1,720,982 research outputs found
Cariprazine specificity profile in the treatment of acute schizophrenia: A meta-Analysis and meta-regression of randomized-controlled trials
No full textCariprazine is a new dopamine D2 and D3 receptor partial agonist antipsychotic. Meta-Analytic evidence of efficacy in acute schizophrenia and specific groups of patients is lacking. We carried out a meta-Analysis in patients with acute schizophrenia to evaluate the efficacy of cariprazine over placebo and active comparators in overall symptoms, positive and negative symptoms and quality of life. Low and high (â¥6 mg/day) doses were tested separately. The possible effect of clinical-demographic modulators was also tested. Four studies (2144 patients) were included. Both high and low cariprazine doses proved superior to placebo in all symptom domains. The standardized mean difference (SMD) to placebo showed a modest impact on overall symptoms compared with meta-Analytic results for other antipsychotics (SMD was similar to lurasidone, asenapine, ziprasidone and aripiprazole, but lower than risperidone, quetiapine and olanzapine). The SMD to placebo on negative symptoms was superior to many antipsychotics including aripiprazole, with a slightly more relevant effect of cariprazine low doses. This effect was probably on secondary negative symptoms since the short-Term follow-up of the studies included. Meta-regression data further refined the compound clinical profile, suggesting that cariprazine may be particularly useful in young patients with a relatively short duration of disease
Pharmacogenetics in Psychiatry
No full textMental illness represents a major health issue both at the individual and at the socioeconomical level. This is partly due to the current suboptimal treatment options: existing psychotropic medications, including antidepressants, antipsychotics, and mood stabilizers, are effective only in a subset of patients or produce partial response and they are often associated with debilitating side effects that discourage adherence. Pharmacogenetics is the study of how genetic information impacts on drug response/side effects with the goal to provide tailored treatments, thereby maximizing efficacy and tolerability. The first pharmacogenetic studies focused on candidate genes, previously known to be relevant to the pharmacokinetics and pharmacodynamics of psychotropic drugs. Results were mainly inconclusive, but some replicated candidates were identified and included as pharmacogenetic biomarkers in drug labeling and in some commercial kits. With the advent of the genomic revolution, it became possible to study the genetic variation on an unprecedented scale, throughout the whole genome with no need of a priori hypothesis. This may lead to the personalized prescription of existing medications and potentially to the development of innovative ones, thanks to new insights into the genetics of mental illness. Promising findings were obtained, but methods for the generation and analysis of genome-wide and sequencing data are still in evolution. Future pharmacogenetic tests may consist of hundreds/thousands of polymorphisms throughout the genome or selected pathways in order to take into account the complex interactions across variants in a number of genes
Corrected QT Interval Prolongation in Psychopharmacological Treatment and Its Modulation by Genetic Variation
No full textSeveral antipsychotics and antidepressants have been associated with electrocardiogram alterations, the most clinically relevant of which is the heart rate-corrected QT interval (QTc) prolongation, a risk factor for sudden cardiac death. Genetic variants influence drug-induced QTc prolongation and can provide valuable information for precision medicine. The effect of genetic variants on QTc prolongation as well as the possible interaction between polymorphisms and risk medications in determining QTc prolongation were investigated. Medications were classified according to their known risk of inducing QTc prolongation (high-to-moderate, low, and no risk). QTc duration and risk of QTc > median value were investigated in a sample of 77 patients with mood or psychotic disorders being treated with antidepressants and antipsychotics, and who had at least 1 ECG recording. A secondary analysis considered QTc percentage change in patients (n = 25) with 2 ECG recordings. Single-nucleotide polymorphisms previously associated with QTc prolongation during treatment with psychotropic medications were investigated. No association survived after multiple-testing correction. The best results for modulation of QTc duration were identified for rs10808071 (the ABCB1 gene, nominal p = 0.007) when at least 1 medication with a moderate-to-high risk was prescribed, and for rs12029454 (the NOS1AP gene) in patients taking at least 1 medication with a cardiovascular risk (nominal p = 0.008). In the secondary analysis, rs2072413 (the KCNH2 gene) was the top finding for the modulation of QTc percentage change (nominal p = 0.001) when 1 drug with a moderate-to-high risk was added compared to baseline. Despite the limited power of this study, our results suggest that ABCB1, NOS1AP, and KCNH2 may play a role in QTc duration/prolongation during treatment with psychotropic drugs
Genetic basis of psychopathological dimensions shared between schizophrenia and bipolar disorder
Full text linkShared genetic vulnerability between schizophrenia (SCZ) and bipolar disorder (BP) was demonstrated, but the genetic underpinnings of specific symptom domains are unclear. This study investigated which genes and gene sets may modulate specific psychopathological domains and if genome-wide significant loci previously associated with SCZ or BP may play a role. Genome-wide data were available in patients with SCZ (n = 226) or BP (n = 228). Phenotypes under investigation were depressive and positive symptoms severity, suicidal ideation, onset age and substance use disorder comorbidity. Genome-wide analyses were performed at gene and gene set level, while 148 genome-wide significant loci previously associated with SCZ and/or BP were investigated. Each sample was analyzed separately then a meta-analysis was performed. SH3GL2 and CLVS1 genes were associated with suicidal ideation in SCZ (p = 5.62e-08 and 0.01, respectively), the former also in the meta-analysis (p = .01). SHC4 gene was associated with depressive symptoms severity in BP (p = .003). A gene set involved in cellular differentiation (GO:0048661) was associated with substance disorder comorbidity in the meta-analysis (p = .03). Individual loci previously associated with SCZ or BP did not modulate the phenotypes of interest. This study provided confirmatory and new findings. SH3GL2 (endophilin A1) showed a role in suicidal ideation that may be due to its relevance to the glutamate system. SHC4 regulates BDNF-induced MAPK activation and was previously associated with depression. CLVS1 is involved in lysosome maturation and was for the first time associated with a psychiatric trait. GO:0048661 may mediate the risk of substance disorder through an effect on neurodevelopment/neuroplasticity
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Clinically-interpretable and large-scale machine learning to monitor mood disorders with wearables
Full text linkMood Disorders (MDs) are common and severe psychiatric conditions with a relapsing-remitting course. Timely intervention during impending episodes improves outcomes, but pre-emptive measures are limited due to infrequent patient reviews and limited symptom reporting from patients. MDs involve changes in energy levels, circadian rhythms, and neurovegetative functions, correlating with changes in physiological data, like acceleration and galvanic skin conductance. Personal sensing, leveraging data from wearables, offers a way to monitor MDs remotely with objective biomarkers, which psychiatry currently lacks, relying mainly on clinical observation and patient self-reports. AI can harness wearable data to realise remote monitoring. I outline a unifying perspective on personal sensing for MDs and make original contributions to the field, using a prospective, observational cohort (TIMEBASE/INTREPIBD), recorded with an Emaptica E4 device.
Our first contribution focuses on Heart Rate Variability (HRV), an indicator of the
autonomic nervous system functionality. I find HRV increases as symptoms subside
after acute episodes, suggesting it as a potential symptom improvement biomarker. Due to limited HRV study samples, I use Bayesian statistics to propose an interpretable probabilistic model, explaining the HRV data generating process. Longitudinal HRV data collection is indeed labour-intensive, often resulting in small samples that undermine frequentist statistics reliability.
Personal sensing research typically attempted to detect the mere presence of acute
episodes or the total score on a psychometric scale, missing actionable clinical information. I propose inferring all symptoms from two popular scales assessing the full MD symptom spectrum, akin to a concept bottleneck. This approach ensures AI output is interpretable, recognizing that different symptom combinations require varied therapeutic strategies. I developed a model for this task and investigate key AI challenges.
Lastly, to address labelled data scarcity in AI systems for personal sensing, I gather
open-access datasets using the E4 wearable, regardless of the task they are concerned with, and make such collection publicly available. I propose a Transformer model tailored to the E4 and show that self-supervised learning, repurposing unlabelled data to learn useful representations through surrogate tasks, is viable in personal sensing. This method outperforms fully supervised models, whether using deep learning or classical machine learning with hand-crafted features
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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