Which women stop smoking during pregnancy and the effect on breastfeeding duration

BACKGROUND: Cigarette smoking during pregnancy increases the risk of adverse pregnancy outcomes and women who quit smoking at this time are able to reduce the risk of low birth weight, preterm labour, spontaneous abortion and perinatal death. This study investigates the socio-demographic characteristics of pregnant women who stop smoking during pregnancy and the association between stopping smoking and breastfeeding duration. METHODS: A 12 month longitudinal study was conducted in two public maternity hospitals in Perth, Australia between mid-September 2002 and mid-July 2003. While in hospital, participating mothers completed a self-administered baseline questionnaire. Follow up telephone interviews were conducted at 4, 10, 16, 22, 32, 40 and 52 weeks. RESULTS: A total of 587 (55%) mothers participated in the study. Two hundred and twenty six (39%) mothers reported smoking prior to pregnancy and 77 (34%) of these stopped smoking during pregnancy. Women who were pregnant for the first time were twice as likely (OR = 2.05; 95% CI 1.047 – 4.03; p < 0.05) to quit smoking as multiparous women. Women who smoked more than 10 cigarettes per day were significantly less likely to quit smoking during pregnancy (OR = 0.36; 95% CI 0.18 – 0.69; p < 0.05). Women who consumed alcohol before pregnancy were three times more likely to quit smoking (OR = 2.58; 95% CI 1.00 – 6.66; p < 0.05). Quitting smoking during pregnancy was significantly associated with breastfeeding for longer than six months (OR = 3.70; 95% CI 1.55 – 8.83; p < 0.05). CONCLUSION: Pregnancy is a time when many women are motivated to quit smoking and providing targeted smoking cessation interventions at this time, which take into account factors predictive of quitting smoking, are more likely to be successful

Microbial Forensics: Predicting Phenotypic Characteristics and Environmental Conditions from Large-Scale Gene Expression Profiles

<div><p>A tantalizing question in cellular physiology is whether the cellular state and environmental conditions can be inferred by the expression signature of an organism. To investigate this relationship, we created an extensive normalized gene expression compendium for the bacterium <i>Escherichia coli</i> that was further enriched with meta-information through an iterative learning procedure. We then constructed an ensemble method to predict environmental and cellular state, including strain, growth phase, medium, oxygen level, antibiotic and carbon source presence. Results show that gene expression is an excellent predictor of environmental structure, with multi-class ensemble models achieving balanced accuracy between 70.0% (±3.5%) to 98.3% (±2.3%) for the various characteristics. Interestingly, this performance can be significantly boosted when environmental and strain characteristics are simultaneously considered, as a composite classifier that captures the inter-dependencies of three characteristics (medium, phase and strain) achieved 10.6% (±1.0%) higher performance than any individual models. Contrary to expectations, only 59% of the top informative genes were also identified as differentially expressed under the respective conditions. Functional analysis of the respective genetic signatures implicates a wide spectrum of Gene Ontology terms and KEGG pathways with condition-specific information content, including iron transport, transferases, and enterobactin synthesis. Further experimental phenotypic-to-genotypic mapping that we conducted for knock-out mutants argues for the information content of top-ranked genes. This work demonstrates the degree at which genome-scale transcriptional information can be predictive of latent, heterogeneous and seemingly disparate phenotypic and environmental characteristics, with far-reaching applications.</p></div