Physical interactions between genetic elements located throughout the genome play important roles in gene regulation and can be identified with the Chromosome Conformation Capture (3C) methodology. 3C converts physical chromatin interactions into specific ligation products, which are quantified individually by PCR. Here we present a high-throughput 3C approach, 3C-Carbon Copy (5C), that employs microarrays or quantitative DNA sequencing using 454-technology as detection methods. We applied 5C to analyze a 400-kb region containing the human ␤-globin locus and a 100-kb conserved gene desert region. We validated 5C by detection of several previously identified looping interactions in the ␤-globin locus. We also identified a new looping interaction in K562 cells between the ␤-globin Locus Control Region and the ␥-␦-globin intergenic region. Interestingly, this region has been implicated in the control of developmental globin gene switching. 5C should be widely applicable for large-scale mapping of cis-and trans-interaction networks of genomic elements and for the study of higher-order chromosome structure.

BackgroundLaboratory testing is the single highest-volume medical activity and drives clinical decision-making across medicine. However, the overall landscape of inappropriate testing, which is thought to be dominated by repeat testing, is unclear. Systematic differences in initial vs. repeat testing, measurement criteria, and other factors would suggest new priorities for improving laboratory testing.MethodsA multi-database systematic review was performed on published studies from 1997–2012 using strict inclusion and exclusion criteria. Over- vs. underutilization, initial vs. repeat testing, low- vs. high-volume testing, subjective vs. objective appropriateness criteria, and restrictive vs. permissive appropriateness criteria, among other factors, were assessed.ResultsOverall mean rates of over- and underutilization were 20.6% (95% CI 16.2–24.9%) and 44.8% (95% CI 33.8–55.8%). Overutilization during initial testing (43.9%; 95% CI 35.4–52.5%) was six times higher than during repeat testing (7.4%; 95% CI 2.5–12.3%; P for stratum difference <0.001). Overutilization of low-volume tests (32.2%; 95% CI 25.0–39.4%) was three times that of high-volume tests (10.2%; 95% CI 2.6–17.7%; P<0.001). Overutilization measured according to restrictive criteria (44.2%; 95% CI 36.8–51.6%) was three times higher than for permissive criteria (12.0%; 95% CI 8.0–16.0%; P<0.001). Overutilization measured using subjective criteria (29.0%; 95% CI 21.9–36.1%) was nearly twice as high as for objective criteria (16.1%; 95% CI 11.0–21.2%; P = 0.004). Together, these factors explained over half (54%) of the overall variability in overutilization. There were no statistically significant differences between studies from the United States vs. elsewhere (P = 0.38) or among chemistry, hematology, microbiology, and molecular tests (P = 0.05–0.65) and no robust statistically significant trends over time.ConclusionsThe landscape of overutilization varies systematically by clinical setting (initial vs. repeat), test volume, and measurement criteria. Underutilization is also widespread, but understudied. Expanding the current focus on reducing repeat testing to include ordering the right test during initial evaluation may lead to fewer errors and better care.

Echocardiography is essential to cardiology. However, the need for human interpretation has limited echocardiography’s full potential for precision medicine. Deep learning is an emerging tool for analyzing images but has not yet been widely applied to echocardiograms, partly due to their complex multi-view format. The essential first step toward comprehensive computer-assisted echocardiographic interpretation is determining whether computers can learn to recognize these views. We trained a convolutional neural network to simultaneously classify 15 standard views (12 video, 3 still), based on labeled still images and videos from 267 transthoracic echocardiograms that captured a range of real-world clinical variation. Our model classified among 12 video views with 97.8% overall test accuracy without overfitting. Even on single low-resolution images, accuracy among 15 views was 91.7% vs. 70.2–84.0% for board-certified echocardiographers. Data visualization experiments showed that the model recognizes similarities among related views and classifies using clinically relevant image features. Our results provide a foundation for artificial intelligence-assisted echocardiographic interpretation.