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Repeated biopsy sampling from one muscle is necessary to investigate muscular adaptation to different forms of exercise as adaptation is thought to be the result of cumulative effects of transient changes in gene expression in response to single exercise bouts. In a crossover study, we obtained four fine needle biopsies from one vastus lateralis muscle of 11 male subjects (25.9 ± 3.8 yr, 179.2 ± 4.8 cm, 76.5 ± 7.0 kg), taken before (baseline), 1, 4, and 24 h after one bout of squatting exercise performed as conventional squatting or as whole body vibration exercise. To investigate if the repeated biopsy sampling has a confounding effect on the observed changes in gene expression, four fine needle biopsies from one vastus lateralis muscle were also taken from 8 male nonexercising control subjects (24.5 ± 3.7 yr, 180.6 ± 1.2 cm, 81.2 ± 1.6 kg) at the equivalent time points. Using RT-PCR, we observed similar patterns of change in the squatting as well as in the control group for the mRNAs of interleukin 6 (IL-6), IL-6 receptor, insulin-like growth factor 1, p21, phosphofructokinase, and glucose transporter in relation to the baseline biopsy. In conclusion, multiple fine needle biopsies obtained from the same muscle region can per se influence the expression of marker genes induced by an acute bout of resistance exercise.

Accurate segmentations in medical images are the foundations for various clinical applications. Advances in machine learning-based techniques show great potential for automatic image segmentation, but these techniques usually require a huge amount of accurately annotated reference segmentations for training. The guiding hypothesis of this paper was that crowd-algorithm collaboration could evolve as a key technique in large-scale medical data annotation. As an initial step toward this goal, we evaluated the performance of untrained individuals to detect and correct errors made by three-dimensional (3-D) medical segmentation algorithms. To this end, we developed a multistage segmentation pipeline incorporating a hybrid crowd-algorithm 3-D segmentation algorithm integrated into a medical imaging platform. In a pilot study of liver segmentation using a publicly available dataset of computed tomography scans, we show that the crowd is able to detect and refine inaccurate organ contours with a quality similar to that of experts (engineers with domain knowledge, medical students, and radiologists). Although the crowds need significantly more time for the annotation of a slice, the annotation rate is extremely high. This could render crowdsourcing a key tool for cost-effective large-scale medical image annotation.

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