Machine learning models of plasma proteomic data predict mood in chronic stroke and tie it to aberrant peripheral immune responses.

Post-stroke depression is common, long-lasting and associated with severe morbidity and death, but mechanisms are not well-understood. We used a broad proteomics panel and developed a machine learning algorithm to determine whether plasma protein data can predict mood in people with chronic stroke, and to identify proteins and pathways associated with mood. We used Olink to measure 1,196 plasma proteins in 85 participants aged 25 and older who were between 5 months and 9 years after ischemic stroke. Mood was assessed with the Stroke Impact Scale mood questionnaire (SIS3). Machine learning multivariable regression models were constructed to estimate SIS3 using proteomics data, age, and time since stroke. We also dichotomized participants into better mood (SIS3 > 63) or worse mood (SIS3 ≤ 63) and analyzed candidate proteins. Machine learning models verified that there is indeed a relationship between plasma proteomic data and mood in chronic stroke, with the most accurate prediction of mood occurring when we add age and time since stroke. At the individual protein level, no single protein or set of proteins predicts mood. But by using univariate analyses of the proteins most highly associated with mood we produced a model of chronic post-stroke depression. We utilized the fact that this list contained many proteins that are also implicated in major depression. Also, over 80% of immune proteins that correlate with mood were higher with worse mood, implicating a broadly overactive immune system in chronic post-stroke depression. Finally, we used a comprehensive literature review of major depression and acute post-stroke depression. We propose that in chronic post-stroke depression there is over-activation of the immune response that then triggers changes in serotonin activity and neuronal plasticity leading to depressed mood.

Precision Care in Cardiac Arrest: ICECAP (PRECICECAP) Study Protocol and Informatics Approach.

BACKGROUND: Most trials in critical care have been neutral, in part because between-patient heterogeneity means not all patients respond identically to the same treatment. The Precision Care in Cardiac Arrest: Influence of Cooling duration on Efficacy in Cardiac Arrest Patients (PRECICECAP) study will apply machine learning to high-resolution, multimodality data collected from patients resuscitated from out-of-hospital cardiac arrest. We aim to discover novel biomarker signatures to predict the optimal duration of therapeutic hypothermia and 90-day functional outcomes. In parallel, we are developing a freely available software platform for standardized curation of intensive care unit-acquired data for machine learning applications. METHODS: The Influence of Cooling duration on Efficacy in Cardiac Arrest Patients (ICECAP) study is a response-adaptive, dose-finding trial testing different durations of therapeutic hypothermia. Twelve ICECAP sites will collect data for PRECICECAP from multiple modalities routinely used after out-of-hospital cardiac arrest, including ICECAP case report forms, detailed medication data, cardiopulmonary and electroencephalographic waveforms, and digital imaging and communications in medicine files (DICOMs). We partnered with Moberg Analytics to develop a freely available software platform to allow high-resolution critical care data to be used efficiently and effectively. We will use an autoencoder neural network to create low-dimensional representations of all raw waveforms and derivative features, censored at rewarming to ensure clinical usability to guide optimal duration of hypothermia. We will also consider simple features that are historically considered to be important. Finally, we will create a supervised deep learning neural network algorithm to directly predict 90-day functional outcome from large sets of novel features. RESULTS: PRECICECAP is currently enrolling and will be completed in late 2025. CONCLUSIONS: Cardiac arrest is a heterogeneous disease that causes substantial morbidity and mortality. PRECICECAP will advance the overarching goal of titrating personalized neurocritical care on the basis of robust measures of individual need and treatment responsiveness. The software platform we develop will be broadly applicable to hospital-based research after acute illness or injury.

Women's experiences of receiving a diagnosis of premenstrual dysphoric disorder: a qualitative investigation.

BACKGROUND: Premenstrual dysphoric disorder (PMDD) is a complex and disabling condition that affects women of reproductive age, characterised by severe physical and psychological symptoms that occur cyclically and remit following the onset of menses. As the psychological nature and consequences of PMDD often seem indistinguishable from symptoms of other mental health difficulties, this condition presents distinct diagnostic challenges for healthcare professionals. Therefore, this study aimed to explore women's experiences of both having PMDD and of receiving this diagnosis. METHODS: Participant recruitment took place in the United Kingdom during 2018. Seventeen women who had been diagnosed with PMDD by a medical specialist and met the clinical criteria for PMDD on the premenstrual symptoms screening tool were interviewed. The data from these semi-structured interviews were audio-recorded, transcribed and inductively analysed using reflexive thematic analysis. RESULTS: Twelve subthemes were identified and organised around four main themes: (1) A broken woman, (2) Misdiagnosis and the lost decades, (3) A life transformed and (4) Negotiating the aftermath. CONCLUSIONS: The findings of this study highlight the critical importance of the accurate and timely detection of PMDD, with the aim of preventing women from experiencing severe and prolonged psychological distress. In order to achieve this, there needs to be a greater understanding and awareness of PMDD within both the medical and lay communities, alongside training for healthcare practitioners in PMDD assessment.

A longitudinal study of the post-stroke immune response and cognitive functioning: the StrokeCog study protocol.

BACKGROUND: Stroke increases the risk of cognitive impairment even several years after the stroke event. The exact mechanisms of post-stroke cognitive decline are unclear, but the immunological response to stroke might play a role. The aims of the StrokeCog study are to examine the associations between immunological responses and long-term post-stroke cognitive trajectories in individuals with ischemic stroke. METHODS: StrokeCog is a single-center, prospective, observational, cohort study. Starting 6-12 months after stroke, comprehensive neuropsychological assessment, plasma and serum, and psychosocial variables will be collected at up to 4 annual visits. Single cell sequencing of peripheral blood monocytes and plasma proteomics will be conducted. The primary outcome will be the change in global and domain-specific neuropsychological performance across annual evaluations. To explain the differences in cognitive change amongst participants, we will examine the relationships between comprehensive immunological measures and these cognitive trajectories. It is anticipated that 210 participants will be enrolled during the first 3 years of this 4-year study. Accounting for attrition, an anticipated final sample size of 158 participants with an average of 3 annual study visits will be available at the completion of the study. Power analyses indicate that this sample size will provide 90% power to detect an average cognitive change of at least 0.23 standard deviations in either direction. DISCUSSION: StrokeCog will provide novel insight into the relationships between immune events and cognitive change late after stroke.

Towards universal health coverage: governance and organisational change in ministries of health.

Countries have implemented a range of reforms in health financing and provision to advance towards universal health coverage (UHC). These reforms often change the role of a ministry of health (MOH) in traditionally unitary national health service systems. An exploratory comparative case study of four upper middle-income and high-income countries provides insights into how these reforms in pursuit of UHC are likely to affect health governance and the organisational functioning of an MOH accustomed to controlling the financing and delivery of healthcare. These reforms often do not result in simple transfers of responsibility from MOH to other actors in the health system. The resulting configuration of responsibilities and organisational changes within a health system is specific to the capacities within the health system and the sociopolitical context. Formal prescriptions that accompany reform proposals often do not fully represent what actually takes place. An MOH may retain considerable influence in financing and delivery even when reforms appear to formally shift those powers to other organisational units. MOHs have limited ability to independently achieve fundamental system restructuring in health systems that are strongly subject to public sector rules and policies. Our comparative study shows that within these constraints, MOHs can drive organisational change through four mechanisms: establishing a high-level interministerial team to provide political commitment and reduce institutional barriers; establishing an MOH 'change team' to lead implementation of organisational change; securing key components of systemic change through legislation; and leveraging emerging political change windows of opportunity for the introduction of health reforms.

There's nowhere to go: counting the costs of extreme weather to the homeless community.

People experiencing homelessness are vulnerable to extreme weather in unique ways. The entrenched inequalities that underpin disaster vulnerability are compounded by extreme isolation and the stress of transient living on mental and physical health. However, the impacts of extreme weather on the homeless in Australia are largely undocumented and rarely incorporated in emergency planning. Interviews with and surveys of emergency and homeless services and service users revealed that the primary ramifications of losing shelter and worsening mental health deepen the cycle of homelessness and trauma. Consequently, homeless shelter losses, such as tents, should be included in pre- and post-event impact statistics and subsequent recovery support. Extreme weather response plans should include early triggers and strategies for 'non-severe' weather events, as the homeless community is affected earlier and by a wider range of meteorological conditions. Moreover, this study also explores the benefits of a trauma-informed response to extreme weather when working with the homeless.

A year-long immune profile of the systemic response in acute stroke survivors.

Stroke is a leading cause of cognitive impairment and dementia, but the mechanisms that underlie post-stroke cognitive decline are not well understood. Stroke produces profound local and systemic immune responses that engage all major innate and adaptive immune compartments. However, whether the systemic immune response to stroke contributes to long-term disability remains ill-defined. We used a single-cell mass cytometry approach to comprehensively and functionally characterize the systemic immune response to stroke in longitudinal blood samples from 24 patients over the course of 1 year and correlated the immune response with changes in cognitive functioning between 90 and 365 days post-stroke. Using elastic net regularized regression modelling, we identified key elements of a robust and prolonged systemic immune response to ischaemic stroke that occurs in three phases: an acute phase (Day 2) characterized by increased signal transducer and activator of transcription 3 (STAT3) signalling responses in innate immune cell types, an intermediate phase (Day 5) characterized by increased cAMP response element-binding protein (CREB) signalling responses in adaptive immune cell types, and a late phase (Day 90) by persistent elevation of neutrophils, and immunoglobulin M+ (IgM+) B cells. By Day 365 there was no detectable difference between these samples and those from an age- and gender-matched patient cohort without stroke. When regressed against the change in the Montreal Cognitive Assessment scores between Days 90 and 365 after stroke, the acute inflammatory phase Elastic Net model correlated with post-stroke cognitive trajectories (r = -0.692, Bonferroni-corrected P = 0.039). The results demonstrate the utility of a deep immune profiling approach with mass cytometry for the identification of clinically relevant immune correlates of long-term cognitive trajectories.

Antipsychotic drugs disrupt normal development in Caenorhabditis elegans via additional mechanisms besides dopamine and serotonin receptors.

Antipsychotic drugs may produce adverse effects during development in humans and rodents. However, the extent of these effects has not been systematically characterized nor have molecular mechanisms been identified. Consequently, we sought to evaluate the effects of an extensive panel of antipsychotic drugs in a model organism, Caenorhabditis elegans, whose development is well characterized and which offers the possibility of identifying novel molecular targets. For these studies, animals were grown from hatching in the presence of vehicle (control) or antipsychotic drugs over a range of concentrations (20-160microM) and growth was analyzed by measuring head-to-tail length at various intervals. First-generation antipsychotics (e.g., fluphenazine) generally slowed growth and maturation more than second-generation drugs such as quetiapine and olanzapine. This is consistent with in vitro effects on human neuronal cell lines. Clozapine, a second-generation drug, produced similar growth deficits as haloperidol. Converging lines of evidence, including the failure to rescue growth with high concentrations of agonists, suggested that the drug-induced delay in development was not mediated by the major neurotransmitter receptors recognized by the antipsychotic drugs. Moreover, in serotonin-deficient tph-1 mutants, the drugs dramatically slowed development and led to larval arrest (including dauer formation) and neuronal abnormalities. Evaluation of alternative targets of the antipsychotics revealed a potential role for calmodulin and underscored the significance of Ca(2+)-calmodulin signaling in development. These findings suggest that antipsychotic drugs may interfere with normal developmental processes and provide a tool for investigating the key signaling pathways involved.