Undiagnosed Disease Network collaborative approach in diagnosing rare disease in a patient with a mosaic CACNA1D variant.

The Undiagnosed Disease Network (UDN) is comprised of clinical and research experts collaborating to diagnose rare disease. The UDN is funded by the National Institutes of Health and includes 12 different clinical sites (About Us, 2022). Here we highlight the success of collaborative efforts within the UDN Clinical Site at Vanderbilt University Medical Center (VUMC) in utilizing a cohort of experts in bioinformatics, structural biology, and genetics specialists in diagnosing rare disease. Our UDN team identified a de novo mosaic CACNA1D variant c.2299T>C in a 5-year-old female with a history of global developmental delay, dystonia, dyskinesis, and seizures. Using a collaborative multidisciplinary approach, our VUMC UDN team diagnosed the participant with Primary Aldosteronism, Seizures, and Neurologic abnormalities (PASNA) OMIM: 615474 due to a rare mosaic CACNA1D variant (O'Neill, 2013). Interestingly, this patient was mosaic, a phenotypic trait previously unreported in PASNA cases. This report highlights the importance of a multidisciplinary approach in diagnosing rare disease.

Efficient Model-Based Anthropometry under Clothing Using Low-Cost Depth Sensors.

Measuring human body dimensions is critical for many engineering and product design domains. Nonetheless, acquiring body dimension data for populations using typical anthropometric methods poses challenges due to the time-consuming nature of manual methods. The measurement process for three-dimensional (3D) whole-body scanning can be much faster, but 3D scanning typically requires subjects to change into tight-fitting clothing, which increases time and cost and introduces privacy concerns. To address these and other issues in current anthropometry techniques, a measurement system was developed based on portable, low-cost depth cameras. Point-cloud data from the sensors are fit using a model-based method, Inscribed Fitting, which finds the most likely body shape in the statistical body shape space and providing accurate estimates of body characteristics. To evaluate the system, 144 young adults were measured manually and with two levels of military ensembles using the system. The results showed that the prediction accuracy for the clothed scans remained at a similar level to the accuracy for the minimally clad scans. This approach will enable rapid measurement of clothed populations with reduced time compared to manual and typical scan-based methods.

MedJEx: A Medical Jargon Extraction Model with Wiki's Hyperlink Span and Contextualized Masked Language Model Score.

This paper proposes a new natural language processing (NLP) application for identifying medical jargon terms potentially difficult for patients to comprehend from electronic health record (EHR) notes. We first present a novel and publicly available dataset with expert-annotated medical jargon terms from 18K+ EHR note sentences (MedJ). Then, we introduce a novel medical jargon extraction (MedJEx) model which has been shown to outperform existing state-of-the-art NLP models. First, MedJEx improved the overall performance when it was trained on an auxiliary Wikipedia hyperlink span dataset, where hyperlink spans provide additional Wikipedia articles to explain the spans (or terms), and then fine-tuned on the annotated MedJ data. Secondly, we found that a contextualized masked language model score was beneficial for detecting domain-specific unfamiliar jargon terms. Moreover, our results show that training on the auxiliary Wikipedia hyperlink span datasets improved six out of eight biomedical named entity recognition benchmark datasets. Both MedJ and MedJEx are publicly available.

A three-dimensional parametric adult head model with representation of scalp shape variability under hair.

Modeling the shape of the scalp and face is essential for the design of protective helmets and other head-borne equipment. However, head anthropometry studies using optical scanning rarely capture scalp shape because of hair interference. Data on scalp shape is available from bald men, but female data are generally not available. To address this issue, scalp shape was digitized in an ethnically diverse sample of 100 adult women, age 18-59, under a protocol that included whole head surface scanning and scalp measurement using a three-dimensional (3D) coordinate digitizer. A combined male and female sample was created by adding 3D surface scans of a similarly diverse sample of 80 bald men. A statistical head shape model was created by standardizing the head scan data. A total of 58 anatomical head landmarks and 12 head dimensions were obtained from each scan and processed along with the scans. A parametric model accounting for the variability of the head shape under the hair as a function of selected head dimensions was developed. The full-variable model has a mean shape error of 3.8 mm; the 95th percentile error was 7.4 mm, which were measured at the vertices. The model will be particularly useful for generating a series of representing a target population as well as for generating subject-specific head shapes along with predicted landmarks and dimensions. The model is publicly available online at http://humanshape.org/head/.

Comparison of body fat estimates using 3D digital laser scans, direct manual anthropometry, and DXA in men.

OBJECTIVES: The purpose of this study was to assess the feasibility of utilizing three dimensional whole body laser surface scanning (3DS) to obtain specific anthropometric measurements to estimate percent body fat (BF). METHODS: Percent BF estimates from 37 male volunteers, of age 18-62 yr, were determined by inputting manual anthropometric (MA) and 3DS anthropometric measurements into the current Army BF prediction equation for males. The results were compared with each other and to BF values from Dual Energy X-ray Absorptiometry (DXA), employed as a reference method. RESULTS: Mean percent BF estimates (+/-SD) derived from MA, 3DS and from DXA were 18.4(+/-3.8), 18.8(+/-3.9), and 18.9(+/-4.7), respectively. Analysis of Variance tests revealed no statistical difference between the mean values. Correlation analysis comparing MA and 3DS derived percent BF estimates to each other and to those measured by DXA revealed moderate to strong Pearson correlation coefficients (r), small to moderate standard errors of the estimate (SEE), and were statistically significant (p < 0.05). CONCLUSIONS: Correlation coefficients and SEE results for this sample were: (1) DXA vs 3DS; r = 0.74, SEE = 3.2, (2) MA vs DXA; r = 0.82, SEE = 2.8, and (3) MA vs 3DS; r = 0.96, SEE = 1.0. Lin's concordance analysis, including Bland-Altman limits of agreement (LOA), revealed statistically significant measurement agreement among the three measurement modalities (p < 0.05). The application of 3DS scanning to estimate percent BF from commonly used anthropometric measurements are in close agreement with BF estimates derived from analogous MA measurements and from DXA scanning.

Whiplash-Associated Disorders: Occupant Kinematics and Neck Morphology.

Synopsis Despite considerable research effort, the incidence of whiplash injury during automotive collisions has continued to rise. This is due, at least in part, to a limited recognition of biomechanical injury mechanisms and factors influencing injury risk. While automotive safety modifications reduced injury risk in some cases, impact on the overall whiplash incidence was limited. This is likely attributable to significant occupant-related differences that have a profound impact on injury risk. Many of those differences were outlined in research studies, and examples include female sex and the associated sex-based anthropometrical variation that can affect seating orientation; cervical spinal posture; and anatomical attributes, including cervical column slenderness and neck muscle morphometry. This review highlights these anatomical attributes and explains, based on biomechanical concepts, the method by which these attributes may alter cervical spine response during automotive rear impacts to affect injury risk. The biomechanical explanations are based on existing studies that have incorporated postmortem human subjects, computational models, and anthropomorphic test devices (ie, crash test dummies), as well as medical imaging in human volunteers. These biomechanical explanations may provide improved understanding of injury risk. J Orthop Sports Phys Ther 2016;46(10):834-844. doi:10.2519/jospt.2016.6846.

Characterization of 3D joint space morphology using an electrostatic model (with application to osteoarthritis).

Joint space morphology can be indicative of the risk, presence, progression, and/or treatment response of disease or trauma. We describe a novel methodology of characterizing joint space morphology in high-resolution 3D images (e.g. cone-beam CT (CBCT)) using a model based on elementary electrostatics that overcomes a variety of basic limitations of existing 2D and 3D methods. The method models each surface of a joint as a conductor at fixed electrostatic potential and characterizes the intra-articular space in terms of the electric field lines resulting from the solution of Gauss' Law and the Laplace equation. As a test case, the method was applied to discrimination of healthy and osteoarthritic subjects (N = 39) in 3D images of the knee acquired on an extremity CBCT system. The method demonstrated improved diagnostic performance (area under the receiver operating characteristic curve, AUC > 0.98) compared to simpler methods of quantitative measurement and qualitative image-based assessment by three expert musculoskeletal radiologists (AUC = 0.87, p-value = 0.007). The method is applicable to simple (e.g. the knee or elbow) or multi-axial joints (e.g. the wrist or ankle) and may provide a useful means of quantitatively assessing a variety of joint pathologies.

Soldier-relevant loads impact lower limb biomechanics during anticipated and unanticipated single-leg cutting movements.

This study quantified how body borne load impacts hip and knee biomechanics during anticipated and unanticipated single-leg cutting maneuvers. Fifteen male military personnel performed a series of single-leg cutting maneuvers with three different load configurations (light, ~6 kg, medium, ~20 kg, and heavy, ~40 kg). Subject-based means of the specific lower limb biomechanical variables were submitted to repeated measures ANOVA to test the main and interaction effects of body borne load and movement type. With body borne load, stance time (P<0.001) increased, while larger hip (P=0.027) and knee flexion (P=0.004), and hip adduction (P<0.001) moments, and decreased hip (P=0.002) and knee flexion (P<0.001), and hip adduction (P=0.003) postures were evident. Further, the hip (P<0.001) and ankle (P=0.024) increased energy absorption, while the knee (P=0.020) increased energy generation with body borne load. During the unanticipated maneuvers, the hip (P=0.009) and knee (P=0.032) increased energy generation, and peak hip flexion moment (P=0.002) increased relative to the anticipated movements. With the body borne load, participants adopted biomechanical patterns that decreased their locomotive ability including larger moments and reduced flexion postures of the lower limb. During the single-leg cut, participants used greater energy absorption from the large, proximal muscles of the hip and greater energy generation from the knee with the addition of load. Participant's performance when carrying a range of loads was not compromised by anticipation, as they did not exhibit the hip and knee kinetic and kinematic adaptations previously demonstrated when reacting to an unplanned stimulus.