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Negative symptoms are a key feature of several psychiatric disorders. Difficulty identifying common neurobiological mechanisms that cut across diagnostic boundaries might result from equifinality (i.e., multiple mechanistic pathways to the same clinical profile), both within and across disorders. This study used a data-driven approach to identify unique subgroups of participants with distinct reward processing profiles to determine which profiles predicted negative symptoms.
Methods
Participants were a transdiagnostic sample of youth from a multisite study of psychosis risk, including 110 individuals at clinical high-risk for psychosis (CHR; meeting psychosis-risk syndrome criteria), 88 help-seeking participants who failed to meet CHR criteria and/or who presented with other psychiatric diagnoses, and a reference group of 66 healthy controls. Participants completed clinical interviews and behavioral tasks assessing four reward processing constructs indexed by the RDoC Positive Valence Systems: hedonic reactivity, reinforcement learning, value representation, and effort–cost computation.
Results
k-means cluster analysis of clinical participants identified three subgroups with distinct reward processing profiles, primarily characterized by: a value representation deficit (54%), a generalized reward processing deficit (17%), and a hedonic reactivity deficit (29%). Clusters did not differ in rates of clinical group membership or psychiatric diagnoses. Elevated negative symptoms were only present in the generalized deficit cluster, which also displayed greater functional impairment and higher psychosis conversion probability scores.
Conclusions
Contrary to the equifinality hypothesis, results suggested one global reward processing deficit pathway to negative symptoms independent of diagnostic classification. Assessment of reward processing profiles may have utility for individualized clinical prediction and treatment.
The association between cannabis and psychosis is established, but the role of underlying genetics is unclear. We used data from the EU-GEI case-control study and UK Biobank to examine the independent and combined effect of heavy cannabis use and schizophrenia polygenic risk score (PRS) on risk for psychosis.
Methods
Genome-wide association study summary statistics from the Psychiatric Genomics Consortium and the Genomic Psychiatry Cohort were used to calculate schizophrenia and cannabis use disorder (CUD) PRS for 1098 participants from the EU-GEI study and 143600 from the UK Biobank. Both datasets had information on cannabis use.
Results
In both samples, schizophrenia PRS and cannabis use independently increased risk of psychosis. Schizophrenia PRS was not associated with patterns of cannabis use in the EU-GEI cases or controls or UK Biobank cases. It was associated with lifetime and daily cannabis use among UK Biobank participants without psychosis, but the effect was substantially reduced when CUD PRS was included in the model. In the EU-GEI sample, regular users of high-potency cannabis had the highest odds of being a case independently of schizophrenia PRS (OR daily use high-potency cannabis adjusted for PRS = 5.09, 95% CI 3.08–8.43, p = 3.21 × 10−10). We found no evidence of interaction between schizophrenia PRS and patterns of cannabis use.
Conclusions
Regular use of high-potency cannabis remains a strong predictor of psychotic disorder independently of schizophrenia PRS, which does not seem to be associated with heavy cannabis use. These are important findings at a time of increasing use and potency of cannabis worldwide.
This review aims to identify the mechanistic relationships related to periodontal diseases and its possible association with changes in human milk composition and the composition and function of infants’ gut microbiome.
Background:
Maternal health conditions, especially inflammatory, are associated with altered human milk composition. It is not known whether maternal oral inflammatory diseases, including periodontal diseases, deleteriously affect human milk composition.
Methods:
A narrative review was conducted according to SANRA, the Scale for the Assessment of Narrative Review Articles, guidelines. PubMed, Google Scholar, and Cochrane database of systematic reviews were searched from September 2019 up to December 2023 using keywords such as breast/human milk, maternal health/infections, and periodontal diseases. Reference lists of relevant articles were also screened. Our primary outcome of interest was human milk composition (i.e., any changes in macronutrients, immunological components, etc.). Secondary outcomes included changes in human milk microbiome and subsequent changes in the infant gut microbiome. Outcomes were synthesized using a narrative approach where the existing evidence and current literature were summarized. No risk of bias assessment of the studies was performed in this review.
Findings:
The search yielded no studies investigating the relationship between periodontal diseases in nursing mothers and changes in human milk composition. However, a dose–response relationship exists between the severity of periodontal diseases and the risk of adverse pregnancy outcomes such as preterm birth. Mastitis and diabetes affected milk lipids. Immunoglobulin A (sIgA) was increased in mastitis, whereas reduced concentrations were reported in diabetes. Potential biological pathways through which periodontal diseases can negatively affect human milk composition include the systemic dissemination of inflammatory cytokines like IL-6, PGE2, and tumor necrosis factor (TNF)-β that can be up-regulated by bacterial by-products. This biological plausibility needs to be investigated, given the potentially negative impact on the quality of human milk that could be caused by periodontal inflammation.
NASA’s all-sky survey mission, the Transiting Exoplanet Survey Satellite (TESS), is specifically engineered to detect exoplanets that transit bright stars. Thus far, TESS has successfully identified approximately 400 transiting exoplanets, in addition to roughly 6 000 candidate exoplanets pending confirmation. In this study, we present the results of our ongoing project, the Validation of Transiting Exoplanets using Statistical Tools (VaTEST). Our dedicated effort is focused on the confirmation and characterisation of new exoplanets through the application of statistical validation tools. Through a combination of ground-based telescope data, high-resolution imaging, and the utilisation of the statistical validation tool known as TRICERATOPS, we have successfully discovered eight potential super-Earths. These planets bear the designations: TOI-238b (1.61$^{+0.09} _{-0.10}$ R$_\oplus$), TOI-771b (1.42$^{+0.11} _{-0.09}$ R$_\oplus$), TOI-871b (1.66$^{+0.11} _{-0.11}$ R$_\oplus$), TOI-1467b (1.83$^{+0.16} _{-0.15}$ R$_\oplus$), TOI-1739b (1.69$^{+0.10} _{-0.08}$ R$_\oplus$), TOI-2068b (1.82$^{+0.16} _{-0.15}$ R$_\oplus$), TOI-4559b (1.42$^{+0.13} _{-0.11}$ R$_\oplus$), and TOI-5799b (1.62$^{+0.19} _{-0.13}$ R$_\oplus$). Among all these planets, six of them fall within the region known as ‘keystone planets’, which makes them particularly interesting for study. Based on the location of TOI-771b and TOI-4559b below the radius valley we characterised them as likely super-Earths, though radial velocity mass measurements for these planets will provide more details about their characterisation. It is noteworthy that planets within the size range investigated herein are absent from our own solar system, making their study crucial for gaining insights into the evolutionary stages between Earth and Neptune.
Incidence of first-episode psychosis (FEP) varies substantially across geographic regions. Phenotypes of subclinical psychosis (SP), such as psychotic-like experiences (PLEs) and schizotypy, present several similarities with psychosis. We aimed to examine whether SP measures varied across different sites and whether this variation was comparable with FEP incidence within the same areas. We further examined contribution of environmental and genetic factors to SP.
Methods
We used data from 1497 controls recruited in 16 different sites across 6 countries. Factor scores for several psychopathological dimensions of schizotypy and PLEs were obtained using multidimensional item response theory models. Variation of these scores was assessed using multi-level regression analysis to estimate individual and between-sites variance adjusting for age, sex, education, migrant, employment and relational status, childhood adversity, and cannabis use. In the final model we added local FEP incidence as a second-level variable. Association with genetic liability was examined separately.
Results
Schizotypy showed a large between-sites variation with up to 15% of variance attributable to site-level characteristics. Adding local FEP incidence to the model considerably reduced the between-sites unexplained schizotypy variance. PLEs did not show as much variation. Overall, SP was associated with younger age, migrant, unmarried, unemployed and less educated individuals, cannabis use, and childhood adversity. Both phenotypes were associated with genetic liability to schizophrenia.
Conclusions
Schizotypy showed substantial between-sites variation, being more represented in areas where FEP incidence is higher. This supports the hypothesis that shared contextual factors shape the between-sites variation of psychosis across the spectrum.
Domesticated cattle were brought to Ireland during the Neolithic. By the early medieval period, 4000 years later, these animals were central to social and economic status in Irish communities and the landscape was organised around cattle husbandry to a degree unattested elsewhere in Europe. How this socio-economic importance developed is unclear. Here, using isotope data spanning six millennia, the authors identify a culturally driven shift towards the creation and management of open pastures, which began in the Iron Age, eventually supplanting woodland grazing. Cattle continued to dominate the economy until the later medieval period when a shift to participate in silver-based trade led to a reassessment of Ireland's unique human-cattle relationship.
Insufficient recruitment of groups underrepresented in medical research threatens the generalizability of research findings and compounds inequity in research and medicine. In the present study, we examined barriers and facilitators to recruitment of underrepresented research participants from the perspective of clinical research coordinators (CRCs).
Methods:
CRCs from one adult and one pediatric academic medical centers completed an online survey in April-May 2022. Survey topics included: participant language and translations, cultural competency training, incentives for research participation, study location, and participant research literacy. CRCs also reported their success in recruiting individuals from various backgrounds and completed an implicit bias measure.
Results:
Surveys were completed by 220 CRCs. CRCs indicated that recruitment is improved by having translated study materials, providing incentives to compensate participants, and reducing the number of in-person study visits. Most CRCs had completed some form of cultural competency training, but most also felt that the training either had no effect or made them feel less confident in approaching prospective participants from backgrounds different than their own. In general, CRCs reported having greater success in recruiting prospective participants from groups that are not underrepresented in research. Results of the implicit bias measure did not indicate that bias was associated with intentions to approach a prospective participant.
Conclusions:
CRCs identified several strategies to improve recruitment of underrepresented research participants, and CRC insights aligned with insights from research participants in previous work. Further research is needed to understand the impact of cultural competency training on recruitment of underrepresented research participants.
Turfgrass managers apply nonselective herbicides to control winter annual weeds during dormancy of warm-season turfgrass. Zoysiagrass subcanopies, however, retain green leaves and stems during winter dormancy, especially in warmer climates. The partially green zoysiagrass often deters the use of nonselective herbicides due to variable injury concerns in transition and southern climatic zones. This study evaluated zoysiagrass response to glyphosate and glufosinate applied at four different growing degree day (GDD)-based application timings during postdormancy transition in different locations, including Blacksburg, VA; Starkville, MS; and Virginia Beach, VA, in 2018 and 2019. GDD was calculated using a 5 C base temperature with accumulation beginning January 1 each year, and targeted application timings were 125, 200, 275, and 350 GDD5C. Zoysiagrass injury response to glyphosate and glufosinate was consistent across a broad growing region from northern Mississippi to coastal Virginia, but it varied by application timing. Glyphosate application at 125 and 200 GDD5C can be used safely for weed control during the postdormancy period of zoysiagrass, while glufosinate caused unacceptable turf injury regardless of application timing. Glyphosate and glufosinate exhibited a stepwise increase to maximum injury with increasing targeted GDD5C application timings. Glyphosate applied at 125 or 200 GDD5C did not injure zoysiagrass above a threshold of 30%, whereas glufosinate caused greater than 30% injury for 28 and 29 d when applied at 125 and 200 GDD5C, respectively. Likewise, glyphosate application at 125 or 200 GDD5C did not affect the zoysiagrass green cover area under the progress curve per day, whereas later applications reduced it. Glyphosate and glufosinate caused greater injury to zoysiagrass when applied at greater cumulative heat units and this was attributed to increasing turfgrass green leaf density, because heat unit accumulation is positively correlated with green leaf density. Accumulated heat unit-based application timing will allow practitioners to apply nonselective herbicides with reduced injury concerns.
Coarse spatial resolution in gridded precipitation datasets, reanalysis, and climate model outputs restricts their ability to characterize the localized extreme rain events and limits the use of the coarse resolution information for local to regional scale climate management strategies. Deep learning models have recently been developed to rapidly downscale the coarse resolution precipitation to the high local scale resolution at a much lower cost than dynamic downscaling. However, these existing super-resolution deep learning modeling studies have not rigorously evaluated the model’s skill in producing fine-scale spatial variability, particularly over topographic features. These current deep-learning models also have difficulty predicting the complex spatial structure of extreme events. Here, we develop a model based on super-resolution deconvolution neural network (SRDN) to downscale the hourly precipitation and evaluate the predictions. We apply three versions of the SRDN model: (a) SRDN (no orography), (b) SRDN-O (orography only at final resolution enhancement), and (c) SRDN-SO (orography at each step of resolution enhancement). We assess the ability of SRDN-based models to reproduce the fine-scale spatial variability and compare it with the previously used deep learning model (DeepSD). All the models are trained and tested using the Conformal Cubic Atmospheric Model (CCAM) data to perform a 100 to 12.5 km of hourly precipitation downscaling over the Australian region. We found that SRDN-based models, including orography, deliver better fine-scale spatial structures of both climatology and extremes, and significantly improved the deep-learning downscaling. The SRDN-SO model performs well both qualitatively and quantitatively in reconstructing the fine-scale spatial variability of climatology and rainfall extremes over complex orographic regions.
A survey of academic medical-center hospital epidemiologists indicated substantial deviation from Centers for Disease Control and Prevention guidance regarding healthcare providers (HCPs) recovering from coronavirus disease 2019 (COVID-19) returning to work. Many hospitals continue to operate under contingency status and have HCPs return to work earlier than recommended.
Current psychiatric diagnoses, although heritable, have not been clearly mapped onto distinct underlying pathogenic processes. The same symptoms often occur in multiple disorders, and a substantial proportion of both genetic and environmental risk factors are shared across disorders. However, the relationship between shared symptoms and shared genetic liability is still poorly understood.
Aims
Well-characterised, cross-disorder samples are needed to investigate this matter, but few currently exist. Our aim is to develop procedures to purposely curate and aggregate genotypic and phenotypic data in psychiatric research.
Method
As part of the Cardiff MRC Mental Health Data Pathfinder initiative, we have curated and harmonised phenotypic and genetic information from 15 studies to create a new data repository, DRAGON-Data. To date, DRAGON-Data includes over 45 000 individuals: adults and children with neurodevelopmental or psychiatric diagnoses, affected probands within collected families and individuals who carry a known neurodevelopmental risk copy number variant.
Results
We have processed the available phenotype information to derive core variables that can be reliably analysed across groups. In addition, all data-sets with genotype information have undergone rigorous quality control, imputation, copy number variant calling and polygenic score generation.
Conclusions
DRAGON-Data combines genetic and non-genetic information, and is available as a resource for research across traditional psychiatric diagnostic categories. Algorithms and pipelines used for data harmonisation are currently publicly available for the scientific community, and an appropriate data-sharing protocol will be developed as part of ongoing projects (DATAMIND) in partnership with Health Data Research UK.
Hospitals are increasingly consolidating into health systems. Some systems have appointed healthcare epidemiologists to lead system-level infection prevention programs. Ideal program infrastructure and support resources have not been described. We informally surveyed 7 healthcare epidemiologists with recent experience building and leading system-level infection prevention programs. Key facilitators and barriers for program structure and implementation are described.
Published guidelines for sports restriction for children with a bicuspid aortic valve remain controversial. We sought to describe practice variation and factors influencing sports restrictions in these children.
Methods:
This retrospective single-centre study included children (7–18 years old) with an isolated bicuspid aortic valve at baseline from 1 January, 2005 to 31 December, 2014. Sports restrictions, factors potentially influencing decision-making, and outcomes were collected. Descriptive statistics and multivariable mixed-effects logistic regression models were performed with providers and patients as random effects. Provider variation was estimated using intraclass correlation coefficients. Odds ratios, 95% confidence intervals, and p-values were reported from the models.
Results:
In 565 encounters (253 children; 34 providers), 41% recommended no sports restrictions, 40% recommended high-static and high-dynamic restrictions, and 19% had no documented recommendations. Based on published guidelines, 22% of children were inappropriately restricted while 30% were not appropriately restricted. The paediatric cardiology provider contributed to 37% of observed practice variation (p < 0.001). Sports restriction was associated with older age, males, greater ascending aorta z-score, and shorter follow-up interval. There were no aortic dissections or deaths and one cardiac intervention.
Conclusion:
Physicians frequently fail to document sports restrictions for children with a bicuspid aortic valve, and documented recommendations often conflict with published guidelines. Despite this, no adverse outcomes occurred. Providers accounted for a significant proportion of the variation in sports restrictions. Further research to provide evidence-based guidelines may improve provider compliance with activity recommendations in this population.
A standardised multi-site approach to manage paediatric post-operative chylothorax does not exist and leads to unnecessary practice variation. The Chylothorax Work Group utilised the Pediatric Critical Care Consortium infrastructure to address this gap.
Methods:
Over 60 multi-disciplinary providers representing 22 centres convened virtually as a quality initiative to develop an algorithm to manage paediatric post-operative chylothorax. Agreement was objectively quantified for each recommendation in the algorithm by utilising an anonymous survey. “Consensus” was defined as ≥ 80% of responses as “agree” or “strongly agree” to a recommendation. In order to determine if the algorithm recommendations would be correctly interpreted in the clinical environment, we developed ex vivo simulations and surveyed patients who developed the algorithm and patients who did not.
Results:
The algorithm is intended for all children (<18 years of age) within 30 days of cardiac surgery. It contains rationale for 11 central chylothorax management recommendations; diagnostic criteria and evaluation, trial of fat-modified diet, stratification by volume of daily output, timing of first-line medical therapy for “low” and “high” volume patients, and timing and duration of fat-modified diet. All recommendations achieved “consensus” (agreement >80%) by the workgroup (range 81–100%). Ex vivo simulations demonstrated good understanding by developers (range 94–100%) and non-developers (73%–100%).
Conclusions:
The quality improvement effort represents the first multi-site algorithm for the management of paediatric post-operative chylothorax. The algorithm includes transparent and objective measures of agreement and understanding. Agreement to the algorithm recommendations was >80%, and overall understanding was 94%.
The purpose of this document is to highlight practical recommendations to assist acute care hospitals to prioritize and implement strategies to prevent ventilator-associated pneumonia (VAP), ventilator-associated events (VAE), and non-ventilator hospital-acquired pneumonia (NV-HAP) in adults, children, and neonates. This document updates the Strategies to Prevent Ventilator-Associated Pneumonia in Acute Care Hospitals published in 2014. This expert guidance document is sponsored by the Society for Healthcare Epidemiology (SHEA), and is the product of a collaborative effort led by SHEA, the Infectious Diseases Society of America, the American Hospital Association, the Association for Professionals in Infection Control and Epidemiology, and The Joint Commission, with major contributions from representatives of a number of organizations and societies with content expertise.
Schizophrenia (SZ), bipolar disorder (BD) and depression (D) run in families. This susceptibility is partly due to hundreds or thousands of common genetic variants, each conferring a fractional risk. The cumulative effects of the associated variants can be summarised as a polygenic risk score (PRS). Using data from the EUropean Network of national schizophrenia networks studying Gene-Environment Interactions (EU-GEI) first episode case–control study, we aimed to test whether PRSs for three major psychiatric disorders (SZ, BD, D) and for intelligent quotient (IQ) as a neurodevelopmental proxy, can discriminate affective psychosis (AP) from schizophrenia-spectrum disorder (SSD).
Methods
Participants (842 cases, 1284 controls) from 16 European EU-GEI sites were successfully genotyped following standard quality control procedures. The sample was stratified based on genomic ancestry and analyses were done only on the subsample representing the European population (573 cases, 1005 controls). Using PRS for SZ, BD, D, and IQ built from the latest available summary statistics, we performed simple or multinomial logistic regression models adjusted for 10 principal components for the different clinical comparisons.
Results
In case–control comparisons PRS-SZ, PRS-BD and PRS-D distributed differentially across psychotic subcategories. In case–case comparisons, both PRS-SZ [odds ratio (OR) = 0.7, 95% confidence interval (CI) 0.54–0.92] and PRS-D (OR = 1.31, 95% CI 1.06–1.61) differentiated AP from SSD; and within AP categories, only PRS-SZ differentiated BD from psychotic depression (OR = 2.14, 95% CI 1.23–3.74).
Conclusions
Combining PRS for severe psychiatric disorders in prediction models for psychosis phenotypes can increase discriminative ability and improve our understanding of these phenotypes. Our results point towards the potential usefulness of PRSs in specific populations such as high-risk or early psychosis phases.