Harnessing microbial phylum-specific molecular markers for assessment of environmental estrogen degradation.

Steroidal estrogens are ubiquitous contaminants that have garnered attention worldwide due to their endocrine-disrupting and carcinogenic activities at sub-nanomolar concentrations. Microbial degradation is one of the main mechanisms through which estrogens can be removed from the environment. Numerous bacteria have been isolated and identified as estrogen degraders; however, little is known about their contribution to environmental estrogen removal. Here, our global metagenomic analysis indicated that estrogen degradation genes are widely distributed among bacteria, especially among aquatic actinobacterial and proteobacterial species. Thus, by using the Rhodococcus sp. strain B50 as the model organism, we identified three actinobacteria-specific estrogen degradation genes, namely aedGHJ, by performing gene disruption experiments and metabolite profile analysis. Among these genes, the product of aedJ was discovered to mediate the conjugation of coenzyme A with a unique actinobacterial C17 estrogenic metabolite, 5-oxo-4-norestrogenic acid. However, proteobacteria were found to exclusively adopt an α-oxoacid ferredoxin oxidoreductase (i.e., the product of edcC) to degrade a proteobacterial C18 estrogenic metabolite, namely 3-oxo-4,5-seco-estrogenic acid. We employed actinobacterial aedJ and proteobacterial edcC as specific biomarkers for quantitative polymerase chain reaction (qPCR) to elucidate the potential of microbes for estrogen biodegradation in contaminated ecosystems. The results indicated that aedJ was more abundant than edcC in most environmental samples. Our results greatly expand the understanding of environmental estrogen degradation. Moreover, our study suggests that qPCR-based functional assays are a simple, cost-effective, and rapid approach for holistically evaluating estrogen biodegradation in the environment.

From Data to Value: How Artificial Intelligence Augments the Radiology Business to Create Value.

The radiology practice has access to a wealth of data in the radiologist information system, dictation reports, and electronic health records. Although many artificial intelligence applications in radiology have focused on computer vision and the interpretive use cases, many opportunities exist to enhance the radiologist's value proposition through business analytics. This article explores how AI lends an analytical lens to the radiology practice to create value.

Teaching Radiology Trainees From the Perspective of a Millennial.

The millennial generation consists of today's medical students, radiology residents, fellows, and junior staff. Millennials' comfort with immersive technology, high expectations for success, and desire for constant feedback differentiate them from previous generations. Drawing from an author's experiences through radiology residency and fellowship as a millennial, from published literature, and from the mentorship of a long-time radiology educator, this article explores educational strategies that embrace these characteristics to engage today's youngest generation both in and out of the reading room.