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As one of the major causes of antimicrobial resistance, beta-lactamase develops rapidly among bacteria. Detection of beta-lactamase in an efficient and low-cost point-of-care testing (POCT) way is urgently needed. However, due to the volatile environmental factors, the quantitative measurement of current POCT is often inaccurate. Herein, we demonstrate an artificial intelligence (AI)-assisted mobile health system that consists of a paper-based beta-lactamase fluorogenic probe analytical device and a smartphone-based AI cloud. An ultrafast broad-spectrum fluorogenic probe (B1) that could respond to beta-lactamase within 20 s was first synthesized, and the detection limit was determined to be 0.13 nmol/L. Meanwhile, a three-dimensional microfluidic paper-based analytical device was fabricated for integration of B1. Also, a smartphone-based AI cloud was developed to correct errors automatically and output results intelligently. This smart system could calibrate the temperature and pH in the beta-lactamase level detection in complex samples and mice infected with various bacteria, which shows the problem-solving ability in interdisciplinary research, and demonstrates potential clinical benefits., Funding Agencies|National Key R&D Program of China [2020YFA0709900]; National Natural Science Foundation of China [62288102, 22077101, 52073230]; Joint Research Funds of Department of Science & Technology of Shaanxi Province; Northwestern~Polytechnical University [2020GXLH-Z-008, 2020GXLH-Z-013]; Shaanxi Provincial Science Fund for Distinguished Young Scholars [2023-JC-JQ-32]; Key Research and Development Program of Shaanxi [2020ZDLGY13-04]; Fundamental Research Funds for the Central Universities and Innovation Foundation for Doctorate Dissertation of Northwestern Polytechnical University [CX2021121]

As one of the major causes of antimicrobial resistance, beta-lactamase develops rapidly among bacteria. Detection of beta-lactamase in an efficient and low-cost point-of-care testing (POCT) way is urgently needed. However, due to the volatile environmental factors, the quantitative measurement of current POCT is often inaccurate. Herein, we demonstrate an artificial intelligence (AI)-assisted mobile health system that consists of a paper-based beta-lactamase fluorogenic probe analytical device and a smartphone-based AI cloud. An ultrafast broad-spectrum fluorogenic probe (B1) that could respond to beta-lactamase within 20 s was first synthesized, and the detection limit was determined to be 0.13 nmol/L. Meanwhile, a three-dimensional microfluidic paper-based analytical device was fabricated for integration of B1. Also, a smartphone-based AI cloud was developed to correct errors automatically and output results intelligently. This smart system could calibrate the temperature and pH in the beta-lactamase level detection in complex samples and mice infected with various bacteria, which shows the problem-solving ability in interdisciplinary research, and demonstrates potential clinical benefits., Funding Agencies|National Key R&D Program of China [2020YFA0709900]; National Natural Science Foundation of China [62288102, 22077101, 52073230]; Joint Research Funds of Department of Science & Technology of Shaanxi Province; Northwestern~Polytechnical University [2020GXLH-Z-008, 2020GXLH-Z-013]; Shaanxi Provincial Science Fund for Distinguished Young Scholars [2023-JC-JQ-32]; Key Research and Development Program of Shaanxi [2020ZDLGY13-04]; Fundamental Research Funds for the Central Universities and Innovation Foundation for Doctorate Dissertation of Northwestern Polytechnical University [CX2021121]

As one of the major causes of antimicrobial resistance, beta-lactamase develops rapidly among bacteria. Detection of beta-lactamase in an efficient and low-cost point-of-care testing (POCT) way is urgently needed. However, due to the volatile environmental factors, the quantitative measurement of current POCT is often inaccurate. Herein, we demonstrate an artificial intelligence (AI)-assisted mobile health system that consists of a paper-based beta-lactamase fluorogenic probe analytical device and a smartphone-based AI cloud. An ultrafast broad-spectrum fluorogenic probe (B1) that could respond to beta-lactamase within 20 s was first synthesized, and the detection limit was determined to be 0.13 nmol/L. Meanwhile, a three-dimensional microfluidic paper-based analytical device was fabricated for integration of B1. Also, a smartphone-based AI cloud was developed to correct errors automatically and output results intelligently. This smart system could calibrate the temperature and pH in the beta-lactamase level detection in complex samples and mice infected with various bacteria, which shows the problem-solving ability in interdisciplinary research, and demonstrates potential clinical benefits., Funding Agencies|National Key R&D Program of China [2020YFA0709900]; National Natural Science Foundation of China [62288102, 22077101, 52073230]; Joint Research Funds of Department of Science & Technology of Shaanxi Province; Northwestern~Polytechnical University [2020GXLH-Z-008, 2020GXLH-Z-013]; Shaanxi Provincial Science Fund for Distinguished Young Scholars [2023-JC-JQ-32]; Key Research and Development Program of Shaanxi [2020ZDLGY13-04]; Fundamental Research Funds for the Central Universities and Innovation Foundation for Doctorate Dissertation of Northwestern Polytechnical University [CX2021121]

As one of the major causes of antimicrobial resistance, beta-lactamase develops rapidly among bacteria. Detection of beta-lactamase in an efficient and low-cost point-of-care testing (POCT) way is urgently needed. However, due to the volatile environmental factors, the quantitative measurement of current POCT is often inaccurate. Herein, we demonstrate an artificial intelligence (AI)-assisted mobile health system that consists of a paper-based beta-lactamase fluorogenic probe analytical device and a smartphone-based AI cloud. An ultrafast broad-spectrum fluorogenic probe (B1) that could respond to beta-lactamase within 20 s was first synthesized, and the detection limit was determined to be 0.13 nmol/L. Meanwhile, a three-dimensional microfluidic paper-based analytical device was fabricated for integration of B1. Also, a smartphone-based AI cloud was developed to correct errors automatically and output results intelligently. This smart system could calibrate the temperature and pH in the beta-lactamase level detection in complex samples and mice infected with various bacteria, which shows the problem-solving ability in interdisciplinary research, and demonstrates potential clinical benefits., Funding Agencies|National Key R&D Program of China [2020YFA0709900]; National Natural Science Foundation of China [62288102, 22077101, 52073230]; Joint Research Funds of Department of Science & Technology of Shaanxi Province; Northwestern~Polytechnical University [2020GXLH-Z-008, 2020GXLH-Z-013]; Shaanxi Provincial Science Fund for Distinguished Young Scholars [2023-JC-JQ-32]; Key Research and Development Program of Shaanxi [2020ZDLGY13-04]; Fundamental Research Funds for the Central Universities and Innovation Foundation for Doctorate Dissertation of Northwestern Polytechnical University [CX2021121]

As one of the major causes of antimicrobial resistance, beta-lactamase develops rapidly among bacteria. Detection of beta-lactamase in an efficient and low-cost point-of-care testing (POCT) way is urgently needed. However, due to the volatile environmental factors, the quantitative measurement of current POCT is often inaccurate. Herein, we demonstrate an artificial intelligence (AI)-assisted mobile health system that consists of a paper-based beta-lactamase fluorogenic probe analytical device and a smartphone-based AI cloud. An ultrafast broad-spectrum fluorogenic probe (B1) that could respond to beta-lactamase within 20 s was first synthesized, and the detection limit was determined to be 0.13 nmol/L. Meanwhile, a three-dimensional microfluidic paper-based analytical device was fabricated for integration of B1. Also, a smartphone-based AI cloud was developed to correct errors automatically and output results intelligently. This smart system could calibrate the temperature and pH in the beta-lactamase level detection in complex samples and mice infected with various bacteria, which shows the problem-solving ability in interdisciplinary research, and demonstrates potential clinical benefits., Funding Agencies|National Key R&D Program of China [2020YFA0709900]; National Natural Science Foundation of China [62288102, 22077101, 52073230]; Joint Research Funds of Department of Science & Technology of Shaanxi Province; Northwestern~Polytechnical University [2020GXLH-Z-008, 2020GXLH-Z-013]; Shaanxi Provincial Science Fund for Distinguished Young Scholars [2023-JC-JQ-32]; Key Research and Development Program of Shaanxi [2020ZDLGY13-04]; Fundamental Research Funds for the Central Universities and Innovation Foundation for Doctorate Dissertation of Northwestern Polytechnical University [CX2021121]

To determine the energy attenuation and energy reflection coefficients in layered foundation is the key factor to reveal the dynamic response characteristics of the high-speed railway foundation. Based on the foundation test model under the dynamic loading of the high-speed railway, the energy attenuation and energy reflection coefficients were introduced and the attenuation formulas of the vibration acceleration in the layered foundation were deduced. Five scale models of 1:1, 1:2, 1:5, 1:10 and 1:20 are established respectively by using Abaqus technique. The energy attenuation mechanism and interfacial energy reflection characteristics in the layered foundation were analyzed. Results show that it is appropriate to use vibration acceleration to characterize the propagation rule of the energy attenuation in the layered foundation. The size effect equations of the energy attenuation and energy reflection coefficients in the layered foundation are deduced and the size effect of the energy attenuation is revealed. Based on the nonlinear relationships among the energy attenuation coefficient, the model scale, the loading amplitude and the vibration frequency, the energy attenuation equation of the scale test model is constructed. The reliability of the theoretical and simulation results is verified by the scale test model. The conclusions obtained in this study can provide a reference for a similar engineering practice.