ABSTRACT
BACKGROUND: The DSM5-defined mixed features in depression do not include psychomotor agitation, irritability or distractibility because they are considered overlapping symptoms. A growing number of modern psychiatrists have expressed dissatisfaction with this and proposed alternative sets of mixed symptoms that are much more common and clinically relevant. Among such alternative criteria were those proposed by Koukopoulos. He utilized the research diagnostic criteria of agitated depression (RDC-A) as a mixed depression subtype, and validated another form of mixed depression, the Koukopoulos criteria for mixed depression (K-DMX). PURPOSE: This study provides psychometric validation for the first self-rated scale designed to measure the most common mixed symptoms in depression as proposed by Koukopoulos. PATIENTS AND METHODS: We conducted a multicenter cross-sectional study of 170 patients with unipolar depression. They completed the Shahin Mixed Depression Scale (SMDS) and underwent expert interviews as a gold standard reference. SMDS' psychometric properties were assessed, including Cronbach's alpha, factor analysis, sensitivity, specificity, predictive value and accuracy. RESULTS: We found significant association and agreement between mixity according to SMDS and the gold standard (K-DMX and RDC-A according to expert interview) with good internal consistency (Cronbach's alpha=0.87), high sensitivity (=91.4%), specificity (=98.0%), positive predictive value (=96.9%), negative predictive value (= 94.2%) and accuracy (=95.2%). Factor analysis identified one factor for psychomotor agitation and another for mixity without psychomotor agitation. CONCLUSION: SMDS was a reliable and valid instrument for assessing the frequently encountered and clinically relevant mixed features in depression.
ABSTRACT
BACKGROUND: Volatile organic compounds (VOCs) emitted from exhaled breath from human bodies have been proven to be a useful source of information for early lung cancer diagnosis. To date, there are still arguable information on the production and origin of significant VOCs of cancer cells. Thus, this study aims to conduct in-vitro experiments involving related cell lines to verify the capability of VOCs in providing information of the cells. METHOD: The performances of e-nose technology with different statistical methods to determine the best classifier were conducted and discussed. The gas sensor study has been complemented using solid phase micro-extraction-gas chromatography mass spectrometry. For this purpose, the lung cancer cells (A549 and Calu-3) and control cell lines, breast cancer cell (MCF7) and non-cancerous lung cell (WI38VA13) were cultured in growth medium. RESULTS: This study successfully provided a list of possible volatile organic compounds that can be specific biomarkers for lung cancer, even at the 24th hour of cell growth. Also, the Linear Discriminant Analysis-based One versus All-Support Vector Machine classifier, is able to produce high performance in distinguishing lung cancer from breast cancer cells and normal lung cells. CONCLUSION: The findings in this work conclude that the specific VOC released from the cancer cells can act as the odour signature and potentially to be used as non-invasive screening of lung cancer using gas array sensor devices.
