Exploring Challenges in Conducting E-Mental Health Research Among Asian American Women

In this discussion paper, we explore the challenges of conducting e-mental health intervention research among Asian American women and propose a model for addressing these barriers. Based on an extensive literature review, we identify two main types of barriers to conducting e-mental health intervention research among Asian American women: recruitment barriers and adherence barriers. Recruitment barriers are further subcategorized into those related to (1) stigmatized cultural beliefs about mental illness and mental health services; (2) lack of awareness about mental health services; and (3) language barrier. As to adherence barriers, the two identified subtypes concern (1) acuity and severity of mental health condition; and (2) lack of time. In order to enhance recruitment and adherence in e-mental health intervention research among the studied population, we formulate the following three main research strategies, namely: (1) considering the cultural and social contexts of Asian American women in the development of e-mental health interventions; (2) determining appropriate program length; and (3) conducting feasibility studies to test e-mental health interventions. We suggest that nurse researchers integrate our proposed model in conducting e-mental health interventions among Asian American women. Our proposed model also implies that nurses play an important role in encouraging Asian American women’s acceptance of and adherence to e-mental health interventions. In order to overcome the obstacles to conducting e-mental health research among Asian American women, we recommend that nurses familiarize themselves with credible, relevant, and evidence-based e-mental health resources and integrate online mental health services and information within their nursing practice

Sodium content as a predictor of the advanced evolution of globular cluster stars

The asymptotic giant branch (AGB) phase is the final stage of nuclear burning for low-mass stars. Although Milky Way globular clusters are now known to harbour (at least) two generations of stars they still provide relatively homogeneous samples of stars that are used to constrain stellar evolution theory. It is predicted by stellar models that the majority of cluster stars with masses around the current turn-off mass (that is, the mass of the stars that are currently leaving the main sequence phase) will evolve through the AGB phase. Here we report that all of the second-generation stars in the globular cluster NGC 6752 -- 70 per cent of the cluster population -- fail to reach the AGB phase. Through spectroscopic abundance measurements, we found that every AGB star in our sample has a low sodium abundance, indicating that they are exclusively first-generation stars. This implies that many clusters cannot reliably be used for star counts to test stellar evolution timescales if the AGB population is included. We have no clear explanation for this observation.Comment: Published in Nature (online 29 May 2013, hard copy 13 June), 12 pages, 3 figures + supplementary information sectio

Quantitative autism symptom patterns recapitulate differential mechanisms of genetic transmission in single and multiple incidence families

Abstract Background Previous studies have demonstrated aggregation of autistic traits in undiagnosed family members of children with autism spectrum disorder (ASD), which has significant implications for ASD risk in their offspring. This study capitalizes upon a large, quantitatively characterized clinical-epidemiologic family sample to establish the extent to which family transmission pattern and sex modulate ASD trait aggregation. Methods Data were analyzed from 5515 siblings (2657 non-ASD and 2858 ASD) included in the Interactive Autism Network. Autism symptom levels were measured using the Social Responsiveness Scale (SRS) and by computing Diagnostic and Statistical Manual of Mental Disorders—Fifth Edition (DSM-5) symptom scores based on items from the SRS and Social Communication Questionnaire. Generalized estimating equation models evaluated the influence of family incidence types (single versus multiple incidence families; male-only ASD-affected families versus families with female ASD-affected children), diagnostic group (non-ASD children with and without a history of language delay with autistic speech and ASD-affected children), and sibling sex on ASD symptom levels. Results Non-ASD children manifested elevated ASD symptom burden when they were members of multiple incidence families—this effect was accentuated for male children in female ASD-containing families—or when they had a history of language delay with autistic qualities of speech. In this sample, ASD-affected children from multiple incidence families had lower symptom levels than their counterparts in single incidence families. Recurrence risk for ASD was higher for children from female ASD-containing families than for children from male-only families. Conclusions Sex and patterns of family transmission modulate the risk of autism symptom burden in undiagnosed siblings of ASD-affected children. Identification of these symptoms/traits and their molecular genetic causes may have significant implications for genetic counseling and for understanding inherited liabilities that confer risk for ASD in successive generations. Autism symptom elevations were more dramatic in non-ASD children from multiple incidence families and those with a history of language delay and autistic qualities of speech, identifying sub-groups at substantially greater transmission risk. Higher symptom burden and greater recurrence in children from female ASD-containing families indicate that familial aggregation patterns are further qualified by sex-specific thresholds, supportive of the notion that females require a higher burden of deleterious liability to cross into categorical ASD diagnosis

Demographic and clinical correlates of autism symptom domains and autism spectrum diagnosis

Demographic and clinical factors may influence assessment of autism symptoms. This study evaluated these correlates and also examined whether social communication and interaction and restricted/repetitive behavior provided unique prediction of autism spectrum disorder diagnosis. We analyzed data from 7352 siblings included in the Interactive Autism Network registry. Social communication and interaction and restricted/repetitive behavior symptoms were obtained using caregiver-reports on the Social Responsiveness Scale. Demographic and clinical correlates were covariates in regression models predicting social communication and interaction and restricted/repetitive behavior symptoms. Logistic regression and receiver operating characteristic curve analyses evaluated the incremental validity of social communication and interaction and restricted/repetitive behavior domains over and above global autism symptoms. Autism spectrum disorder diagnosis was the strongest correlate of caregiver-reported social communication and interaction and restricted/repetitive behavior symptoms. The presence of comorbid diagnoses also increased symptom levels. Social communication and interaction and restricted/repetitive behavior symptoms provided significant, but modest, incremental validity in predicting diagnosis beyond global autism symptoms. These findings suggest that autism spectrum disorder diagnosis is by far the largest determinant of quantitatively measured autism symptoms. Externalizing (attention deficit hyperactivity disorder) and internalizing (anxiety) behavior, low cognitive ability, and demographic factors may confound caregiver-report of autism symptoms, potentially necessitating a continuous norming approach to the revision of symptom measures. Social communication and interaction and restricted/repetitive behavior symptoms may provide incremental validity in the diagnosis of autism spectrum disorder

E-AHPBA-ESSO-ESSR Innsbruck consensus guidelines for preoperative liver function assessment before hepatectomy

Background Posthepatectomy liver failure (PHLF) contributes significantly to morbidity and mortality after liver surgery. Standardized assessment of preoperative liver function is crucial to identify patients at risk. These European consensus guidelines provide guidance for preoperative patient assessment. Methods A modified Delphi approach was used to achieve consensus. The expert panel consisted of hepatobiliary surgeons, radiologists, nuclear medicine specialists, and hepatologists. The guideline process was supervised by a methodologist and reviewed by a patient representative. A systematic literature search was performed in PubMed/MEDLINE, the Cochrane library, and the WHO International Clinical Trials Registry. Evidence assessment and statement development followed Scottish Intercollegiate Guidelines Network methodology. Results Based on 271 publications covering 4 key areas, 21 statements (at least 85 per cent agreement) were produced (median level of evidence 2− to 2+). Only a few systematic reviews (2++) and one RCT (1+) were identified. Preoperative liver function assessment should be considered before complex resections, and in patients with suspected or known underlying liver disease, or chemotherapy-associated or drug-induced liver injury. Clinical assessment and blood-based scores reflecting liver function or portal hypertension (for example albumin/bilirubin, platelet count) aid in identifying risk of PHLF. Volumetry of the future liver remnant represents the foundation for assessment, and can be combined with indocyanine green clearance or LiMAx® according to local expertise and availability. Functional MRI and liver scintigraphy are alternatives, combining FLR volume and function in one examination. Conclusion These guidelines reflect established methods to assess preoperative liver function and PHLF risk, and have uncovered evidence gaps of interest for future research.publishedVersio

The Aarhus red giants challenge: II. Stellar oscillations in the red giant branch phase

[Contact] The large quantity of high-quality asteroseismic data that have been obtained from space-based photometric missions and the accuracy of the resulting frequencies motivate a careful consideration of the accuracy of computed oscillation frequencies of stellar models, when applied as diagnostics of the model properties.[Aims] Based on models of red-giant stars that have been independently calculated using different stellar evolution codes, we investigate the extent to which the differences in the model calculation affect the model oscillation frequencies and other asteroseismic diagnostics.[Methods] For each of the models, which cover four different masses and different evolution stages on the red-giant branch, we computed full sets of low-degree oscillation frequencies using a single pulsation code and, from these frequencies, typical asteroseismic diagnostics. In addition, we carried out preliminary analyses to relate differences in the oscillation properties to the corresponding model differences.[Results] In general, the differences in asteroseismic properties between the different models greatly exceed the observational precision of these properties. This is particularly true for the nonradial modes whose mixed acoustic and gravity-wave character makes them sensitive to the structure of the deep stellar interior and, hence, to details of their evolution. In some cases, identifying these differences led to improvements in the final models presented here and in Paper I; here we illustrate particular examples of this.[Conclusions] Further improvements in stellar modelling are required in order fully to utilise the observational accuracy to probe intrinsic limitations in the modelling and improve our understanding of stellar internal physics. However, our analysis of the frequency differences and their relation to stellar internal properties provides a striking illustration of the potential, in particular, of the mixed modes of red-giant stars for the diagnostics of stellar interiors.Funding for the Stellar Astrophysics Centre is provided by The Danish National Research Foundation (Grant agreement No. DNRF106). The research was supported by the ASTERISK project (ASTERoseismic Investigations with SONG and Kepler) funded by the European Research Council (Grant agreement No. 267864). This research was supported in part by the National Science Foundation under Grant No. NSF PHY-1748958. VSA acknowledges support from VILLUM FONDEN (research grant 10118) and the Independent Research Fund Denmark (Research grant 7027-00096B). DS is the recipient of an Australian Research Council Future Fellowship (project number FT1400147). SC acknowledges support from Premiale INAF MITiC, from INAF “Progetto mainstream” (PI: S. Cassisi), and grant AYA2013-42781P from the Ministry of Economy and Competitiveness of Spain. AMS is partially supported by grants ESP2017-82674-R (Spanish Government) and 2017-SGR-1131 (General-itat de Catalunya). TC acknowledges support from the European Research Council AdG No 320478-TOFU and the STFC Consolidated Grant ST/R000395/1. SH received funding for this research from the European Research Council under the European Community’s Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement no 338251 (StellarAges). AM acknowledges the support of the Government of India, Department of Atomic Energy, under Project No. 12-R&D-TFR-6.04-0600. DB is supported in the form of work contract FCT/MCTES through national funds and by FEDER through COMPETE2020 in connection to these grants: UID/FIS/04434/2019; PTDC/FIS-AST/30389/2017 & POCI-01-0145-FEDER-030389

The Aarhus red giants challenge: II. Stellar oscillations in the red giant branch phase

Contact. The large quantity of high-quality asteroseismic data that have been obtained from space-based photometric missions and the accuracy of the resulting frequencies motivate a careful consideration of the accuracy of computed oscillation frequencies of stellar models, when applied as diagnostics of the model properties. Aims. Based on models of red-giant stars that have been independently calculated using different stellar evolution codes, we investigate the extent to which the differences in the model calculation affect the model oscillation frequencies and other asteroseismic diagnostics. Methods. For each of the models, which cover four different masses and different evolution stages on the red-giant branch, we computed full sets of low-degree oscillation frequencies using a single pulsation code and, from these frequencies, typical asteroseismic diagnostics. In addition, we carried out preliminary analyses to relate differences in the oscillation properties to the corresponding model differences. Results. In general, the differences in asteroseismic properties between the different models greatly exceed the observational precision of these properties. This is particularly true for the nonradial modes whose mixed acoustic and gravity-wave character makes them sensitive to the structure of the deep stellar interior and, hence, to details of their evolution. In some cases, identifying these differences led to improvements in the final models presented here and in Paper I; here we illustrate particular examples of this. Conclusions. Further improvements in stellar modelling are required in order fully to utilise the observational accuracy to probe intrinsic limitations in the modelling and improve our understanding of stellar internal physics. However, our analysis of the frequency differences and their relation to stellar internal properties provides a striking illustration of the potential, in particular, of the mixed modes of red-giant stars for the diagnostics of stellar interiors. © ESO 2020

Genetic risk for autism spectrum disorders and neuropsychiatric variation in the general population

Almost all genetic risk factors for autism spectrum disorders (ASDs) can be found in the general population, but the effects of that risk are unclear in people not ascertained for neuropsychiatric symptoms. Using several large ASD consortia and population based resources, we find genetic links between ASDs and typical variation in social behavior and adaptive functioning. This finding is evidenced through both inherited and de novo variation, indicating that multiple types of genetic risk for ASDs influence a continuum of behavioral and developmental traits, the severe tail of which can result in an ASD or other neuropsychiatric disorder diagnosis. A continuum model should inform the design and interpretation of studies of neuropsychiatric disease biology