Post‐stroke anxiety (PSA) is a common neuropsychiatric affective disorder occurring after a stroke. Animal experiments have indicated that serum S‐100β levels are closely related to anxiety disorder. No clinical study has been done to explore the relationship between serum S‐100β levels and anxiety symptoms in patients with acute stroke. The aim of our study was to investigate the association between serum S‐100β levels and PSA.
{"title":"The relationship between serum levels of S‐100β and anxiety symptoms in patients with acute stroke","authors":"Qiongzhang Wang, Minjie Xu, Meijuan Xiao, Xiaoqian Luan, Huijun Chen, Yiting Ruan, Liuyuan Wang, Yujie Tu, Guiqian Huang, Jincai He","doi":"10.1111/psyg.12799","DOIUrl":"https://doi.org/10.1111/psyg.12799","url":null,"abstract":"Post‐stroke anxiety (PSA) is a common neuropsychiatric affective disorder occurring after a stroke. Animal experiments have indicated that serum S‐100β levels are closely related to anxiety disorder. No clinical study has been done to explore the relationship between serum S‐100β levels and anxiety symptoms in patients with acute stroke. The aim of our study was to investigate the association between serum S‐100β levels and PSA.","PeriodicalId":20784,"journal":{"name":"Psychogeriatrics","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49554983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01Epub Date: 2022-02-25DOI: 10.1007/s10489-021-03150-3
Rucha Golwalkar, Ninad Mehendale
The coronavirus disease 2019 (COVID-19) has made it mandatory for people all over the world to wear facial masks to prevent the spread of the virus. The conventional face recognition systems used for security purposes have become ineffective in the current situation since the face mask covers most of the important facial features such as nose, mouth, etc. making it very difficult to recognize the person. We have proposed a system that uses the deep metric learning technique and our own FaceMaskNet-21 deep learning network to produce 128-d encodings that help in the face recognition process from static images, live video streams, as well as, static video files. We achieved a testing accuracy of 88.92% with an execution time of fewer than 10 ms. The ability of the system to perform masked face recognition in real-time makes it suitable to recognize people in CCTV footage in places like malls, banks, ATMs, etc. Due to its fast performance, our system can be used in schools and colleges for attendance, as well as in banks and other high-security zones to grant access to only the authorized ones without asking them to remove the mask.
Supplementary information: The online version contains supplementary material available at 10.1007/s10489-021-03150-3.
{"title":"Masked-face recognition using deep metric learning and FaceMaskNet-21.","authors":"Rucha Golwalkar, Ninad Mehendale","doi":"10.1007/s10489-021-03150-3","DOIUrl":"10.1007/s10489-021-03150-3","url":null,"abstract":"<p><p>The coronavirus disease 2019 (COVID-19) has made it mandatory for people all over the world to wear facial masks to prevent the spread of the virus. The conventional face recognition systems used for security purposes have become ineffective in the current situation since the face mask covers most of the important facial features such as nose, mouth, etc. making it very difficult to recognize the person. We have proposed a system that uses the deep metric learning technique and our own FaceMaskNet-21 deep learning network to produce 128-d encodings that help in the face recognition process from static images, live video streams, as well as, static video files. We achieved a testing accuracy of 88.92% with an execution time of fewer than 10 ms. The ability of the system to perform masked face recognition in real-time makes it suitable to recognize people in CCTV footage in places like malls, banks, ATMs, etc. Due to its fast performance, our system can be used in schools and colleges for attendance, as well as in banks and other high-security zones to grant access to only the authorized ones without asking them to remove the mask.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s10489-021-03150-3.</p>","PeriodicalId":20784,"journal":{"name":"Psychogeriatrics","volume":"7 1","pages":"13268-13279"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8874736/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85312372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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