eprintid: 10196300 rev_number: 9 eprint_status: archive userid: 699 dir: disk0/10/19/63/00 datestamp: 2024-08-30 08:32:29 lastmod: 2024-08-30 08:32:29 status_changed: 2024-08-30 08:32:29 type: article metadata_visibility: show sword_depositor: 699 creators_name: Cui, Rongwei creators_name: Tang, Huijing creators_name: Huang, Qing creators_name: Ye, Tingsong creators_name: Chen, Jiyang creators_name: Huang, Yinshen creators_name: Hou, Chongchao creators_name: Wang, Sihua creators_name: Ramadan, Sami creators_name: Li, Bing creators_name: Xu, Yunsheng creators_name: Xu, Lizhou creators_name: Li, Danyang title: AI-assisted smartphone-based colorimetric biosensor for visualized, rapid and sensitive detection of pathogenic bacteria ispublished: pub divisions: UCL divisions: B04 divisions: C06 divisions: F62 divisions: ZZN keywords: Bacteria detection, Colorimetric biosensor, Hyaluronidase, Smartphone, YOLOv5 note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. abstract: Accurate and effective detection is essential to against bacterial infection and contamination. Novel biosensors, which detect bacterial bioproducts and convert them into measurable signals, are attracting attention. We developed an artificial intelligence (AI)-assisted smartphone-based colorimetric biosensor for the visualized, rapid, sensitive detection of pathogenic bacteria by measuring the bacteria secreted hyaluronidase (HAase). The biosensor consists of the chlorophenol red-β-D-galactopyranoside (CPRG)-loaded hyaluronic acid (HA) hydrogel as the bioreactor and the β-galactosidase (β-gal)-loaded agar hydrogel as the signal generator. The HAase degrades the bioreactor and subsequently determines the release of CPRG, which could further react with β-gal to generate signal colors. The self-developed YOLOv5 algorithm was utilized to analyze the signal colors acquired by smartphone. The biosensor can provide a report within 60 min with an ultra-low limit of detection (LoD) of 10 CFU/mL and differentiate between gram-positive (G+) and gram-negative (G-) bacteria. The proposed biosensor was successfully applied in various areas, especially the evaluation of infections in clinical samples with 100% sensitivity. We believe the designed biosensor has the potential to represent a new paradigm of "ASSURED" bacterial detection, applicable for broad biomedical uses. date: 2024-09 date_type: published publisher: Elsevier official_url: http://dx.doi.org/10.1016/j.bios.2024.116369 full_text_type: other language: eng verified: verified_manual elements_id: 2278278 doi: 10.1016/j.bios.2024.116369 medium: Print-Electronic pii: S0956-5663(24)00374-9 lyricists_name: Li, Bing lyricists_id: BLICX39 actors_name: Li, Bing actors_id: BLICX39 actors_role: owner funding_acknowledgements: 2023A1515010067 [Applied Basic Research Foundation of Guangdong Province]; LR23C130001 [Zhejiang Provincial Natural Science Foundation of China]; RCBS20210609104333005 [Science, Technology and Innovation Com-mission of Shenzhen Municipality]; JCYJ20220530145001003 [Science, Technology and Innovation Com-mission of Shenzhen Municipality] full_text_status: restricted publication: Biosensors and Bioelectronics volume: 259 article_number: 116369 event_location: England citation: Cui, Rongwei; Tang, Huijing; Huang, Qing; Ye, Tingsong; Chen, Jiyang; Huang, Yinshen; Hou, Chongchao; ... Li, Danyang; + view all <#> Cui, Rongwei; Tang, Huijing; Huang, Qing; Ye, Tingsong; Chen, Jiyang; Huang, Yinshen; Hou, Chongchao; Wang, Sihua; Ramadan, Sami; Li, Bing; Xu, Yunsheng; Xu, Lizhou; Li, Danyang; - view fewer <#> (2024) AI-assisted smartphone-based colorimetric biosensor for visualized, rapid and sensitive detection of pathogenic bacteria. Biosensors and Bioelectronics , 259 , Article 116369. 10.1016/j.bios.2024.116369 <https://doi.org/10.1016/j.bios.2024.116369>. document_url: https://discovery.ucl.ac.uk/id/eprint/10196300/2/Li_AI-assisted%20smartphone-based%20colorimetric%20biosensor%20for%20visualized%2C%20rapid%20and%20sensitive%20detection%20of%20pathogenic%20bacteria_AAM.pdf