Sankar Jyoti Bora, D. J. Deka, Chinmoy Malakar, Nancy Kashyap, Bhrigu Kumar Das
{"title":"Breast Cancer Management: The Role of Nutrition, Exercise and Psychosocial Well-being","authors":"Sankar Jyoti Bora, D. J. Deka, Chinmoy Malakar, Nancy Kashyap, Bhrigu Kumar Das","doi":"10.2174/0115733947286944240223101937","DOIUrl":"https://doi.org/10.2174/0115733947286944240223101937","url":null,"abstract":"\u0000\u0000Breast cancer incidence and mortality rates are rising worldwide, which presents\u0000a formidable challenge for women. The advancement of targeted drug therapies offers promising\u0000avenues for treatment, but resource constraints prevent their widespread implementation in advanced\u0000clinical trials, highlighting the need for sustained research funding. Nutritional support is critical\u0000in cancer management, affecting key cancer hallmarks. The anti-inflammatory effects of exercise\u0000and a healthy diet are critical in reducing cancer incidence and tumor growth. A comprehensive approach\u0000to breast cancer treatment requires addressing health challenges and psychological symptoms.\u0000\u0000\u0000\u0000In this context, we aim to address modifiable risk factors, including nutrition, physical\u0000activity, and psychosocial factors, which can serve as non-pharmacological adjuncts in reducing\u0000breast cancer risk, incidence, and mortality.\u0000\u0000\u0000\u0000This study conducted a thorough literature search on breast cancer, nutrition, physical activity,\u0000psychosocial problems, clinical trial/study, mechanisms, in-vitro and in-vivo. The search was\u0000performed using multiple search engines and the main keywords, and only English publications until\u0000August 2023 were included.\u0000\u0000\u0000\u0000Nutrition plays a critical role in influencing breast cancer risk, but its exact role needs to be\u0000explored. Diet diversity and exercise are recommended to reduce risk, while psychosocial support is\u0000vital for patient well-being.\u0000\u0000\u0000\u0000In light of rising global breast cancer challenges, our study underscores the urgent need\u0000for enhanced clinical trial availability, exploration of nutrition-cancer links, and refined psychosocial\u0000interventions to comprehensively address prevention and treatment.\u0000","PeriodicalId":503819,"journal":{"name":"Current Cancer Therapy Reviews","volume":"134 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140251445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Revolutionizing Cancer Research and Drug Discovery: The Role of Artificial\u0000Intelligence and Machine Learning","authors":"Ajita Paliwal, Md Aftab Alam, Preeti Sharma, Smita Jain, Shivang Dhoundiyal","doi":"10.2174/0115733947288355240305080236","DOIUrl":"https://doi.org/10.2174/0115733947288355240305080236","url":null,"abstract":"\u0000\u0000Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized various industries, including cancer research and drug discovery. This article provides a summary of the history of\u0000AI and ML, highlighting their resurgence in the 1990s with advancements in computational power\u0000and new algorithms. In the context of drug discovery, AI and ML techniques have been applied to\u0000accelerate the development of new drugs, from target identification and lead generation to drug repurposing. AI applications in drug design and virtual screening have improved the efficiency of identifying potential drug candidates. Deep learning, a division of ML, has been particularly effective in\u0000predicting protein structures and optimizing lead compounds. In anti-cancer drug target prediction, AI\u0000and ML algorithms analyze large-scale genomic, proteomic, and clinical data to identify potential\u0000targets for cancer therapy. AI has also transformed cancer imaging and diagnosis by enhancing the\u0000accuracy and efficiency of cancer detection, classification, and prognosis. Medical imaging analysis,\u0000pathology, and radiology have benefited from AI algorithms’ ability to interpret and analyze various\u0000imaging modalities. Moreover, AI applications in cancer treatment have facilitated the development\u0000of predictive models for treatment response, enabling personalized and targeted therapies based on\u0000individual patient characteristics. The purpose of the study was to give facts regarding the integration\u0000of artificial intelligence and machine learning in drug discovery and cancer therapy and its significant\u0000prospects for improving efficiency, decreasing costs, and improving patient outcomes.\u0000","PeriodicalId":503819,"journal":{"name":"Current Cancer Therapy Reviews","volume":"16 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140252797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}