Integrating artificial intelligence (AI) and robotics in prostate cancer (PCa) offers a game-changing breakthrough with far-reaching implications for diagnosis, treatment, and research. AI-driven algorithms have tremendous promise for assisting early diagnosis by analyzing invisible trends within medical imaging devices such as MRI and ultrasounds. In addition, by evaluating big datasets containing patient data, genetic attributes, and treatment outcomes, these AI algorithms offer the possibility of allowing individualized treatment regimens. This ability to personalize actions to specific patients might improve therapy efficacy while reducing side effects. Robotics can increase accuracy in less invasive surgery, revolutionize therapies like prostatectomies, and improve recovery time for patients. Robotic-assisted procedures provide clinicians with remarkable skills and flexibility, allowing clinicians to negotiate complicated anatomical structures more precisely. However, the symbiotic combination of AI and robotics has several drawbacks. Concerns about data privacy, algorithm biases, and the need to continually assess AI’s diagnostic proficiency offer significant hurdles. To ensure patient privacy and data security, the ethical and regulatory aspects of integrating AI and robotics require proper attention. However, combining AI and robotics opens up a galaxy of possibilities. The joint use of AI and robotics can potentially speed up drug development procedures by filtering through massive databases, resulting in the identification of new medicinal compounds. Furthermore, combining AI and robotics might usher in an innovative era of personalized medicine, allowing healthcare providers to design therapies based on detailed patient profiles. The merging of AI and robotics in PCa care gives up unprecedented prospects. While limitations highlight the necessity for caution, the possibilities of better diagnostics, tailored therapies, and new research pathways highlight the transformational abilities of AI and robotics in determining the future of PCa management. This study explores the limitations and opportunities presented by using AI and robotics in the context of PCa.

The distribution of coronavirus disease 2019 (COVID-19) infection across the historically marginalized populations in the United States (US) has consistently been inequitable. In addition, systemic racism and prejudice, which have existed for decades, have caused a lack of faith in public health and medical experts and have resulted in the epidemic of misinformation. To counteract the COVID-19 pandemic and widespread misinformation, the political establishment and public health experts must work collaboratively. And because they are closely associated, there had been a significant increase in the prevalence of the disease as well as a spike in the number of hospitalizations and fatalities. Public health professionals have investigated a number of epidemiological strategies to stop the spread of the virus and mitigate its effects, but false information released via various media sources has caused serious harm to a number of people. To create the framework and guidelines for protecting audiences from lies and deceit, and eradicating false information before taking root in society, it is essential to understand the types of misinformation that are being spread since the disadvantaged and uneducated communities suffer disproportionately as a result. According to studies, spreading false information could have a negative impact on a country's health outcomes, as well as its economic and social well-being, if not immediately refuted. Public health themes, such as evidence-based programs, health communication, and health policy, among others need to be evaluated and put into action in order to prevent the dissemination of incorrect information. This review examines a number of public health themes, such as policy and evidence-based strategies that might help in the fight against misinformation that has wreaked havoc on families and communities, particularly the underserved and uninformed populations.

Hunger, Food Security, and Sovereignty: A Need for Evidence-Based Public Health Approaches to Meet Sustainable Development Goals

Being cognizant of the pronounced health advantages of breastfeeding for both the nursing mother and her infant, the breastfeeding dyad, we examined breastfeeding rates among Floridian women who gave birth from 2012 to 2014 (N = 639,052). We investigated the associations between breastfeeding initiation and WIC-based breastfeeding support (the Special Supplemental Nutrition Program for Women, Infants, and Children), education level, and race and ethnicity. We compared the percentage of breastfeeding mothers between those in the WIC program and those who were not, and we compared breastfeeding rates across racial and ethnic groups. Consistent with previous reports, black newborns in this study were breastfed at lower rates than other racial groups, and WIC program participants were less likely to breastfeed than non-WIC program participants. However, by breaking down the data by education level and race, and ethnicity, we see a significantly increased rate of breastfeeding due to WIC participation for both Hispanic and black women with less than a high school education. Further, we assessed differences by insurance type, race, and WIC participation. In multivariable logistic regression, we showed that the WIC program has a significant positive impact on breastfeeding rates for all but white non-Hispanic mothers, independent of sociodemographic and geographic variables. We also note a trend of increasing breastfeeding rates over the study period (p-value < 0.0001), which has positive public health implications.

We describe barriers and supports for the practice of breastfeeding, with particular focus on Black and Hispanic women in the United States. We note that breastfeeding patterns reported by WIC agencies is highly variable across the country and within states. The global campaign to support breastfeeding, Baby Friendly Hospital Initiative, and its implementation in the US is described, as well as Healthy People goals and the mixture of policies across the US that provide incomplete support for breastfeeding mothers.

Issue: Prostate cancer (Pca) is still among the most frequently diagnosed cancers in adults. The most often mutated gene in PCa is PTEN (Phosphatase and Tensin Homolog), making it a possible biomarker for prospective customized therapy. The root causes of health inequalities in PCa have not received much attention. Numerous online databases, like The Cancer Genome Atlas (TCGA), provide gene expression data and other data, allowing researchers to further investigate new processes or therapy options. However, there hasn't been much study on tailored therapy up to this point since there may have been difficulties with access to genomic information and bioinformatic tools. Methods: The PCa data in cBioPortal comprises 494 samples. We selected 154 samples comprising 7 Blacks and 147 Whites. We used the TCGA database to obtain the PCa gene expression and DNA methylation data for study. In order to examine the data by race/ethnicity, gene expression, DNA methylation, and genomic changes, we evaluated at the differentially expressed and methylated genes using online tools (cBioPortal). To evaluate gene methylation, expression, and alterations, log ratios of means in Blacks to means in Whites were computed across the data. Additionally, Kaplan-Meier survival analysis, protein interaction analysis, and multivariable data analysis were carried out. Results: PCa data in the TCGA database showed PTEN mutation in 20% patients. The median age of participants was 63 years (range: 42-78). For every category of data assessed, there were significant differences by race/ethnicity (q < 0.05). In the survival analysis, SLC2A1, NUDT12, CUTALP, and other genes were associated with survival. Conclusion: Genetic alteration, expression, and DNA methylation are each significantly different by the two race groups compared. According to our research, PTEN mutation in PCa may influence several genes and biological processes. Citation Format: Olumide Arigbede, Sarah G. Buxbaum, Sara Falzarano, Suhn K. Rhie. Genomic landscape of prostate cancer: Bioinformatic analysis of TCGA PanCancer data by race and identification of key pathways and genes in PTEN mutation. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4266.