A useful scale for identification of mixed features in major depressive episodes (MDE) patients is urgent in China. This study aimed to evaluate the reliability and validity of the Chinese version of the Clinically Useful Depression Outcome Scale supplemented with questions for the DSM-5 mixed features specifier (Chinese-CUDOS-M) in MDE patients.
A total of 152 MDE patients were recruited and assessed using Chinese-CUDOS-M, Patient Health Questionnaire-9 (PHQ-9) and 32-item Hypomania Checklist (HCL-32). Principal component analysis (PCA) and exploratory factor analysis (EFA) were conducted. The predictive validity was calculated by the area under the receiver operating characteristic curve (AUROC).
The Cronbach\'s alpha of Chinese-CUDOS-M was 0.85. PCA showed three common factors with eigenvalue greater than 1; the eigenvalue of factor I was 4.96, with 38.1% of variance explanation. Chinese-CUDOS-M depression subscale was associated with PHQ-9 (r = 0.83, p<0.01), and manic subscale was associated with HCL-32 (r = 0.73, p< 0.01). AUROC of the Chinese-CUDOS-M for patients with mixed depression was 0.90 (95%CI: 0.85-0.95), with a cut-off value of 7, sensitivity of 0.95, and specificity of 0.73. Furthermore, AUROC was 0.88 in patients with major depressive disorder (MDD), with a cut-off value of 7, sensitivity of 0.96, and specificity of 0.71. AUROC was 0.92 in bipolar disorder (BD) depression patients, with a cut-off value of 9, sensitivity of 0.89, and specificity of 0.87.
Our study shows that the Chinese-CUDOS-M can identify mixed features in both MDD and BD depression with satisfactory reliability and validity.

This study aims to explore the reliability, validity, and feasibility of Clinically Useful Depression Outcome Scale (CUDOS) in screening mixed features in patients diagnosed with mania.
A total of 109 patients with (hypo-) manic episode were recruited. The reliability of Chinese version of CUDOS (CUDOS-C) were analyzed with Cronbach\'s alpha and intraclass correlation coefficient (ICC). Spearman correlation coefficient was used to analyze the validity by comparing the correlation between CUDOS-C and Patient Health Questionnaire-9 (PHQ-9), 32-item Hypomania Checklist (HCL-32). The score of MINI (hypo-) manic episode with mixed features-DSM-5 Module-Chinese version(MINI-M-C) ≥ 2 was considered as the gold standard of mixed features, and the receiver operating characteristic (ROC) curve analysis was used to calculate the optimal cut-off values of CUDOS-C score.
The Cronbach\'s alpha value of CUDOS-C was 0.898, and the ICC of CUDOS-C test-retest was 0.880 (95% CI: 0.812-0.923, p < .05).The CUDOS-C score was significantly correlated with PHQ-9 score (r = 0.893, p = .000), but not with HCL-32 score(r = 0.088, p = .364).The area under ROC curve was 0.909 (95% CI: 0.855 to 0.963, p < .001) for CUDOS-C identifying mixed features in mania. The optimal cut-off value was 11 with a sensitivity of 0.854 and a specificity of 0.868. The CUDOS-C (score ≥ 12) identified 40.4% of the patients with mixed features, which was higher than those diagnosed by clinicians (18.3%) and screened using MINI-M-C (37.6%).
The results indicate the CUDOS-C is a reliable and valid self-administered questionnaire for assessing depressive symptoms and screening patients with mixed mania.

With the modification of DSM-5 mixed features specifier, a brief scale to screen mixed features in patients with mood disorders is needed in clinical practice. This study aimed to explore the psychometric properties of the Chinese version of the Clinically Useful Depression Outcome Scale supplemented with DSM-5 Mixed subtype (CUDOS-M-C) for the Chinese patients with mood disorders.
Overall, 300 patients with major depressive episode were recruited. All participants were assessed using CUDOS-M-C, Young Mania Rating Scale, Hamilton Anxiety Scale and Montgomery-Asberg Depression Rating Scale. The receiver operating characteristic (ROC) curve analysis was used to calculate the optimal cut-off values of CUDOS-M-C score. The reliability and validity of CUDOS-M-C were examined using Cronbach\'s alpha, intraclass correlation coefficient (ICC) and principal component analysis (PCA).
The results of PCA indicated two-factor structure as the best solution for CUDOS-M-C, which explained 54.82% of cumulative variance. The Cronbach\'s alpha was 0.892 and the ICC was 0.853. The area under the ROC curve of the CUDOS-M-C for participants with mixed depression was 0.927 (p<0.001) and the suitable cut-off value was 8, with a sensitivity of 91.6% and specificity of 79.9%.
Most of the patients were recruited from eastern China and further research with larger sample is warranted. And this study did not perform confirmatory factor analysis to identify the generalization of factor structure of CUDOS-M-C. Besides, the study performed the test-retest reliability of CUDOS-M-C and further analysis is needed to ascertain the patient\'s post-treatment changes.
The CUDOS-M-C demonstrated to have satisfactory psychometric properties as a self-report scale, and could be applied to screen patients with mixed depression in clinical practice.

The thermostability of proteins is a key factor considered during enzyme engineering, and finding a method that can identify thermophilic and non-thermophilic proteins will be helpful for enzyme design. In this study, we established a novel method combining mixed features and machine learning to achieve this recognition task. In this method, an amino acid reduction scheme was adopted to recode the amino acid sequence. Then, the physicochemical characteristics, auto-cross covariance (ACC), and reduced dipeptides were calculated and integrated to form a mixed feature set, which was processed using correlation analysis, feature selection, and principal component analysis (PCA) to remove redundant information. Finally, four machine learning methods and a dataset containing 500 random observations out of 915 thermophilic proteins and 500 random samples out of 793 non-thermophilic proteins were used to train and predict the data. The experimental results showed that 98.2% of thermophilic and non-thermophilic proteins were correctly identified using 10-fold cross-validation. Moreover, our analysis of the final reserved features and removed features yielded information about the crucial, unimportant and insensitive elements, it also provided essential information for enzyme design.

OBJECTIVE: This post-hoc analysis assessed rates of symptomatic and functional remission, as well as recovery (combination of symptomatic and functional remission), in patients treated with lurasidone for major depressive disorder (MDD) associated with subthreshold hypomanic symptoms (mixed features).
METHODS: Patients with MDD plus two or three manic symptoms (defined as per the DSM-5 mixed-features specifier) were randomly assigned to flexible-dose lurasidone 20-60 mg/day (n=109) or placebo (n=100) for 6 weeks, followed by a 3-month open-label, flexible-dose extension study for U.S. sites only (n=48). Cross-sectional recovery was defined as the presence of both symptomatic remission (Montgomery-Åsberg Depression Rating Scale score ≤ 12) and functional remission (all Sheehan Disability Scale [SDS] domain scores ≤3) at week 6, and at both months 1 and 3 of the extension study (\"sustained recovery\").
RESULTS: A significantly higher proportion of lurasidone-treated patients (31.3%) achieved recovery (assessed cross-sectionally) compared to placebo (12.2%, p=0.002) at week 6. The number of manic symptoms at baseline moderated the effect size for attaining cross-sectional recovery for lurasidone treatment (vs. placebo) (p=0.028). Sustained recovery rates were higher in patients initially treated with lurasidone (20.8%) versus placebo (12.5%).
CONCLUSIONS: In this post-hoc analysis of a placebo-controlled study with open-label extension that involved patients with MDD and mixed features, lurasidone was found to significantly improve the rate of recovery at 6 weeks (vs. placebo) that was sustained at month 3 of the extension study. The presence of two (as opposed to three) manic symptoms moderated recovery at the acute study endpoint.