Risk of Gestational Diabetes Due to Maternal and Partner Smoking

Pregnant women are among the most vulnerable to environmental exposure to tobacco smoke (EET); which has been linked to problems in the mothers’ health; one of the most frequent is gestational diabetes (GD). For this reason, there are specific interventions and prevention strategies designed to reduce this exposure risk. However, currently, they are mostly aimed only at aiding the pregnant women with smoking cessation during pregnancy and do not assess or address the risk from passive exposure due to partner smoking. The aim of this work is to study the exposure to EET of pregnant women considering active and passive smoking and to evaluate its effect on the development of GD. This is an observational case-control study within a retrospective cohort of pregnant women. Information on smoking habits was obtained from both personal interviews and recorded medical history. In total, 16.2% of mothers and 28.3% of partners declared having been active smokers during pregnancy; 36.5% of the women presented EET during pregnancy when both active and passive smoking were considered. After adjustments, the association with the EET and GD of the mother was (aOR 1.10 95% CI: 0.64–1.92); for the EET of the partner, it was (aOR 1.66 95% CI: 1.01–2.77); for both partners, it was (aOR 1.82 95% CI: 1.15–2.89), adjusted by the mother’s age and body mass index. There is a lack of education regarding the effects of passive exposure to tobacco smoke. It is essential that pregnant women and their partners are educated on the risks of active and passive smoking; this could improve the effectiveness of other GD prevention strategies.

Passive Exposure Sparsifies Neural Activity in the Primary Visual Cortex

A notable feature of neural activity is sparseness - namely, that only a small fraction of neurons in a local circuit have high activity at any moment. Not only is sparse neural activity observed experimentally in most areas of the brain, but sparseness has been proposed as an optimization or design principle for neural circuits. Sparseness can increase the energy efficiency of the neu- ral code as well as allow for beneficial computations to be carried out. But how does the brain achieve sparse- ness? Here, we found that when neurons in the primary visual cortex were passively exposed to a set of images over several days, neural responses became more sparse. Sparsification was driven by a decrease in the response of neurons with low or moderate activity, while highly active neurons retained similar responses. We also observed a net decorrelation of neural activity. These changes sculpt neural activity for greater coding efficiency.

Feature distribution learning by passive exposure

Humans can rapidly estimate the statistical properties of groups of stimuli, including their average and variability. But recent studies of so-called Feature Distribution Learning (FDL) have shown that observers can quickly learn even more complex aspects of feature distributions. In FDL, observers learn the full shape of a distribution of features in a set of distractor stimuli and use this information to improve visual search: response times (RT) are slowed if the target feature lies inside the previous distractor distribution, and the RT patterns closely reflect the distribution shape. FDL requires only a few trials and is markedly sensitive to different distribution types. It is unknown, however, whether our perceptual system encodes feature distributions automatically and by passive exposure, or whether this learning requires active engagement with the stimuli. In two experiments, we sought to answer this question. During an initial exposure stage, participants passively viewed a display of 36 lines that included one orientation singleton or no singletons. In the following search display, they had to find an oddly oriented target. The orientations of the lines were determined either by a Gaussian or a uniform distribution. We found evidence for FDL only when the passive trials contained an orientation singleton. Under these conditions, RT decreased as a function of the orientation distance between the target and the exposed distractor distribution. These results suggest that FDL can occur by passive exposure, but only if an orientation singleton appears during exposure to the distribution.

Genomic Profiling of Lung Adenocarcinoma in Never-Smokers

PURPOSE Approximately 10%-40% of patients with lung cancer report no history of tobacco smoking (never-smokers). We analyzed whole-exome and RNA-sequencing data of 160 tumor and normal lung adenocarcinoma (LUAD) samples from never-smokers to identify clinically actionable alterations and gain insight into the environmental and hereditary risk factors for LUAD among never-smokers. METHODS We performed whole-exome and RNA-sequencing of 88 and 69 never-smoker LUADs. We analyzed these data in conjunction with data from 76 never-smoker and 299 smoker LUAD samples sequenced by The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium. RESULTS We observed a high prevalence of clinically actionable driver alterations in never-smoker LUADs compared with smoker LUADs (78%-92% v 49.5%; P < .0001). Although a subset of never-smoker samples demonstrated germline alterations in DNA repair genes, the frequency of samples showing germline variants in cancer predisposing genes was comparable between smokers and never-smokers (6.4% v 6.9%; P = .82). A subset of never-smoker samples (5.9%) showed mutation signatures that were suggestive of passive exposure to cigarette smoke. Finally, analysis of RNA-sequencing data showed distinct immune transcriptional subtypes of never-smoker LUADs that varied in their expression of clinically relevant immune checkpoint molecules and immune cell composition. CONCLUSION In this comprehensive genomic and transcriptome analysis of never-smoker LUADs, we observed a potential role for germline variants in DNA repair genes and passive exposure to cigarette smoke in the pathogenesis of a subset of never-smoker LUADs. Our findings also show that clinically actionable driver alterations are highly prevalent in never-smoker LUADs, highlighting the need for obtaining biopsies with adequate cellularity for clinical genomic testing in these patients.

Effects of E-Cigarette Exposure on Prenatal Life and Childhood Respiratory Health: A Review of Current Evidence

In the last decade, widespread use of E-cigarettes (EC) has occurred all over the world. Whereas, a large amount of evidence on harm to children from conventional cigarette exposure is available, data on health effects in this population throughout different vulnerability windows are still a matter of concern. Exposure to EC during pregnancy may compromise placental function, resulting in fetal structural abnormalities. Specifically, this may cause physio-pathologic changes in the developing lung, which in turn may impair respiratory health later in life. Furthermore, there is evidence that using EC can cause both short- and long-term respiratory problems in the pediatric population and there is great concern for future young people with nicotine addiction. The low parental perception of the risks connected to EC exposure for children increases their susceptibility to harmful effects from passive vaping. This minireview aims to summarize the current evidence focusing on: (i) prenatal effects of EC passive exposure; (ii) post-natal respiratory effects of EC exposure in youth; (iii) parental attitudes toward EC use and perception of children's health risks connected to EC exposure; and (iv) addressing gaps in our current evidence.

Investigating human prediction: sequence termination provides insights into predictive statistics

Prediction is a fundamental process in human cognition. Prediction means extracting one or more statistics from the distribution of past inputs and using that information to make a decision. What are the statistics underlying human predictions, and how do they change with training? To investigate these questions, we designed a sequence termination task, where participants watch temporally unfolding sequences and terminate them when they can predict the next item. We then test how well the participants’ termination points are predicted by computational models. We contrast frequency estimation models (How often did this symbol appear in the sequence?), transition models (How often did symbol A follow symbol B?), and a chunking model (What are the patterns of symbols?). In an online experiment with 65 adults, we find that participants are best fit by a transition-counting model. To assess the effect of training, we manipulated passive exposure to the sequences prior to the sequence termination task. Contrary to our expectations, prior exposure to sequences had no effect on termination performance– whether tested statistically or computationally, and despite good power. Lastly, training specifically on the termination task may shift responses towards chunking. These results provide insight into the representations, or information in mind, behind prediction. However, the lack of an effect of prior exposure makes it clear that sequence termination measures explicit, or conscious, prediction. Future work could examine whether representations in explicit prediction tasks like sequence termination are different from implicit, or unconscious, tasks like the serial reaction time task.