Explore the vast range of titles available.

Advances in technology are progressively more relevant to the clinical practice of psychology and mental health services generally. Studies indicate that technology facilitates the delivery of interventions, such as cognitive behavioral therapy, in the treatment of psychological disorders in adults, such as depression, anxiety, obsessive-compulsive disorder, panic symptoms, and eating disorders. Fewer data exist for computer-based (stand-alone, self-help) and computer-assisted (in combination with face-to-face therapy, or therapist guided) programs for youth. Our objective was to summarize and critically review the literature evaluating the acceptability and efficacy of using technology with treatment and prevention programs for anxiety in young children and adolescents. The aim was to improve the understanding of what would be critical for future development of effective technology-based interventions. We conducted an exploratory review of the literature through searches in 3 scientific electronic databases (PsycINFO, ScienceDirect, and PubMed). We used keywords in various combinations: child or children, adolescent, preschool children, anxiety, intervention or treatment or program, smartphone applications or apps, online or Web-based tool, computer-based tool, internet-based tool, serious games, cognitive behavioral therapy or CBT, biofeedback, and mindfulness. For inclusion, articles had to (1) employ a technological therapeutic tool with or without the guidance of a therapist; (2) be specific for treatment or prevention of anxiety disorders in children or adolescents; (3) be published between 2000 and 2018; and (4) be published in English and in scientific peer-reviewed journals. We identified and examined 197 articles deemed to be relevant. Of these, we excluded 164 because they did not satisfy 1 or more of the requirements. The final review comprised 19 programs. Published studies demonstrated promising results in reducing anxiety, especially relative to the application of cognitive behavioral therapy with technology. For those programs demonstrating efficacy, no difference was noted when compared with traditional interventions. Other approaches have been applied to technology-based interventions with inconclusive results. Most programs were developed to be used concurrently with traditional treatments and lacked long-term evaluation. Very little has been done in terms of prevention interventions. Future development of eHealth programs for anxiety management in children will have to address several unmet needs and overcome key challenges. Although developmental stages may limit the applicability to preschool children, prevention should start in early ages. Self-help formats and personalization are highly relevant for large-scale dissemination. Automated data collection should be built in for program evaluation and effectiveness assessment. And finally, a strategy to stimulate motivation to play and maintain high adherence should be carefully considered.

Depression and obesity are highly prevalent, and major impacts on public health frequently co-occur. Recently, we reported that having depression moderates the effect of the gene, suggesting its implication in the association between depression and obesity. To confirm these findings by investigating the polymorphism rs9939609 in new cohorts, and subsequently in a meta-analysis. The sample consists of 6902 individuals with depression and 6799 controls from three replication cohorts and two original discovery cohorts. Linear regression models were performed to test for association between rs9939609 and body mass index (BMI), and for the interaction between rs9939609 and depression status for an effect on BMI. Fixed and random effects meta-analyses were performed using METASOFT. In the replication cohorts, we observed a significant interaction between , BMI and depression with fixed effects meta-analysis (β = 0.12, = 2.7 × 10 ) and with the Han/Eskin random effects method ( = 1.4 × 10 ) but not with traditional random effects (β = 0.1, = 0.35). When combined with the discovery cohorts, random effects meta-analysis also supports the interaction (β = 0.12, = 0.027) being highly significant based on the Han/Eskin model ( = 6.9 × 10 ). On average, carriers of the risk allele who have depression have a 2.2% higher BMI for each risk allele, over and above the main effect of This meta-analysis provides additional support for a significant interaction between , depression and BMI, indicating that depression increases the effect of on BMI. The findings provide a useful starting point in understanding the biological mechanism involved in the association between obesity and depression.

Despite significant research efforts aimed at understanding the neurobiological underpinnings of psychiatric disorders, the diagnosis and the evaluation of treatment of these disorders are still based solely on relatively subjective assessment of symptoms. Therefore, biological markers which could improve the current classification of psychiatry disorders, and in perspective stratify patients on a biological basis into more homogeneous clinically distinct subgroups, are highly needed. In order to identify novel candidate biological markers for major depression and schizophrenia, we have applied a focused proteomic approach using plasma samples from a large case-control collection. Patients were diagnosed according to DSM criteria using structured interviews and a number of additional clinical variables and demographic information were assessed. Plasma samples from 245 depressed patients, 229 schizophrenic patients and 254 controls were submitted to multi analyte profiling allowing the evaluation of up to 79 proteins, including a series of cytokines, chemokines and neurotrophins previously suggested to be involved in the pathophysiology of depression and schizophrenia. Univariate data analysis showed more significant p-values than would be expected by chance and highlighted several proteins belonging to pathways or mechanisms previously suspected to be involved in the pathophysiology of major depression or schizophrenia, such as insulin and MMP-9 for depression, and BDNF, EGF and a number of chemokines for schizophrenia. Multivariate analysis was carried out to improve the differentiation of cases from controls and identify the most informative panel of markers. The results illustrate the potential of plasma biomarker profiling for psychiatric disorders, when conducted in large collections. The study highlighted a set of analytes as candidate biomarker signatures for depression and schizophrenia, warranting further investigation in independent collections.

Purpose The aim of the study was to assess the differences between impulsive and non-impulsive patients in response to a multidisciplinary intensive inpatient treatment for eating disorders (EDs). Methods 320 patients with EDs were consecutively recruited in an eating disorders unit (EDU). They were assessed by clinical interviews and self-reported questionnaires. The treatment was characterized by a patient-centric approach and included both an intensive and comprehensive standardized multidisciplinary program based on cognitive–behavioral therapy and a flexible and personalized component according to the needs and the history of each patient. Results Impulsive ED patients showed greater improvement in specific psychopathological areas, in particular: interpersonal sensitivity of Symptom Checklist-90 (SCL-90) ( p  = 0.007); Eating Disorder Examination Questionnaire (EDE-Q) Global Score ( p  = 0.009), EDE-Q eating concern ( p  < 0.001) and EDE-Q shape concern ( p  = 0.025). The two groups also showed a different pattern on the Body Uneasiness Test, with impulsive patients uniquely showing improvement on Global Severity Index ( p  = 0.006), body image concern ( p  = 0.008), compulsive self monitoring ( p  = 0.002), and weight phobia ( p  = 0.037). Discussion Results support the hypothesis that patients with impulsive behaviors might benefit from treatments characterized by a standardized cognitive behavioral therapy implemented by third-wave interventions according to each patient’s clinical profile. Personalized treatment approaches could be an answer to the complexity of ED, addressing individual psychopathology. Further studies are needed to confirm these preliminary findings. Level of Evidence III, cohort or case-control analytic studies.

Bipolar disorder (BP) is a disabling and often life-threatening disorder that affects [almost equal to]1% of the population worldwide. To identify genetic variants that increase the risk of BP, we genotyped on the Illumina HumanHap550 Beadchip 2,076 bipolar cases and 1,676 controls of European ancestry from the National Institute of Mental Health Human Genetics Initiative Repository, and the Prechter Repository and samples collected in London, Toronto, and Dundee. We imputed SNP genotypes and tested for SNP-BP association in each sample and then performed meta-analysis across samples. The strongest association P value for this 2-study meta-analysis was 2.4 x 10⁻⁶. We next imputed SNP genotypes and tested for SNP-BP association based on the publicly available Affymetrix 500K genotype data from the Wellcome Trust Case Control Consortium for 1,868 BP cases and a reference set of 12,831 individuals. A 3-study meta-analysis of 3,683 nonoverlapping cases and 14,507 extended controls on >2.3 M genotyped and imputed SNPs resulted in 3 chromosomal regions with association P [almost equal to] 10⁻⁷: 1p31.1 (no known genes), 3p21 (>25 known genes), and 5q15 (MCTP1). The most strongly associated nonsynonymous SNP rs1042779 (OR = 1.19, P = 1.8 x 10⁻⁷) is in the ITIH1 gene on chromosome 3, with other strongly associated nonsynonymous SNPs in GNL3, NEK4, and ITIH3. Thus, these chromosomal regions harbor genes implicated in cell cycle, neurogenesis, neuroplasticity, and neurosignaling. In addition, we replicated the reported ANK3 association results for SNP rs10994336 in the nonoverlapping GSK sample (OR = 1.37, P = 0.042). Although these results are promising, analysis of additional samples will be required to confirm that variant(s) in these regions influence BP risk.

Background Recently, genome-wide association studies (GWAS) for cases versus controls using single nucleotide polymorphism microarray data have shown promising findings for complex neuropsychiatric disorders, including bipolar disorder (BD). Methods Here we describe a comprehensive genome-wide study of bipolar disorder (BD), cross-referencing analysis from a family-based study of 229 small families with association analysis from over 950 cases and 950 ethnicity-matched controls from the UK and Canada. Further, loci identified in these analyses were supported by pathways identified through pathway analysis on the samples. Results Although no genome-wide significant markers were identified, the combined GWAS findings have pointed to several genes of interest that support GWAS findings for BD from other groups or consortia, such as at SYNE1 on 6q25, PPP2R2C on 4p16.1, ZNF659 on 3p24.3, CNTNAP5 (2q14.3), and CDH13 (16q23.3). This apparent corroboration across multiple sites gives much confidence to the likelihood of genetic involvement in BD at these loci. In particular, our two-stage strategy found association in both our combined case/control analysis and the family-based analysis on 1q21.2 (closest gene: sphingosine-1-phosphate receptor 1 gene, S1PR1 ) and on 1q24.1 near the gene TMCO1 , and at CSMD1 on 8p23.2, supporting several previous GWAS reports for BD and for schizophrenia. Pathway analysis suggests association of pathways involved in calcium signalling, neuropathic pain signalling, CREB signalling in neurons, glutamate receptor signalling and axonal guidance signalling. Conclusions The findings presented here show support for a number of genes previously implicated genes in the etiology of BD, including CSMD1 and SYNE1 , as well as evidence for previously unreported genes such as the brain-expressed genes ADCY2 , NCALD , WDR60 , SCN7A and SPAG16 .

Background The Psychiatric arm of the population-based CoLaus study (PsyCoLaus) is designed to: 1) establish the prevalence of threshold and subthreshold psychiatric syndromes in the 35 to 66 year-old population of the city of Lausanne (Switzerland); 2) test the validity of postulated definitions for subthreshold mood and anxiety syndromes; 3) determine the associations between psychiatric disorders, personality traits and cardiovascular diseases (CVD), 4) identify genetic variants that can modify the risk for psychiatric disorders and determine whether genetic risk factors are shared between psychiatric disorders and CVD. This paper presents the method as well as sociodemographic and somatic characteristics of the sample. Methods All 35 to 66 year-old persons previously selected for the population-based CoLaus survey on risk factors for CVD were asked to participate in a substudy assessing psychiatric conditions. This investigation included the Diagnostic Interview for Genetic Studies to elicit diagnostic criteria for threshold disorders according to DSM-IV and algorithmically defined subthreshold syndromes. Complementary information was collected on potential risk and protective factors for psychiatric disorders, migraine and on the morbidity of first-degree relatives, whereas the collection of DNA and plasma samples was already part of the original CoLaus survey. Results A total of 3,691 individuals completed the psychiatric evaluation (67% participation). The gender distribution of the sample did not differ significantly from that of the general population in the same age range. Although the youngest 5-year band of the cohort was underrepresented and the oldest 5-year band overrepresented, participants of PsyCoLaus and individuals who refused to participate revealed comparable scores on the General Health Questionnaire, a self-rating instrument completed at the somatic exam. Conclusion Despite limitations resulting from the relatively low participation in the context of a comprehensive and time-consuming investigation, the PsyCoLaus study should significantly contribute to the current understanding of psychiatric disorders and comorbid somatic conditions by: 1) establishing the clinical relevance of specific psychiatric syndromes below the DSM-IV threshold; 2) determining comorbidity between risk factors for CVD and psychiatric disorders; 3) assessing genetic variants associated with common psychiatric disorders and 4) identifying DNA markers shared between CVD and psychiatric disorders.

Breast cancer patients are at a particularly high risk of cardiotoxicity from chemotherapy having a detrimental effect on quality-of-life parameters and increasing the risk of mortality. Prognostic biomarkers would allow the management of therapies to mitigate the risks of cardiotoxicity in vulnerable patients and a key potential candidate for such biomarkers are microRNAs (miRNA). miRNAs are post-transcriptional regulators of gene expression which can also be released into the circulatory system and have been associated with the progression of many chronic diseases including many types of cancer. In this review, the evidence for the potential application of miRNAs as biomarkers for chemotherapy-induced cardiotoxicity (CIC) in breast cancer patientsis evaluated and a simple meta-analysis is performed to confirm the replication status of each reported miRNA. Further selection of miRNAs is performed by reviewing the reported associations of each miRNA with other cardiovascular conditions. Based on this research, the most representative panels targeting specific chemotherapy agents and treatment regimens are suggested, that contain several informative miRNAs, including both general markers of cardiac damage as well as those for the specific cancer treatments.

Suicidal behaviour can be conceptualised as a continuum from suicidal ideation, to suicidal attempts to completed suicide. In this study we identify genes contributing to suicidal behaviour in the depression study RADIANT. A quantitative suicidality score was composed of two items from the SCAN interview. In addition, the 251 depression cases with a history of serious suicide attempts were classified to form a discrete trait. The quantitative trait was correlated with younger onset of depression and number of episodes of depression, but not with gender. A genome-wide association study of 2,023 depression cases was performed to identify genes that may contribute to suicidal behaviour. Two Munich depression studies were used as replication cohorts to test the most strongly associated SNPs. No SNP was associated at genome-wide significance level. For the quantitative trait, evidence of association was detected at GFRA1, a receptor for the neurotrophin GDRA (p = 2e-06). For the discrete trait of suicide attempt, SNPs in KIAA1244 and RGS18 attained p-values of <5e-6. None of these SNPs showed evidence for replication in the additional cohorts tested. Candidate gene analysis provided some support for a polymorphism in NTRK2, which was previously associated with suicidality. This study provides a genome-wide assessment of possible genetic contribution to suicidal behaviour in depression but indicates a genetic architecture of multiple genes with small effects. Large cohorts will be required to dissect this further.

Neuroticism is a moderately heritable personality trait considered to be a risk factor for developing major depression, anxiety disorders and dementia. We performed a genome-wide association study in 2,235 participants drawn from a population-based study of neuroticism, making this the largest association study for neuroticism to date. Neuroticism was measured by the Eysenck Personality Questionnaire. After Quality Control, we analysed 430,000 autosomal SNPs together with an additional 1.2 million SNPs imputed with high quality from the Hap Map CEU samples. We found a very small effect of population stratification, corrected using one principal component, and some cryptic kinship that required no correction. NKAIN2 showed suggestive evidence of association with neuroticism as a main effect (p < 10(-6)) and GPC6 showed suggestive evidence for interaction with age (p approximately = 10(-7)). We found support for one previously-reported association (PDE4D), but failed to replicate other recent reports. These results suggest common SNP variation does not strongly influence neuroticism. Our study was powered to detect almost all SNPs explaining at least 2% of heritability, and so our results effectively exclude the existence of loci having a major effect on neuroticism.