Proteomic profiling of extracellular vesicles (EVs) represents a promising approach for early detection and therapeutic monitoring of diseases such as cancer. The focus of this study was to apply robust EV isolation and subsequent data-independent acquisition mass spectrometry (DIA-MS) for urinary EV proteomics of prostate cancer and prostate inflammation patients. Urinary EVs were isolated by functionalized magnetic beads through chemical affinity on an automatic station, and EV proteins were analyzed by integrating three library-base analyses (Direct-DIA, GPF-DIA, and Fractionated DDA-base DIA) to improve the coverage and quantitation. We assessed the levels of urinary EV-associated proteins based on 40 samples consisting of 20 cases and 20 controls, where 18 EV proteins were identified to be differentiated in prostate cancer outcome, of which three (i.e., SERPINA3, LRG1, and SCGB3A1) were shown to be consistently upregulated. We also observed 6 out of the 18 (33%) EV proteins that had been developed as drug targets, while some of them showed protein-protein interactions. Moreover, the potential mechanistic pathways of 18 significantly different EV proteins were enriched in metabolic, immune, and inflammatory activities. These results showed consistency in an independent cohort with 20 participants. Using a random forest algorithm for classification assessment, including the identified EV proteins, we found that SERPINA3, LRG1, or SCGB3A1 add predictable value in addition to age, prostate size, body mass index (BMI), and prostate-specific antigen (PSA). In summary, the current study demonstrates a translational workflow to identify EV proteins as molecular markers to improve the clinical diagnosis of prostate cancer.
As the focus on higher education in China gradually shifts from rapid development to an emphasis on quality, the need for campus environments to become facilitators of education has gained increasing attention. The accelerated development of information technology has also led to tremendous changes in both teaching and learning methods, with informal learning taking on an increasingly important role. Furthermore, the development of human sensing technology, especially visual perception technology, has brought in new opportunities for the research and optimization of informal learning spaces (ILSs) in universities. This paper focuses on the ILS in Chinese universities by exploring optimal design approaches based on visual perception analysis. Through research and field investigation, this paper proposes revised theoretical research of classifications and spatial elements of ILS in universities more applicable to the architectural study of space. This paper also explores practical optimal design methods with two case studies and makes experiments with wearable eye trackers to study the users’ perception in these spaces before and after optimization. The optimal design is made from the aspects of physical space, facilities, and environment. Visual perception experiments and quantitative analysis were used to obtain a higher level of experimental accuracy than the previous studies and thus to study the real feeling of users in spaces. By these means, the effect of the optimized design was verified and the relation between users’ perceptions and the spatial environments was explored for further improvements to optimal design methods. This article can provide theoretical and practical references for campus space optimization research and design, especially for ILS on university campuses.
With the rapid development of information and sensory technology, the construction mode of universities and the planning of campus public spaces are confronting great challenges and opportunities. It also brings about new perspectives for reconsidering the relationship between users’ perceptions and the campus environment. This paper reviews the research on the perception of university public spaces over the past 20 years and summarizes the research hotspots by using co-citation analysis, co-occurrence analysis, and burst detection analysis through CiteSpace software. The results demonstrate that the overall development of this field experienced three stages: the initial development stage (2000–2007), the rapid growth stage (2008–2017), and the stable development stage (2018–2021). In terms of research content, hotspot studies are emphasized from the perspectives of thermal perceptions, health impact perception, spatial configuration perception, and user activity perception of on-campus space. In addition, this literature review concluded the emerging research tendencies and new quantification methods in recent years, proposing an enormous potential for quantifying campus space research based on new perceptual technologies. It also encourages the research and optimal design of campus spaces for a more student-oriented campus environment based on the study of the student’s perception of the spaces.
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