{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Zheng Y"],"funding":["Basic and Applied Basic Research Foundation of Guangdong Province","National Natural Science Foundation of China"],"pagination":["e2405681"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11578317"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["11(43)"],"pubmed_abstract":["Accurate non-invasive monitoring of blood glucose (BG) is a challenging issue in the therapy of diabetes. Here near-infrared (NIR) photoplethysmography (PPG) sensor based on a vapor-deposited mixed tin-lead hybrid perovskite photodetector is developed. The device shows a high detectivity of 5.32 × 10<sup>12</sup> Jones and a large linear dynamic range (LDR) of 204 dB under NIR light, guaranteeing accurate extraction of eleven features from the PPG signal. By a combination of machine learning, accurate prediction of blood glucose level with mean absolute relative difference (MARD) as small as 2.48% is realized. The self-powered PPG sensor also works for real-time outdoor healthcare monitors using sunlight as a light source. The potential for early diabetes diagnoses by the perovskite PPG sensor is demonstrated."],"journal":["Advanced science (Weinheim, Baden-Wurttemberg, Germany)"],"pubmed_title":["Highly Sensitive Perovskite Photoplethysmography Sensor for Blood Glucose Sensing Using Machine Learning Techniques."],"pmcid":["PMC11578317"],"funding_grant_id":["62174072","2019B151502049"],"pubmed_authors":["Zheng Y","Chen Q","Xie W","Chen K","Chen J","Luo J","Cai J","Zhou Y","Zhan Z"],"additional_accession":[]},"is_claimable":false,"name":"Highly Sensitive Perovskite Photoplethysmography Sensor for Blood Glucose Sensing Using Machine Learning Techniques.","description":"Accurate non-invasive monitoring of blood glucose (BG) is a challenging issue in the therapy of diabetes. Here near-infrared (NIR) photoplethysmography (PPG) sensor based on a vapor-deposited mixed tin-lead hybrid perovskite photodetector is developed. The device shows a high detectivity of 5.32 × 10<sup>12</sup> Jones and a large linear dynamic range (LDR) of 204 dB under NIR light, guaranteeing accurate extraction of eleven features from the PPG signal. By a combination of machine learning, accurate prediction of blood glucose level with mean absolute relative difference (MARD) as small as 2.48% is realized. The self-powered PPG sensor also works for real-time outdoor healthcare monitors using sunlight as a light source. The potential for early diabetes diagnoses by the perovskite PPG sensor is demonstrated.","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Nov","modification":"2025-04-26T01:54:17.378Z","creation":"2025-04-06T10:18:29.171Z"},"accession":"S-EPMC11578317","cross_references":{"pubmed":["39303203"],"doi":["10.1002/advs.202405681"]}}