| 1. |
- zhang, xin, 1990-, et al.
(författare)
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Single-wavelength-excited fluorogenic nanoprobe for accurate real-time ratiometric analysis of broad pH fluctuations in mitophagy
- 2022
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Ingår i: Nano Reseach. - : Tsinghua University Press. - 1998-0124 .- 1998-0000. ; 15, s. 6515-6521
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Tidskriftsartikel (refereegranskat)abstract
- Mitophagy has a critical role in maintaining cellular homeostasis through acidic lysosomes engulfing excess or impaired mitochondria, thereby pH fluctuation is one of the most significant indicators for tracking mitophagy. Then such precise pH tracking demands the fluorogenic probe that has tailored contemporaneous features, including mitochondrial-specificity, excellent biocompatibility, wide pH-sensitive range of 8.0-4.0, and especially quantitative ability. However, available molecular probes cannot simultaneously meet all the requirements since it is extremely difficult to integrate multiple functionalities into a single molecule. To fully address this issue, we herein integrate two fluorogenic pH sensitive units, a mitochondria-specific block, cell-penetrating facilitator, and biocompatible segments into an elegant silica nano scaffold, which greatly ensures the applicability for real-time tracking of pH fluctuations in mitophagy. Most significantly, at a single wavelength excitation, the integrated pH-sensitive units have spectra-distinguishable fluorescence towards alkaline and acidic pH in a broad range that covers mitochondrial and lysosomal pH, thus enabling a ratiometric analysis of pH variations during the whole mitophagy. This work also provides constructive insights into the fabrication of advanced fluorescent nanoprobes for diverse biomedical applications.
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| 2. |
- Ren, Shan, et al.
(författare)
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An Advanced Operation Mode with Product-Service System Using Lifecycle Big Data and Deep Learning
- 2022
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Ingår i: International Journal of Precision Engineering and Manufacturing-Green Technology. - : Springer Science and Business Media LLC. - 2288-6206 .- 2198-0810. ; 9:1, s. 287-303
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Tidskriftsartikel (refereegranskat)abstract
- As a successful business strategy for enhancing environmental sustainability and decreasing the natural resource consumption of societies, the product-service system (PSS) has raised significant interests in the academic and industrial community. However, with the digitisation of the industry and the advancement of multisensory technologies, the PSS providers face many challenges. One major challenge is how the PSS providers can fully capture and efficiently analyse the operation and maintenance big data of different products and different customers in different conditions to obtain insights to improve their production processes, products and services. To address this challenge, a new operation mode and procedural approach are proposed for operation and maintenance of bigger cluster products, when these products are provided as a part of PSS and under exclusive control by the providers. The proposed mode and approach are driven by lifecycle big data of large cluster products and employs deep learning to train the neural networks to identify the fault features, thereby monitoring the products health status. This new mode is applied to a real case of a leading CNC machine provider to illustrate its feasibility. Higher accuracy and shortened time for fault prediction are realised, resulting in the providers saving of the maintenance and operation cost.
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