Prediction of perinatal depression from adolescence and before conception (VIHCS): 20-year prospective cohort study

BACKGROUND: Perinatal depression is a neglected global health priority, affecting 10-15% of women in high-income countries and a greater proportion in low-income countries. Outcomes for children include cognitive, behavioural, and emotional difficulties and, in low-income settings, perinatal depression is associated with stunting and physical illness. In the Victorian Intergenerational Health Cohort Study (VIHCS), we aimed to assess the extent to which women with perinatal depressive symptoms had a history of mental health problems before conception. METHODS: VIHCS is a follow-up study of participants in the Victorian Adolescent Health Cohort Study (VAHCS), which was initiated in August, 1992, in the state of Victoria, Australia. In VAHCS, participants were assessed for health outcomes at nine timepoints (waves) from age 14 years to age 29 years. Depressive symptoms were measured with the Revised Clinical Interview Schedule and the General Health Questionnaire. Enrolment to VIHCS began in September, 2006, during the ninth wave of VAHCS; depressive symptoms at this timepoint were measured with the Composite International Diagnostic Interview. We contacted women every 6 months (from age 29 years to age 35 years) to identify any pregnancies. We assessed perinatal depressive symptoms with the Edinburgh Postnatal Depression Scale (EPDS) by computer-assisted telephone interview at 32 weeks of gestation, 8 weeks after birth, and 12 months after birth. We defined perinatal depression as an EPDS score of 10 or more. FINDINGS: From a stratified random sample of 1000 female participants in VAHCS, we enrolled 384 women with 564 pregnancies. 253 (66%) of these women had a previous history of mental health problems at some point in adolescence or young adulthood. 117 women with a history of mental health problems in both adolescence and young adulthood had 168 pregnancies, and perinatal depressive symptoms were reported for 57 (34%) of these pregnancies, compared with 16 (8%) of 201 pregnancies in 131 women with no preconception history of mental health problems (adjusted odds ratio 8·36, 95% CI 3·34-20·87). Perinatal depressive symptoms were reported at one or more assessment points in 109 pregnancies; a preconception history of mental health problems was reported in 93 (85%) of these pregnancies. INTERPRETATION: Perinatal depressive symptoms are mostly preceded by mental health problems that begin before pregnancy, in adolescence or young adulthood. Women with a history of persisting common mental disorders before pregnancy are an identifiable high-risk group, deserving of clinical support throughout the childbearing years. Furthermore, the window for considering preventive intervention for perinatal depression should extend to the time before conception. FUNDING: National Health and Medical Research Council (Australia), Victorian Health Promotion Foundation, Colonial Foundation, Australian Rotary Health Research and Perpetual Trustees.

Lung cancer prediction from microarray data by gene expression programming

Lung cancer is a leading cause of cancer-related death worldwide. The early diagnosis of cancer has demonstrated to be greatly helpful for curing the disease effectively. Microarray technology provides a promising approach of exploiting gene profiles for cancer diagnosis. In this study, the authors propose a gene expression programming (GEP)-based model to predict lung cancer from microarray data. The authors use two gene selection methods to extract the significant lung cancer related genes, and accordingly propose different GEP-based prediction models. Prediction performance evaluations and comparisons between the authors' GEP models and three representative machine learning methods, support vector machine, multi-layer perceptron and radial basis function neural network, were conducted thoroughly on real microarray lung cancer datasets. Reliability was assessed by the cross-data set validation. The experimental results show that the GEP model using fewer feature genes outperformed other models in terms of accuracy, sensitivity, specificity and area under the receiver operating characteristic curve. It is concluded that GEP model is a better solution to lung cancer prediction problems.