Notes

Thorough glycoproteomics excels fresh mild on the complexity as well as magnitude of glycosylation throughout archaea.
Achieving a specific diagnosis in leukodystrophies is often difficult due to clinical and genetic heterogeneity. Mitochondrial defects cause 5%-10% of leukodystrophies. Our objective was to define MR imaging features commonly shared by mitochondrial leukodystrophies and to distinguish MR imaging patterns related to specific genetic defects.

One hundred thirty-two patients with a mitochondrial leukodystrophy with known genetic defects were identified in the data base of the Amsterdam Leukodystrophy Center. Numerous anatomic structures were systematically assessed on brain MR imaging. Additionally, lesion characteristics were scored. Statistical group analysis was performed for 57 MR imaging features by hierarchic testing on clustered genetic subgroups.

MR imaging features indicative of mitochondrial disease that were frequently found included white matter rarefaction (
= 50 patients), well-delineated cysts (
= 20 patients), T2 hyperintensity of the middle blade of the corpus callosum (
= 85 patient in future patients.
In a large group of patients with a mitochondrial leukodystrophy, general MR imaging features suggestive of mitochondrial disease were found. Additionally, we identified several MR imaging patterns correlating with specific genotypes. Recognition of these patterns facilitates the diagnosis in future patients.
Cerebral venous oxygen saturation can be used as an indirect measure of brain health, yet it often requires either an invasive procedure or a noninvasive technique with poor sensitivity. We aimed to test whether cerebral venous oxygen saturation could be measured using quantitative susceptibility mapping, an MR imaging technique, in 3 distinct groups healthy term neonates, injured term neonates, and preterm neonates.

We acquired multiecho gradient-echo MR imaging data in 16 neonates with perinatal asphyxia and moderate or severe hypoxic-ischemic encephalopathy (8 term age average, 40.0 [SD, 0.8]  weeks' gestational age; 8 preterm, 33.5 [SD, 2.0]  weeks' gestational age) and in 8 healthy term-age controls (39.3 [SD, 0.6] weeks, for a total of
 = 24. Data were postprocessed as quantitative susceptibility mapping images, and magnetic susceptibility was measured in cerebral veins by thesholding out 99.95% of lower magnetic susceptibility values.

The mean magnetic susceptibility value of the cerebral veinsnificantly different between neonates or healthy controls.Ectopic cerebellar tissue is a rare entity likely secondary to multiple, interacting, developmental errors during embryogenesis. Multiple sites of ectopic cerebellar tissue have been reported, including extracranial locations; however, an intracranial location is most common. We report on the MR imaging findings of a multi-institutional series of 7 ectopic cerebellar tissue cases (2 males, 4 females, 1 fetal) ranging from 22 weeks 5 days' gestational age to 18 years of age. All cases of ectopic cerebellar tissue were diagnosed incidentally, while imaging was performed for other causes. Ectopic cerebellar tissue was infratentorial in 6/7 patients and supratentorial in 1/7 patients. All infratentorial ectopic cerebellar tissue was connected with the brain stem or cerebellum. MR imaging signal intensity was identical to the cerebellar gray and white matter signal intensity on all MR imaging sequences in all cases. Ectopic cerebellar tissue should be considered in the differential diagnoses of extra-axial masses with signal characteristics similar to those of the cerebellum. selleck Surgical biopsy or resection is rarely necessary, and in most cases, MR imaging is diagnostic.
Multidetector CT has emerged as the standard of care imaging technique to evaluate cervical spine trauma. Our aim was to evaluate the performance of a convolutional neural network in the detection of cervical spine fractures on CT.

We evaluated C-spine, an FDA-approved convolutional neural network developed by Aidoc to detect cervical spine fractures on CT. A total of 665 examinations were included in our analysis. Ground truth was established by retrospective visualization of a fracture on CT by using all available CT, MR imaging, and convolutional neural network output information. The ĸ coefficients, sensitivity, specificity, and positive and negative predictive values were calculated with 95% CIs comparing diagnostic accuracy and agreement of the convolutional neural network and radiologist ratings, respectively, compared with ground truth.

Convolutional neural network accuracy in cervical spine fracture detection was 92% (95% CI, 90%-94%), with 76% (95% CI, 68%-83%) sensitivity and 97% (95% CI, 95%nesses of the convolutional neural network is essential before its successful incorporation into clinical practice. Further refinements in sensitivity will improve convolutional neural network diagnostic utility.
The increasing burden of noncommunicable diseases that are prevalent in low- and middle-income countries (LMICs) is largely attributed to modifiable behavioral risk factors such as unhealthy diets and insufficient physical activity (PA). The adolescent stage, defined as 10 to 24 years of age, is an important formative phase of life and offers an opportunity to reduce the risk of noncommunicable diseases across the life course and for future generations.

The aim of this paper is to describe a protocol for a study using a convergent mixed methods design to explore exposures in the household, neighborhood, school, and the journey from home to school that may influence diet and PA behaviors in adolescents from LMICs.

Male and female adolescents (n≥150) aged between 13 and 24 years will be recruited from selected high schools or households in project site countries to ensure the socioeconomic diversity of perspectives and experiences at the individual, home, and neighborhood levels. The project will be condutes (South Africa, Cameroun, and Jamaica in 2019, and Kenya in 2020). As of December 23, 2020, we had completed data collection from adolescents (n≥150) in all the country sites, except Kenya, and data collection for the subgroup (n=30-45) is ongoing. Data analysis is ongoing and the output of findings from the study described in this protocol is expected to be published by 2022.

This project protocol contributes to research that focuses on adolescents and the socioecological determinants of food intake and PA in LMIC settings. It includes innovative methodologies to interrogate and map the contexts of these determinants and will generate much-needed data to understand the multilevel system of factors that can be leveraged through upstream and downstream strategies and interventions to improve health outcomes.

DERR1-10.2196/26739.
DERR1-10.2196/26739.
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