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Differences in breast and colorectal cancer screening adherence among women residing in urban and rural communities in the United States

Women who live in urban and rural areas get screened for breast cancer at similar rates, but rural women get screened for colorectal cancer at significantly lower rates than their urban counterparts, new research reveals (JAMA Netw Open 2021; doi: 10.1001/jamanetworkopen.2021.28000). The disparity may stem from a lack of access to colorectal screening in rural areas, the researchers believe. To understand how often women in rural and urban areas get screened for breast and colorectal cancer, researchers looked at responses from 2,897 women, ages 50-75, to surveys at 11 sites around the country, including UVA, Virginia Commonwealth University, the University of Pittsburgh, and the University of Alabama at Birmingham. About 81 percent of both urban and rural women were up to date on their breast cancer screenings, but only 78 percent of rural women were following the colorectal cancer screening guidelines. In comparison, 82 percent of urban women were getting screened for colorectal cancer. The researchers said the disparity in colorectal cancer screenings may stem from the slow arrival of new screening methods to rural areas. They hypothesized that fewer rural residents may be getting screened for colorectal cancer because they don't have easy access to testing. Travel times, for example, may be a barrier, and it may be difficult for people to get time off work, especially for those with lower-paying jobs. The researchers noted that women who had health insurance were 2-3 times more likely to comply with breast cancer and colorectal cancer screening guidelines than women without insurance. "Increase in health care coverage as a result of the Patient Protection and Affordable Care Act, which included elimination of copay for preventive health services, including breast and colorectal cancer screening, has been well-documented and found to be associated with an increase in screening rates for breast and colorectal cancer," the researchers wrote in a new scientific paper outlining their findings. The researchers expected that there would be differences in mammography rates based on race, as African-American cancer outcomes tend to be much worse than among Whites. But the results came as a surprise: Mammography screening was significantly higher in non-Hispanic Black women than in non-Hispanic White women. This difference could not be explained by location or other factors. In response to their findings, the researchers are urging public health interventions to increase colorectal cancer screening among rural women. For example, rural residents may benefit from campaigns to increase awareness of home colorectal cancer testing that can be done by mail.



Biomimetic nanoparticles deliver mRNAs encoding costimulatory receptors and enhance T cell mediated cancer immunotherapy

Driving up the immune response at the site of a cancer tumor with nanotechnology may help enhance immunotherapy treatments in advanced stages of the disease, new research in mice suggests (Nat Commun 2021; In mouse models of numerous types of cancer, scientists boosted activation of T cells, important fighters in an immune response, inside tumors in a way that improved their interactions with an antibody therapy currently being tested in clinical trials. The researchers injected nanobodies carrying messenger RNA, molecules that translate genetic information into functional proteins, directly into the tumor site to help T cells generate specific receptors on their surfaces. Experimental monoclonal antibodies delivered 6 hours later could then bind to those receptors to carry out their cancer cell-killing functions. The technique left six of 10 mice with lymphoma tumor-free, and was effective in melanoma when combined with additional existing drugs that help amplify the immune response. While increasing T-cell activation was the end goal of the research, designing the most effective nanoparticle to carry the messenger RNA was equally important. Yizhou Dong's lab at The Ohio State University has long focused on nanoparticle delivery of messenger RNA as a therapeutic strategy, producing promising results in animal studies against sepsis, genetic disorders, and COVID-19. The team designed nanoparticles for this purpose using one of many compounds that make up cell membranes. Researchers then loaded the nanoparticle cargo: messenger RNA carrying instructions for the production of molecules that T cells express as part of their immune system function. These nanoparticles were injected directly into tumors in mouse models of specific cancers, and entered tumor-infiltrating T cells to amplify their expression of the receptors. Tests of the combined treatment produced the best results in mouse models of melanoma and B-cell lymphoma. The nanoparticle and antibody delivery completely eliminated tumors in 60 percent of the mice-a significantly better result than treatment with the antibody alone. The immune response enhancement had staying power as well. Lymphoma cells injected later into the treated tumor-free mice were unable to survive long enough to form tumors. Melanoma proved to be a tougher fight. However, when researchers supplemented the combination treatment with the addition of two antibodies that disrupt cancer cells' ability to block the immune response, this approach resulted in a 50 percent complete response in the mice and protection against a later tumor rechallenge. This multi-therapy approach also reduced cancer's spread in a mouse model of metastasis to the lungs. Focusing treatment directly at the tumor site is a way of training the immune system to recognize local and circulating cancer cells while lowering the chances for whole-body side effects, the authors concluded. The study provided evidence that this technology platform could be used to enhance immunotherapy.



A machine and human reader study on AI diagnosis model safety under attacks of adversarial images

Artificial intelligence (AI) models that evaluate medical images have potential to speed up and improve accuracy of cancer diagnoses, but they may also be vulnerable to cyberattacks. In a new study, researchers simulated an attack that falsified mammogram images, fooling both an AI breast cancer diagnosis model and human breast imaging radiologist experts (Nat Commun 2021; The study brings attention to a potential safety issue for medical AI known as "adversarial attacks," which seek to alter images or other inputs to make models arrive at incorrect conclusions. AI technologies are at risk from cyberthreats, such as adversarial attacks. Potential motivations for such attacks include insurance fraud from health care providers looking to boost revenue or companies trying to adjust clinical trial outcomes in their favor. Adversarial attacks on medical images range from tiny manipulations that change the AI's decision, but are imperceptible to the human eye, to more sophisticated versions that target sensitive contents of the image, such as cancerous regions-making them more likely to fool a human. To understand how AI would behave under this more complex type of adversarial attack, researchers used mammogram images to develop a model for detecting breast cancer. First, they trained a deep learning algorithm to distinguish cancerous and benign cases with more than 80 percent accuracy. Next, they developed a so-called "generative adversarial network" (GAN)-a computer program that generates false images by inserting or removing cancerous regions from negative or positive images, respectively, and then they tested how the model classified these adversarial images. Of 44 positive images made to look negative by the GAN, 42 were classified as negative by the model, and of 319 negative images made to look positive, 209 were classified as positive. In all, the model was fooled by 69.1 percent of the fake images. In the second part of the experiment, the researchers asked five human radiologists to distinguish whether mammogram images were real or fake. The experts accurately identified the images' authenticity with accuracy of between 29 percent and 71 percent, depending on the individual.