Aspartate-glutamate carrier 1 (AGC1) is one of two exchangers within the malate-aspartate shuttle. AGC1 is encoded by the SLC25A12 gene. Three patients with pathogenic variants in SLC25A12 have been reported in the literature. These patients were clinically characterized by neurodevelopmental delay, epilepsy, hypotonia, cerebral atrophy, and hypomyelination; however, there has been discussion in the literature as to whether this hypomyelination is primary or secondary to a neuronal defect. Here we report a 12-year-old patient with variants in SLC25A12 and magnetic resonance imaging (MRI) at multiple ages. Novel compound heterozygous, recessive variants in SLC25A12 were identified: c.1295C>T (p.A432V) and c.1447-2_1447-1delAG. Clinical presentation is characterized by severe intellectual disability, nonambulatory, nonverbal status, hypotonia, epilepsy, spastic quadriplegia, and a happy disposition. The serial neuroimaging findings are notable for cerebral atrophy with white matter involvement, namely, early hypomyelination yet subsequent progression of myelination. The longitudinal MRI findings are most consistent with a leukodystrophy of the leuko-axonopathy category, that is, white matter abnormalities that are most suggestive of mechanisms that result from primary neuronal defects. We present here the first case of a patient with compound heterozygous variants in SLC25A12, including brain MRI findings, in the oldest individual reported to date with this neurogenetic condition.
Computed tomography (CT) is commonly used to assess traumatic brain injury (TBI) in the emergency department (ED). Radiologists at a Level 1 trauma center implemented a novel tool, the RADiology CATegorization (RADCAT) system, to communicate injuries to clinicians in real time. Using this categorization system, we aimed to determine the rates of positive head CTs among pediatric and adult ED patients evaluated for TBI.We performed a retrospective analysis of all patients who received a head CT to assess for TBI. We classified head CTs using the RADCAT tool. On a 5-point scale, scores of 3 or less are considered normal or routine. Scores of 4-5 are considered high priority, representing findings such as intracranial bleeding.Of the 5,341 head CT's obtained during the study period, 992 (18.5%) had high priority results (scores 4-5). A large number of pediatric studies, 30.8%, were positive for high priority results. Among the adult population, 18.0 % contained high priority results.The pediatric population had a higher rate of high priority reads among those undergoing non- contrast head CT for TBI compared to adult patients.
We deflne a methodology for training deformable shape mod- els as a basis for studying anatomic shape spaces. We present a complete implementation using a sampled medial representation and provide quan- titative results of the method applied to both synthetic and real medical images.
Healthcare providers rely on complex biomedical devices to assess, treat, and monitor patients. Ongoing research efforts are attempting to generate and implement better algorithms and mechanisms to ensure the early, accurate, automated, and clinically meaningful recognition of patterns and changes in patient health and pathology. Effecting such evolutionary advances in patient monitoring will likely require large collections of high-resolution physiologic parameter datasets from a broad spectrum of patients. As part of a research program to scientifically improve patient monitoring (with a focus on alarm fatigue mitigation), investigators developed the Medical Technology Interface-Open/Research toolkit with modular conduit components that provide the following capabilities: 1) access to select bedside monitor physiologic signals in real-world clinical settings for near-real-time acquisition, storage, and export of high-resolution patient datastreams in a portable format (.json); 2) establishment of a safe, parallel test environment at the bedside for experimental datastream analyses in a research framework. Deployment and interfacing of toolkit elements with off-the-shelf software solutions in a live emergency department setting enabled the construction of a bedside clinical informatics (BCI) research pipeline infrastructure that featured 1) indexing, search/query, and retrieval of datastreams for sophisticated analyses, experimental processing, and algorithm development; and 2) dataset visualization for expert adjudication of datastream interpretability, alarm clinical significance and severity, and experimental algorithm performance. In order to help institute a collaborative biomedical engineering research resource, this article shares details of the active ED BCI data pipeline and presents preliminary examples of ongoing multimodal data fusion applications.
Accurate non-contact acquisition of patient vital signs will advance emergency care. In order to assess promising candidate technologies., an observational study was conducted with healthy volunteers to test two hypotheses: 1. Video photoplethysmography and motion analysis (vPPG-MA) and infrared thermography (IR) will accurately and concurrently measure heart rate (HR) and respiratory rate (RR), and body temperature, respectively. 2. Non-contact approaches will exhibit comparable and reliable performance against standard contact cardiorespiratory monitors (CM). HR and RR were measured with CM and vPPG-MA; core and surface temperatures were obtained using oral thermometry and two IR cameras, respectively. Subjects were videorecorded at rest; during sustained exercise at 50%, 60%, and 70% of age-predicted maximum HR; and 1, 3, and 5 min post-exertion. vPPG-MA HR and RR measurements were calculated for video segments corresponding to ED use-cases: Triage (unprimed) 30s check, Routine 30s check, Abbreviated "Spot" 10s check, and Full 60s check. Descriptive statistics and Bland-Altman analyses were performed on vPPG-MA and IR measurements against synchronous CM measurements. Thirty volunteers exhibited a HR range of 43-146bpm, a RR range of 8-29bpm, and an oral temperature range of 96.2-99.5°F on CM. vPPG-MA obtained 972 (98.2% of scheduled) HR and 591 (98.5%) RR measurements; mean differences between Full 60s vPPG-MA and CM were -0.9±5.5bpm (-0.9±5.3%; 95% CI: -11.6-9.8bpm) for HR, and 0.9±3.1bpm (4.8±17.6%; -5.1-6.9bpm) for RR; other video segments performed similarly. IR acquired temperatures ~4°F lower than oral thermometers. vPPG-MA and IR thermography successfully measured select vital signs concurrently. vPPG-MA`s observed level of agreement with CM, along with temperature offsets identified for IR-based thermometry, have set the foundation for live ED clinical studies.
Deformable shape models require correspondence across the training population in order to generate a statistical model for use as a future geometric prior. Traditional methods use flxed sampling and assume correspondence, or attempt to induce correspondence by min- imizing variance. In this paper, we deflne a training methodology for sampled medial deformable shape models (m-reps) which generates cor- respondence implicitly via a geometric prior. We present quantitative results of the method applied to real medical images. Automatic segmentation of medical images is a vital step in processing large populations for quantitative study of biological shape variability. In this paper, we present a methodology for statistically training the geometry of deformable model templates that can be used as geometric prior and basis for intensity train- ing for automatic segmentation of gray images. Our method frames the problem as a special case of the general segmentation problem. Given a data set of hu- man or otherwise expertly segmented training cases, we flt models to the labeled data, and then create our template by statistical analysis of the flt population. The fltting is an optimization over model parameters in a Bayesian framework, searching for the model with the highest posterior probability of fltting the data. Our posterior is decomposed into data likelihood and geometric prior terms. The data likelihood accounts for both image match and optional landmark match. The geometric prior encourages models to stay in a legal shape-space. We de- scribe an implementation of the method using m-reps and present results showing that the method is accurate and yields models suitable for statistical analysis. Deformable Models We desire a statistical model for a population of training data. Probabilistic deformable models describe shape variability via a probability distribution on the shape-space. Under the Gaussian model, the distribution of the training data can be modeled by the mean, a point in the space, and several eigenmodes of deformation. This model describes all possible shapes in the training data, and by extension, estimates the actual ambient shape-space from which the training data is drawn. This statistical model can then be used as
