Nitrogen cycle disruption through the application of de-icing salts on upland highways

It is hypothesized that episodic introductions of road salt severely disrupt the soil nitrogen cycle at a range of spatial and temporal scales. A field-scale study has confirmed impacts on the nitrogen cycle in soil, soil solution and river samples. There is evidence that ammonium-N retention on cation exchange sites has been reduced by the presence of sodium ions, and that ammonium-N has been flushed from the exchange sites. Increases in soil pH have been caused in naturally acidic uplands. These have enhanced mineralization of organic-N, especially nitrification, leading to a reduction in the mineralizable-N pool of roadside soils. There is evidence to support the hypothesis that organic matter content has been lowered over decades either through desorption or dispersal processes. Multiple drivers are identified that contribute to the disruption of nitrogen cycling processes, but their relative importance is difficult to quantify unequivocally. The influence of road salt on soil and soil solution declines with distance from the highway, but impacts on water chemistry in a local stream are still strongly evident at some distance from the road

The psychological impact of prolonged disorders of consciousness on caregivers:a systematic review of quantitative studies

Objective: Systematic review of the nature, frequency and severity of psychological experiences of people who have a close relationship with a person with a prolonged disorder of consciousness. Data sources: Cochrane Library, Web of Science, PsycINFO, PubMed, Embase®, MEDLINE®, Allied and Complementary Medicine™, were searched from inceptions until December 2016 with additional hand searching of reference lists of included articles. Review methods: Studies were included that used quantitative methodologies and psychological measures to investigate experiences. The PRISMA statement was followed with inclusion criteria set a priori. A data synthesis summarized psychological constructs studied. Results: A total of 18 studies (ranging between n = 16–487 participants) met the inclusion criteria with 15 of 18 studies focused on the primary caregiver. A total of 23 standardized psychological measures were identified to assess four primary psychological constructs: Loss and grief, psychological wellbeing changes, burden and use of coping strategies. Conclusions: Small sample sizes, limited variables and reliance on observational methods affected quality. Caregivers do find ways to manage independently, but some exhibit clinically significant psychological distress that does not change over time alone and may get worse

Interaction testing and polygenic risk scoring to estimate the association of common genetic variants with treatment resistance in schizophrenia

Importance About 20% to 30% of people with schizophrenia have psychotic symptoms that do not respond adequately to first-line antipsychotic treatment. This clinical presentation, chronic and highly disabling, is known as treatment-resistant schizophrenia (TRS). The causes of treatment resistance and their relationships with causes underlying schizophrenia are largely unknown. Adequately powered genetic studies of TRS are scarce because of the difficulty in collecting data from well-characterized TRS cohorts. Objective To examine the genetic architecture of TRS through the reassessment of genetic data from schizophrenia studies and its validation in carefully ascertained clinical samples. Design, Setting, and Participants Two case-control genome-wide association studies (GWASs) of schizophrenia were performed in which the case samples were defined as individuals with TRS (n=10 501) and individuals with non-TRS (n=20 325). The differences in effect sizes for allelic associations were then determined between both studies, the reasoning being such differences reflect treatment resistance instead of schizophrenia. Genotype data were retrieved from the CLOZUK and Psychiatric Genomics Consortium (PGC) schizophrenia studies. The output was validated using polygenic risk score (PRS) profiling of 2 independent schizophrenia cohorts with TRS and non-TRS: a prevalence sample with 817 individuals (Cardiff Cognition in Schizophrenia [CardiffCOGS]) and an incidence sample with 563 individuals (Genetics Workstream of the Schizophrenia Treatment Resistance and Therapeutic Advances [STRATA-G]). Main Outcomes and Measures GWAS of treatment resistance in schizophrenia. The results of the GWAS were compared with complex polygenic traits through a genetic correlation approach and were used for PRS analysis on the independent validation cohorts using the same TRS definition. Results The study included a total of 85 490 participants (48 635 [56.9%] male) in its GWAS stage and 1380 participants (859 [62.2%] male) in its PRS validation stage. Treatment resistance in schizophrenia emerged as a polygenic trait with detectable heritability (1% to 4%), and several traits related to intelligence and cognition were found to be genetically correlated with it (genetic correlation, 0.41-0.69). PRS analysis in the CardiffCOGS prevalence sample showed a positive association between TRS and a history of taking clozapine (r² = 2.03%; P = .001), which was replicated in the STRATA-G incidence sample (r² = 1.09%; P = .04). Conclusions and Relevance In this GWAS, common genetic variants were differentially associated with TRS, and these associations may have been obscured through the amalgamation of large GWAS samples in previous studies of broadly defined schizophrenia. Findings of this study suggest the validity of meta-analytic approaches for studies on patient outcomes, including treatment resistance.Funding/Support: This work was supported by Medical Research Council Centre grant MR/ L010305/1, Medical Research Council Program grant MR/P005748/1, and Medical Research Council Project grants MR/L011794/1 and MC_PC_17212 to Cardiff University and by the National Centre for Mental Health, funded by the Welsh Government through Health and Care Research Wales. This work acknowledges the support of the Supercomputing Wales project, which is partially funded by the European Regional Development Fund via the Welsh Government. Dr Pardiñas was supported by an Academy of Medical Sciences Springboard Award (SBF005\1083). Dr Andreassen was supported by the Research Council of Norway (grants 283798, 262656, 248980, 273291, 248828, 248778, and 223273); KG Jebsen Stiftelsen, South-East Norway Health Authority, and the European Union’s Horizon 2020 Research and Innovation Programme (grant 847776). Dr Ajnakina was supported by an National Institute for Health Research postdoctoral fellowship (PDF-2018-11-ST2-020). Dr Joyce was supported by the University College London Hospitals/UCL University College London Biomedical Research Centre. Dr Kowalec received funding from the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie grant agreement (793530) from the government of Canada Banting postdoctoral fellowship programme and the University of Manitoba. Dr Sullivan was supported by the Swedish Research Council (Vetenskapsrådet, D0886501), the European Union’s Horizon 2020 programme (COSYN, 610307) and the US National Institute of Mental Health (U01 MH109528 and R01 MH077139). The Psychiatric Genomics Consortium was partly supported by the National Institute Of Mental Health (grants R01MH124873). The Sweden Schizophrenia Study was supported by the National Institute Of Mental Health (grant R01MH077139). The STRATA consortium was supported by a Stratified Medicine Programme grant to Dr MacCabe from the Medical Research Council (grant MR/L011794/1), which funded the research and supported Drs Pardiñas, Smart, Kassoumeri, Murray, Walters, and MacCabe. Dr Smart was supported by a Collaboration for Leadership in Applied Health Research and Care South London at King’s College Hospital National Health Service Foundation Trust. The AESOP (US) cohort was funded by the UK Medical Research Council (grant G0500817). The Belfast (UK) cohort was funded by the Research and Development Office of Northern Ireland. The Bologna (Italy) cohort was funded by the European Community’s Seventh Framework program (HEALTH-F2-2010–241909, project EU-GEI). The Genetics and Psychosis project (London, UK) cohort was funded by the UK National Institute of Health Research Specialist Biomedical Research Centre for Mental Health, South London and the Maudsley National Health Service Mental Health Foundation Trust (SLAM) and the Institute of Psychiatry, Psychology, and Neuroscience at King’s College London; Psychiatry Research Trust; Maudsley Charity Research Fund; and the European Community’s Seventh Framework program (HEALTH-F2-2009-241909, project EU-GEI). The Lausanne (Switzerland) cohort was funded by the Swiss National Science Foundation (grants 320030_135736/1, 320030-120686, 324730-144064, 320030-173211, and 171804); the National Center of Competence in Research Synaptic Bases of Mental Diseases from the Swiss National Science Foundation (grant 51AU40_125759); and Fondation Alamaya. The Oslo (Norway) cohort was funded by the Research Council of Norway (grant 223273/F50, under the Centers of Excellence funding scheme, 300309, 283798) and the South-Eastern Norway Regional Health Authority (grants 2006233, 2006258, 2011085, 2014102, 2015088, and 2017-112). The Paris (France) cohort was funded by European Community’s Seventh Framework program (HEALTH-F2-2010–241909, project EU-GEI). The Prague (Czech Republic) cohort was funded by the Ministry of Health of the Czech Republic (grant NU20-04-00393). The Santander (Spain) cohort was funded by the following grants to Dr Crespo-Facorro: Instituto de Salud Carlos III (grants FIS00/3095, PI020499, PI050427, and PI060507), Plan Nacional de Drogas Research (grant 2005-Orden sco/3246/2004), SENY Fundatio Research (grant 2005-0308007), Fundacion Marques de Valdecilla (grant A/02/07, API07/011) and Ministry of Economy and Competitiveness and the European Fund for Regional Development (grants SAF2016-76046-R and SAF2013-46292-R). The West London (UK) cohort was funded by The Wellcome Trust (grants 042025, 052247, and 064607)

Integrated metastate functional connectivity networks predict change in symptom severity in clinical high risk for psychosis

The ability to identify biomarkers of psychosis risk is essential in defining effective preventive measures to potentially circumvent the transition to psychosis. Using samples of people at clinical high risk for psychosis (CHR) and Healthy controls (HC) who were administered a task fMRI paradigm, we used a framework for labelling time windows of fMRI scans as ‘integrated’ FC networks to provide a granular representation of functional connectivity (FC). Periods of integration were defined using the ‘cartographic profile’ of time windows and k‐means clustering, and sub‐network discovery was carried out using Network Based Statistics (NBS). There were no network differences between CHR and HC groups. Within the CHR group, using integrated FC networks, we identified a sub‐network negatively associated with longitudinal changes in the severity of psychotic symptoms. This sub‐network comprised brain areas implicated in bottom‐up sensory processing and in integration with motor control, suggesting it may be related to the demands of the fMRI task. These data suggest that extracting integrated FC networks may be useful in the investigation of biomarkers of psychosis risk

Evaluation of a Smartwatch-based Intervention Providing Feedback of Daily Activity within a Research-Naive Stroke Ward: a pilot randomised controlled trial

Background. The majority of stroke patients are inactive outside formal therapy sessions. Tailored activity feedback via a Smartwatch has the potential to increase inpatient activity. Objective. to identify the challenges and support needed by ward staff and researchers and to examine the feasibility of conducting a randomised controlled trial (RCT) using Smartwatch activity monitors in research naive rehabilitation wards. Objectives (Phase 1 and 2) were to report any challenges and support needed and determine the recruitment and retention rate, completion of outcome measures, Smartwatch adherence rate (Phase 2 only) readiness to randomise, adherence to protocol (intervention fidelity) and potential for effect. Methods. First admission, stroke patients (onset <4 months) aged 40-75, able to walk 10m prior to stroke and follow a two stage command with sufficient cognition and vision (clinically judged) were recruited within the Second Affiliated Hospital of Anhui University of Traditional Chinese Medicine. Phase 1: a non-randomised observation phase (to allow practice of protocol) - patients received no activity feedback. Phase 2: a parallel single-blind pilot RCT. Patients were randomised into one of two groups: to receive daily activity feedback over a nine hour period, or to receive no activity feedback. EQ-5D-5L, WHODAS and RMI were conducted at baseline, discharge and three months post-discharge. Descriptives statistics were performed on recruitment, retention, completion and activity counts as well as adherence to protocol. Results. Out of 470 ward admissions, 11% were recruited across the two phases, over a 30-week period. Retention rate at the three months post-discharge was 48%. 22% of patients dropped out post-baseline assessment, 78% completed baseline and discharge admissions, from which 62% were assessed three months post-discharge. Smartwatch data was received from all patients. Patients were correctly randomised into each RCT group. RCT adherence rate to wearing the Smartwatch was 80%. Baseline activity was exceeded for 65% of days in the feedback group compared to 55% of days in the no-feedback group. Conclusions. Delivery of a Smartwatch RCT is feasible in a research naive rehabilitation ward. However, frequent support and guidance of research-naive staff is required to ensure completeness of clinical assessment data and protocol adherence

Stratified, precision or personalised medicine? Cancer services in the 'real world' of a London hospital

We conducted ethnographic research in collaboration with a large research-intensive London breast cancer service in 2013-14 so as to understand the practices and potential effects of stratified medicine. Stratified medicine is often seen as a synonym for both personalised and precision medicine but these three terms, we found, also related to distinct facets of treatment and care. Personalised medicine is the term adopted for the developing 2016 NHS England Strategy, in which breast cancer care is considered a prime example of improved biological precision and better patient outcomes. We asked how this biologically stratified medicine affected wider relations of care and treatment. We interviewed formally 33 patients and 23 of their carers, including healthcare workers; attended meetings associated with service improvements, medical decision-making, public engagement, and scientific developments as well as following patients through waiting rooms, clinical consultations and other settings. We found that the translation of new protocols based on biological research introduced further complications into an already-complex patient pathway. Combinations of new and historic forms of stratification had an impact on almost all patients, carers and staff, resulting in care that often felt less rather than more personal

Using a statistical learning approach to identify sociodemographic and clinical predictors of response to clozapine

Background: A proportion of people with treatment-resistant schizophrenia fail to show improvement on clozapine treatment. Knowledge of the sociodemographic and clinical factors predicting clozapine response may be useful in developing personalised approaches to treatment. Methods: This retrospective cohort study used data from the electronic health records of the South London and Maudsley (SLaM) hospital between 2007 and 2011. Using the Least Absolute Shrinkage and Selection Operator (LASSO) regression statistical learning approach, we examined 35 sociodemographic and clinical factors’ predictive ability of response to clozapine at 3 months of treatment. Response was assessed by the level of change in the severity of the symptoms using the Clinical Global Impression (CGI) scale. Results: We identified 242 service-users with a treatment-resistant psychotic disorder who had their first trial of clozapine and continued the treatment for at least 3 months. The LASSO regression identified three predictors of response to clozapine: higher severity of illness at baseline, female gender and having a comorbid mood disorder. These factors are estimated to explain 18% of the variance in clozapine response. The model’s optimism-corrected calibration slope was 1.37, suggesting that the model will underfit when applied to new data. Conclusions: These findings suggest that women, people with a comorbid mood disorder and those who are most ill at baseline respond better to clozapine. However, the accuracy of the internally validated and recalibrated model was low. Therefore, future research should indicate whether a prediction model developed by including routinely collected data, in combination with biological information, presents adequate predictive ability to be applied in clinical settings

A predictor model of treatment resistance in schizophrenia using data from electronic health records

Objectives: To develop a prognostic tool of treatment resistant schizophrenia (TRS) in a large and diverse clinical cohort, with comprehensive coverage of patients using mental health services in four London boroughs. Methods: We used the Least Absolute Shrinkage and Selection Operator (LASSO) for time-to-event data, to develop a risk prediction model from the first antipsychotic prescription to the development of TRS, using data from electronic health records. Results: We reviewed the clinical records of 1,515 patients with a schizophrenia spectrum disorder and observed that 253 (17%) developed TRS. The Cox LASSO survival model produced an internally validated Harrel’s C index of 0.60. A Kaplan-Meier curve indicated that the hazard of developing TRS remained constant over the observation period. Predictors of TRS were: having more inpatient days in the three months before and after the first antipsychotic, more community face-to-face clinical contact in the three months before the first antipsychotic, minor cognitive problems, and younger age at the time of the first antipsychotic. Conclusions: Routinely collected information, readily available at the start of treatment, gives some indication of TRS but is unlikely to be adequate alone. These results provide further evidence that earlier onset is a risk factor for TRS

Clinical correlates of early onset antipsychotic treatment resistance

Background:: There is evidence of heterogeneity within treatment-resistant schizophrenia (TRS), with some people not responding to antipsychotic treatment from illness onset and others becoming treatment-resistant after an initial response period. These groups may have different aetiologies. Aim:: This study investigates sociodemographic and clinical correlates of early onset of TRS. Method:: Employing a retrospective cohort design, we do a secondary analysis of data from a cohort of people with TRS attending the South London and Maudsley. Regression analyses were conducted to identify the correlates of the length of treatment to TRS. Predictors included the following: gender, age, ethnicity, problems with positive symptoms, problems with activities of daily living, psychiatric comorbidities, involuntary hospitalisation and treatment with long-acting injectable antipsychotics. Results:: In a cohort of 164 people with TRS (60% were men), the median length of treatment to TRS was 3 years and 8 months. We observed no cut-off on the length of treatment until TRS presentation differentiating between early and late TRS (i.e. no bimodal distribution). Having mild to very severe problems with hallucinations and delusions at the treatment start was associated with earlier TRS (~19 months earlier). In sensitivity analyses, including only complete cases (subject to selection bias), treatment with a long-acting injectable antipsychotic was additionally associated with later TRS (~15 months later). Conclusion:: Our findings do not support a clear separation between early and late TRS but rather a continuum of the length of treatment before TRS onset. Having mild to very severe problems with positive symptoms at treatment start predicts earlier onset of TRS

Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial

Background Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy