Search Results (4)

Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide, due to the limited efficacy of current therapeutic strategies in advanced stages. Necroptosis, a regulated form of necrotic cell death mediated by receptor-interacting protein kinases RIPK1 and RIPK3, and the pseudokinase MLKL, has emerged as a potential alternative pathway to induce cancer cell death. Recent studies suggest that modulation of necroptosis may enhance antitumor immunity, overcome therapeutic resistance, and improve clinical outcomes in CRC. In this review, we systematically analyzed the current literature on the role of necroptosis in CRC, focusing on molecular mechanisms, experimental models, and therapeutic implications. By critically evaluating the available evidence, we aimed to determine whether targeting RIPK1, RIPK3, and MLKL, or other novel agents, represents a promising horizon or a blind alley in the development of novel CRC therapies. Full article

The modern era of systemic treatment of urological cancers is definitely marked by checkpoint inhibitors. Over the past 30 years, checkpoint inhibitors have changed the oncological world, especially in chemoresistant malignancies. Multiple investigations focused on immunotherapy in urological cancers have carved new paradigms and changed clinical guidelines. However, some clinical trials have been blind alleys for systemic therapy. After a scrutinized review of electronic databases, we want to present the natural history and courses of clinical trials in urological malignancies. All of them contribute to expanding the knowledge and experience of clinicians, and some of them improve the prognosis and prolong the overall survival of oncological patients. In conclusion, checkpoint inhibitors open a new modern era in some urological cancers, but not overall. Future perspectives are focused on combination with targeted therapy and could be a new way forward in the systemic treatment of urological cancers. Full article

The detrimental impact of toxic chemicals, gas, and oil spills in aquatic environments poses a severe threat to plants, animals, and human life. Regions such as Cancer Alley exemplify the profound consequences of inadequately controlled chemical spills, significantly affecting the local community. Given the far-reaching effects of these spills, it has become imperative to devise an efficient method for early monitoring, estimation, and cleanup, utilizing affordable and effective techniques. In this research, we explore the application of U-shaped neural Network (UNET) and U-shaped neural network transformer (UNETR) neural network models designed for the image segmentation of chemical and oil spills. Our models undergo training using the Commonwealth Scientific and Industrial Research Organization (CSIRO) dataset and the Oil Spill Detection dataset, employing a specialized filtering technique to enhance detection accuracy. We achieved training accuracies of 95.35% and 91% by applying UNET on the Oil Spill and the CSIRO datasets after 50 epochs of training, respectively. We also achieved a training accuracy of 75% by applying UNETR to the Oil Spill dataset. Additionally, we integrated mixed precision to expedite the model training process, thus maximizing data throughput. To further accelerate our implementation, we propose the utilization of the Field Programmable Gate Array (FPGA) architecture. The results obtained from our study demonstrate improvements in inference latency on FPGA. Full article

This study examines race- and income-based disparities in cancer risks from air toxics in Cancer Alley, LA, USA. Risk estimates were obtained from the 2005 National Air Toxics Assessment and socioeconomic and race data from the 2005 American Community Survey, both at the census tract level. Disparities were assessed using spatially weighted ordinary least squares (OLS) regression and quantile regression (QR) for five major air toxics, each with cancer risk greater than 10⁻⁶. Spatial OLS results showed that disparities in cancer risks were significant: People in low-income tracts bore a cumulative risk 12% more than those in high-income tracts (p < 0.05), and those in black-dominant areas 16% more than in white-dominant areas (p < 0.01). Formaldehyde and benzene were the two largest contributors to the disparities. Contributions from emission sources to disparities varied by compound. Spatial QR analyses showed that magnitude of disparity became larger at the high end of exposure range, indicating worsened disparity in the poorest and most highly concentrated black areas. Cancer risk of air toxics not only disproportionately affects socioeconomically disadvantaged and racial minority communities, but there is a gradient effect within these groups with poorer and higher minority concentrated segments being more affected than their counterparts. Risk reduction strategies should target emission sources, risk driver chemicals, and especially the disadvantaged neighborhoods. Full article