Pub Date : 2026-03-03DOI: 10.1016/j.scib.2026.03.001
Lei Wang, Zhiyu Xia, Kai Wang, Yingli Li, Tianhui Shao, Sitong Chen, Jingwei Zhou, Yan Li
{"title":"Machine learning-based predictive system for cycloplegic autorefraction in children: advancing early myopia detection and warning.","authors":"Lei Wang, Zhiyu Xia, Kai Wang, Yingli Li, Tianhui Shao, Sitong Chen, Jingwei Zhou, Yan Li","doi":"10.1016/j.scib.2026.03.001","DOIUrl":"https://doi.org/10.1016/j.scib.2026.03.001","url":null,"abstract":"","PeriodicalId":421,"journal":{"name":"Science Bulletin","volume":" ","pages":""},"PeriodicalIF":21.1,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147455142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Proteins serve as the fundamental executors of cellular function and the key mediators linking genome to phenome, offering unique insights into the molecular mechanisms underlying human diseases. Recent technological breakthroughs have enabled the quantification of thousands of proteins, opening an unprecedented avenue for exploring human health at the proteome level. As proteomic data generation expands in scale and depth, new opportunities are emerging to translate proteomic signatures into clinical practice, paving the way toward precision medicine. In this review, we summarize current progress using proteomics for biomarker discovery, disease diagnosis and prediction, and drug development. We further highlight innovative artificial intelligence strategies that have revolutionized proteomics analysis and interpretation. Finally, we discuss the key challenges, limitations, and future perspectives essential for realizing the full potential of proteomics in precision medicine.
{"title":"The emerging role of high-throughput proteomics in advancing precision medicine.","authors":"Jia You, Yue-Ting Deng, Luo Chen, Lin-Bo Wang, Yu Guo, Ze-Yu Li, Jian-Feng Feng, Jin-Tai Yu, Wei Cheng","doi":"10.1016/j.scib.2026.02.054","DOIUrl":"https://doi.org/10.1016/j.scib.2026.02.054","url":null,"abstract":"<p><p>Proteins serve as the fundamental executors of cellular function and the key mediators linking genome to phenome, offering unique insights into the molecular mechanisms underlying human diseases. Recent technological breakthroughs have enabled the quantification of thousands of proteins, opening an unprecedented avenue for exploring human health at the proteome level. As proteomic data generation expands in scale and depth, new opportunities are emerging to translate proteomic signatures into clinical practice, paving the way toward precision medicine. In this review, we summarize current progress using proteomics for biomarker discovery, disease diagnosis and prediction, and drug development. We further highlight innovative artificial intelligence strategies that have revolutionized proteomics analysis and interpretation. Finally, we discuss the key challenges, limitations, and future perspectives essential for realizing the full potential of proteomics in precision medicine.</p>","PeriodicalId":421,"journal":{"name":"Science Bulletin","volume":" ","pages":""},"PeriodicalIF":21.1,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147455135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-02DOI: 10.1016/j.scib.2026.02.055
Yuanyuan Zheng, Qian Ye, Haicheng Yao, Yichi Zhang, Huanhuan Liu, Yifeng Zhang, Duo Li, Jingxia Wu, Kun Zhang, Songlin Zhang, Xuemei Sun, Zhaohui Wang, Bingjie Wang, Huisheng Peng, Peining Chen
Adverse driver states account for approximately 30% of global traffic injuries, highlighting the need for real-time monitoring strategies that remain robust under complex real-world driving conditions. However, current vision-based driver monitoring systems are constrained by occlusion sensitivity, environmental interference, and privacy concerns, posing a risk of monitoring interruption. Herein, we report a skin-contact, privacy-preserving intelligent textile interface featuring a topologically invariant knitted architecture that enables robust, continuous multimodal biophysical sensing in vehicles. This is achieved through intrinsic mesoscale structural self-adaptation to complex contact interfaces. The synergistic effect of loop stretching, twisting, and sliding within the textile architecture promotes uniform stress distribution, resulting in exceptional adaptability to irregular surfaces. This design exhibits a bending modulus nearly five orders of magnitude lower than that of traditional polyimide film electrodes, along with an approximately 21-fold reduction in skin-contact impedance. It delivers clinical-grade ECG waveform fidelity and quantifies grip force with a sensitivity of 0.348 kPa-1 across a 1.25 MPa dynamic range. Real-world validation over 70 km of mixed urban traffic demonstrates real-time monitoring of driver posture and behaviours through 25-channel multimodal signal fusion, while integrated electroluminescent modules provide posture-responsive visual feedback. The textile interface also meets automotive durability requirements, withstanding 100,000 compression cycles, 50,000 abrasion cycles, and 7 d of thermal and chemical exposure (-40 to 100 °C, ultraviolet light, sweat) without signal degradation, confirming its suitability for long-term in-vehicle use. This study establishes a privacy-preserving, skin-contact paradigm for robust biophysical signal acquisition, enabled by a highly adaptive, topologically invariant intelligent textile interface.
{"title":"Intelligent textile interface for robust multimodal biophysical sensing to enhance driving safety.","authors":"Yuanyuan Zheng, Qian Ye, Haicheng Yao, Yichi Zhang, Huanhuan Liu, Yifeng Zhang, Duo Li, Jingxia Wu, Kun Zhang, Songlin Zhang, Xuemei Sun, Zhaohui Wang, Bingjie Wang, Huisheng Peng, Peining Chen","doi":"10.1016/j.scib.2026.02.055","DOIUrl":"https://doi.org/10.1016/j.scib.2026.02.055","url":null,"abstract":"<p><p>Adverse driver states account for approximately 30% of global traffic injuries, highlighting the need for real-time monitoring strategies that remain robust under complex real-world driving conditions. However, current vision-based driver monitoring systems are constrained by occlusion sensitivity, environmental interference, and privacy concerns, posing a risk of monitoring interruption. Herein, we report a skin-contact, privacy-preserving intelligent textile interface featuring a topologically invariant knitted architecture that enables robust, continuous multimodal biophysical sensing in vehicles. This is achieved through intrinsic mesoscale structural self-adaptation to complex contact interfaces. The synergistic effect of loop stretching, twisting, and sliding within the textile architecture promotes uniform stress distribution, resulting in exceptional adaptability to irregular surfaces. This design exhibits a bending modulus nearly five orders of magnitude lower than that of traditional polyimide film electrodes, along with an approximately 21-fold reduction in skin-contact impedance. It delivers clinical-grade ECG waveform fidelity and quantifies grip force with a sensitivity of 0.348 kPa<sup>-1</sup> across a 1.25 MPa dynamic range. Real-world validation over 70 km of mixed urban traffic demonstrates real-time monitoring of driver posture and behaviours through 25-channel multimodal signal fusion, while integrated electroluminescent modules provide posture-responsive visual feedback. The textile interface also meets automotive durability requirements, withstanding 100,000 compression cycles, 50,000 abrasion cycles, and 7 d of thermal and chemical exposure (-40 to 100 °C, ultraviolet light, sweat) without signal degradation, confirming its suitability for long-term in-vehicle use. This study establishes a privacy-preserving, skin-contact paradigm for robust biophysical signal acquisition, enabled by a highly adaptive, topologically invariant intelligent textile interface.</p>","PeriodicalId":421,"journal":{"name":"Science Bulletin","volume":" ","pages":""},"PeriodicalIF":21.1,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147493411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-28Epub Date: 2025-08-25DOI: 10.1016/j.scib.2025.08.045
Wen Li , Weiyang Shi , Haiyan Wang , Jin Li , Congying Chu , Yu Zhang , Yue Cui , Luqi Cheng , Kaixin Li , Yuheng Lu , Liang Ma , Ming Song , Zhengyi Yang , Tobias Banaschewski , Arun L.W. Bokde , Sylvane Desrivières , Herta Flor , Antoine Grigis , Hugh Garavan , Penny Gowland , Tianzi Jiang
{"title":"Anatomical connectivity development constrains medial–lateral topography in the dorsal prefrontal cortex","authors":"Wen Li , Weiyang Shi , Haiyan Wang , Jin Li , Congying Chu , Yu Zhang , Yue Cui , Luqi Cheng , Kaixin Li , Yuheng Lu , Liang Ma , Ming Song , Zhengyi Yang , Tobias Banaschewski , Arun L.W. Bokde , Sylvane Desrivières , Herta Flor , Antoine Grigis , Hugh Garavan , Penny Gowland , Tianzi Jiang","doi":"10.1016/j.scib.2025.08.045","DOIUrl":"10.1016/j.scib.2025.08.045","url":null,"abstract":"","PeriodicalId":421,"journal":{"name":"Science Bulletin","volume":"71 4","pages":"Pages 701-706"},"PeriodicalIF":21.1,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145008092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-28Epub Date: 2025-12-15DOI: 10.1016/j.scib.2025.12.013
Yong Huang , Quan Gao , Qibao He , Jinjing Xiao , Qiongqiong Liu , Yuying Liu , Min Liao , Yanhong Shi , Tengfei Shi , Yancan Wu , Qing Yang , Linsheng Yu , Bruce D. Hammock , Sibao Wang , Haiqun Cao
{"title":"Diamide insecticides impair honey bee queen reproduction and colony development","authors":"Yong Huang , Quan Gao , Qibao He , Jinjing Xiao , Qiongqiong Liu , Yuying Liu , Min Liao , Yanhong Shi , Tengfei Shi , Yancan Wu , Qing Yang , Linsheng Yu , Bruce D. Hammock , Sibao Wang , Haiqun Cao","doi":"10.1016/j.scib.2025.12.013","DOIUrl":"10.1016/j.scib.2025.12.013","url":null,"abstract":"","PeriodicalId":421,"journal":{"name":"Science Bulletin","volume":"71 4","pages":"Pages 697-700"},"PeriodicalIF":21.1,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145825541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-28Epub Date: 2025-12-01DOI: 10.1016/j.scib.2025.11.062
Pengfei Guan , Lanhao Qin , Ke Ning , Jie Liu , Decai Ouyang , Yimeng Yu , Jinsong Wu , Xing Lu , Yingshuang Fu , Yuan Li , Huiqiao Li , Tianyou Zhai
Vast power grid infrastructure generates enormous volumes of inspection data from smart meters, unmanned aerial vehicle (UAV) patrols, and high-definition video monitoring. Meeting the demand for real-time analysis places stringent requirements on latency, energy efficiency, and on-device intelligence at the edge. Here, we present a molecular crystal memristor-based edge artificial intelligence (AI) hardware platform that can be directly deployed in inspection devices, enabling real-time grid monitoring with drastically reduced computational and storage overheads. The memristor exhibits highly controllable filamentary switching behavior, stable multi-level conductance states, femtowatt-scale power consumption, and outstanding retention. Leveraging these properties, the platform enables fully hardware-integrated convolution, achieving 97% feature-extraction accuracy and 67.75 TOPS/W energy efficiency, thereby substantially alleviating the computational and storage load of cloud servers. This work establishes a scalable and energy-efficient in-memory computing framework for smart grid inspection and provides a powerful foundation for broader edge AI applications.
{"title":"Molecular crystal memristor-based edge AI platform for energy-efficient and real-time smart grid inspection","authors":"Pengfei Guan , Lanhao Qin , Ke Ning , Jie Liu , Decai Ouyang , Yimeng Yu , Jinsong Wu , Xing Lu , Yingshuang Fu , Yuan Li , Huiqiao Li , Tianyou Zhai","doi":"10.1016/j.scib.2025.11.062","DOIUrl":"10.1016/j.scib.2025.11.062","url":null,"abstract":"<div><div>Vast power grid infrastructure generates enormous volumes of inspection data from smart meters, unmanned aerial vehicle (UAV) patrols, and high-definition video monitoring. Meeting the demand for real-time analysis places stringent requirements on latency, energy efficiency, and on-device intelligence at the edge. Here, we present a molecular crystal memristor-based edge artificial intelligence (AI) hardware platform that can be directly deployed in inspection devices, enabling real-time grid monitoring with drastically reduced computational and storage overheads. The memristor exhibits highly controllable filamentary switching behavior, stable multi-level conductance states, femtowatt-scale power consumption, and outstanding retention. Leveraging these properties, the platform enables fully hardware-integrated convolution, achieving 97% feature-extraction accuracy and 67.75 TOPS/W energy efficiency, thereby substantially alleviating the computational and storage load of cloud servers. This work establishes a scalable and energy-efficient in-memory computing framework for smart grid inspection and provides a powerful foundation for broader edge AI applications.</div></div>","PeriodicalId":421,"journal":{"name":"Science Bulletin","volume":"71 4","pages":"Pages 759-765"},"PeriodicalIF":21.1,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145766777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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