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Genetic variants in SLC16A2, encoding the thyroid hormone transporter MCT8, can cause intellectual and motor disability and abnormal serum thyroid function tests, known as MCT8 deficiency. selleckchem The C-terminal domain of MCT8 is poorly conserved, which complicates prediction of the deleteriousness of variants in this region. We studied the functional consequences of 5 novel variants within this domain and their relation to the clinical phenotypes.
We enrolled male subjects with intellectual disability in whom genetic variants were identified in exon 6 of SLC16A2. The impact of identified variants was evaluated in transiently transfected cell lines and patient-derived fibroblasts.
Seven individuals from 5 families harbored potentially deleterious variants affecting the C-terminal domain of MCT8. Two boys with clinical features considered atypical for MCT8 deficiency had a missense variant [c.1724A>G;p.(His575Arg) or c.1796A>G;p.(Asn599Ser)] that did not affect MCT8 function in transfected cells or patiente clinical guidance in the assessment of the pathogenicity of variants within the C-terminal domain of MCT8.
To highlight geographic differences and the socio-structural determinants of SARS-CoV-2 test positivity within Los Angeles County (LAC).
A geographic information system was used to integrate, map, and analyze SARS-CoV-2 testing data reported by LAC DPH, and data from the American Community Survey. Structural determinants included race/ethnicity, poverty, insurance status, education, population and household density. We examined which factors were associated with positivity rates, using a 5% test positivity threshold, with spatial analysis and spatial regression.
Between 1 March and 30 June 2020 there were 843,440 SARS-CoV-2 tests and 86,383 diagnoses reported, for an overall positivity rate of 10.2% within the study area. Communities with high proportions of Latino/a residents, those living below the federal poverty line and with high household densities had higher crude positivity rates. Age- adjusted diagnosis rates were significantly associated with the proportion of Latino/as, individuals living below the poverty line, population, and household density.
There are significant local variations in test positivity within LAC and several socio-structural determinants contribute to ongoing disparities. Public health interventions, beyond shelter in place, are needed to address and target such disparities.
There are significant local variations in test positivity within LAC and several socio-structural determinants contribute to ongoing disparities. Public health interventions, beyond shelter in place, are needed to address and target such disparities.
Higher maternal prepregnancy body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) is associated with adverse long-term outcomes for offspring, including obesity, poorer cognitive and social abilities, and increased risk of psychiatric disorders. Less clear is whether higher maternal BMI disrupts fetal growth and brain development.
To investigate the association of maternal prepregnancy BMI with fetal growth and neonatal functional connectivity.
This prospective longitudinal cohort study was conducted from 2012 to 2017. Participants included nulliparous pregnant adolescent and young adult women, aged 14 to 19 years who were recruited in the second trimester through Columbia University Irving Medical Center and Weill Cornell Medical College. Women received routine prenatal care and had no major health problems at the time of recruitment. Data were analyzed from January 2018 to March 2020.
Maternal prepregnancy BMI.
The main outcomes were fetal growth, measured anal BMI was associated with greater local thalamic (both hemispheres) and lower frontothalamic connectivity.
These results suggest that maternal prepregnancy BMI was associated with the development of regulation of body weight and thalamic functional brain connectivity in offspring even during fetal development.
These results suggest that maternal prepregnancy BMI was associated with the development of regulation of body weight and thalamic functional brain connectivity in offspring even during fetal development.
Machine-learning algorithms offer better predictive accuracy than traditional prognostic models but are too complex and opaque for clinical use.
To compare different machine learning methods in predicting overall mortality in cirrhosis and to use machine learning to select easily scored clinical variables for a novel cirrhosis prognostic model.
This prognostic study used a retrospective cohort of adult patients with cirrhosis or its complications seen in 130 hospitals and affiliated ambulatory clinics in the integrated, national Veterans Affairs health care system from October 1, 2011, to September 30, 2015. Patients were followed up through December 31, 2018. Data were analyzed from October 1, 2017, to May 31, 2020.
Potential predictors included demographic characteristics; liver disease etiology, severity, and complications; use of health care resources; comorbid conditions; and comprehensive laboratory and medication data. Patients were randomly selected for model development (66.7%) and validationanced ensemble gradient boosting. Using the clinical variables identified from simple machine learning in a cirrhosis mortality model produced a new score more transparent than machine learning and more predictive than the MELD-Na score.
The association between poverty and unfavorable cognitive outcomes is robust, but most research has focused on individual household socioeconomic status (SES). There is increasing evidence that neighborhood context explains unique variance not accounted for by household SES.
To evaluate whether neighborhood poverty (NP) is associated with cognitive function and prefrontal and hippocampal brain structure in ways that are dissociable from household SES.
This cross-sectional study used a baseline sample of the ongoing longitudinal Adolescent Brain Cognitive Development (ABCD) Study. The ABCD Study will follow participants for assessments each year for 10 years. Data were collected at 21 US sites, mostly within urban and suburban areas, between September 2019 and October 2018. School-based recruitment was used to create a participant sample reflecting the US population. Data analysis was conducted from March to June 2019.
NP and household SES were included as factors potentially associated with National Institutes of Health Toolbox Cognitive Battery subtests and hippocampal and prefrontal (dorsolateral prefrontal cortex [DLPFC], dorsomedial PFC [DMPFC], superior frontal gyrus [SFG]) volumes.
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