Monitoring of Tumor Promotion and Progression in a Mouse Model of Inflammation-Induced Colon Cancer with Magnetic Resonance Colonography

AbstractEarly detection of precancerous tissue has significantly improved survival of most cancers including colorectal cancer (CRC). Animal models designed to study the early stages of cancer are valuable for identifying molecular events and response indicators that correlate with the onset of disease. The goal of this work was to investigate magnetic resonance (MR) colonography in a mouse model of CRC on a clinical MR imager. Mice treated with azoxymethane and dextran sulfate sodium were imaged by serial MR colonography (MRC) from initiation to euthanasia. Magnetic resonance colonography was obtained with both T1- and T2-weighted images after administration of a Fluorinert enema to remove residual luminal signal and intravenous contrast to enhance the colon wall. Individual tumor volumes were calculated and validated ex vivo. The Fluorinert enema provided a clear differentiation of the lumen of the colon from the mucosal lining. Inflammation was detected 3 days after dextran sulfate sodium exposure and subsided during the next week. Tumors as small as 1.2 mm3 were detected and as early as 29 days after initiation. Individual tumor growths were followed over time, and tumor volumes were measured by MR imaging correlated with volumes measured ex vivo. The use of a Fluorinert enema during MRC in mice is critical for differentiating mural processes from intraluminal debris. Magnetic resonance colonography with Fluorinert enema and intravenous contrast enhancement will be useful in the study of the initial stages of colon cancer and will reduce the number of animals needed for preclinical trials of prevention or intervention

An FPT Approach for Predicting Protein Localization from Yeast Genomic Data

Accurately predicting the localization of proteins is of paramount importance in the quest to determine their respective functions within the cellular compartment. Because of the continuous and rapid progress in the fields of genomics and proteomics, more data are available now than ever before. Coincidentally, data mining methods been developed and refined in order to handle this experimental windfall, thus allowing the scientific community to quantitatively address long-standing questions such as that of protein localization. Here, we develop a frequent pattern tree (FPT) approach to generate a minimum set of rules (mFPT) for predicting protein localization. We acquire a series of rules according to the features of yeast genomic data. The mFPT prediction accuracy is benchmarked against other commonly used methods such as Bayesian networks and logistic regression under various statistical measures. Our results show that mFPT gave better performance than other approaches in predicting protein localization. Meanwhile, setting 0.65 as the minimum hit-rate, we obtained 138 proteins that mFPT predicted differently than the simple naive bayesian method (SNB). In our analysis of these 138 proteins, we present novel predictions for the location for 17 proteins, which currently do not have any defined localization. These predictions can serve as putative annotations and should provide preliminary clues for experimentalists. We also compared our predictions against the eukaryotic subcellular localization database and related predictions by others on protein localization. Our method is quite generalized and can thus be applied to discover the underlying rules for protein-protein interactions, genomic interactions, and structure-function relationships, as well as those of other fields of research

An integrated ultrasound curriculum (iUSC) for medical students: 4-year experience

A review of the development and implementation of a 4-year medical student integrated ultrasound curriculum is presented. Multiple teaching and assessment modalities are discussed as well as results from testing and student surveys. Lessons learned while establishing the curriculum are summarized. It is concluded that ultrasound is a well received, valuable teaching tool across all 4 years of medical school, and students learn ultrasound well, and they feel their ultrasound experience enhances their medical education

Comparative Microbial Modules Resource: Generation and Visualization of Multi-species Biclusters

The increasing abundance of large-scale, high-throughput datasets for many closely related organisms provides opportunities for comparative analysis via the simultaneous biclustering of datasets from multiple species. These analyses require a reformulation of how to organize multi-species datasets and visualize comparative genomics data analyses results. Recently, we developed a method, multi-species cMonkey, which integrates heterogeneous high-throughput datatypes from multiple species to identify conserved regulatory modules. Here we present an integrated data visualization system, built upon the Gaggle, enabling exploration of our method's results (available at http://meatwad.bio.nyu.edu/cmmr.html). The system can also be used to explore other comparative genomics datasets and outputs from other data analysis procedures – results from other multiple-species clustering programs or from independent clustering of different single-species datasets. We provide an example use of our system for two bacteria, Escherichia coli and Salmonella Typhimurium. We illustrate the use of our system by exploring conserved biclusters involved in nitrogen metabolism, uncovering a putative function for yjjI, a currently uncharacterized gene that we predict to be involved in nitrogen assimilation

Exome-wide association study to identify rare variants influencing COVID-19 outcomes : Results from the Host Genetics Initiative

Publisher Copyright: Copyright: © 2022 Butler-Laporte et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Host genetics is a key determinant of COVID-19 outcomes. Previously, the COVID-19 Host Genetics Initiative genome-wide association study used common variants to identify multiple loci associated with COVID-19 outcomes. However, variants with the largest impact on COVID-19 outcomes are expected to be rare in the population. Hence, studying rare variants may provide additional insights into disease susceptibility and pathogenesis, thereby informing therapeutics development. Here, we combined whole-exome and whole-genome sequencing from 21 cohorts across 12 countries and performed rare variant exome-wide burden analyses for COVID-19 outcomes. In an analysis of 5,085 severe disease cases and 571,737 controls, we observed that carrying a rare deleterious variant in the SARS-CoV-2 sensor toll-like receptor TLR7 (on chromosome X) was associated with a 5.3-fold increase in severe disease (95% CI: 2.75–10.05, p = 5.41x10-7). This association was consistent across sexes. These results further support TLR7 as a genetic determinant of severe disease and suggest that larger studies on rare variants influencing COVID-19 outcomes could provide additional insights.Peer reviewe