Gambling disorders, gambling type preferences, and psychiatric comorbidity among the Thai general population: Results of the 2013 National Mental Health Survey

Background and aims To estimate the prevalence of problem and pathological gambling, gender and age-group differences in gambling types, and comorbidities with other psychiatric disorders among the Thai general population. Methods Analysis was conducted on 4,727 participants of Thailand’s 2013 National Mental Health Survey, a multistage stratified cluster survey, using the Composite International Diagnostic Interview. Diagnoses of problem and pathological gambling and other psychiatric disorders were based on the DSM-IV-TR criteria with the following additional criteria for gamblers: more than 10 lifetime gambling episodes and a single year loss of at least 365 USD from gambling. Results The estimated lifetime prevalence rates of pathological and problem gambling were 0.90% [95% confidence interval (CI): 0.51–1.29] and 1.14% (95% CI: 0.58–1.70), respectively. The most popular type of gambling was playing lotteries [69.5%, standard error (SE) = 1.9], the prevalence of which was significantly higher among females and older age groups. The most common psychiatric disorders seen among pathological gamblers were alcohol abuse (57.4%), nicotine dependence (49.5%), and any drug use disorder (16.2%). Pathological gambling was highly prevalent among those who ever experienced major depressive episodes (5.5%), any drug dependence (5.1%), and intermittent explosive disorder (4.8%). The association between pathological gambling was strongest with a history of major depressive episode [adjusted odds ratio (AOR) = 10.4, 95% CI: 2.80–38.4]. Conclusion The study confirms the recognition of gambling disorders as a public health concern in Thailand and suggests a need for culturally specific preventive measures for pathological gamblers and those with a history of substance use disorders or major depression

Acute Kidney Injury in Myeloid Leukemia of Down Syndrome: Risk Factors and Outcomes

Objective: To identify the risk factors and describe the outcomes of patients who developed acute kidney injury (AKI)during treatment for myeloid leukemia of Down syndrome (ML-DS). Material and Methods: The medical records of 23 Down syndrome patients under the age of 15 who had been diagnosedwith acute myeloid leukemia (AML) and were being treated at a major tertiary care referral facility in Southern Thailandwere reviewed. The identification of factors associated with AKI was done using logistic regression. The Kaplan-Meiermethod was used to calculate survival probabilities. Results: Eight (34.8%) patients developed AKI during their course of chemotherapy with a median time from the firstvisit to the AKI event of 1.1 (IQR 0.7, 3.1) months. Higher levels of blast cells (OR: 1.19, 95% CI: 1.05-1.98) and septicshock during the course of chemotherapy (OR: 621.1, 95% CI: 2.40-Inf.) were independently associated with AKI. The1-year overall survival rate was 26.1%. The median survival times among those who developed AKI and those who didnot were 1.94 and 10.7 months, respectively. Conclusion: About one-third of the cases with ML-DS in our cohort developed AKI during the course of chemotherapy.The risk factors of AKI were higher peripheral blast count and septic shock during chemotherapy

Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy.

MotivationMultiple biological clocks govern a healthy pregnancy. These biological mechanisms produce immunologic, metabolomic, proteomic, genomic and microbiomic adaptations during the course of pregnancy. Modeling the chronology of these adaptations during full-term pregnancy provides the frameworks for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia.ResultsWe performed a multiomics analysis of 51 samples from 17 pregnant women, delivering at term. The datasets included measurements from the immunome, transcriptome, microbiome, proteome and metabolome of samples obtained simultaneously from the same patients. Multivariate predictive modeling using the Elastic Net (EN) algorithm was used to measure the ability of each dataset to predict gestational age. Using stacked generalization, these datasets were combined into a single model. This model not only significantly increased predictive power by combining all datasets, but also revealed novel interactions between different biological modalities. Future work includes expansion of the cohort to preterm-enriched populations and in vivo analysis of immune-modulating interventions based on the mechanisms identified.Availability and implementationDatasets and scripts for reproduction of results are available through: https://nalab.stanford.edu/multiomics-pregnancy/.Supplementary informationSupplementary data are available at Bioinformatics online

A phenomenological exploration of transgender people's experiences of mental health services in Ireland

AIM: This study sought to explore the personal experiences of transgender people with Irish mental health services. BACKGROUND: The transgender community have been identified as an underserved, under-researched community in Ireland and abroad. While there has been a surge in research carried out with the community in recent years, there is little known about the personal experiences of Irish transgender people with mental health services. METHOD: Interpretative phenomenological analysis was used to inform data collection and analysis of semi-structured interviews carried out with four research participants all identifying as transgender and having experienced accessing Irish mental health services. RESULTS: Three themes emerged: affirmative experiences, non-affirmative experiences and clinician relationship. CONCLUSION: Lack of information and non-affirmative experiences are contributing to poor clinician-patient relationships with transgender populations and impacting attrition. IMPLICATIONS FOR NURSING MANAGEMENT: Nurse managers have a central role in supporting a transgender-positive organisational approach to care by ensuring policies, care practices and the environment are supportive of sexual and gender expression by role modelling attitudes of respect and inclusivity. In order to provide appropriate and responsive services to transgender people, there needs to be in place strategies to enable the development of confident, competent and knowledgeable staff

What drives long-run biodiversity change? New insights from combining economics, palaeoecology and environmental history

This paper presents a new approach to understanding the effects of economic factors on biodiversity change over the long run. We illustrate this approach by studying the determinants of biodiversity change in upland Scotland from 1600-2000. The measure of biodiversity used is a proxy for plant species diversity, constructed using statistical analysis of paleoecological (pollen) data. We assemble a new data set of historical land use and prices over 11 sites during this 400 year period; this data set also includes information on changes in agricultural technology, climate and land ownership. A panel model is then estimated, which controls for both supply and demand shifts over time. A main result is that prices, which act in our model as a proxy for livestock numbers, do indeed impact on biodiversity, with higher prices leading to lower biodiversity

Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy

Motivation Multiple biological clocks govern a healthy pregnancy. These biological mechanisms produce immunologic, metabolomic, proteomic, genomic and microbiomic adaptations during the course of pregnancy. Modeling the chronology of these adaptations during full-term pregnancy provides the frameworks for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia. Results We performed a multiomics analysis of 51 samples from 17 pregnant women, delivering at term. The datasets included measurements from the immunome, transcriptome, microbiome, proteome and metabolome of samples obtained simultaneously from the same patients. Multivariate predictive modeling using the Elastic Net (EN) algorithm was used to measure the ability of each dataset to predict gestational age. Using stacked generalization, these datasets were combined into a single model. This model not only significantly increased predictive power by combining all datasets, but also revealed novel interactions between different biological modalities. Future work includes expansion of the cohort to preterm-enriched populations and in vivo analysis of immune-modulating interventions based on the mechanisms identified. Availability and implementation Datasets and scripts for reproduction of results are available through: Https://nalab.stanford.edu/multiomics-pregnancy/

An immune clock of human pregnancy

The maintenance of pregnancy relies on finely tuned immune adaptations. We demonstrate that these adaptations are precisely timed, reflecting an immune clock of pregnancy in women delivering at term. Using mass cytometry, the abundance and functional responses of all major immune cell subsets were quantified in serial blood samples collected throughout pregnancy. Cell signaling-based Elastic Net, a regularized regression method adapted from the elastic net algorithm, was developed to infer and prospectively validate a predictive model of interrelated immune events that accurately captures the chronology of pregnancy. Model components highlighted existing knowledge and revealed previously unreported biology, including a critical role for the interleukin-2-dependent STAT5ab signaling pathway in modulating T cell function during pregnancy. These findings unravel the precise timing of immunological events occurring during a term pregnancy and provide the analytical framework to identify immunological deviations implicated in pregnancy-related pathologies

A framework to assess biogeochemical response to ecosystem disturbance using nutrient partitioning ratios

Author Posting. © The Author(s), 2015. This is the author's version of the work. It is posted here by permission of Springer for personal use, not for redistribution. The definitive version was published in Ecosystems 19 (2016): 387-395, doi:10.1007/s10021-015-9934-1.Disturbances affect almost all terrestrial ecosystems, but it has been difficult to identify general principles regarding these influences. To improve our understanding of the long-term consequences of disturbance on terrestrial ecosystems, we present a conceptual framework that analyzes disturbances by their biogeochemical impacts. We posit that the ratio of soil and plant nutrient stocks in mature ecosystems represents a characteristic site property. Focusing on nitrogen (N), we hypothesize that this partitioning ratio (soil N: plant N) will undergo a predictable trajectory after disturbance. We investigate the nature of this partitioning ratio with three approaches: (1) nutrient stock data from forested ecosystems in North America, (2) a process-based ecosystem model, and (3) conceptual shifts in site nutrient availability with altered disturbance frequency. Partitioning ratios could be applied to a variety of ecosystems and successional states, allowing for improved temporal scaling of disturbance events. The generally short-term empirical evidence for recovery trajectories of nutrient stocks and partitioning ratios suggests two areas for future research. First, we need to recognize and quantify how disturbance effects can be accreting or depleting, depending on whether their net effect is to increase or decrease ecosystem nutrient stocks. Second, we need to test how altered disturbance frequencies from the present state may be constructive or destructive in their effects on biogeochemical cycling and nutrient availability. Long-term studies, with repeated sampling of soils and vegetation, will be essential in further developing this framework of biogeochemical response to disturbance.This material is based upon work supported by the National Science Foundation under Grant No. DEB-1145815 and 0949420.2016-11-1

Genome-wide association study of colorectal cancer identifies six new susceptibility loci

El document inclou una pàgina final amb una correcció (corrigendum). Aquesta, per si sola, té el següent DOI: 10.1038/ncomms9739 i es va publicar al mateix vol. 6.Genetic susceptibility to colorectal cancer is caused by rare pathogenic mutations and common genetic variants that contribute to familial risk. Here we report the results of a two-stage association study with 18,299 cases of colorectal cancer and 19,656 controls, with follow-up of the most statistically significant genetic loci in 4,725 cases and 9,969 controls from two Asian consortia. We describe six new susceptibility loci reaching a genome-wide threshold of P<5.0E-08. These findings provide additional insight into the underlying biological mechanisms of colorectal cancer and demonstrate the scientific value of large consortia-based genetic epidemiology studies

The SOLAS air-sea gas exchange experiment (SAGE) 2004

Author Posting. © The Author(s), 2010. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Deep Sea Research Part II: Topical Studies in Oceanography 58 (2011): 753-763, doi:10.1016/j.dsr2.2010.10.015.The SOLAS air-sea gas exchange experiment (SAGE) was a multiple-objective study investigating gas-transfer processes and the influence of iron fertilisation on biologically driven gas exchange in high-nitrate low-silicic acid low-chlorophyll (HNLSiLC) Sub-Antarctic waters characteristic of the expansive Subpolar Zone of the southern oceans. This paper provides a general introduction and summary of the main experimental findings. The release site was selected from a pre-voyage desktop study of environmental parameters to be in the south-west Bounty Trough (46.5°S 172.5°E) to the south-east of New Zealand and the experiment conducted between mid-March and mid-April 2004. In common with other mesoscale iron addition experiments (FeAX’s), SAGE was designed as a Lagrangian study quantifying key biological and physical drivers influencing the air-sea gas exchange processes of CO2, DMS and other biogenic gases associated with an iron-induced phytoplankton bloom. A dual tracer SF6/3He release enabled quantification of both the lateral evolution of a labelled volume (patch) of ocean and the air-sea tracer exchange at the 10’s of km’s scale, in conjunction with the iron fertilisation. Estimates from the dual-tracer experiment found a quadratic dependency of the gas exchange coefficient on windspeed that is widely applicable and describes air-sea gas exchange in strong wind regimes. Within the patch, local and micrometeorological gas exchange process studies (100 m scale) and physical variables such as near-surface turbulence, temperature microstructure at the interface, wave properties, and wind speed were quantified to further assist the development of gas exchange models for high-wind environments. There was a significant increase in the photosynthetic competence (Fv/Fm) of resident phytoplankton within the first day following iron addition, but in contrast to other FeAX’s, rates of net primary production and column-integrated chlorophyll a concentrations had only doubled relative to the unfertilised surrounding waters by the end of the experiment. After 15 days and four iron additions totalling 1.1 tonne Fe2+, this was a very modest response compared to the other mesoscale iron enrichment experiments. An investigation of the factors limiting bloom development considered co- limitation by light and other nutrients, the phytoplankton seed-stock and grazing regulation. Whilst incident light levels and the initial Si:N ratio were the lowest recorded in all FeAX’s to date, there was only a small seed-stock of diatoms (less than 1% of biomass) and the main response to iron addition was by the picophytoplankton. A high rate of dilution of the fertilised patch relative to phytoplankton growth rate, the greater than expected depth of the surface mixed layer and microzooplankton grazing were all considered as factors that prevented significant biomass accumulation. In line with the limited response, the enhanced biological draw-down of pCO2 was small and masked by a general increase in pCO2 due to mixing with higher pCO2 waters. The DMS precursor DMSP was kept in check through grazing activity and in contrast to most FeAX’s dissolved dimethylsulfide (DMS) concentration declined through the experiment. SAGE is an important low-end member in the range of responses to iron addition in FeAX’s. In the context of iron fertilisation as a geoengineering tool for atmospheric CO2 removal, SAGE has clearly demonstrated that a significant proportion of the low iron ocean may not produce a phytoplankton bloom in response to iron addition.SAGE was jointly funded through the New Zealand Foundation for Research, Science and Technology (FRST) programs (C01X0204) "Drivers and Mitigation of Global Change" and (C01X0223) "Ocean Ecosystems: Their Contribution to NZ Marine Productivity." Funding was also provided for specific collaborations by the US National Science Foundation from grants OCE-0326814 (Ward), OCE-0327779 (Ho), and OCE 0327188 OCE-0326814 (Minnett) and the UK Natural Environment Research Council NER/B/S/2003/00282 (Archer). The New Zealand International Science and Technology (ISAT) linkages fund provided additional funding (Archer and Ziolkowski), and the many collaborator institutions also provided valuable support