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Advancements in cell-penetrating monoclonal antibody treatment. 细胞穿透单克隆抗体治疗的进展。
Oncotarget Pub Date : 2024-11-22 DOI: 10.18632/oncotarget.28674
Sai Pallavi Pradeep, Raman Bahal
{"title":"Advancements in cell-penetrating monoclonal antibody treatment.","authors":"Sai Pallavi Pradeep, Raman Bahal","doi":"10.18632/oncotarget.28674","DOIUrl":"10.18632/oncotarget.28674","url":null,"abstract":"","PeriodicalId":19499,"journal":{"name":"Oncotarget","volume":"15 ","pages":"815-816"},"PeriodicalIF":0.0,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11584028/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142687617","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
B7-H4: A potential therapeutic target in adenoid cystic carcinoma. B7-H4：腺样囊性癌的潜在治疗靶点。
Oncotarget Pub Date : 2024-11-22 DOI: 10.18632/oncotarget.28661
Luana Guimaraes de Sousa, Renata Ferrarotto
{"title":"B7-H4: A potential therapeutic target in adenoid cystic carcinoma.","authors":"Luana Guimaraes de Sousa, Renata Ferrarotto","doi":"10.18632/oncotarget.28661","DOIUrl":"10.18632/oncotarget.28661","url":null,"abstract":"","PeriodicalId":19499,"journal":{"name":"Oncotarget","volume":"15 ","pages":"807-808"},"PeriodicalIF":0.0,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11584030/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142687622","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
Retraction: MicroRNA-34a regulates liver regeneration and the development of liver cancer in rats by targeting Notch signaling pathway. 撤回：MicroRNA-34a通过靶向Notch信号通路调控大鼠肝脏再生和肝癌发展
Oncotarget Pub Date : 2024-11-22 DOI: 10.18632/oncotarget.28676
Xiao-Ping Wang, Jian Zhou, Ming Han, Chuan-Bao Chen, Yi-Tao Zheng, Xiao-Shun He, Xiao-Peng Yuan
{"title":"Retraction: MicroRNA-34a regulates liver regeneration and the development of liver cancer in rats by targeting Notch signaling pathway.","authors":"Xiao-Ping Wang, Jian Zhou, Ming Han, Chuan-Bao Chen, Yi-Tao Zheng, Xiao-Shun He, Xiao-Peng Yuan","doi":"10.18632/oncotarget.28676","DOIUrl":"10.18632/oncotarget.28676","url":null,"abstract":"","PeriodicalId":19499,"journal":{"name":"Oncotarget","volume":"15 ","pages":"814"},"PeriodicalIF":0.0,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11584034/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142687692","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
Retraction: Hyperglycemia via activation of thromboxane A2 receptor impairs the integrity and function of blood-brain barrier in microvascular endothelial cells. 撤回：高血糖通过激活血栓素 A2 受体损害微血管内皮细胞血脑屏障的完整性和功能
Oncotarget Pub Date : 2024-11-22 DOI: 10.18632/oncotarget.28675
Zhihong Zhao, Jue Hu, Xiaoping Gao, Hui Liang, Haiya Yu, Suosi Liu, Zhan Liu
{"title":"Retraction: Hyperglycemia via activation of thromboxane A2 receptor impairs the integrity and function of blood-brain barrier in microvascular endothelial cells.","authors":"Zhihong Zhao, Jue Hu, Xiaoping Gao, Hui Liang, Haiya Yu, Suosi Liu, Zhan Liu","doi":"10.18632/oncotarget.28675","DOIUrl":"10.18632/oncotarget.28675","url":null,"abstract":"","PeriodicalId":19499,"journal":{"name":"Oncotarget","volume":"15 ","pages":"806"},"PeriodicalIF":0.0,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11584033/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142687655","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
Mesenchymal stem cells - the secret agents of cancer immunotherapy: Promises, challenges, and surprising twists. 间充质干细胞--癌症免疫疗法的秘密药剂：承诺、挑战和令人惊讶的转折。
Oncotarget Pub Date : 2024-11-22 DOI: 10.18632/oncotarget.28672
Theia Minev, Shani Balbuena, Jaya Mini Gill, Francesco M Marincola, Santosh Kesari, Feng Lin
{"title":"Mesenchymal stem cells - the secret agents of cancer immunotherapy: Promises, challenges, and surprising twists.","authors":"Theia Minev, Shani Balbuena, Jaya Mini Gill, Francesco M Marincola, Santosh Kesari, Feng Lin","doi":"10.18632/oncotarget.28672","DOIUrl":"10.18632/oncotarget.28672","url":null,"abstract":"<p><p>Mesenchymal stem cells (MSCs) are recognized for their immunomodulatory capabilities, tumor-homing abilities, and capacity to serve as carriers for therapeutic agents. This review delves into the role of adoptively transferred MSCs in tumor progression, their interactions with the tumor microenvironment, and their use in delivering anti-cancer drugs, oncolytic viruses, and genetic material. It also addresses the challenges and limitations associated with MSC therapy, such as variability in MSC preparations and potential tumorigenic effects emphasizing the need for advanced genetic engineering and personalized approaches to enhance therapeutic efficacy. The review concludes with an optimistic outlook on the future of MSC-based therapies, underscoring their promise to develop effective and personalized cancer treatments.</p>","PeriodicalId":19499,"journal":{"name":"Oncotarget","volume":"15 ","pages":"793-805"},"PeriodicalIF":0.0,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11584032/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142687649","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
Computed tomography-based radiomics and body composition model for predicting hepatic decompensation. 基于计算机断层扫描的放射组学和身体成分模型用于预测肝功能失代偿。
Oncotarget Pub Date : 2024-11-22 DOI: 10.18632/oncotarget.28673
Yashbir Singh, John E Eaton, Sudhakar K Venkatesh, Bradley J Erickson
{"title":"Computed tomography-based radiomics and body composition model for predicting hepatic decompensation.","authors":"Yashbir Singh, John E Eaton, Sudhakar K Venkatesh, Bradley J Erickson","doi":"10.18632/oncotarget.28673","DOIUrl":"10.18632/oncotarget.28673","url":null,"abstract":"<p><p>Primary sclerosing cholangitis (PSC) is a chronic liver disease characterized by inflammation and scarring of the bile ducts, which can lead to cirrhosis and hepatic decompensation. The study aimed to explore the potential value of computational radiomics, a field that extracts quantitative features from medical images, in predicting whether or not PSC patients had hepatic decompensation. We used an in-house developed deep learning model called the body composition model, which quantifies body composition from computed tomography (CT) into four compartments: subcutaneous adipose tissue (SAT), skeletal muscle (SKM), visceral adipose tissue (VAT), and intermuscular adipose tissue (IMAT). We extracted radiomics features from all four body composition compartments and used them to build a predictive model in the training cohort. The predictive model demonstrated good performance in validation cohorts for predicting hepatic decompensation, with an accuracy score of 0.97, a precision score of 1.0, and an area under the curve (AUC) score of 0.97. Computational radiomics using CT images shows promise in predicting hepatic decompensation in primary sclerosing cholangitis patients. Our model achieved high accuracy, but predicting future events remains challenging. Further research is needed to validate clinical utility and limitations.</p>","PeriodicalId":19499,"journal":{"name":"Oncotarget","volume":"15 ","pages":"809-813"},"PeriodicalIF":0.0,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11584029/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142687646","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
Mitigating bias in radiology: The promise of topological data analysis and simplicial complexes. 减少放射学中的偏差：拓扑数据分析和简单复合物的前景。
Oncotarget Pub Date : 2024-11-12 DOI: 10.18632/oncotarget.28668
Yashbir Singh, Colleen Farrelly, Quincy A Hathaway, Gunnar Carlsson
{"title":"Mitigating bias in radiology: The promise of topological data analysis and simplicial complexes.","authors":"Yashbir Singh, Colleen Farrelly, Quincy A Hathaway, Gunnar Carlsson","doi":"10.18632/oncotarget.28668","DOIUrl":"https://doi.org/10.18632/oncotarget.28668","url":null,"abstract":"<p><p>Topological Data Analysis (TDA) and simplicial complexes offer a novel approach to address biases in AI-assisted radiology. By capturing complex structures, n-way interactions, and geometric relationships in medical images, TDA enhances feature extraction, improves representation robustness, and increases interpretability. This mathematical framework has the potential to significantly improve the accuracy and fairness of radiological assessments, paving the way for more equitable patient care.</p>","PeriodicalId":19499,"journal":{"name":"Oncotarget","volume":"15 ","pages":"782-783"},"PeriodicalIF":0.0,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11559658/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142625258","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
Visualizing radiological data bias through persistence images. 通过持久图像可视化放射数据偏差。
Oncotarget Pub Date : 2024-11-12 DOI: 10.18632/oncotarget.28670
Yashbir Singh, Colleen Farrelly, Quincy A Hathaway, Gunnar Carlsson
{"title":"Visualizing radiological data bias through persistence images.","authors":"Yashbir Singh, Colleen Farrelly, Quincy A Hathaway, Gunnar Carlsson","doi":"10.18632/oncotarget.28670","DOIUrl":"https://doi.org/10.18632/oncotarget.28670","url":null,"abstract":"<p><p>Persistence images, derived from topological data analysis, emerge as a powerful tool for visualizing and mitigating biases in radiological data interpretation and AI model development. This technique transforms complex topological features into stable, interpretable representations, offering unique insights into medical imaging data structure. By providing intuitive visualizations, persistence images enable the identification of subtle structural differences and potential biases in data acquisition, interpretation, and AI model training. Persistence images can also facilitate stratified sampling, matching statistics, and noise filtration, enhancing the accuracy and equity of radiological analysis. Despite challenges in computational complexity and workflow integration, persistence images show promise in developing more accurate, equitable, and trustworthy AI systems in radiology, potentially improving patient outcomes and personalized healthcare delivery.</p>","PeriodicalId":19499,"journal":{"name":"Oncotarget","volume":"15 ","pages":"787-789"},"PeriodicalIF":0.0,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11559657/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142625261","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
Persistence landscapes: Charting a path to unbiased radiological interpretation. 持久性景观：为无偏见的放射学解释指明方向。
Oncotarget Pub Date : 2024-11-12 DOI: 10.18632/oncotarget.28671
Yashbir Singh, Colleen Farrelly, Quincy A Hathaway, Gunnar Carlsson
{"title":"Persistence landscapes: Charting a path to unbiased radiological interpretation.","authors":"Yashbir Singh, Colleen Farrelly, Quincy A Hathaway, Gunnar Carlsson","doi":"10.18632/oncotarget.28671","DOIUrl":"https://doi.org/10.18632/oncotarget.28671","url":null,"abstract":"<p><p>Persistence landscapes, a sophisticated tool from topological data analysis, offer a promising approach to address biases in radiological interpretation and AI model development. By transforming complex topological features into statistically analyzable functions, they enable robust comparisons between populations and datasets. Persistence landscapes excel in noise filtration, fusion bias mitigation, and enhancing machine learning models. Despite challenges in computation and integration, they show potential to improve the accuracy and equity of radiological analysis, particularly in multi-modal imaging and AI-assisted interpretation.</p>","PeriodicalId":19499,"journal":{"name":"Oncotarget","volume":"15 ","pages":"790-792"},"PeriodicalIF":0.0,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11559655/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142625260","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
Persistence barcodes: A novel approach to reducing bias in radiological analysis. 持久性条形码：减少放射分析偏差的新方法。
Oncotarget Pub Date : 2024-11-12 DOI: 10.18632/oncotarget.28667
Yashbir Singh, Colleen Farrelly, Quincy A Hathaway, Gunnar Carlsson
{"title":"Persistence barcodes: A novel approach to reducing bias in radiological analysis.","authors":"Yashbir Singh, Colleen Farrelly, Quincy A Hathaway, Gunnar Carlsson","doi":"10.18632/oncotarget.28667","DOIUrl":"https://doi.org/10.18632/oncotarget.28667","url":null,"abstract":"<p><p>Persistence barcodes emerge as a promising tool in radiological analysis, offering a novel approach to reduce bias and uncover hidden patterns in medical imaging. By leveraging topological data analysis, this technique provides a robust, multi-scale perspective on image features, potentially overcoming limitations in traditional methods and Graph Neural Networks. While challenges in interpretation and implementation remain, persistence barcodes show significant potential for improving diagnostic accuracy, standardization, and ultimately, patient outcomes in the evolving field of radiology.</p>","PeriodicalId":19499,"journal":{"name":"Oncotarget","volume":"15 ","pages":"784-786"},"PeriodicalIF":0.0,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11559656/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142625259","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
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