Policing and Health Collaboration in England and Wales. Landscape review.

The links between health, offending and policing are complex but inextricable. Collaborative working between the police and health has a long history but is still not commonplace. This landscape review aims to consider the breadth of the subject, and also to look at emerging themes and to influence future approaches

A machine learning algorithm to differentiate bipolar disorder from major depressive disorder using an online mental health questionnaire and blood biomarker data.

The vast personal and economic burden of mood disorders is largely caused by their under- and misdiagnosis, which is associated with ineffective treatment and worsening of outcomes. Here, we aimed to develop a diagnostic algorithm, based on an online questionnaire and blood biomarker data, to reduce the misdiagnosis of bipolar disorder (BD) as major depressive disorder (MDD). Individuals with depressive symptoms (Patient Health Questionnaire-9 score ≥5) aged 18-45 years were recruited online. After completing a purpose-built online mental health questionnaire, eligible participants provided dried blood spot samples for biomarker analysis and underwent the World Health Organization World Mental Health Composite International Diagnostic Interview via telephone, to establish their mental health diagnosis. Extreme Gradient Boosting and nested cross-validation were used to train and validate diagnostic models differentiating BD from MDD in participants who self-reported a current MDD diagnosis. Mean test area under the receiver operating characteristic curve (AUROC) for separating participants with BD diagnosed as MDD (N = 126) from those with correct MDD diagnosis (N = 187) was 0.92 (95% CI: 0.86-0.97). Core predictors included elevated mood, grandiosity, talkativeness, recklessness and risky behaviour. Additional validation in participants with no previous mood disorder diagnosis showed AUROCs of 0.89 (0.86-0.91) and 0.90 (0.87-0.91) for separating newly diagnosed BD (N = 98) from MDD (N = 112) and subclinical low mood (N = 120), respectively. Validation in participants with a previous diagnosis of BD (N = 45) demonstrated sensitivity of 0.86 (0.57-0.96). The diagnostic algorithm accurately identified patients with BD in various clinical scenarios, and could help expedite accurate clinical diagnosis and treatment of BD

A machine learning algorithm to differentiate bipolar disorder from major depressive disorder using an online mental health questionnaire and blood biomarker data.

The vast personal and economic burden of mood disorders is largely caused by their under- and misdiagnosis, which is associated with ineffective treatment and worsening of outcomes. Here, we aimed to develop a diagnostic algorithm, based on an online questionnaire and blood biomarker data, to reduce the misdiagnosis of bipolar disorder (BD) as major depressive disorder (MDD). Individuals with depressive symptoms (Patient Health Questionnaire-9 score ≥5) aged 18-45 years were recruited online. After completing a purpose-built online mental health questionnaire, eligible participants provided dried blood spot samples for biomarker analysis and underwent the World Health Organization World Mental Health Composite International Diagnostic Interview via telephone, to establish their mental health diagnosis. Extreme Gradient Boosting and nested cross-validation were used to train and validate diagnostic models differentiating BD from MDD in participants who self-reported a current MDD diagnosis. Mean test area under the receiver operating characteristic curve (AUROC) for separating participants with BD diagnosed as MDD (N = 126) from those with correct MDD diagnosis (N = 187) was 0.92 (95% CI: 0.86-0.97). Core predictors included elevated mood, grandiosity, talkativeness, recklessness and risky behaviour. Additional validation in participants with no previous mood disorder diagnosis showed AUROCs of 0.89 (0.86-0.91) and 0.90 (0.87-0.91) for separating newly diagnosed BD (N = 98) from MDD (N = 112) and subclinical low mood (N = 120), respectively. Validation in participants with a previous diagnosis of BD (N = 45) demonstrated sensitivity of 0.86 (0.57-0.96). The diagnostic algorithm accurately identified patients with BD in various clinical scenarios, and could help expedite accurate clinical diagnosis and treatment of BD

A machine learning algorithm to differentiate bipolar disorder from major depressive disorder using an online mental health questionnaire and blood biomarker data

The vast personal and economic burden of mood disorders is largely caused by their under- and misdiagnosis, which is associated with ineffective treatment and worsening of outcomes. Here, we aimed to develop a diagnostic algorithm, based on an online questionnaire and blood biomarker data, to reduce the misdiagnosis of bipolar disorder (BD) as major depressive disorder (MDD). Individuals with depressive symptoms (Patient Health Questionnaire-9 score >= 5) aged 18-45 years were recruited online. After completing a purpose-built online mental health questionnaire, eligible participants provided dried blood spot samples for biomarker analysis and underwent the World Health Organization World Mental Health Composite International Diagnostic Interview via telephone, to establish their mental health diagnosis. Extreme Gradient Boosting and nested cross-validation were used to train and validate diagnostic models differentiating BD from MDD in participants who self-reported a current MDD diagnosis. Mean test area under the receiver operating characteristic curve (AUROC) for separating participants with BD diagnosed as MDD (N = 126) from those with correct MDD diagnosis (N = 187) was 0.92 (95% CI: 0.86-0.97). Core predictors included elevated mood, grandiosity, talkativeness, recklessness and risky behaviour. Additional validation in participants with no previous mood disorder diagnosis showed AUROCs of 0.89 (0.86-0.91) and 0.90 (0.87-0.91) for separating newly diagnosed BD (N = 98) from MDD (N = 112) and subclinical low mood (N = 120), respectively. Validation in participants with a previous diagnosis of BD (N = 45) demonstrated sensitivity of 0.86 (0.57-0.96). The diagnostic algorithm accurately identified patients with BD in various clinical scenarios, and could help expedite accurate clinical diagnosis and treatment of BD

Missense variants in the N-terminal domain of the A isoform of FHF2/FGF13 cause an X-linked developmental and epileptic encephalopathy

Fibroblast growth factor homologous factors (FHFs) are intracellular proteins which regulate voltage-gated sodium (Na v) channels in the brain and other tissues. FHF dysfunction has been linked to neurological disorders including epilepsy. Here, we describe two sibling pairs and three unrelated males who presented in infancy with intractable focal seizures and severe developmental delay. Whole-exome sequencing identified hemi- and heterozygous variants in the N-terminal domain of the A isoform of FHF2 (FHF2A). The X-linked FHF2 gene (also known as FGF13) has alternative first exons which produce multiple protein isoforms that differ in their N-terminal sequence. The variants were located at highly conserved residues in the FHF2A inactivation particle that competes with the intrinsic fast inactivation mechanism of Na v channels. Functional characterization of mutant FHF2A co-expressed with wild-type Na v1.6 (SCN8A) revealed that mutant FHF2A proteins lost the ability to induce rapid-onset, long-term blockade of the channel while retaining pro-excitatory properties. These gain-of-function effects are likely to increase neuronal excitability consistent with the epileptic potential of FHF2 variants. Our findings demonstrate that FHF2 variants are a cause of infantile-onset developmental and epileptic encephalopathy and underline the critical role of the FHF2A isoform in regulating Na v channel function

Masses, radii, and orbits of small Kepler planets : The transition from gaseous to rocky planets

We report on the masses, sizes, and orbits of the planets orbiting 22 Kepler stars. There are 49 planet candidates around these stars, including 42 detected through transits and 7 revealed by precise Doppler measurements of the host stars. Based on an analysis of the Kepler brightness measurements, along with high-resolution imaging and spectroscopy, Doppler spectroscopy, and (for 11 stars) asteroseismology, we establish low false-positive probabilities (FPPs) for all of the transiting planets (41 of 42 have an FPP under 1%), and we constrain their sizes and masses. Most of the transiting planets are smaller than three times the size of Earth. For 16 planets, the Doppler signal was securely detected, providing a direct measurement of the planet's mass. For the other 26 planets we provide either marginal mass measurements or upper limits to their masses and densities; in many cases we can rule out a rocky composition. We identify six planets with densities above 5 g cm-3, suggesting a mostly rocky interior for them. Indeed, the only planets that are compatible with a purely rocky composition are smaller than 2 R ⊕. Larger planets evidently contain a larger fraction of low-density material (H, He, and H2O).Peer reviewedFinal Accepted Versio

Solving the conundrum of intra-specific variation in metabolic rate: A multidisciplinary conceptual and methodological toolkit

Researchers from diverse disciplines, including organismal and cellular physiology, sports science, human nutrition, evolution and ecology, have sought to understand the causes and consequences of the surprising variation in metabolic rate found among and within individual animals of the same species. Research in this area has been hampered by differences in approach, terminology and methodology, and the context in which measurements are made. Recent advances provide important opportunities to identify and address the key questions in the field. By bringing together researchers from different areas of biology and biomedicine, we describe and evaluate these developments and the insights they could yield, highlighting the need for more standardisation across disciplines. We conclude with a list of important questions that can now be addressed by developing a common conceptual and methodological toolkit for studies on metabolic variation in animals

The Help for People with Money, Employment or Housing Problems (HOPE) Intervention:pilot randomised trial with mixed methods feasibility research

Background:Job loss, austerity measures, financial difficulties and house repossession contribute to the risk of self-harm and suicide during recessions. Navigating the benefits system and accessing sources of welfare and debt advice is a difficult experience for vulnerable people, further contributing to their distress. Whilst there is some evidence that advice-type interventions can lead to financial gain, there is mixed evidence for their effectiveness in improving mental health in those experiencing financial difficulties. There have been no interventions targeting those who have self-harmed due to economic hardship. Methods:Our aim was to determine the feasibility and acceptability of a brief psychosocial intervention (the 'HOPE' service) for people presenting to hospital emergency departments (ED) following self-harm or in acute distress because of financial, employment or welfare (benefit) difficulties. Nineteen people consented to random allocation to the intervention or control arm on a 2:1 basis. Participants randomised to the intervention arm (n = 13) received up to six sessions of 1:1 support provided by community support staff trained in Motivational Interviewing (MI). Control participants (n = 6) received a one-off session signposting them to relevant support organisations. Fourteen participants were followed up after 3 months. Participants and mental health workers took part in qualitative interviews. The acceptability of outcome measures including the PHQ-9, GAD-7, repeat self-harm, EQ5D-5 L and questions about debt, employment and welfare benefits were explored. Results:Interviews indicated the main benefits of the service as the resolution of specific financial problems and receiving support when participants were feeling most vulnerable. Randomisation was acceptable to most participants although not always fully understood and control participants could be disappointed. Recruitment was slow (1-2 per month). The outcome measures were acceptable and appeared sensitive to change. Discussion:The HOPE intervention is feasible and acceptable. There was evidence of need and it is a relatively inexpensive intervention. Refining aspects of the intervention would be straightforward. A full-scale RCT would be feasible, if broadened eligibility criteria led to increased recruitment and improvements were made to staff training and support. Trial registration:ISRCTN58531248

Mast Cells and Gastrointestinal Dysmotility in the Cystic Fibrosis Mouse

BACKGROUND: Cystic fibrosis (CF) has many effects on the gastrointestinal tract and a common problem in this disease is poor nutrition. In the CF mouse there is an innate immune response with a large influx of mast cells into the muscularis externa of the small intestine and gastrointestinal dysmotility. The aim of this study was to evaluate the potential role of mast cells in gastrointestinal dysmotility using the CF mouse (Cftr(tm1UNC), Cftr knockout). METHODOLOGY: Wild type (WT) and CF mice were treated for 3 weeks with mast cell stabilizing drugs (ketotifen, cromolyn, doxantrazole) or were treated acutely with a mast cell activator (compound 48/80). Gastrointestinal transit was measured using gavage of a fluorescent tracer. RESULTS: In CF mice gastric emptying at 20 min post-gavage did not differ from WT, but was significantly less than in WT at 90 min post-gavage. Gastric emptying was significantly increased in WT mice by doxantrazole, but none of the mast cell stabilizers had any significant effect on gastric emptying in CF mice. Mast cell activation significantly enhanced gastric emptying in WT mice but not in CF mice. Small intestinal transit was significantly less in CF mice as compared to WT. Of the mast cell stabilizers, only doxantrazole significantly affected small intestinal transit in WT mice and none had any effect in CF mice. Mast cell activation resulted in a small but significant increase in small intestinal transit in CF mice but not WT mice. CONCLUSIONS: The results indicate that mast cells are not involved in gastrointestinal dysmotility but their activation can stimulate small intestinal transit in cystic fibrosis