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Antibody-reactive course I epitopes determined by frames regarding mismatched eplets and self-eplets.
OBJECTIVE To relate empirically derived dietary patterns identified using the Treelet Transform (TT) to risk of stroke. DESIGN A prospective cohort study using the Danish Diet, Cancer and Health cohort. Dietary information was obtained in 1993-1997 using a validated semi-quantitative FFQ. Incident stroke diagnoses, obtained from the Danish National Patient Register, were verified by record review. Dietary patterns were generated using TT, and participants were categorised into quintiles based on their adherence to each pattern. Sex-specific Cox proportional hazard models estimated associations between dietary patterns and stroke. SETTING Denmark. PARTICIPANTS 55 061 men and women aged 50-64 years at the time of enrolment. RESULTS Three dietary patterns explaining 15·4 % of the total variance were identified a Prudent pattern, a Western pattern and a Wine & Snacks pattern. During a follow-up time of 10 years, 1513 cases occurred. Comparing the highest to lowest quintiles of intake, adherence to a Prudent pattern was inversely associated with stroke (HRmen 0·74, 95 % CI 0·60, 0·91; HRwomen 0·82, 95 % CI 0·62, 1·08), while adherence to a Western pattern was associated with greater risk (HRmen 1·61, 95 % CI 1·23, 2·10; HRwomen 2·01, 95 % CI 1·48, 2·72). No association was found for a Wine & Snacks pattern for women, but a weak inverse association was found for men (HR 0·81, 95 % CI 0·67, 0·99). CONCLUSIONS The results of this study are broadly in line with current recommendations for a healthy diet to prevent stroke.OBJECTIVE To describe the consumption of ultra-processed foods according to demographic and socioeconomic characteristics in three birth cohorts. DESIGN Cross-sectional analysis. SETTING Data from the 2004, 1993 and 1982 Pelotas Birth Cohorts were used at 11, 22 and 30 years, respectively, collected between 2012 and 2015. Outcome was the relative contribution of ultra-processed foods from the total daily energy intake. Maternal-independent variables were self-reported skin colour, schooling, age and family income (obtained in the perinatal study), and variables of the cohort member, sex, skin colour, schooling and current family income (the last two obtained at the 11-, 22- and 30-year follow-ups of the respective cohorts). We calculated crude and adjusted means of the outcome for the whole cohorts and according to the independent variables. PARTICIPANTS 11-, 22- and 30-year-old individuals. RESULTS Daily energetic contribution from ultra-processed foods was higher in the younger cohort (33·7, 29·8 and 25·1 % at 11, 22 and 30 years, respectively). Maternal schooling and family income at birth showed an inverse dose-response relationship at 11 and 22 years, but a positive dose-response at 30 years. Female sex, lower schooling and family income at 22 years and higher schooling at 30 years were associated to a higher contribution from ultra-processed foods in the daily energy intake. CONCLUSIONS Information from food and nutrition policies needs a higher dissemination, mostly among women and population groups of lower income and schooling, including its promotion in media and health services, aiming for a decreased consumption of ultra-processed foods.BACKGROUND No evidence-based therapy for borderline personality disorder (BPD) exhibits a clear superiority. However, BPD is highly heterogeneous, and different patients may specifically benefit from the interventions of a particular treatment. METHODS From a randomized trial comparing a year of dialectical behavior therapy (DBT) to general psychiatric management (GPM) for BPD, long-term (2-year-post) outcome data and patient baseline variables (n = 156) were used to examine individual and combined patient-level moderators of differential treatment response. L(+)-Monosodium glutamate monohydrate chemical structure A two-step bootstrapped and partially cross-validated moderator identification process was employed for 20 baseline variables. For identified moderators, 10-fold bootstrapped cross-validated models estimated response to each therapy, and long-term outcomes were compared for patients randomized to their model-predicted optimal v. non-optimal treatment. RESULTS Significant moderators surviving the two-step process included psychiatric symptom severity, BPD impulsivity symptoms (both GPM > DBT), dependent personality traits, childhood emotional abuse, and social adjustment (all DBT > GPM). Patients randomized to their model-predicted optimal treatment had significantly better long-term outcomes (d = 0.36, p = 0.028), especially if the model had a relatively stronger (top 60%) prediction for that patient (d = 0.61, p = 0.004). Among patients with a stronger prediction, this advantage held even when applying a conservative statistical check (d = 0.46, p = 0.043). CONCLUSIONS Patient characteristics influence the degree to which they respond to two treatments for BPD. Combining information from multiple moderators may help inform providers and patients as to which treatment is the most likely to lead to long-term symptom relief. Further research on personalized medicine in BPD is needed.Increased amygdala responsiveness is the hallmark of fear and a characteristic across patients with anxiety disorders. The amygdala is embedded in a complex regulatory circuit. Multiple different mechanisms may elevate amygdala responsiveness and lead to the occurrence of an anxiety disorder. While top-down control by the prefrontal cortex (PFC) downregulates amygdala responses, the locus coeruleus (LC) drives up amygdala activation via noradrenergic projections. This indicates that the same fearful phenotype may result from different neural mechanisms. We propose a mechanistic model that defines three different neural biomarkers causing amygdala hyper-responsiveness in patients with anxiety disorders (a) inherent amygdala hypersensitivity, (b) low prefrontal control and (c) high LC drive. First-line treatment for anxiety disorders is exposure-based cognitive behavioural therapy, which strengthens PFC recruitment during emotion regulation and thus targets low-prefrontal control. A treatment response rate around 50% (Loerinc et al., 2015, Clinical Psychological Reviews, 42, 72-82) might indicate heterogeneity of underlying neurobiological mechanisms among patients, presumably leading to high variation in treatment benefit. Transforming insights from cognitive neuroscience into applicable clinical heuristics to categorise patients based on their underlying biomarker may support individualised treatment selection in psychiatry. We review literature on the three anxiety-related mechanisms and present a mechanistic model that may serve as a rational for pathology-based diagnostic and biomarker-guided treatment selection in psychiatry.
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