Developing a Flipped Classroom for Clinical Anatomy: Approaches to Pre-class Recordings and a Novel Approach to In-Class Active Learning.

For centuries the established pedagogy for teaching in academia has been the lecture, in which an expert in a given subject speaks from a pulpit while a collection of students listens and takes notes. In recent decades, however, advances in technology have allowed educators to rethink the way in which they deliver course content. One approach that has been given attention is that of the flipped classroom, in which lectures are delivered outside of the class through video recording, and the class session is dedicated to more interactive activities. This chapter provides a rationale for this approach to learning, provides guidance in the development of pre-recorded lectures, and introduces a novel approach to the classroom sessions using audience response software.

The role of anatomical context in soft-tissue multi-organ segmentation of cadaveric non-contrast-enhanced whole body CT.

BACKGROUND: Cadaveric computed tomography (CT) image segmentation is a difficult task to solve, especially when applied to whole-body image volumes. Traditional algorithms require preprocessing using registration, or highly conserved organ morphologies. These requirements cannot be fulfilled by cadaveric specimens, so deep learning must be used to overcome this limitation. Further, the widespread use of 2D algorithms for volumetric data ignores the role of anatomical context. The use of 3D spatial context for volumetric segmentation of CT scans as well as the anatomical context required to optimize the segmentation has not been adequately explored. PURPOSE: To determine whether 2D slice-by-slice UNet algorithms or 3D volumetric UNet (VNet) algorithms provide a more effective method for segmenting 3D volumes, and to what extent anatomical context plays in the segmentation of soft-tissue organs in cadaveric, noncontrast-enhanced (NCE) CT. METHODS: We tested five CT segmentation algorithms: 2D UNets with and without 3D data augmentation (3D rotations) as well as VNets with three levels of anatomical context (implemented via image downsampling at 1X, 2X, and 3X) for their performance via 3D Dice coefficients, and Hausdorff distance calculations. The classifiers were trained to segment the kidneys and liver and the performance was evaluated using Dice coefficient and Hausdorff distance on the segmentation versus the ground truth annotation. RESULTS: Our results demonstrate that VNet algorithms perform significantly better ( p < 0.05 $p<0.05$ ) than 2D models. Among the VNet classifiers, those that use some level of image downsampling outperform (as calculated through Dice coefficients) the VNet without downsampling. Additionally, the optimal amount of downsampling depends on the target organ. CONCLUSIONS: Anatomical context is an important component of soft-tissue, multi-organ segmentation in cadaveric, NCE CT imaging of the whole body. Different amounts of anatomical contexts are optimal depending on the size, position, and surrounding tissue of the organ.

Clinical implications of the distinct anatomy and innervation of the long head biceps tendon.

The proximal long head of the biceps tendon (LHBT) has been recognized as a well-known cause of anterior shoulder pain. Previous studies have identified a heterogeneous distribution of nerve fibers in the tendon, with a higher abundance of fibers in the proximal and distal thirds of the tendon. This suggests that the proximal portion of the long head biceps tendon may have a different source of innervation than the distal portion. The purpose of this study was to review the innervation of the superior shoulder and identify the proximal source of sensory innervation of the LHBT. The relevant hypothesis was that the suprascapular nerve (SSN) was the proximal source of sensory innervation to the LHBT. Gross and microdissection of eight fresh human cadaver shoulders were performed, with a focus on the distal articular branches of suprascapular nerve (SSN). Utilizing 3.5× magnification loupes, the medial subacromial branch (MSAb), lateral subacromial branch (LSAb), and posterior glenohumeral branch (PGHb) were identified and followed distally to their terminal branches. In all specimens, terminal branches of the lateral subacromial branch supplied the proximal LHBT and the superior labrum. Terminal branches of the posterior glenohumeral branch supplied the posterosuperior labrum and, to a lesser extent, the labral attachment of the LHBT. These findings confirm branches of the suprascapular nerve as the proximal source of sensory innervation to the LHBT. Identification of the suprascapular nerve as a source of proximal innervation of the LHBT may influence clinical decisions related to nonsurgical and surgical intervention, nerve blocks, and nerve ablation procedures.

Joint variant and de novo mutation identification on pedigrees from high-throughput sequencing data.

The analysis of whole-genome or exome sequencing data from trios and pedigrees has been successfully applied to the identification of disease-causing mutations. However, most methods used to identify and genotype genetic variants from next-generation sequencing data ignore the relationships between samples, resulting in significant Mendelian errors, false positives and negatives. Here we present a Bayesian network framework that jointly analyzes data from all members of a pedigree simultaneously using Mendelian segregation priors, yet providing the ability to detect de novo mutations in offspring, and is scalable to large pedigrees. We evaluated our method by simulations and analysis of whole-genome sequencing (WGS) data from a 17-individual, 3-generation CEPH pedigree sequenced to 50× average depth. Compared with singleton calling, our family caller produced more high-quality variants and eliminated spurious calls as judged by common quality metrics such as Ti/Tv, Het/Hom ratios, and dbSNP/SNP array data concordance, and by comparing to ground truth variant sets available for this sample. We identify all previously validated de novo mutations in NA12878, concurrent with a 7× precision improvement. Our results show that our method is scalable to large genomics and human disease studies.

Physiological and metabolic responses of late pregnant women to 40 min of steady-state exercise followed by an oral glucose tolerance perturbation.

We examined the physiological and metabolic responses of 24 active late pregnant women to 40 min of vigorous (95% ventilatory threshold) steady-state treadmill exercise followed by a metabolic perturbation [oral glucose tolerance test (OGTT), 75 g] after exercise. Heart rate and respiratory measures were taken throughout exercise, and blood samples were collected during exercise and every 30 min during the 2-h OGTT. Values were compared with those for a group of physically active nonpregnant women (n = 16) in the luteal phase of the menstrual cycle. Although late pregnant women were heavier, they performed the same work rate (182 vs. 208 W, P > 0.05), with the same oxygen pulse, but responded to the exercise with a blunted heart rate and relative oxygen consumption, with less carbon dioxide expired, possibly due to pregnancy-related adaptations in heart efficiency. Resting glucose concentrations were the same between groups, but by 40 min of exercise (3.8 ± 0.1 vs. 4.6 ± 0.1 mmol/l) and into 15 min of recovery (4.3 ± 0.2 vs. 5.0 ± 0.1 mmol/l), glucose concentrations were diminished in late pregnant women (P ≤ 0.05, respectively). The pregnancy-induced delay of glucose uptake was seen in response to the postexercise OGTT compared with the nonpregnant women, but insulin sensitivity (ISI) remained (7.4 ± 0.9 vs. 9.7 ± 1.4 ISI, P > 0.05, respectively), with the preservation of the sensitivity of lipolysis inhibition of nonesterified free fatty acids to insulin. These adaptations may be fetoprotective, because our research suggests that 40 min of continuous treadmill exercise is well tolerated by physically active pregnant women. No adverse effects on birth outcome (3.53 ± 0.08 kg birth weight; 39.6 ± 0.33 wk gestational age) were observed.

Dietary intake, weight gain, and birth outcomes of physically active pregnant women: a pilot study.

Pregnant women have increased energy and nutrient needs. Exercise also increases energy and glucose demands. This pilot study examined whether women who were physically active during pregnancy met their dietary needs, gained weight adequately, avoided hypoglycemia, and had normal birth outcomes. Based on a convenience sample, 11 active women were studied from 18 weeks gestation to delivery. They exercised for 40 min at 70% VO2 peak, 3-4 times per week. Weekly measures included capillary blood glucose concentrations, body mass, and food records. Birth outcome measures included baby mass, length, and activity, pulse, grimace, appearance, and respiration (APGAR) scores. Our findings suggest that these subjects had lower average energy intake (8680 +/- 490 kJ/d) than recommended for the 2nd and 3rd trimesters (13,020 +/-185 kJ/d; 13,480 +/- 185 kJ/d, respectively, p < 0.0001). Protein requirements for pregnancy were exceeded. Average intakes of iron (19 +/- 2 mg/d) and folate (356 +/- 33 microg/d) were lower than Recommended Dietary Allowances (RDAs) (27 mg/d and 600 microg/d, respectively) and milk products consumed were less than the recommended daily servings. Four women gained less weight per week than recommended for pregnancy. No hypoglycemia occurred post-exercise. Birth outcome measures were within normal ranges. Comparative studies with a larger number of subjects are required to determine if active pregnant women have adequate nutrient intakes and weight gain.

VO2peak prediction and exercise prescription for pregnant women.

PURPOSE: The present study was designed to develop and validate a prediction equation for peak oxygen consumption VO2peak) using a progressive treadmill test and to refine the current target HR exercise guidelines for pregnancy (PARmed-X for Pregnancy). METHODS: One hundred fifty-six women between 16 and 22 wk of gestation performed the test to volitional fatigue (peak exercise test). Data from every fourth subject were used to form the cross-validation group. The women were separated into two age groups; 20-29 (N = 60) and 30-39 (N = 96) yr of age and then further separated into fit (VO2peak at the top 25th percentile), unfit (VO2peak at the bottom 25th percentile), and active (between these two ranges). HR and VO2peak values were used in the regression equation to predict target HR ranges at 60 and 80% VO2peak. RESULTS: The prediction equation (R2 = 0.72, R2adjusted = 0.71 and SEE = 2.7) was compared with cross validation (N = 39; P = 0.78). Fit women had a VO2peak > or = 27.2 mL.kg(-1).min(-1) and > or = 26.1 mL.kg.min for ages 20-29 and 30-39 yr, respectively, representing the top 25th percentile. Unfit women had a VO2peak of < or = 21.0 mL.kg(-1).min(-1) and < or = 19.6 mL.kg(-1).min(-1), respectively, representing the bottom 25th percentile. HR/VO2peak regression lines for each fitness level were used to generate the target HR zones in each age group. CONCLUSION: This is the first study to provide a validated prediction equation of VO2peak for pregnant women using a progressive treadmill exercise test. The defined target HR zones based on age and the appropriate fitness levels can be used for exercise prescription in healthy pregnant women.