Annals of biomedical research最新文献

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Grk1 Missense Mutations in Type II Oguchi Disease: A Literature Review. 小口病 II 型中的 Grk1 缺义突变：文献综述。

Annals of biomedical research Pub Date : 2024-01-01 DOI: 10.61545/abr-5-128

Theodore Edward Margo, Frank Sungping Chen, Yu-Jiun Chen, Ching-Kang Chen

{"title":"Grk1 Missense Mutations in Type II Oguchi Disease: A Literature Review.","authors":"Theodore Edward Margo, Frank Sungping Chen, Yu-Jiun Chen, Ching-Kang Chen","doi":"10.61545/abr-5-128","DOIUrl":"10.61545/abr-5-128","url":null,"abstract":"Oguchi disease is a rare form of congenital stationary night blindness resulting from arrestin-1 (SAG) or rhodopsin kinase (GRK1) loss-of-function mutations. Unlike other congenital nyctalopias, patients with Oguchi disease can reach the dark-adapted state, albeit only after several hours of sustained darkness exposure. The mechanism underlying rhodopsin kinase dysfunction in Oguchi disease remains understudied. Previous research utilized the Grk1 knockout mice to reveal its role in phototransduction, the process that transduces light into neuronal signals in rod and cone photoreceptors. By studying Grk1 missense mutations via a knock-in approach, a more complete picture of the Oguchi disease mechanism involving GRK1 may be readily harvested. We summarize here the current knowledge on the Type II Oguchi disease with Grk1 missense mutations by focusing on the interaction of GRK1 with other proteins, and how these interactions influence dark adaptation. We call for more detailed analyses of GRK1 missense mutations in animal models, particularly V380D and L157P, to reveal novel disease mechanisms to gain further insight onto GRK1's action and function.","PeriodicalId":510902,"journal":{"name":"Annals of biomedical research","volume":"5 2","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11793915/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143191657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Deep-Learning Error Detection System in Radiation Therapy. 放射治疗中的深度学习错误检测系统

Annals of biomedical research Pub Date : 2023-01-01 DOI: 10.61545/abr-5-126

P M Kump, J Xia, S Yaddanapudi, E Bai

{"title":"A Deep-Learning Error Detection System in Radiation Therapy.","authors":"P M Kump, J Xia, S Yaddanapudi, E Bai","doi":"10.61545/abr-5-126","DOIUrl":"10.61545/abr-5-126","url":null,"abstract":"Delivering radiation therapy based on erroneous or corrupted treatment plan data has previously and unfortunately resulted in severe, sometimes grave patient harm. Aiming to prevent such harm and improve safety in radiation therapy treatment, this work introduces a novel, yet intuitive algorithm for strategically structuring the complex and unstructured data typical of modern treatment plans so their treatment sites may automatically be verified with deep-learning architectures. The proposed algorithm utilizes geometric and dose plan parameters to represent each plan's data as a heat map to feed a deep-learning classifier that will predict the plan's treatment site. Once it is returned by the classifier, a plan's predicted site can be compared to its documented intended site, and a warning raised should the two differ. Using real head-neck, breast, and prostate treatment plan data retrieved at two hospitals in the United States, the algorithm is evaluated by observing the accuracy of convolutional neural networks (ConvNets) in correctly classifying the structured heat map data. Many well-known ConvNet architectures are tested, and ResNet-18 performs the best with a testing accuracy of 97.8% and 0.979 F-1 score. Clearly, the heat maps generated by the proposed algorithm, despite using only a few of the many available plan parameters, retain enough information for correct treatment site classification. The simple construction and ease of interpretation make the heat maps an attractive choice for classification and error detection.","PeriodicalId":510902,"journal":{"name":"Annals of biomedical research","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10766422/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139099472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0