Gene Profiling of Nucleus Basalis Tau Containing Neurons in Chronic Traumatic Encephalopathy: A Chronic Effects of Neurotrauma Consortium Study.

Military personnel and athletes exposed to traumatic brain injury may develop chronic traumatic encephalopathy (CTE). Brain pathology in CTE includes intracellular accumulation of abnormally phosphorylated tau proteins (p-tau), the main constituent of neurofibrillary tangles (NFTs). Recently, we found that cholinergic basal forebrain (CBF) neurons within the nucleus basalis of Meynert (nbM), which provide the major cholinergic innervation to the cortex, display an increased number of NFTs across the pathological stages of CTE. However, molecular mechanisms underlying nbM neurodegeneration in the context of CTE pathology remain unknown. Here, we assessed the genetic signature of nbM neurons containing the p-tau pretangle maker pS422 from CTE subjects who came to autopsy and received a neuropathological CTE staging assessment (Stages II, III, and IV) using laser capture microdissection and custom-designed microarray analysis. Quantitative analysis revealed dysregulation of key genes in several gene ontology groups between CTE stages. Specifically, downregulation of the nicotinic cholinergic receptor subunit ß-2 gene (CHRNB2), monoaminergic enzymes catechol-O-methyltransferase (COMT) and dopa decarboxylase (DDC), chloride channels CLCN4 and CLCN5, scaffolding protein caveolin 1 (CAV1), cortical development/cytoskeleton element lissencephaly 1 (LIS1), and intracellular signaling cascade member adenylate cyclase 3 (ADCY3) was observed in pS422-immunreactive nbM neurons in CTE patients. By contrast, upregulation of calpain 2 (CAPN2) and microtubule-associated protein 2 (MAP2) transcript levels was found in Stage IV CTE patients. These single-population data in vulnerable neurons indicate alterations in gene expression associated with neurotransmission, signal transduction, the cytoskeleton, cell survival/death signaling, and microtubule dynamics, suggesting novel molecular pathways to target for drug discovery in CTE.

Patterns of Twitter Behavior Among Networks of Cannabis Dispensaries in California.

BACKGROUND: Twitter represents a social media platform through which medical cannabis dispensaries can rapidly promote and advertise a multitude of retail products. Yet, to date, no studies have systematically evaluated Twitter behavior among dispensaries and how these behaviors influence the formation of social networks. OBJECTIVES: This study sought to characterize common cyberbehaviors and shared follower networks among dispensaries operating in two large cannabis markets in California. METHODS: From a targeted sample of 119 dispensaries in the San Francisco Bay Area and Greater Los Angeles, we collected metadata from the dispensary accounts using the Twitter API. For each city, we characterized the network structure of dispensaries based upon shared followers, then empirically derived communities with the Louvain modularity algorithm. Principal components factor analysis was employed to reduce 12 Twitter measures into a more parsimonious set of cyberbehavioral dimensions. Finally, quadratic discriminant analysis was implemented to verify the ability of the extracted dimensions to classify dispensaries into their derived communities. RESULTS: The modularity algorithm yielded three communities in each city with distinct network structures. The principal components factor analysis reduced the 12 cyberbehaviors into five dimensions that encompassed account age, posting frequency, referencing, hyperlinks, and user engagement among the dispensary accounts. In the quadratic discriminant analysis, the dimensions correctly classified 75% (46/61) of the communities in the San Francisco Bay Area and 71% (41/58) in Greater Los Angeles. CONCLUSIONS: The most centralized and strongly connected dispensaries in both cities had newer accounts, higher daily activity, more frequent user engagement, and increased usage of embedded media, keywords, and hyperlinks. Measures derived from both network structure and cyberbehavioral dimensions can serve as key contextual indicators for the online surveillance of cannabis dispensaries and consumer markets over time.

Predictors of Neurocognitive Syndromes in Combat Veterans.

Traumatic brain injury, depression and posttraumatic stress disorder (PTSD) are neurocognitive syndromes often associated with impairment of physical and mental health, as well as functional status. These syndromes are also frequent in military service members (SMs) after combat, although their presentation is often delayed until months after their return. The objective of this prospective cohort study was the identification of independent predictors of neurocognitive syndromes upon return from deployment could facilitate early intervention to prevent disability. We completed a comprehensive baseline assessment, followed by serial evaluations at three, six, and 12 months, to assess for new-onset PTSD, depression, or postconcussive syndrome (PCS) in order to identify baseline factors most strongly associated with subsequent neurocognitive syndromes. On serial follow-up, seven participants developed at least one neurocognitive syndrome: five with PTSD, one with depression and PTSD, and one with PCS. On univariate analysis, 60 items were associated with syndrome development at p < 0.15. Decision trees and ensemble tree multivariate models yielded four common independent predictors of PTSD: right superior longitudinal fasciculus tract volume on MRI; resting state connectivity between the right amygdala and left superior temporal gyrus (BA41/42) on functional MRI; and single nucleotide polymorphisms in the genes coding for myelin basic protein as well as brain-derived neurotrophic factor. Our findings require follow-up studies with greater sample size and suggest that neuroimaging and molecular biomarkers may help distinguish those at high risk for post-deployment neurocognitive syndromes.

Estimating model-adjusted risks, risk differences, and risk ratios from complex survey data.

There is increasing interest in estimating and drawing inferences about risk or prevalence ratios and differences instead of odds ratios in the regression setting. Recent publications have shown how the GENMOD procedure in SAS (SAS Institute Inc., Cary, North Carolina) can be used to estimate these parameters in non-population-based studies. In this paper, the authors show how model-adjusted risks, risk differences, and risk ratio estimates can be obtained directly from logistic regression models in the complex sample survey setting to yield population-based inferences. Complex sample survey designs typically involve some combination of weighting, stratification, multistage sampling, clustering, and perhaps finite population adjustments. Point estimates of model-adjusted risks, risk differences, and risk ratios are obtained from average marginal predictions in the fitted logistic regression model. The model can contain both continuous and categorical covariates, as well as interaction terms. The authors use the SUDAAN software package (Research Triangle Institute, Research Triangle Park, North Carolina) to obtain point estimates, standard errors (via linearization or a replication method), confidence intervals, and P values for the parameters and contrasts of interest. Data from the 2006 National Health Interview Survey are used to illustrate these concepts.

Prenatal developmental toxicity evaluation of 2',3'-dideoxyinosine (ddI) and 2',3'-didehydro-3'-deoxythymidine (d4T) co-administered to Swiss Albino (CD-1) mice.

BACKGROUND: In pregnant women, antiretroviral drugs improve maternal health and reduce vertical transmission of human immunodeficiency virus to the infant. However, few nonclinical studies have examined the potential for adverse drug interactions. METHODS: On gestational days (GD) 6-16, mice were dosed with vehicle, ddI (360, 1440, or 2,880 mg/kg/day, p.o.), d4T (60, 240, or 480), or ddI/d4T combinations (360/60, 1,440/240, or 2,880/480). Daily doses were divided into two equal parts that were administered >or=6-hr apart. Body weight, clinical signs, and feed consumption were monitored. Pregnancies (22-24/group) were confirmed at necropsy. Maternal liver and gravid uterine weights (GUW), uterine implants (resorption, live or dead fetus), fetal body weight, gender, and morphologic anomalies (external, visceral, skeletal) were recorded. RESULTS: Maternal body weight, clinical signs, and GUW were unaffected. Maternal weight change corrected for GUW was greater than controls at 60 and 480 d4T. Relative feed consumption during treatment was increased relative to controls at 1,440 and 2,880 ddI and 2,880/480 ddI/d4T. Relative maternal liver weight was elevated above controls at 240 and 480 d4T and 2,880/480 ddI/d4T, and above the constituent dose of ddI at 1,440/240 and 2,880/480 ddI/d4T. Liver weight was not affected by ddI and there was no significant drug interaction. Prenatal mortality and morphologic anomalies were not increased. Fetal body weight showed only a decreasing trend for ddI/d4T, no effect for ddI or d4T, and no statistically significant drug interaction. CONCLUSIONS: In pregnant mice, ddI/d4T combinations were not associated with well-defined developmental toxicity or adverse drug interactions.