialization, construction, operation and maintenance, and disposal and recycling phases considered. The method is applied on a 110 kV wind power IES project in China, to analyze and calculate lifecycle carbon emissions, identify the key influence factors of carbon footprints and provide suggestions for carbon reduction. The findings can identify key influence factors and provide suggestions for carbon reduction for the development of IES projects.

Augmentation-Consistent Clustering Network for Diabetic Retinopathy Grading with Fewer Annotations.

Diabetic retinopathy (DR) is currently one of the severe complications leading to blindness, and computer-aided, diagnosis technology-assisted DR grading has become a popular research trend especially for the development of deep learning methods. However, most deep learning-based DR grading models require a large number of annotations to provide data guidance, and it is laborious for experts to find subtle lesion areas from fundus images, making accurate annotation more expensive than other vision tasks. In contrast, large-scale unlabeled data are easily accessible, becoming a potential solution to reduce the annotating workload in DR grading. Thus, this paper explores the internal correlations from unknown fundus images assisted by limited labeled fundus images to solve the semisupervised DR grading problem and proposes an augmentation-consistent clustering network (ACCN) to address the above-mentioned challenges. Specifically, the augmentation provides an efficient cue for the similarity information of unlabeled fundus images, assisting the supervision from the labeled data. By mining the consistent correlations from augmentation and raw images, the ACCN can discover subtle lesion features by clustering with fewer annotations. Experiments on Messidor and APTOS 2019 datasets show that the ACCN surpasses many state-of-the-art methods in a semisupervised manner.

Attitudes of medical workers in China toward artificial intelligence in ophthalmology: a comparative survey.

BACKGROUND: In the development of artificial intelligence in ophthalmology, the ophthalmic AI-related recognition issues are prominent, but there is a lack of research into people's familiarity with and their attitudes toward ophthalmic AI. This survey aims to assess medical workers' and other professional technicians' familiarity with, attitudes toward, and concerns about AI in ophthalmology. METHODS: This is a cross-sectional study design study. An electronic questionnaire was designed through the app Questionnaire Star, and was sent to respondents through WeChat, China's version of Facebook or WhatsApp. The participation was voluntary and anonymous. The questionnaire consisted of four parts, namely the respondents' background, their basic understanding of AI, their attitudes toward AI, and their concerns about AI. A total of 562 respondents were counted, with 562 valid questionnaires returned. The results of the questionnaires are displayed in an Excel 2003 form. RESULTS: There were 291 medical workers and 271 other professional technicians completed the questionnaire. About 1/3 of the respondents understood AI and ophthalmic AI. The percentages of people who understood ophthalmic AI among medical workers and other professional technicians were about 42.6 % and 15.6 %, respectively. About 66.0 % of the respondents thought that AI in ophthalmology would partly replace doctors, about 59.07 % having a relatively high acceptance level of ophthalmic AI. Meanwhile, among those with AI in ophthalmology application experiences (30.6 %), above 70 % of respondents held a full acceptance attitude toward AI in ophthalmology. The respondents expressed medical ethics concerns about AI in ophthalmology. And among the respondents who understood AI in ophthalmology, almost all the people said that there was a need to increase the study of medical ethics issues in the ophthalmic AI field. CONCLUSIONS: The survey results revealed that the medical workers had a higher understanding level of AI in ophthalmology than other professional technicians, making it necessary to popularize ophthalmic AI education among other professional technicians. Most of the respondents did not have any experience in ophthalmic AI but generally had a relatively high acceptance level of AI in ophthalmology, and there was a need to strengthen research into medical ethics issues.