Cannabis use in patients with early psychosis is associated with alterations in putamen and thalamic shape

Around half of patients with early psychosis have a history of cannabis use. We aimed to determine if there are neurobiological differences in these the subgroups of persons with psychosis with and without a history of cannabis use. We expected to see regional deflations in hippocampus as a neurotoxic effect and regional inflations in striatal regions implicated in addictive processes. Volumetric, T1w MRIs were acquired from people with a diagnosis psychosis with (PwP + C = 28) or without (PwP − C = 26) a history of cannabis use; and Controls with (C + C = 16) or without (C − C = 22) cannabis use. We undertook vertex‐based shape analysis of the brainstem, amygdala, hippocampus, globus pallidus, nucleus accumbens, caudate, putamen, thalamus using FSL FIRST. Clusters were defined through Threshold Free Cluster Enhancement and Family Wise Error was set at p < .05. We adjusted analyses for age, sex, tobacco and alcohol use. The putamen (bilaterally) and the right thalamus showed regional enlargement in PwP + C versus PwP − C. There were no areas of regional deflation. There were no significant differences between C + C and C − C. Cannabis use in participants with psychosis is associated with morphological alterations in subcortical structures. Putamen and thalamic enlargement may be related to compulsivity in patients with a history of cannabis use

Super-resolution imaging as a method to study GPCR dimers and higher-order oligomers

The study of G protein-coupled receptor (GPCR) dimers and higher-order oligomers has unveiled mechanisms for receptors to diversify signaling and potentially uncover novel therapeutic targets. The functional and clinical significance of these receptor–receptor associations has been facilitated by the development of techniques and protocols, enabling researchers to unpick their function from the molecular interfaces, to demonstrating functional significance in vivo, in both health and disease. Here we describe our methodology to study GPCR oligomerization at the single-molecule level via super-resolution imaging. Specifically, we have employed photoactivated localization microscopy, with photoactivatable dyes (PD-PALM) to visualize the spatial organization of these complexes to <10 nm resolution, and the quantitation of GPCR monomer, dimer, and oligomer in both homomeric and heteromeric forms. We provide guidelines on optimal sample preparation, imaging parameters, and necessary controls for resolving and quantifying single-molecule data. Finally, we discuss advantages and limitations of this imaging technique and its potential future applications to the study of GPCR function

Protein Networks Reveal Detection Bias and Species Consistency When Analysed by Information-Theoretic Methods

We apply our recently developed information-theoretic measures for the characterisation and comparison of protein–protein interaction networks. These measures are used to quantify topological network features via macroscopic statistical properties. Network differences are assessed based on these macroscopic properties as opposed to microscopic overlap, homology information or motif occurrences. We present the results of a large–scale analysis of protein–protein interaction networks. Precise null models are used in our analyses, allowing for reliable interpretation of the results. By quantifying the methodological biases of the experimental data, we can define an information threshold above which networks may be deemed to comprise consistent macroscopic topological properties, despite their small microscopic overlaps. Based on this rationale, data from yeast–two–hybrid methods are sufficiently consistent to allow for intra–species comparisons (between different experiments) and inter–species comparisons, while data from affinity–purification mass–spectrometry methods show large differences even within intra–species comparisons

Parameter selection for and implementation of a web-based decision-support tool to predict extubation outcome in premature infants

BACKGROUND: Approximately 30% of intubated preterm infants with respiratory distress syndrome (RDS) will fail attempted extubation, requiring reintubation and mechanical ventilation. Although ventilator technology and monitoring of premature infants have improved over time, optimal extubation remains challenging. Furthermore, extubation decisions for premature infants require complex informational processing, techniques implicitly learned through clinical practice. Computer-aided decision-support tools would benefit inexperienced clinicians, especially during peak neonatal intensive care unit (NICU) census. METHODS: A five-step procedure was developed to identify predictive variables. Clinical expert (CE) thought processes comprised one model. Variables from that model were used to develop two mathematical models for the decision-support tool: an artificial neural network (ANN) and a multivariate logistic regression model (MLR). The ranking of the variables in the three models was compared using the Wilcoxon Signed Rank Test. The best performing model was used in a web-based decision-support tool with a user interface implemented in Hypertext Markup Language (HTML) and the mathematical model employing the ANN. RESULTS: CEs identified 51 potentially predictive variables for extubation decisions for an infant on mechanical ventilation. Comparisons of the three models showed a significant difference between the ANN and the CE (p = 0.0006). Of the original 51 potentially predictive variables, the 13 most predictive variables were used to develop an ANN as a web-based decision-tool. The ANN processes user-provided data and returns the prediction 0–1 score and a novelty index. The user then selects the most appropriate threshold for categorizing the prediction as a success or failure. Furthermore, the novelty index, indicating the similarity of the test case to the training case, allows the user to assess the confidence level of the prediction with regard to how much the new data differ from the data originally used for the development of the prediction tool. CONCLUSION: State-of-the-art, machine-learning methods can be employed for the development of sophisticated tools to aid clinicians' decisions. We identified numerous variables considered relevant for extubation decisions for mechanically ventilated premature infants with RDS. We then developed a web-based decision-support tool for clinicians which can be made widely available and potentially improve patient care world wide

Genetic Signatures of Exceptional Longevity in Humans

Like most complex phenotypes, exceptional longevity is thought to reflect a combined influence of environmental (e.g., lifestyle choices, where we live) and genetic factors. To explore the genetic contribution, we undertook a genome-wide association study of exceptional longevity in 801 centenarians (median age at death 104 years) and 914 genetically matched healthy controls. Using these data, we built a genetic model that includes 281 single nucleotide polymorphisms (SNPs) and discriminated between cases and controls of the discovery set with 89% sensitivity and specificity, and with 58% specificity and 60% sensitivity in an independent cohort of 341 controls and 253 genetically matched nonagenarians and centenarians (median age 100 years). Consistent with the hypothesis that the genetic contribution is largest with the oldest ages, the sensitivity of the model increased in the independent cohort with older and older ages (71% to classify subjects with an age at death>102 and 85% to classify subjects with an age at death>105). For further validation, we applied the model to an additional, unmatched 60 centenarians (median age 107 years) resulting in 78% sensitivity, and 2863 unmatched controls with 61% specificity. The 281 SNPs include the SNP rs2075650 in TOMM40/APOE that reached irrefutable genome wide significance (posterior probability of association = 1) and replicated in the independent cohort. Removal of this SNP from the model reduced the accuracy by only 1%. Further in-silico analysis suggests that 90% of centenarians can be grouped into clusters characterized by different “genetic signatures” of varying predictive values for exceptional longevity. The correlation between 3 signatures and 3 different life spans was replicated in the combined replication sets. The different signatures may help dissect this complex phenotype into sub-phenotypes of exceptional longevity

An update on vitamin B12-related gene polymorphisms and B12 status.

Vitamin B12 is an essential micronutrient in humans needed for health maintenance. Deficiency of vitamin B12 has been linked to dietary, environmental and genetic factors. Evidence for the genetic basis of vitamin B12 status is poorly understood. However, advancements in genomic techniques have increased the knowledge-base of the genetics of vitamin B12 status. Based on the candidate gene and genome-wide association (GWA) studies, associations between genetic loci in several genes involved in vitamin B12 metabolism have been identified. The objective of this literature review was to identify and discuss reports of associations between single-nucleotide polymorphisms (SNPs) in vitamin B12 pathway genes and their influence on the circulating levels of vitamin B12. Relevant articles were obtained through a literature search on PubMed through to May 2017. An article was included if it examined an association of a SNP with serum or plasma vitamin B12 concentration. Beta coefficients and odds ratios were used to describe the strength of an association, and a  < 0.05 was considered as statistically significant. Two reviewers independently evaluated the eligibility for the inclusion criteria and extracted the data. From 23 studies which fulfilled the selection criteria, 16 studies identified SNPs that showed statistically significant associations with vitamin B12 concentrations. Fifty-nine vitamin B12-related gene polymorphisms associated with vitamin B12 status were identified in total, from the following populations: African American, Brazilian, Canadian, Chinese, Danish, English, European ancestry, Icelandic, Indian, Italian, Latino, Northern Irish, Portuguese and residents of the USA. Overall, the data analyzed suggests that ethnic-specific associations are involved in the genetic determination of vitamin B12 concentrations. However, despite recent success in genetic studies, the majority of identified genes that could explain variation in vitamin B12 concentrations were from Caucasian populations. Further research utilizing larger sample sizes of non-Caucasian populations is necessary in order to better understand these ethnic-specific associations