Acetaldehyde and hexanaldehyde from cultured white cells

<p>Abstract</p> <p>Background</p> <p>Noninvasive detection of innate immune function such as the accumulation of neutrophils remains a challenge in many areas of clinical medicine. We hypothesized that granulocytes could generate volatile organic compounds.</p> <p>Methods</p> <p>To begin to test this, we developed a bioreactor and analytical GC-MS system to accurately identify and quantify gases in trace concentrations (parts per billion) emitted solely from cell/media culture. A human promyelocytic leukemia cell line, HL60, frequently used to assess neutrophil function, was grown in serum-free medium.</p> <p>Results</p> <p>HL60 cells released acetaldehyde and hexanaldehyde in a time-dependent manner. The mean ± SD concentration of acetaldehyde in the headspace above the cultured cells following 4-, 24- and 48-h incubation was 157 ± 13 ppbv, 490 ± 99 ppbv, 698 ± 87 ppbv. For hexanaldehyde these values were 1 ± 0.3 ppbv, 8 ± 2 ppbv, and 11 ± 2 ppbv. In addition, our experimental system permitted us to identify confounding trace gas contaminants such as styrene.</p> <p>Conclusion</p> <p>This study demonstrates that human immune cells known to mimic the function of innate immune cells, like neutrophils, produce volatile gases that can be measured <it>in vitro </it>in trace amounts.</p

Analysis of shared heritability in common disorders of the brain

ience, this issue p. eaap8757 Structured Abstract INTRODUCTION Brain disorders may exhibit shared symptoms and substantial epidemiological comorbidity, inciting debate about their etiologic overlap. However, detailed study of phenotypes with different ages of onset, severity, and presentation poses a considerable challenge. Recently developed heritability methods allow us to accurately measure correlation of genome-wide common variant risk between two phenotypes from pools of different individuals and assess how connected they, or at least their genetic risks, are on the genomic level. We used genome-wide association data for 265,218 patients and 784,643 control participants, as well as 17 phenotypes from a total of 1,191,588 individuals, to quantify the degree of overlap for genetic risk factors of 25 common brain disorders. RATIONALE Over the past century, the classification of brain disorders has evolved to reflect the medical and scientific communities' assessments of the presumed root causes of clinical phenomena such as behavioral change, loss of motor function, or alterations of consciousness. Directly observable phenomena (such as the presence of emboli, protein tangles, or unusual electrical activity patterns) generally define and separate neurological disorders from psychiatric disorders. Understanding the genetic underpinnings and categorical distinctions for brain disorders and related phenotypes may inform the search for their biological mechanisms. RESULTS Common variant risk for psychiatric disorders was shown to correlate significantly, especially among attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder (MDD), and schizophrenia. By contrast, neurological disorders appear more distinct from one another and from the psychiatric disorders, except for migraine, which was significantly correlated to ADHD, MDD, and Tourette syndrome. We demonstrate that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine. We also identify significant genetic sharing between disorders and early life cognitive measures (e.g., years of education and college attainment) in the general population, demonstrating positive correlation with several psychiatric disorders (e.g., anorexia nervosa and bipolar disorder) and negative correlation with several neurological phenotypes (e.g., Alzheimer's disease and ischemic stroke), even though the latter are considered to result from specific processes that occur later in life. Extensive simulations were also performed to inform how statistical power, diagnostic misclassification, and phenotypic heterogeneity influence genetic correlations. CONCLUSION The high degree of genetic correlation among many of the psychiatric disorders adds further evidence that their current clinical boundaries do not reflect distinct underlying pathogenic processes, at least on the genetic level. This suggests a deeply interconnected nature for psychiatric disorders, in contrast to neurological disorders, and underscores the need to refine psychiatric diagnostics. Genetically informed analyses may provide important "scaffolding" to support such restructuring of psychiatric nosology, which likely requires incorporating many levels of information. By contrast, we find limited evidence for widespread common genetic risk sharing among neurological disorders or across neurological and psychiatric disorders. We show that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures. Further study is needed to evaluate whether overlapping genetic contributions to psychiatric pathology may influence treatment choices. Ultimately, such developments may pave the way toward reduced heterogeneity and improved diagnosis and treatment of psychiatric disorders

Genome-wide identification and phenotypic characterization of seizure-associated copy number variations in 741,075 individuals

Copy number variants (CNV) are established risk factors for neurodevelopmental disorders with seizures or epilepsy. With the hypothesis that seizure disorders share genetic risk factors, we pooled CNV data from 10,590 individuals with seizure disorders, 16,109 individuals with clinically validated epilepsy, and 492,324 population controls and identified 25 genome-wide significant loci, 22 of which are novel for seizure disorders, such as deletions at 1p36.33, 1q44, 2p21-p16.3, 3q29, 8p23.3-p23.2, 9p24.3, 10q26.3, 15q11.2, 15q12-q13.1, 16p12.2, 17q21.31, duplications at 2q13, 9q34.3, 16p13.3, 17q12, 19p13.3, 20q13.33, and reciprocal CNVs at 16p11.2, and 22q11.21. Using genetic data from additional 248,751 individuals with 23 neuropsychiatric phenotypes, we explored the pleiotropy of these 25 loci. Finally, in a subset of individuals with epilepsy and detailed clinical data available, we performed phenome-wide association analyses between individual CNVs and clinical annotations categorized through the Human Phenotype Ontology (HPO). For six CNVs, we identified 19 significant associations with specific HPO terms and generated, for all CNVs, phenotype signatures across 17 clinical categories relevant for epileptologists. This is the most comprehensive investigation of CNVs in epilepsy and related seizure disorders, with potential implications for clinical practice

GWAS meta-analysis of over 29,000 people with epilepsy identifies 26 risk loci and subtype-specific genetic architecture

Epilepsy is a highly heritable disorder affecting over 50 million people worldwide, of which about one-third are resistant to current treatments. Here we report a multi-ancestry genome-wide association study including 29,944 cases, stratified into three broad categories and seven subtypes of epilepsy, and 52,538 controls. We identify 26 genome-wide significant loci, 19 of which are specific to genetic generalized epilepsy (GGE). We implicate 29 likely causal genes underlying these 26 loci. SNP-based heritability analyses show that common variants explain between 39.6% and 90% of genetic risk for GGE and its subtypes. Subtype analysis revealed markedly different genetic architectures between focal and generalized epilepsies. Gene-set analyses of GGE signals implicate synaptic processes in both excitatory and inhibitory neurons in the brain. Prioritized candidate genes overlap with monogenic epilepsy genes and with targets of current antiseizure medications. Finally, we leverage our results to identify alternate drugs with predicted efficacy if repurposed for epilepsy treatment

Exploring the Collaborative Synthesis of Information During Online Reading

This descriptive study sought to understand the complexities of integrative processing during collaborative online reading. Student pairs constructed a collaborative understanding while reading online information about a controversial issue by connecting, combining and organizing information that originated from prior knowledge, self-selected online texts, and discussions during an online inquiry task. Thirty-eight students from an upper secondary school in Finland worked in pairs to read online information and write an essay with the help of an argument graph tool. Primary data sources consisted of: prior knowledge; discussions; notes recorded with a graphic representational tool; video capture files of online use; and essays. The following results emerged: 1) a methodology and a taxonomic system were developed for the study of information sources involved in collaborative synthesis; 2) the integration of ideas from multiple online texts was difficult for adolescent students; 3) students with better essays used more online information whereas students with less remarkable essays relied more on prior knowledge that was activated during online reading. The methodology used in this study provides initial direction for research on the complexities of synthesis during collaborative online reading, an increasingly important aspect of learning in schools. Limitations and future directions for research are discussed.peerReviewe

Individual differences in the new literacies of online research and comprehension

peerReviewe

Handbook of Research on New Literacies

[Extract] Situated at the intersection of two of the most important areas in educational research today — literacy and technology — this handbook draws on the potential of each while carving out important new territory. It provides leadership for this newly emerging field, directing scholars to the major issues, theoretical perspectives, and interdisciplinary research pertaining to new literacies

Working on understanding during collaborative online reading

This study examines how students in Finland (16-18 years of age) constructed meaning and knowledge in a collaborative online reading situation. Student pairs (n = 19) were asked to write a joint essay on a controversial issue. First, the pairs discussed the topic freely to activate their prior knowledge. Next, they gathered source material on the Internet. Finally, they composed a joint essay. The data were collected using an interaction approach to verbal protocol data, along with video screen captures. In the analysis, three units were employed: episodes (n = 562) for describing online reading practices; utterances (n = 944) for identifying collaborative reading strategies; and collaborative reading patterns (n = 435) for clarifying how the student pairs constructed meaning and knowledge. Collaborative reading patterns were categorized according to a four-part model. A hierarchical cluster analysis was conducted to identify students’ collaborative reading profiles. Five collaborative reading profiles emerged: co-constructers (two pairs), collaborators (two pairs), blenders (six pairs), individually oriented readers (four pairs), and silent readers (five pairs). Overall, it appeared that some students were capable of working in pairs, whereas others had a stronger preference for working alone. Collaborative profiles might offer teachers both an evaluative and an instructional tool to support collaborative interaction in their classrooms.peerReviewe