School of Medicine

Alopecia areata is thought to be a T-cell mediated and cytokine mediated autoimmune disease that results in non-scarring hair loss. Poliosis has been described as a localized depigmentation of hair caused by a deficiency of melanin in hair follicles. A 57-year-old man with a history of alopecia areata developed white hair regrowth in areas of previous hair loss. We retrospectively reviewed the medical literature using PubMed, searching: (1) alopecia areata and (2) poliosis. Poliosis may be associated with autoimmune diseases including alopecia areata, as described in our case. However, it is also reported in patients who have cutaneous lesions, genetic syndromes, infections, medication use, and trauma. Hair regrowth following alopecia areata may be associated with poliosis. We hypothesize that the incidence of poliosis in areas of previous alopecia areata-related hair loss may be greater than reflected in the published literature.

Cannabis use disorder (CUD) is associated with sexually dimorphic behavioral and neurobiological effects, but sex differences in a broader sampling of brain structures in CUD assessed relative to normative reference values have not been examined. Here, we assessed sex differences in brain regions measured via 3 T MRI in 72 adults (50 males, 22 females) with CUD. T1-weighted images, segmented via FreeSurfer, were used to derive Normative Morphometry Imaging Statistics z-scores (accounting for age, sex, intracranial volume, and image quality). Z-scores were then compared between sexes and associated with behavioral data. We found that average z-scores were within normative ranges for both sexes. There were no sex differences in total brain, cerebral white matter, and subcortical gray matter z-scores, but total cortical thickness z-scores were greater in females. Fourteen cortical regions surrounding the central and lateral sulci had greater z-scores in females than in males, but the medial orbitofrontal cortex z-score was greater in males. Of these regions, 3 were positively correlated with cannabis-related problems. Findings suggest sexual dimorphism in brain structure in CUD primarily in the frontal, medial parietal, and superior temporal lobes, with some association with cannabis-related problems even in the context of normative brain structure. Future research is needed to clarify causal mechanisms of morphometric differences in CUD.

Background: Oxycodone has an elevated abuse liability profile compared to other prescription opioid medications. However, many human and rodent metabolomics studies have not been specifically focused on oxycodone. Objectives: Investigating metabolomics changes associated with oxycodone exposure can provide insights into biochemical mechanisms of the addiction cycle and prognosis prediction. Methods: Plasma samples from 16 rats at pre-exposure and intoxication time points were profiled on the Metabolon platform. A total of 941 metabolites were characterized. We employed a k-Nearest Neighbor imputation to impute metabolites with low levels of missingness and binarized metabolites with moderate levels of missingness, respectively. Results: Of the 136 binarized metabolites, 6 showed differential abundance (FDR < 0.05), including 5 that were present at pre-exposure but absent at intoxication (e.g., adenine), while linoleamide (18:2n6) exhibited the opposite behavior. Among the 798 metabolites with low levels of missingness, 364 showed significant changes between pre-exposure and intoxication (FDR < 0.01), including succinate, oleamide, and sarcosine. We identified four pathways, including tryptophan metabolism, that were nominally enriched among the metabolites that change with oxycodone exposure (p < 0.05). Furthermore, we identified several metabolites that showed nominal correlations with the Addiction Index (composite of oxycodone behaviors): 17 at pre-exposure and 8 at intoxication. In addition, the changes in abundance between pre-exposure and intoxication time points of 9 metabolites were nominally correlated with the Addiction Index, including sphingomyelins, methylhistidines, and glycerols. Conclusions: In summary, not only were we able to capture oxy-induced changes in metabolic pathways using easily accessible blood samples, but we also demonstrated the potential of blood metabolomics to better understand addiction liability.

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Results of the impact of reading books and viewing television on neurodevelopment have been mixed, without definitive evaluation to date. Using data from 11,875 US adolescents in the Adolescent Brain and Cognitive Development (ABCD) study, we investigated the associations between reading and television viewing on brain morphology and neurocognitive performance. After quality control, 8,125 participants' MRI scans and cognitive tests were analyzed in relation to their reading and TV habits. Greater reading time was associated with higher cognitive performance and regionally-selective increases in cortical area, while greater TV viewing had a much smaller association with lower cognitive performance and decreased cortical area. Regionally, areas of spatial overlap in associations included the lateral temporal, inferior parietal, and inferior frontal lobes, while significant associations in the ventral and inferior temporal cortex and cingulate cortex were unique to reading habits. These relationships persisted after adjusting for demographics, socioeconomic factors, genetic ancestry, and imaging factors. The magnitude of reading associations exceeded those of TV viewing and was similar to established contributions of parental income and education on neurodevelopment. This study provides a comprehensive evaluation of how these behaviors correlate with early adolescent brain development across a large diverse population.

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BACKGROUND: Systemic inflammation has been associated with lower neurobehavioral performance in diverse populations, yet the evidence in adolescents remains lacking. Cytokines can alter neural network activity to induce neurocognitive changes. This work seeks to investigate the association between inflammation and neurobehavior in adolescents living in a rural region of Ecuador. METHODS: We examined 535 adolescents in rural communities of Ecuador (ESPINA study), 508 of which had neurobehavioral assessments (NEPSY-II) and circulating plasma levels of inflammatory markers (CRP, IL-6, TNF-⍺, sICAM-1, sVCAM-1, SAA, and sCD14). Associations between inflammatory biomarker concentrations and neurobehavioral scores were examined using adjusted bivariate semi-parametric models with generalized estimating equations. A partial least squares regression approach was used to create composite variables from multiple inflammation biomarkers and model their association with cognitive outcomes. RESULTS: Higher sCD14 and TNF-α concentrations were significantly associated with lower social perception scores, by -0.465 units (95% CI: -0.80, -0.13) and -0.418 units (-0.72, -0.12) for every 50% increase in inflammatory marker concentration, respectively. Similarly, every 50% increase in the inflammation summary score was associated with a significantly lower Social Perception score by -0.112 units (-0.19, -0.03). A greater inflammatory composite variable from seven markers was associated with lower scores in language (β = -0.11, p = 0.043), visuospatial processing (β = -0.15, p = 0.086), and social perception (β = -0.22, p = 0.005) domains. CONCLUSIONS: Higher levels of inflammation were associated with lower neurobehavioral performance in adolescents, especially with social perception. In addition, using a robust analytic method to examine an association between a composite inflammatory variable integrating seven markers led to additional findings, including the domains of language and visuospatial processing. A longitudinal follow-up of such investigations could unveil potential changes in inflammation-neurobehavior performance links through developmental stages and intervention opportunities.

Risk of U.S. Army soldier suicide-related behaviors increases substantially after separation from service. As universal prevention programs have been unable to resolve this problem, a previously reported machine learning model was developed using pre-separation predictors to target high-risk transitioning service members (TSMs) for more intensive interventions. This model is currently being used in a demonstration project. The model is limited, though, in two ways. First, the model was developed and trained in a relatively small cross-validation sample (n = 4044) and would likely be improved if a larger sample was available. Second, the model provides no guidance on subtyping high-risk TSMs. This report presents results of an attempt to refine the model to address these limitations by re-estimating the model in a larger sample (n = 5909) and attempting to develop embedded models for differential risk of post-separation stressful life events (SLEs) known to mediate the association of model predictions with post-separation nonfatal suicide attempts (SAs; n = 4957). Analysis used data from the Army STARRS Longitudinal Surveys. The revised model improved prediction of post-separation SAs in the first year (AUC = 0.85) and second-third years (AUC = 0.77) after separation, but embedded models could not predict post-separation SLEs with enough accuracy to support intervention targeting.