Genomics of Black American colon cancer disparities: An RNA sequencing (RNA-Seq) study from an academic, tertiary referral center

A framework to guide research and practice response to emerging infectious diseases: Genomic-to-global considerations

Newly emerging infectious diseases (EIDs), like the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, are becoming increasingly common. Due to geographic, political, social, behavioral, and genomic differences, some populations are more vulnerable to infectious disease spread than others. The purpose of this article is to present a framework for research and practice response to emergent infectious diseases that addresses multiple transdisciplinary actions to limit exposure or mitigate adverse outcomes for individuals and communities. Recent experience with new strains of emergent infectious diseases reinforces the importance of intervening at multiple levels, from genomics to political messaging to create multipronged, transdisciplinary interventions to contain the threat. In particular, incorporation of genomics into public health nursing practice of infectious diseases management can enhance existing regional-, community-, and individual-level health promotion and protection efforts, thus impacting long-term health outcomes.

Texture Features of Computed Tomography Image under the Artificial Intelligence Algorithm and Its Predictive Value for Colorectal Liver Metastasis

The aim of this research was to investigate the predictive role of texture features in computed tomography (CT) images based on artificial intelligence (AI) algorithms for colorectal liver metastases (CRLM). A total of 150 patients with colorectal cancer who were admitted to the hospital were selected as the research objects and randomly divided into three groups with 50 cases in each group. The patients who were found to suffer from the CRLM in the initial examination were included in group A. Patients who were found with CRLM in the follow-up were assigned to group B (B1: metastasis within 0.5 years, 16 cases; B2: metastasis within 0.5–1.0 years, 17 cases; and B3: metastasis within 1.0–2.0 years, 17 cases). Patients without liver metastases during the initial examination and subsequent follow-up were designated as group C. Image textures were analyzed for patients in each group. The prediction accuracy, sensitivity, and specificity of CRLM in patients with six classifiers were calculated, based on which the receiver operator characteristic (ROC) curves were drawn. The results showed that the logistic regression (LR) classifier had the highest prediction accuracy, sensitivity, and specificity, showing the best prediction effect, followed by the linear discriminant (LD) classifier. The prediction accuracy, sensitivity, and specificity of the LR classifier were higher in group B1 and group B3, and the prediction effect was better than that in group B2. The texture features of CT images based on the AI algorithms showed a good prediction effect on CRLM and had a guiding significance for the early diagnosis and treatment of CRLM. In addition, the LR classifier showed the best prediction effect and high clinical value and can be popularized and applied.