Postpartum mental health after Hurricane Katrina: A cohort study

<p>Abstract</p> <p>Background</p> <p>Natural disaster is often a cause of psychopathology, and women are vulnerable to post-traumatic stress disorder (PTSD) and depression. Depression is also common after a woman gives birth. However, no research has addressed postpartum women's mental health after natural disaster.</p> <p>Methods</p> <p>Interviews were conducted in 2006–2007 with women who had been pregnant during or shortly after Hurricane Katrina. 292 New Orleans and Baton Rouge women were interviewed at delivery and 2 months postpartum. Depression was assessed using the Edinburgh Depression Scale and PTSD using the Post-Traumatic Stress Checklist. Women were asked about their experience of the hurricane with questions addressing threat, illness, loss, and damage. Chi-square tests and log-binomial/Poisson models were used to calculate associations and relative risks (RR).</p> <p>Results</p> <p>Black women and women with less education were more likely to have had a serious experience of the hurricane. 18% of the sample met the criteria for depression and 13% for PTSD at two months postpartum. Feeling that one's life was in danger was associated with depression and PTSD, as were injury to a family member and severe impact on property. Overall, two or more severe experiences of the storm was associated with an increased risk for both depression (relative risk (RR) 1.77, 95% confidence interval (CI) 1.08–2.89) and PTSD (RR 3.68, 95% CI 1.80–7.52).</p> <p>Conclusion</p> <p>Postpartum women who experience natural disaster severely are at increased risk for mental health problems, but overall rates of depression and PTSD do not seem to be higher than in studies of the general population.</p

Hybridization in parasites: consequences for adaptive evolution, pathogenesis and public health in a changing world

[No abstract available

Testing A (Stringy) Model of Quantum Gravity

I discuss a specific model of space-time foam, inspired by the modern non-perturbative approach to string theory (D-branes). The model views our world as a three brane, intersecting with D-particles that represent stringy quantum gravity effects, which can be real or virtual. In this picture, matter is represented generically by (closed or open) strings on the D3 brane propagating in such a background. Scattering of the (matter) strings off the D-particles causes recoil of the latter, which in turn results in a distortion of the surrounding space-time fluid and the formation of (microscopic, i.e. Planckian size) horizons around the defects. As a mean-field result, the dispersion relation of the various particle excitations is modified, leading to non-trivial optical properties of the space time, for instance a non-trivial refractive index for the case of photons or other massless probes. Such models make falsifiable predictions, that may be tested experimentally in the foreseeable future. I describe a few such tests, ranging from observations of light from distant gamma-ray-bursters and ultra high energy cosmic rays, to tests using gravity-wave interferometric devices and terrestrial particle physics experients involving, for instance, neutral kaons.Comment: 25 pages LATEX, four figures incorporated, uses special proceedings style. Invited talk at the third international conference on Dark Matter in Astro and Particle Physics, DARK2000, Heidelberg, Germany, July 10-15 200

Use of mental health services among disaster survivors: predisposing factors

<p>Abstract</p> <p>Background</p> <p>Given the high prevalence of mental health problems after disasters it is important to study health services utilization. This study examines predictors for mental health services (MHS) utilization among survivors of a man-made disaster in the Netherlands (May 2000).</p> <p>Methods</p> <p>Electronic records of survivors (n = 339; over 18 years and older) registered in a mental health service (MHS) were linked with general practice based electronic medical records (EMRs) of survivors and data obtained in surveys. EMR data were available from 16 months pre-disaster until 3 years post-disaster. Symptoms and diagnoses in the EMRs were coded according to the International Classification of Primary Care (ICPC). Surveys were carried out 2–3 weeks and 18 months post-disaster, and included validated questionnaires on psychological distress, post-traumatic stress reactions and social functioning. Demographic and disaster-related variables were available. Predisposing factors for MHS utilization 0–18 months and 18–36 months post-disaster were examined using multiple logistic regression models.</p> <p>Results</p> <p>In multiple logistic models, adjusting for demographic and disaster related variables, MHS utilization was predicted by demographic variables (young age, immigrant, public health insurance, unemployment), disaster-related exposure (relocation and injuries), self-reported psychological problems and pre- and post-disaster physician diagnosed health problems (chronic diseases, musculoskeletal problems). After controlling for all health variables, disaster intrusions and avoidance reactions (OR:2.86; CI:1.48–5.53), hostility (OR:2.04; CI:1.28–3.25), pre-disaster chronic diseases (OR:1.82; CI:1.25–2.65), injuries as a result of the disaster (OR:1.80;CI:1.13–2.86), social functioning problems (OR:1.61;CI:1.05–2.44) and younger age (OR:0.98;CI:0.96–0.99) predicted MHS utilization within 18 months post-disaster. Furthermore, disaster intrusions and avoidance reactions (OR:2.29;CI:1.04–5.07) and hostility (OR:3.77;CI:1.51–9.40) predicted MHS utilization following 18 months post-disaster.</p> <p>Conclusion</p> <p>This study showed that several demographic and disaster-related variables and self-reported and physician diagnosed health problems predicted post-disaster MHS-use. The most important factors to predict post-disaster MHS utilization were disaster intrusions and avoidance reactions and symptoms of hostility (which can be identified as symptoms of PTSD) and pre-disaster chronic diseases.</p

Pediatric patient asthma-related emergency department visits and admissions in Washington, DC, from 2001–2004, and associations with air quality, socio-economic status and age group

BACKGROUND: The District of Columbia (DC) Department of Health, under a grant from the US Centers for Disease Control and Prevention, established an Environmental Public Health Tracking Program. As part of this program, the goals of this contextual pilot study are to quantify short-term associations between daily pediatric emergency department (ED) visits and admissions for asthma exacerbations with ozone and particulate concentrations, and broader associations with socio-economic status and age group. METHODS: Data included daily counts of de-identified asthma-related pediatric ED visits for DC residents and daily ozone and particulate concentrations during 2001–2004. Daily temperature, mold, and pollen measurements were also obtained. After a cubic spline was applied to control for long-term seasonal trends in the ED data, a Poisson regression analysis was applied to the time series of daily counts for selected age groups. RESULTS: Associations between pediatric asthma ED visits and outdoor ozone concentrations were significant and strongest for the 5–12 year-old age group, for which a 0.01-ppm increase in ozone concentration indicated a mean 3.2% increase in daily ED visits and a mean 8.3% increase in daily ED admissions. However, the 1–4 yr old age group had the highest rate of asthma-related ED visits. For 1–17 yr olds, the rates of both asthma-related ED visits and admissions increased logarithmically with the percentage of children living below the poverty threshold, slowing when this percentage exceeded 30%. CONCLUSION: Significant associations were found between ozone concentrations and asthma-related ED visits, especially for 5–12 year olds. The result that the most significant ozone associations were not seen in the age group (1–4 yrs) with the highest rate of asthma-related ED visits may be related to the clinical difficulty in accurately diagnosing asthma among this age group. We observed real increases in relative risk of asthma ED visits for children living in higher poverty zip codes versus other zip codes, as well as similar logarithmic relationships for visits and admissions, which implies ED over-utilization may not be a factor. These results could suggest designs for future epidemiological studies that include more information on individual exposures and other risk factors

Variability in C-reactive protein is associated with cognitive impairment in women living with and without HIV: a longitudinal study

Despite the availability of effective antiretroviral therapies, cognitive impairment (CI) remains prevalent in HIV-infected (HIV+) individuals. Evidence from primarily cross-sectional studies, in predominantly male samples, implicates monocyte- and macrophage-driven inflammatory processes linked to HIV-associated CI. Thus, peripheral systemic inflammatory markers may be clinically useful biomarkers in tracking HIV-associated CI. Given sex differences in immune function, we focused here on whether mean and intra-individual variability in inflammatory marker-predicted CI in HIV+ and HIV− women. Seventy-two HIV+ (36 with CI) and 58 HIV− (29 with CI) propensity-matched women participating in the Women’s Interagency HIV Study completed a neuropsychological battery once between 2009 and 2011, and performance was used to determine CI status. Analysis of 13 peripheral immune markers was conducted on stored biospecimens at three time points (7 and 3.5 years before neuropsychological data collection and concurrent with data collection). HIV+ women showed alterations in 8 immune markers compared to HIV− women. The strongest predictors of CI across HIV+ and HIV− women were lower mean soluble tumor necrosis factor receptor I (sTNFRI) levels, higher mean interleukin (IL)-6 levels, and greater variability in C-reactive protein (CRP) and matrix metalloproteinase (MMP)-9 (p values < 0.05). Stratified by HIV, the only significant predictor of CI was greater variability in CRP for both HIV+ and HIV− women (p values < 0.05). This variability predicted lower executive function, attention/working memory, and psychomotor speed in HIV+ but only learning in HIV− women (p values < 0.05). Intra-individual variability in CRP levels over time may be a good predictor of CI in predominately minority low-socioeconomic status midlife women

Discrimination in lexical decision.

In this study we present a novel set of discrimination-based indicators of language processing derived from Naive Discriminative Learning (ndl) theory. We compare the effectiveness of these new measures with classical lexical-distributional measures-in particular, frequency counts and form similarity measures-to predict lexical decision latencies when a complete morphological segmentation of masked primes is or is not possible. Data derive from a re-analysis of a large subset of decision latencies from the English Lexicon Project, as well as from the results of two new masked priming studies. Results demonstrate the superiority of discrimination-based predictors over lexical-distributional predictors alone, across both the simple and primed lexical decision tasks. Comparable priming after masked corner and cornea type primes, across two experiments, fails to support early obligatory segmentation into morphemes as predicted by the morpho-orthographic account of reading. Results fit well with ndl theory, which, in conformity with Word and Paradigm theory, rejects the morpheme as a relevant unit of analysis. Furthermore, results indicate that readers with greater spelling proficiency and larger vocabularies make better use of orthographic priors and handle lexical competition more efficiently

Disrupting education using smart mobile pedagogies

© Springer Nature Switzerland AG 2019. As mobile technologies become more multifaceted and ubiquitous in society, educational researchers are investigating the use of these technologies in education. A growing body of evidence shows that traditional pedagogies still dominate the educational field and are misaligned with the diverse learning opportunities offered by the use of mobile technologies. There is an imperative to question those traditional notions of education, including how, where and when teaching and learning are enacted, and to explore the possible mediating roles of new mobile technologies. New smart pedagogies, which embrace the affordances offered by mobile technologies, have the potential to disrupt notions of schooling. In this chapter, we examine the nature of smart pedagogies and their intersection with mobile pedagogies. We unpack notions of innovation and disruption. We then discuss smart mobile learning activities for school students identified from a Systematic Literature Review, together with the pedagogical principles underpinning them. We argue to encourage smart pedagogies, teacher educators should support teachers to implement ‘feasible disruptions’. Consequently, implications for teacher education are explored

The First Illumina-Based De Novo Transcriptome Sequencing and Analysis of Safflower Flowers

BACKGROUND: The safflower, Carthamus tinctorius L., is a worldwide oil crop, and its flowers, which have a high flavonoid content, are an important medicinal resource against cardiovascular disease in traditional medicine. Because the safflower has a large and complex genome, the development of its genomic resources has been delayed. Second-generation Illumina sequencing is now an efficient route for generating an enormous volume of sequences that can represent a large number of genes and their expression levels. METHODOLOGY/PRINCIPAL FINDINGS: To investigate the genes and pathways that might control flavonoids and other secondary metabolites in the safflower, we used Illumina sequencing to perform a de novo assembly of the safflower tubular flower tissue transcriptome. We obtained a total of 4.69 Gb in clean nucleotides comprising 52,119,104 clean sequencing reads, 195,320 contigs, and 120,778 unigenes. Based on similarity searches with known proteins, we annotated 70,342 of the unigenes (about 58% of the identified unigenes) with cut-off E-values of 10(-5). In total, 21,943 of the safflower unigenes were found to have COG classifications, and BLAST2GO assigned 26,332 of the unigenes to 1,754 GO term annotations. In addition, we assigned 30,203 of the unigenes to 121 KEGG pathways. When we focused on genes identified as contributing to flavonoid biosynthesis and the biosynthesis of unsaturated fatty acids, which are important pathways that control flower and seed quality, respectively, we found that these genes were fairly well conserved in the safflower genome compared to those of other plants. CONCLUSIONS/SIGNIFICANCE: Our study provides abundant genomic data for Carthamus tinctorius L. and offers comprehensive sequence resources for studying the safflower. We believe that these transcriptome datasets will serve as an important public information platform to accelerate studies of the safflower genome, and may help us define the mechanisms of flower tissue-specific and secondary metabolism in this non-model plant

Machine learning for accurate estimation of fetal gestational age based on ultrasound images.

Accurate estimation of gestational age is an essential component of good obstetric care and informs clinical decision-making throughout pregnancy. As the date of the last menstrual period is often unknown or uncertain, ultrasound measurement of fetal size is currently the best method for estimating gestational age. The calculation assumes an average fetal size at each gestational age. The method is accurate in the first trimester, but less so in the second and third trimesters as growth deviates from the average and variation in fetal size increases. Consequently, fetal ultrasound late in pregnancy has a wide margin of error of at least ±2 weeks' gestation. Here, we utilise state-of-the-art machine learning methods to estimate gestational age using only image analysis of standard ultrasound planes, without any measurement information. The machine learning model is based on ultrasound images from two independent datasets: one for training and internal validation, and another for external validation. During validation, the model was blinded to the ground truth of gestational age (based on a reliable last menstrual period date and confirmatory first-trimester fetal crown rump length). We show that this approach compensates for increases in size variation and is even accurate in cases of intrauterine growth restriction. Our best machine-learning based model estimates gestational age with a mean absolute error of 3.0 (95% CI, 2.9-3.2) and 4.3 (95% CI, 4.1-4.5) days in the second and third trimesters, respectively, which outperforms current ultrasound-based clinical biometry at these gestational ages. Our method for dating the pregnancy in the second and third trimesters is, therefore, more accurate than published methods