Significance: Exosomes are tiny vesicles secreted by cells that play an important role in cell-to-cell communication. The RNA, DNA, and proteins in exosomes are reported to be critical biomarkers for precision diagnostics and therapeutics. Exosome analysis thus offers an approach to learn health conditions without tissue biopsy. Clinical application and fundamental study of exosomes require analytical tools capable of physical and biochemical characterization. The challenge in developing such tools lies in that conventional optical microscopy is not suitable for exosome analysis due to its limited sensitivity. In this paper, we report a plasmonic microscopy capable of imaging single exosomes and quantitative analysis of physical (size) and biochemical (interaction with antibody) information.

Background: With the expanding applications of mass cytometry in medical research, a wide variety of clustering methods, both semi-supervised and unsupervised, have been developed for data analysis. Selecting the optimal clustering method can accelerate the identification of meaningful cell populations.Result: To address this issue, we compared three classes of performance measures, “precision” as external evaluation, “coherence” as internal evaluation, and stability, of nine methods based on six independent benchmark datasets. Seven unsupervised methods (Accense, Xshift, PhenoGraph, FlowSOM, flowMeans, DEPECHE, and kmeans) and two semi-supervised methods (Automated Cell-type Discovery and Classification and linear discriminant analysis (LDA)) are tested on six mass cytometry datasets. We compute and compare all defined performance measures against random subsampling, varying sample sizes, and the number of clusters for each method. LDA reproduces the manual labels most precisely but does not rank top in internal evaluation. PhenoGraph and FlowSOM perform better than other unsupervised tools in precision, coherence, and stability. PhenoGraph and Xshift are more robust when detecting refined sub-clusters, whereas DEPECHE and FlowSOM tend to group similar clusters into meta-clusters. The performances of PhenoGraph, Xshift, and flowMeans are impacted by increased sample size, but FlowSOM is relatively stable as sample size increases.Conclusion: All the evaluations including precision, coherence, stability, and clustering resolution should be taken into synthetic consideration when choosing an appropriate tool for cytometry data analysis. Thus, we provide decision guidelines based on these characteristics for the general reader to more easily choose the most suitable clustering tools.

Glucose, as an essential substance directly involved in metabolic processes, is closely related to the occurrence of various diseases such as glucose metabolism disorders and islet cell carcinoma. Therefore, it is crucial to develop sensitive, accurate, rapid, and cost effective methods for frequent and convenient detections of glucose. Microfluidic Paper-based Analytical Devices (μPADs) not only satisfying the above requirements but also occupying the advantages of portability and minimal sample consumption, have exhibited great potential in the field of glucose detection. This article reviews and summarizes the most recent improvements in glucose detection in two aspects of colorimetric and electrochemical μPADs. The progressive techniques for fabricating channels on μPADs are also emphasized in this article. With the growth of diabetes and other glucose indication diseases in the underdeveloped and developing countries, low-cost and reliably commercial μPADs for glucose detection will be in unprecedentedly demand.

Effective therapeutic strategies for myocardial ischemia/reperfusion (I/R) injury remain elusive. Targeting reactive oxygen species (ROS) provides a practical approach to mitigate myocardial damage following reperfusion. In this study, we synthesize an antioxidant nanozyme, equipped with a single-Platinum (Pt)-atom (PtsaN-C), for protecting against I/R injury. PtsaN-C exhibits multiple enzyme-mimicking activities for ROS scavenging with high efficiency and stability. Mechanistic studies demonstrate that the excellent ROS-elimination performance of the single Pt atom center precedes that of the Pt cluster center, owing to its better synergistic effect and metallic electronic property. Systematic in vitro and in vivo studies confirm that PtsaN-C efficiently counteracts ROS, restores cellular homeostasis and prevents apoptotic progression after I/R injury. PtsaN-C also demonstrates good biocompatibility, making it a promising candidate for clinical applications. Our study expands the scope of single-atom nanozyme in combating ROS-induced damage and offers a promising therapeutic avenue for the treatment of I/R injury.