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Residual negative symptoms and cognitive impairment are common for chronic schizophrenia patients. The aim of this study was to investigate the efficacy of a mindfulness-based intervention (MBI) on negative and cognitive symptoms of schizophrenia patients with residual negative symptoms.
In this 6-week, randomized, single-blind, controlled study, a total of 100 schizophrenia patients with residual negative symptoms were randomly assigned to the MBI or control group. The 6-week MBI group and the control group with general rehabilitation programs maintained their original antipsychotic treatments. The scores for the Positive and Negative Syndrome Scale (PANSS), the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), and the Symptom Checklist 90 (SCL-90) were recorded at baseline and week 6 to assess psychotic symptoms, cognitive performance, and emotional state, respectively.
Compared with general rehabilitation programs, MBI alleviated the PANSS-negative subscore, general psychopathology subscore, and PANSS total score in schizophrenia patients with residual negative symptoms (F = 33.77, pBonferroni < 0.001; F = 42.01, pBonferroni < 0.001; F = 52.41, pBonferroni < 0.001, respectively). Furthermore, MBI improved RBANS total score and immediate memory subscore (F = 8.80, pBonferroni = 0.024; F = 11.37, pBonferroni = 0.006), as well as SCL-90 total score in schizophrenia patients with residual negative symptoms (F = 18.39, pBonferroni < 0.001).
Our results demonstrate that MBI helps schizophrenia patients with residual negative symptoms improve clinical symptoms including negative symptom, general psychopathology symptom, and cognitive impairment.
The aim of the present study is to determine whether plasma bile acids (BAs) could be used as an auxiliary diagnostic biomarker to distinguish patients with schizophrenia from healthy controls. Seventeen different BAs were quantitatively measured in plasma of 12 healthy participants and 12 patients with schizophrenia. Then, the data were subjected to correlation and linear discriminant analysis (LDA). The concentrations of cholic acid (CA), taurochenodeoxycholic acid (TCDCA) and taurodeoxycholic acid (TDCA) were significantly decreased in plasma of the schizophrenia patients. Correlation analysis showed the concentrations of CA, TCDCA and TDCA were negatively correlated with schizophrenia. In addition, LDA demonstrated that combination of CA, TCDCA and TDCA with a classification formula could predict correctly classified cases and the accuracy of prediction was up to 95.83%. Combination of the three BAs may be useful to diagnose schizophrenia in plasma samples.
Early identification of patients with bipolar disorder during their first depressive episode is beneficial to the outcome of the disorder and treatment, but traditionally this has been a great challenge to clinicians. Recently, brain-derived neurotrophic factor (BDNF) has been suggested to be involved in the pathophysiology of bipolar disorder and major depressive disorder (MDD), but it is not clear whether BDNF levels can be used to predict bipolar disorder among patients in their first major depressive episode.
To explore whether BDNF levels can differentiate between MDD and bipolar disorder in the first depressive episode.
A total of 203 patients with a first major depressive episode as well as 167 healthy controls were recruited. After 3 years of bi-annual follow-up, 164 patients with a major depressive episode completed the study, and of these, 21 were identified as having bipolar disorder and 143 patients were diagnosed as having MDD. BDNF gene expression and plasma levels at baseline were compared among the bipolar disorder, MDD and healthy control groups. Logistic regression and decision tree methods were applied to determine the best model for predicting bipolar disorder at the first depressive episode.
At baseline, patients in the bipolar disorder and MDD groups showed lower BDNF mRNA levels (P<0.001 and P = 0.02 respectively) and plasma levels (P = 0.002 and P = 0.01 respectively) compared with healthy controls. Similarly, BDNF levels in the bipolar disorder group were lower than those in the MDD group. These results showed that the best model for predicting bipolar disorder during a first depressive episode was a combination of BDNF mRNA levels with plasma BDNF levels (receiver operating characteristics (ROC) = 0.80, logistic regression; ROC = 0.84, decision tree).
Our findings suggest that BDNF levels may serve as a potential differential diagnostic biomarker for bipolar disorder in a patient's first depressive episode.
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