232 research outputs found

    The DSM: mindful science or mindless power? A critical review

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    In this paper we review the Diagnostic and Statistical Manual of mental health (DSM), its scientific bases and utility. The concepts of “normality,” “pathology,” and boundaries between them are critically reviewed. We further use the concepts of mindfulness and mindlessness, and evidence from cognitive and social sciences to investigate the DSM clinical and social impact and we argue against its assigned overpower. We recommend including alternative perspectives to the DSM, such as mindfulness and positive psychology. We also argue for including mindfulness training in psychiatric residency and clinical psychology programs

    Symptom changes in multiple sclerosis following psychological interventions: a systematic review

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    Background: Multiple Sclerosis is a disease of the central nervous system involving a variety of debilitating physical, sensory, cognitive and emotional symptoms. This literature review evaluated the impact of psychological interventions on the physiological symptoms associated with the illness. Methods: A systematic literature search was conducted using Medline, PsycINFO, Scopus, and the Cochrane Library databases, as well as reference lists. Relevant studies were selected and assessed according to a preset protocol. Results: The search produced 220 articles, with 22 meeting inclusion criteria for the review. A total of 5,705 subjects with Multiple Sclerosis were analyzed. Results from the included studies indicate a general improvement in both psychological and physiological outcomes following psychological treatment. The most highly influenced physical symptoms include fatigue, sleep disturbances, pain, and physical vitality. Conclusions: Findings from the review suggest a positive relationship between psychological interventions and physiological Multiple Sclerosis symptoms. Implications for future research are discussed

    The borderline effect for diabetes: when no difference makes a difference

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    We hypothesized that people at the borderline of being labeled as “prediabetic” based on A1c blood test results, who initially face equivalent risks of developing diabetes but who are labeled differently, would be more likely to develop diabetes when labeled as “prediabetic” as a result of the label. Study 1 served to establish the psychological effect of the prediabetes label: we surveyed 260 participants on Amazon Mechanical Turk to test whether risk perception significantly increased when comparing A1c test results that differed by 0.1% and led to different diagnostic labels (5.6 and 5.7%) but did not significantly increase when comparing those that differed by 0.1% but received the same label (5.5%/5.6 and 5.7%/5.8%). Study 2 explored whether labels are associated with different rates of developing diabetes when the initial difference in A1c results suggests equivalent risk. Using data from 8,096 patients, we compared patients whose initial A1c results differed by 0.1% and found those who received results labeled as prediabetic (A1c of 5.7%) were significantly more likely to develop diabetes than patients whose initial results were labeled as normal (5.6%). In contrast, patients whose initial results differed by 0.1% but who received the same “normal” label (5.5 and 5.6%) were equally likely to develop diabetes. These preliminary results suggest that diagnostic labels may become self-fulfilling, especially when the underlying pathology of patients receiving different labels does not meaningfully differ

    Snail promotes the cell-autonomous generation of Flk1 + endothelial cells through the repression of the microRNA-200 family

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    Expression of the transcription factor Snail is required for normal vasculogenesis in the developing mouse embryo. In addition, tumors expressing Snail have been associated with a more malignant phenotype, with both increased invasive properties and angiogenesis. Although the relationship between Snail and vasculogenesis has been noted, no mechanistic analysis has been elucidated. Here, we show that in addition to inducing an epithelial mesenchymal transition, Snail promotes the cell-autonomous induction of Flk1(+) endothelial cells in an early subset of differentiating mouse embryonic stem (ES) cells. Cells that become Flk1+ in response to Snail have a transcriptional profile specific to Gata6+primitive endoderm, but not the early Nanog+epiblast. We further show that Snail's ability to promote Flk1(+) endothelium depends on fibroblast growth factor signaling as well as the repression of the microRNA-200 (miR-200) family, which directly targets the 3′ UTRs of Flk1 and Ets1. Together, our results show that Snail is capable of inducing Flk1+ lineage commitment in a subset of differentiating ES cells through the down-regulation of the miR-200 family. We hypothesize that this mechanism of Snail-induced vasculogenesis may be conserved in both the early developing embryo and malignant cancers

    Forecasting the onset and course of mental illness with Twitter data

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    We developed computational models to predict the emergence of depression and Post-Traumatic Stress Disorder in Twitter users. Twitter data and details of depression history were collected from 204 individuals (105 depressed, 99 healthy). We extracted predictive features measuring affect, linguistic style, and context from participant tweets (N = 279,951) and built models using these features with supervised learning algorithms. Resulting models successfully discriminated between depressed and healthy content, and compared favorably to general practitioners\u27 average success rates in diagnosing depression, albeit in a separate population. Results held even when the analysis was restricted to content posted before first depression diagnosis. State-space temporal analysis suggests that onset of depression may be detectable from Twitter data several months prior to diagnosis. Predictive results were replicated with a separate sample of individuals diagnosed with PTSD (Nusers = 174, Ntweets = 243,775). A state-space time series model revealed indicators of PTSD almost immediately post-trauma, often many months prior to clinical diagnosis. These methods suggest a data-driven, predictive approach for early screening and detection of mental illness
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