Generalized anxiety disorder (GAD) and major depressive disorder (MDD) are highly prevalent and impairing problems, but frequently go undetected, leading to substantial treatment delays. Electronic health records (EHRs) collect a great deal of biometric markers and patient characteristics that could foster the detection of GAD and MDD in primary care settings. We approached the problem of predicting MDD and GAD using a novel machine learning pipeline to re-analyze data from an observational study. The pipeline constitutes an ensemble of algorithmically distinct machine learning methods, including deep learning. A sample of 4,184 undergraduate students completed the study, undergoing a general health screening and completing a psychiatric assessment for MDD and GAD. After explicitly excluding all psychiatric information, 59 biomedical and demographic features from the general health survey in addition to a set of engineered features were used for model training. We assessed the model's performance on a held-out test set and found an AUC of 0.73 (sensitivity: 0.66, specificity: 0.7) and 0.67 (sensitivity: 0.55, specificity: 0.7) for GAD, and MDD, respectively. Additionally, we used advanced techniques (SHAP values) to illuminate which features had the greatest impact on prediction for each disease. The top predictive features for MDD were being satisfied with living conditions and having public health insurance. The top predictive features for GAD were vaccinations being up to date and marijuana use. Our results indicate moderate predictive performance for the application of machine learning methods in detection of GAD and MDD based on EHR data. By identifying important predictors of GAD and MDD, these results may be used in future research to aid in the early detection of MDD and GAD.
This study aimed to identify biomarkers of major depressive disorder (MDD), by relating neuroimage-derived measures to binary (MDD/control), ordinal (severe MDD/mild MDD/control), or continuous (depression severity) outcomes. To address MDD heterogeneity, factors (severity of psychic depression, motivation, anxiety, psychosis, and sleep disturbance) were also used as outcomes. A multisite, multimodal imaging (diffusion MRI [dMRI] and structural MRI [sMRI]) cohort (52 controls and 147 MDD patients) and several modeling techniques-penalized logistic regression, random forest, and support vector machine (SVM)-were used. An additional cohort (25 controls and 83 MDD patients) was used for validation. The optimally performing classifier (SVM) had a 26.0% misclassification rate (binary), 52.2 ± 1.69% accuracy (ordinal) and r = .36 correlation coefficient (p < .001, continuous). Using SVM, R values for prediction of any MDD factors were <10%. Binary classification in the external data set resulted in 87.95% sensitivity and 32.00% specificity. Though observed classification rates are too low for clinical utility, four image-based features contributed to accuracy across all models and analyses-two dMRI-based measures (average fractional anisotropy in the right cuneus and left insula) and two sMRI-based measures (asymmetry in the volume of the pars triangularis and the cerebellum) and may serve as a priori regions for future analyses. The poor accuracy of classification and predictive results found here reflects current equivocal findings and sheds light on challenges of using these modalities for MDD biomarker identification. Further, this study suggests a paradigm (e.g., multiple classifier evaluation with external validation) for future studies to avoid nongeneralizable results.
Pancreatic Cancer (PC) is a deadly disease in need of new therapeutic options. We recently developed a novel tricarbonylmethane agent (CMC2.24) as a therapeutic agent for PC, and evaluated its efficacy in preclinical models of PC. CMC2.24 inhibited the growth of various human PC cell lines in a concentration and time-dependent manner. Normal human pancreatic epithelial cells were resistant to CMC2.24, indicating selectivity. CMC2.24 reduced the growth of subcutaneous and orthotopic PC xenografts in mice by up to 65% (P < 0.02), and the growth of a human patient-derived tumor xenograft by 47.5% (P < 0.03 vs vehicle control). Mechanistically, CMC2.24 inhibited the Ras-RAF-MEK-ERK pathway. Based on Ras Pull-Down Assays, CMC2.24 inhibited Ras-GTP, the active form of Ras, in MIA PaCa-2 cells and in pancreatic acinar explants isolated from Kras mutant mice, by 90.3% and 89.1%, respectively (P < 0.01, for both). The inhibition of active Ras led to an inhibition of c-RAF, MEK, and ERK phosphorylation by 93%, 91%, and 87%, respectively (P < 0.02, for all) in PC xenografts. Furthermore, c-RAF overexpression partially rescued MIA PaCa-2 cells from the cell growth inhibition by CMC2.24. In addition, downstream of ERK, CMC2.24 inhibited STAT3 phosphorylation levels at the serine 727 residue, enhanced the levels of superoxide anion in mitochondria, and induced intrinsic apoptosis as shown by the release of cytochrome c from the mitochondria to the cytosol and the further cleavage of caspase 9 in PC cells. In conclusion, CMC2.24, a potential Ras inhibitor, is an efficacious agent for PC treatment in preclinical models, deserving further evaluation.
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