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Previous phases of the Coupled Model Intercomparison Project (CMIP) have primarily focused on simulations driven by atmospheric concentrations of greenhouse gases (GHGs), for both idealized model experiments and climate projections of different emissions scenarios. We argue that although this approach was practical to allow parallel development of Earth system model simulations and detailed socioeconomic futures, carbon cycle uncertainty as represented by diverse, process-resolving Earth system models (ESMs) is not manifested in the scenario outcomes, thus omitting a dominant source of uncertainty in meeting the Paris Agreement. Mitigation policy is defined in terms of human activity (including emissions), with strategies varying in their timing of net-zero emissions, the balance of mitigation effort between short-lived and long-lived climate forcers, their reliance on land use strategy, and the extent and timing of carbon removals. To explore the response to these drivers, ESMs need to explicitly represent complete cycles of major GHGs, including natural processes and anthropogenic influences. Carbon removal and sequestration strategies, which rely on proposed human management of natural systems, are currently calculated in integrated assessment models (IAMs) during scenario development with only the net carbon emissions passed to the ESM. However, proper accounting of the coupled system impacts of and feedback on such interventions requires explicit process representation in ESMs to build self-consistent physical representations of their potential effectiveness and risks under climate change. We propose that CMIP7 efforts prioritize simulations driven by CO2 emissions from fossil fuel use and projected deployment of carbon dioxide removal technologies, as well as land use and management, using the process resolution allowed by state-of-the-art ESMs to resolve carbon–climate feedbacks. Post-CMIP7 ambitions should aim to incorporate modeling of non-CO2 GHGs (in particular, sources and sinks of methane and nitrous oxide) and process-based representation of carbon removal options. These developments will allow three primary benefits: (1) resources to be allocated to policy-relevant climate projections and better real-time information related to the detectability and verification of emissions reductions and their relationship to expected near-term climate impacts, (2) scenario modeling of the range of possible future climate states including Earth system processes and feedbacks that are increasingly well-represented in ESMs, and (3) optimal utilization of the strengths of ESMs in the wider context of climate modeling infrastructure (which includes simple climate models, machine learning approaches and kilometer-scale climate models).

Abstract The Clinical and Laboratory Standards Institute (CLSI) M27 guidelines are the recommended and most commonly used protocols for broth microdilution antifungal susceptibility testing of yeasts. However, these guidelines are limited to the use of 96-well assay plates, limiting assay capacity. With the increased risk of fungal resistance emerging in the community, it is important to have alternative protocols available, that offer higher throughput and can screen more than eight to ten potential antifungal compounds per plate. This study presents an optimised broth microdilution minimum inhibitory concentration (MIC) method for testing the susceptibility of yeasts in an efficient high throughput screening setup, with minimal growth variability and maximum reproducibility. We extend the M27 guidelines and optimise the conditions for 384-well plates. Validation of the assay was performed with ten clinically used antifungals (fluconazole, amphotericin B, 5-fluorocytosine, posaconazole, voriconazole, ketoconazole, itraconazole, caspofungin diacetate, anidulafungin and micafungin) against Candida albicans and Cryptococcus neoformans.

We review how the international modelling community, encompassing integrated assessment models, global and regional Earth system and climate models, and impact models, has worked together over the past few decades to advance understanding of Earth system change and its impacts on society and the environment and thereby support international climate policy. We go on to recommend a number of priority research areas for the coming decade, a timescale that encompasses a number of newly starting international modelling activities, as well as the IPCC Seventh Assessment Report (AR7) and the second UNFCCC Global Stocktake. Progress in these priority areas will significantly advance our understanding of Earth system change and its impacts, increasing the quality and utility of science support to climate policy. We emphasize the need for continued improvement in our understanding of, and ability to simulate, the coupled Earth system and the impacts of Earth system change. There is an urgent need to investigate plausible pathways and emission scenarios that realize the Paris climate targets – for example, pathways that overshoot 1.5 or 2 °C global warming, before returning to these levels at some later date. Earth system models need to be capable of thoroughly assessing such warming overshoots – in particular, the efficacy of mitigation measures, such as negative CO2 emissions, in reducing atmospheric CO2 and driving global cooling. An improved assessment of the long-term consequences of stabilizing climate at 1.5 or 2 °C above pre-industrial temperatures is also required. We recommend Earth system models run overshoot scenarios in CO2-emission mode to more fully represent coupled climate–carbon-cycle feedbacks and, wherever possible, interactively simulate other key Earth system phenomena at risk of rapid change during overshoot. Regional downscaling and impact models should use forcing data from these simulations, so impact and regional climate projections cover a more complete range of potential responses to a warming overshoot. An accurate simulation of the observed, historical record remains a fundamental requirement of models, as does accurate simulation of key metrics, such as the effective climate sensitivity and the transient climate response to cumulative carbon emissions. For adaptation, a key demand is improved guidance on potential changes in climate extremes and the modes of variability these extremes develop within. Such improvements will most likely be realized through a combination of increased model resolution, improvement of key model parameterizations, and enhanced representation of important Earth system processes, combined with targeted use of new artificial intelligence (AI) and machine learning (ML) techniques. We propose a deeper collaboration across such efforts over the coming decade. With respect to sampling future uncertainty, increased collaboration between approaches that emphasize large model ensembles and those focussed on statistical emulation is required. We recommend an increased focus on high-impact–low-likelihood (HILL) outcomes – in particular, the risk and consequences of exceeding critical tipping points during a warming overshoot and the potential impacts arising from this. For a comprehensive assessment of the impacts of Earth system change, including impacts arising directly as a result of climate mitigation actions, it is important that spatially detailed, disaggregated information used to generate future scenarios in integrated assessment models be available for use in impact models. Conversely, there is a need to develop methods that enable potential societal responses to projected Earth system change to be incorporated into scenario development. The new models, simulations, data, and scientific advances proposed in this article will not be possible without long-term development and maintenance of a robust, globally connected infrastructure ecosystem. This system must be easily accessible and useable by modelling communities across the world, allowing the global research community to be fully engaged in developing and delivering new scientific knowledge to support international climate policy.

The ideology of native speakerism and its effects on the professional lives of 'native' and 'non-native speakers' in English language teaching (ELT) have been widely documented. Nevertheless, little is known about the impact native speakerism might have on the selection of plenary speakers for ELT conferences. Hence, through the analysis of plenary line-ups of seven conferences in the EU and interviews with their organisers, this study aimed to explore whether 'native' and 'non-native speakers' were represented equally as plenary speakers. The results show that overall only 25 per cent of the 416 analysed plenary speaker line-ups were given by 'non-native speakers'. At some conferences, over 80 per cent of all plenary speakers were 'native speakers'. Apart from one conference organiser who highlighted that steps had been taken to ensure a more equal balance of speakers, the other organisers seemed not to place much importance on equal balance of 'native' and 'non-native speakers' when inviting plenary speakers, preferring a 'colourblind' approach. This study suggests native-speakerism impacts the choice of plenary speakers at certain ELT conferences, and that conscious efforts must be made to alter this balance.

Background The radiological trajectory of post-COVID-19 is uncertain. We present a prospective, observational, multicentre cohort study using multimodality imaging to describe the pulmonary sequelae of patients hospitalised with COVID-19, predictors of persistent abnormal radiology and implications on health status.Methods In survivors of COVID-19, we performed convalescent CT pulmonary angiogram and high-resolution CT imaging as part of the CISCO-19 study (ClinicalTrials.gov ID NCT04403607). This included serial blood biomarkers and patient-reported outcomes 28–60 days following discharge from hospital.Results Of the COVID-19 cohort, 88 (56%) patients of the COVID-19 cohort (n = 159; mean age, 55 years; 43% female) had persisting radiological abnormalities at 28–60 days postdischarge. This included ground-glass opacification (45%), reticulation/architectural distortion (30%) or mixed pattern (19%). These features were very infrequent among a group of age-matched, sex-matched and cardiovascular risk factor-matched controls (n=29). The majority of COVID-19 cohort (68%) had less than 20% persisting radiological abnormalities, with 67% demonstrating overall improvement compared with admission imaging. Older age, premorbid performance status, typical acute COVID-19 radiological features, markers of severe acute COVID-19, convalescent ICAM-1 and P-selectin were associated with persisting lung abnormalities (all p

The use of teledermatology has increased significantly in recent years. The objective of this study was to determine Canadian dermatologists’ and dermatology residents’ perspectives on teledermatology. An online survey was created to determine participants’ teledermatology practice patterns and their perception of the challenges, education, training, and research in teledermatology. The survey was distributed through the Canadian Dermatology Association and by administrative staff at Canadian Dermatology departments. There were 33 respondents: 66.7% of respondents started using teledermatology during the COVID-19 pandemic, and 93.8% reported that teledermatology accounted for 0-25% of their practice. Convenience, access, and safety were identified as the primary advantages of teledermatology. Teledermatology was mainly utilized for medication monitoring or refills and to assess, manage, or follow up on dermatitis, other chronic inflammatory conditions, and pre-diagnosed dermatologic conditions. Poor photo quality (32.3%) and the inability to conduct physical examinations or accurately diagnose conditions (74.2%) were reported as significant challenges. Respondents recommended education on the medicolegal considerations of teledermatology and research on how teledermatology compares to in-person consultations. Overall, teledermatology offers convenience, access, and improved safety for patients and healthcare professionals. However, addressing challenges related to physical examinations, accurate diagnoses, and photo quality is essential for optimal care delivery.

Cardiorespiratory signals have long been treated as ''noise'' in functional magnetic resonance imaging (fMRI) research, with the goal of minimizing their impact to isolate neural activity. However, there is a growing recognition that these signals, once seen as confounding variables, provide valuable insights into brain function and overall health. This shift reflects the dynamic interaction between the cardiovascular, respiratory, and neural systems, which together support brain activity. In this review, we explore the role of cardiorespiratory dynamics—such as heart rate variability (HRV), respiratory sinus arrhythmia (RSA), and changes in blood flow, oxygenation, and carbon dioxide levels—embedded within fMRI signals. These physiological signals reflect critical aspects of neurovascular coupling and are influenced by factors such as physiological stress, breathing patterns, and age-related changes. We also discuss the complexities of distinguishing these signals from neuronal activity in fMRI data, given their significant contribution to signal variability and interactions with cerebrospinal fluid (CSF). Recognizing the influence of these cardiorespiratory dynamics is crucial for improving the interpretation of fMRI data, shedding light on heart-brain and respiratory-brain connections, and enhancing our understanding of circulation, oxygen delivery, and waste elimination within the brain.

Abstract Background White matter hyperintensities (WMH) are considered hallmark features of cerebral small vessel disease and have recently been linked to Alzheimer’s disease (AD) pathology. Their distinct spatial distributions, namely periventricular versus deep WMH, may differ by underlying age-related and pathobiological processes contributing to cognitive decline. We aimed to identify the spatial patterns of WMH using the 4-scale Fazekas visual assessment and explore their differential association with age, vascular health, AD imaging markers, namely amyloid and tau burden, and cognition. Because our study consisted of scans from GE and Siemens scanners with different resolutions, we also investigated inter-scanner reproducibility and combinability of WMH measurements on imaging. Methods We identified 1144 participants from the Mayo Clinic Study of Aging consisting of a population-based sample from Olmsted County, Minnesota with available structural magnetic resonance imaging (MRI), amyloid, and tau positron emission tomography (PET). WMH distribution patterns were assessed on FLAIR-MRI, both 2D axial and 3D, using Fazekas ratings of periventricular and deep WMH severity. We compared the association of periventricular and deep WMH scales with vascular risk factors, amyloid-PET, and tau-PET standardized uptake value ratio, automated WMH volume, and cognition using Pearson partial correlation after adjusting for age. We also evaluated vendor compatibility and reproducibility of the Fazekas scales using intraclass correlations (ICC). Results Periventricular and deep WMH measurements showed similar correlations with age, cardiometabolic conditions score (vascular risk), and cognition, (p

Abstract Non-invasive imaging of GSK-3 expression in the brain will help to understand the role of GSK-3 in disease pathology and progression. Herein, we report the radiosynthesis and evaluation of two novel isonicotinamide based 18F labeled PET probes, [18F]2 and [18F]6 for noninvasive imaging of GSK3. Among the developed PET probes, the in vitro blood–brain permeability coefficient of 2 (38 ± 20 × 10–6 cm/s, n = 3) was found to be better than 6 (8.75 ± 3.90 × 10–6 cm/s, n = 5). The reference compounds 2 and 6 showed nanomolar affinity towards GSK-3α and GSK-3β. PET probe [18F]2 showed higher stability (100%) in mouse and human serums compared to [18F]6 (67.01 ± 4.93%, n = 3) in mouse serum and 66.20 ± 6.38%, n = 3) in human serum at 120 min post incubation. The in vivo imaging and blocking studies were performed in wild-type mice only with [18F]2 due to its observed stability. [18F]2 showed a SUV of 0.92 ± 0.28 (n = 6) in mice brain as early as 5 min post-injection followed by gradual clearance over time.

Abstract Background The current UK standard for major trauma patients is to record notes in a paper trauma booklet. Through an innovative collaboration between a major trauma centre and a digital transformation industry partner, a TraumaApp was developed. Electronic notes have been shown to have fewer errors, granular data collection and enable time stamped contemporaneous record keeping. Implementation of digital clinical records presents a challenge within the context of trauma multidisciplinary trauma resuscitation. Data can be easily accessible and shared for quality improvement, audit and research purposes. This study compared paper and electronic notes for completeness and for acceptability data following the implementation of the TraumaApp. Methods Trauma team members who performed scribe function attended training for the newly launched TraumaApp. Two staff members acted as scribe, using either the paper trauma booklet or TraumaApp, and attended major trauma calls. A framework for comparison of paper and electronic notes was created and used for a retrospective review of major trauma patients’ notes. Statistical analysis was performed using a two-tailed t-test. Staff using the TraumaApp completed a System Usability Score questionnaire. Results There was a total of 37 data points for collection per case. The mean numbers collected were paper notes 24.1 of 37 (65.1%) and electronic notes, 25.7 of 37 (69.5%). There was no statistical significance between the completeness of paper and electronic notes. The mean System Usability Score was 68.4. Discussion Recording accurate patient information during a major trauma call can be challenging and the role of the scribe to accurately record events is critical for immediate and future care. There was no statistically significant difference in completeness of paper and electronic notes, however the mean System Usability Score was 68.4, which is greater than the internationally validated standard of acceptable usability. Conclusion It is feasible to introduce digital data collection tools enabling accurate record keeping during trauma resuscitation and improve information sharing between clinicians.

Abstract Studies of morphology and developmental patterning in adult stages of many invertebrates are hindered by opaque structures, such as shells, skeletal elements, and pigment granules that block or refract light and necessitate sectioning for observation of internal features. An inherent challenge in studies relying on surgical approaches is that cutting tissue is semi-destructive, and delicate structures, such as axonal processes within neural networks, are computationally challenging to reconstruct once disrupted. To address this problem, we developed See-Star, a hydrogel-based tissue clearing protocol to render the bodies of opaque and calcified invertebrates optically transparent while preserving their anatomy in an unperturbed state, facilitating molecular labeling and observation of intact organ systems. The resulting protocol can clear large (> 1 cm3) specimens to enable deep-tissue imaging, and is compatible with molecular techniques, such as immunohistochemistry and in situ hybridization to visualize protein and mRNA localization. To test the utility of this method, we performed a whole-mount imaging study of intact nervous systems in juvenile echinoderms and molluscs and demonstrate that See-Star allows for comparative studies to be extended far into development, facilitating insights into the anatomy of juveniles and adults that are usually not amenable to whole-mount imaging.

A recently introduced quantitative susceptibility mapping (QSM) technique, χ-separation, offers the capability to separate paramagnetic (χpara) and diamagnetic (χdia) susceptibility distribution within the brain. In-vivo high-resolution mapping of iron and myelin distribution, estimated by χ-separation, could provide a deeper understanding of brain substructures, assisting the investigation of their functions and alterations. This can be achieved using 7T MRI, which benefits from a high signal-to-noise ratio and susceptibility effects. However, applying χ-separation at 7T presents difficulties due to the requirement of an R2 map, coupled with issues such as high specific absorption rate (SAR), large B1 transmit field inhomogeneities, and prolonged scan time. To address these challenges, we developed a novel deep neural network, R2PRIMEnet7T, designed to convert a 7T R2* map into a 3T R2′ map. Building on this development, we present a new pipeline for χ-separation at 7T, enabling us to generate high-resolution χ-separation maps from multi-echo gradient-echo data. The proposed method is compared with alternative pipelines, such as an end-to-end network and linearly-scaled R2′, and is validated against χ-separation maps at 3T, demonstrating its accuracy. The 7T χ-separation maps generated by the proposed method exhibit similar contrasts to those from 3T, while 7T high-resolution maps offer enhanced clarity and detail. Quantitative analysis confirms that the proposed method surpasses the alternative pipelines. The proposed method results well delineate the detailed brain structures associated with iron and myelin. This new pipeline holds promise for analyzing iron and myelin concentration changes in various neurodegenerative diseases through precise structural examination.

The prevalence of long-term conditions such as cardiovascular disease, chronic obstructive pulmonary disease (COPD), asthma, and diabetes mellitus is rising. These conditions are leading sources of premature mortality, hospital admission, and healthcare expenditure. Machine learning approaches to improve the management of these conditions have been widely explored, with data-driven insights demonstrating the potential to support earlier diagnosis, triage, and treatment selection. The translation of this research into tools used in live clinical practice has however been limited, with many projects lacking clinical involvement and planning beyond the initial model development stage. To support the move toward a more coordinated and collaborative working process from concept to investigative use in a live clinical environment, we present a multistage workflow framework for the co-development and operationalization of machine learning models which use routine clinical data derived from electronic health records. The approach outlined in this framework has been informed by our multidisciplinary team’s experience of co-developing and operationalizing risk prediction models for COPD within NHS Greater Glasgow & Clyde. In this paper, we provide a detailed overview of this framework, alongside a description of the development and operationalization of two of these risk-prediction models as case studies of this approach.

Introduction A proportion of people develop Long Covid after acute COVID-19, but with most studies concentrated in high-income countries (HICs), the global burden is largely unknown. Our study aims to characterise long-term COVID-19 sequelae in populations globally and compare the prevalence of reported symptoms in HICs and low-income and middle-income countries (LMICs).Methods A prospective, observational study in 17 countries in Africa, Asia, Europe and South America, including adults with confirmed COVID-19 assessed at 2 to

Three experiments (N = 336) examined whether participants can systematically adjust levels of mind wandering on command. Participants performed four blocks of the metronome response task (MRT) in which they pressed a spacebar in sync with a steady audio tone. Levels of spontaneous and deliberate mind wandering were measured using intermittent thought probes. Performance was indexed with MRT response time variability and omission errors. Each block started with instructions to mind wander either 20, 40, 60, or 80% of the time. Analysis was primarily conducted using linear mixed effects models. We found that mind wandering (spontaneous and deliberate), response time variability, and omission errors increased progressively with instructions to mind wander more and that these instruction-related changes were larger for deliberate than spontaneous mind wandering (Experiments 1–3). This pattern held regardless of whether participants’ eyes were open or shut (Experiment 2). Relative to a control group receiving no commands to mind wander, instructing people to mind wander 60 or 80% of the time led to more deliberate mind wandering, and strikingly, asking people to mind wander 20% of the time led to less spontaneous mind wandering (Experiment 3). Our results suggest that individuals can titrate mind wandering experiences to roughly match instructed levels indicating that mind wandering can be manipulated through simple instructions. However, other features of the data suggest that such titration is effortful and may come with a cost to performance.

Introduction Diagnosing and treating lung cancer in early stages is essential for survival outcomes. The chest X-ray (CXR) remains the primary screening tool to identify lung cancers in the UK; however, there is a shortfall of radiologists, while demand continues to increase. Image analysis by machine-learning software has the potential to support radiology workflows with a focus on immediate triage of suspicious X-rays. The RADICAL study will evaluate Qure.ai’s ‘qXR’ software in reducing reporting time for suspicious X-rays in NHS Greater Glasgow & Clyde.Methods and analysis This is a stepped-wedge cluster-randomised study consisting of a retrospective technical evaluation and prospective clinical effectiveness study alongside the assessment of acceptability via qualitative work and evaluation of cost-effectiveness via a cost utility analysis. The primary objective is to assess the clinical effectiveness of qXR to prioritise patients suspected with lung cancer on CXR for follow-up CT. Secondary objectives will look at the utility, safety, technical performance, health economics and acceptability of the intervention. The study period is 24 months, consisting of an initial 12 month data collection period and a 12 month follow-up period. All the standard care CXRs from outpatient and primary care requests will be securely transmitted to Qure.ai software ‘qXR’ for interpretation. Images with features of cancer will be flagged as ‘Urgent Suspicion of Cancer’ and be prioritised for radiologist review within the existing reporting workflow.Ethics and dissemination The study will follow the principles of Good Clinical Practice. The protocol was granted REC approval in August 2023 from North West—Greater Manchester West Research Ethics Committee (REC 23/NW/0211). This study was registered on clinicaltrials.gov (NCT06044454). An interim report will be produced for use by the Scottish Government. The results from this study will be presented at artificial intelligence, radiology and respiratory meetings and published in peer-reviewed journals.Trial registration number NCT06044454.

Abstract Dementia with Lewy bodies (DLB) is a neurodegenerative condition often co-occurring with Alzheimer’s disease (AD) pathology. Characterizing white matter tissue microstructure using Neurite Orientation Dispersion and Density Imaging (NODDI) may help elucidate the biological underpinnings of white matter injury in individuals with DLB. In this study, diffusion tensor imaging (DTI) and NODDI metrics were compared in 45 patients within the dementia with Lewy bodies spectrum (mild cognitive impairment with Lewy bodies (n = 13) and probable dementia with Lewy bodies (n = 32)) against 45 matched controls using conditional logistic models. We evaluated the associations of tau and amyloid-β with DTI and NODDI parameters and examined the correlations of AD-related white matter injury with Clinical Dementia Rating (CDR). Structural equation models (SEM) explored relationships among age, APOE ε4, amyloid-β, tau, and white matter injury. The DLB spectrum group exhibited widespread white matter abnormalities, including reduced fractional anisotropy, increased mean diffusivity, and decreased neurite density index. Tau was significantly associated with limbic and temporal white matter injury, which was, in turn, associated with worse CDR. SEM revealed that amyloid-β exerted indirect effects on white matter injury through tau. We observed widespread disruptions in white matter tracts in DLB that were not attributed to AD pathologies, likely due to α-synuclein-related injury. However, a fraction of the white matter injury could be attributed to AD pathology. Our findings underscore the impact of AD pathology on white matter integrity in DLB and highlight the utility of NODDI in elucidating the biological basis of white matter injury in DLB.

Abstract Sex differences permeate many aspects of dementia with Lewy bodies (DLB), yet sex differences in patterns of neurodegeneration in DLB remain largely unexplored. Here, we test whether grey matter networks differ between sexes in DLB and compare these findings to sex differences in healthy controls. In this cross-sectional study, we analysed clinical and neuroimaging data of patients with DLB and cognitively healthy controls matched for age and sex. Grey matter networks were constructed by pairwise correlations between 58 regional volumes after correction for age, intracranial volume, and centre. Network properties were compared between sexes and diagnostic groups. Additional analyses were conducted on w-scored data to identify DLB-specific sex differences. Data from 119 (68.7 ± 8.4 years) men and 45 women (69.9 ± 9.1 years) with DLB, and 164 healthy controls were included in this study. Networks of men had a lower nodal strength compared to women. In comparison to healthy women, the grey matter networks of healthy men showed a higher global efficiency, modularity, and fewer modules. None of the network measures showed significant sex differences in DLB. Comparing DLB patients with healthy controls revealed global differences in women and more local differences in men. Modular analyses showed a more distinct demarcation between cortical and subcortical regions in men compared with women. While topologies of grey matter networks differed between sexes in healthy controls, those sex differences were diluted in DLB patients. These findings suggest a disease-driven convergence of neurodegenerative patterns in women and men with DLB, which may inform precision medicine in DLB.

Abstract INTRODUCTION Age‐related and Alzheimer's disease (AD) dementia–related neurodegeneration impact brain health. While morphometric measures from T1‐weighted scans are established biomarkers, they may be less sensitive to earlier changes. Neurite orientation dispersion and density imaging (NODDI), offering biologically meaningful interpretation of tissue microstructure, may be an advanced brain health biomarker. METHODS We contrasted regional gray matter NODDI and morphometric evaluations concerning their correlation with (1) age, (2) clinical diagnosis stage, and (3) tau pathology as assessed by AV1451 positron emission tomography. RESULTS Our study hypothesizes that NODDI measures are more sensitive to aging and early AD changes than morphometric measures. One NODDI output, free water fraction (FWF), showed higher sensitivity to age‐related changes, generally better effect sizes in separating mild cognitively impaired from cognitively unimpaired participants, and stronger associations with regional tau deposition than morphometric measures. DISCUSSION These findings underscore NODDI's utility in capturing early neurodegenerative changes and enhancing our understanding of aging and AD. Highlights Neurite orientation dispersion and density imaging can serve as an effective brain health biomarker for aging and early Alzheimer's disease (AD). Free water fraction has higher sensitivity to normal brain aging. Free water fraction has stronger associations with early AD and regional tau deposition.

Binge eating is characterized as eating a large amount of food and feeling a loss of control while eating. However, the neurobiological mechanisms associated with the onset and maintenance of binge eating are largely unknown. Recent neuroimaging work has suggested that increased responsivity within reward regions of the brain to the anticipation or receipt of rewards is related to binge eating; however, limited longitudinal data has precluded understanding of the role of reward responsivity in the development of binge eating. The current study used data from the Adolescent Brain and Cognitive Development® (ABCD) longitudinal study dataset to assess whether heightened neural responses to different phases of reward processing (reward anticipation and receipt) (1) differentiated individuals with binge eating from matched controls, and (2) predicted the onset of binge eating in an ''at risk'' sample. Consistent with hypotheses, heightened neural responsivity in the right caudate and bilateral VS during reward anticipation differentiated youth with and without binge eating. Moreover, greater VS response to reward anticipation predicted binge eating two years later. Neural responses to reward receipt also were consistent with hypotheses, such that heightened VS and OFC responses differentiated youth with and without binge eating and predicted the presence of binge eating two years later. Findings from the current study suggest that hypersensitivity to rewards may contribute to the development of binge eating during early adolescence.

Introduction Diagnostic imaging is vital in emergency departments (EDs). Accessibility and reporting impacts ED workflow and patient care. With radiology workforce shortages, reporting capacity is limited, leading to image interpretation delays. Turnaround times for image reporting are an ED bottleneck. Artificial intelligence (AI) algorithms can improve productivity, efficiency and accuracy in diagnostic radiology, contingent on their clinical efficacy. This includes positively impacting patient care and improving clinical workflow. The ACCEPT-AI study will evaluate Qure.ai’s qER software in identifying and prioritising patients with critical findings from AI analysis of non-contrast head CT (NCCT) scans.Methods and analysis This is a multicentre trial, spanning four diverse sites, over 13 months. It will include all individuals above the age of 18 years who present to the ED, referred for an NCCT. The project will be divided into three consecutive phases (pre-implementation, implementation and post-implementation of the qER solution) in a stepped-wedge design to control for adoption bias and adjust for time-based changes in the background patient characteristics. Pre-implementation involves baseline data for standard care to support the primary and secondary outcomes. The implementation phase includes staff training and qER solution threshold adjustments in detecting target abnormalities adjusted, if necessary. The post-implementation phase will introduce a notification (prioritised flag) in the radiology information system. The radiologist can choose to agree with the qER findings or ignore it according to their clinical judgement before writing and signing off the report. Non-qER processed scans will be handled as per standard care.Ethics and dissemination The study will be conducted in accordance with the principles of Good Clinical Practice. The protocol was approved by the Research Ethics Committee of East Midlands (Leicester Central), in May 2023 (REC (Research Ethics Committee) 23/EM/0108). Results will be published in peer-reviewed journals and disseminated in scientific findings (ClinicalTrials.gov: NCT06027411)Trial registration number NCT06027411.

Abstract Background The associations between deprivation and illness trajectory after hospitalisation for coronavirus disease-19 (COVID-19) are uncertain. Methods A prospective, multicentre cohort study was conducted on post-COVID-19 patients, enrolled either in-hospital or shortly post-discharge. Two evaluations were carried out: an initial assessment and a follow-up at 28–60 days post-discharge. The study encompassed research blood tests, patient-reported outcome measures, and multisystem imaging (including chest computed tomography (CT) with pulmonary and coronary angiography, cardiovascular and renal magnetic resonance imaging). Primary and secondary outcomes were analysed in relation to socioeconomic status, using the Scottish Index of Multiple Deprivation (SIMD). The EQ-5D-5L, Brief Illness Perception Questionnaire (BIPQ), Patient Health Questionnaire-4 (PHQ-4) for Anxiety and Depression, and the Duke Activity Status Index (DASI) were used to assess health status. Results Of the 252 enrolled patients (mean age 55.0 ± 12.0 years; 40% female; 23% with diabetes), deprivation status was linked with increased BMI and diabetes prevalence. 186 (74%) returned for the follow-up. Within this group, findings indicated associations between deprivation and lung abnormalities (p = 0.0085), coronary artery disease (p = 0.0128), and renal inflammation (p = 0.0421). Furthermore, patients with higher deprivation exhibited worse scores in health-related quality of life (EQ-5D-5L, p = 0.0084), illness perception (BIPQ, p = 0.0004), anxiety and depression levels (PHQ-4, p = 0.0038), and diminished physical activity (DASI, p = 0.002). At the 3-month mark, those with greater deprivation showed a higher frequency of referrals to secondary care due to ongoing COVID-19 symptoms (p = 0.0438). However, clinical outcomes were not influenced by deprivation. Conclusions In a post-hospital COVID-19 population, socioeconomic deprivation was associated with impaired health status and secondary care episodes. Deprivation influences illness trajectory after COVID-19.

Abstract Background Suboptimal or slow recruitment affects 30–50% of trials. Education and training of trial recruiters has been identified as one strategy for potentially boosting recruitment to randomised controlled trials (hereafter referred to as trials). The Training tRial recruiters, An educational INtervention (TRAIN) project was established to develop and assess the acceptability of an education and training intervention for recruiters to neonatal trials. In this paper, we report the development and acceptability of TRAIN. Methods TRAIN involved three sequential phases, with each phase contributing information to the subsequent phase(s). These phases were 1) evidence synthesis (systematic review of the effectiveness of training interventions and a content analysis of the format, content, and delivery of identified interventions), 2) intervention development using a Partnership (co-design/co-creation) approach, and 3) intervention acceptability assessments with recruiters to neonatal trials. Results TRAIN, accompanied by a comprehensive intervention manual, has been designed for online or in-person delivery. TRAIN can be offered to recruiters before trial recruitment begins or as refresher sessions during a trial. The intervention consists of five core learning outcomes which are addressed across three core training units. These units are the trial protocol (Unit 1, 50 min, trial-specific), understanding randomisation (Unit 2, 5 min, trial-generic) and approaching and engaging with parents (Unit 3, 70 min, trial-generic). Eleven recruiters to neonatal trials registered to attend the acceptability assessment training workshops, although only four took part. All four positively valued the training Units and resources for increasing recruiter preparedness, knowledge, and confidence. More flexibility in how the training is facilitated, however, was noted (e.g., training divided across two workshops of shorter duration). Units 2 and 3 were considered beneficial to incorporate into Good Clinical Practice Training or as part of induction training for new staff joining neonatal units. Conclusion TRAIN offers a comprehensive co-produced training and education intervention for recruiters to neonatal trials. TRAIN was deemed acceptable, with minor modification, to neonatal trial recruiters. The small number of recruiters taking part in the acceptability assessment is a limitation. Scale-up of TRAIN with formal piloting and testing for effectiveness in a large cluster randomised trial is required.

Abstract Background and importance Use of video review in medicine is established in contexts such as surgery. Although not widely used in the emergency department (ED), some centres use it to evaluate non-technical skills (NTS) to support teaching and quality improvement. Objective There is no consensus on assessment of NTS using video review in the ED and the purpose of this review was to identify tools used in this context. Design, setting and participants Studies were identified using Embase, Medline, CINAHL and Google Scholar. Inclusion criterion for the review was NTS of resuscitation teams working within the ED were assessed using video review. A systematic search method was used, and results were synthesised after search criteria was checked by two independent reviewers. Authors settled on the same 9 studies eligible for inclusion. Outcome measures and analysis Reliability and validity of tools identified for use in this context. Due to the heterogeneity of studies, no meta-analysis occurred. Main results There are 9 studies included in the review. The review was registered with PROSPERO (Ref No: CRD42022306129). Four unique tools were identified – 6 studies used T-NOTECHS, 1 used TTCA-24, 1 used CALM and 1 used the Communication tool. T-NOTECHS is validated in the literature for use in this context. Conclusion T-NOTECHS is the tool of choice for assessing ED teams in this context.

Abstract Long-COVID prevalence estimates vary widely and should take account of symptoms that would have occurred anyway. Here we determine the prevalence of symptoms attributable to SARS-CoV-2 infection, taking account of background rates and confounding, in a nationwide population cohort study of 198,096 Scottish adults. 98,666 (49.8%) had symptomatic laboratory-confirmed SARS-CoV-2 infections and 99,430 (50.2%) were age-, sex-, and socioeconomically-matched and never-infected. While 41,775 (64.5%) reported at least one symptom 6 months following SARS-CoV-2 infection, this was also true of 34,600 (50.8%) of those never-infected. The crude prevalence of one or more symptom attributable to SARS-CoV-2 infection was 13.8% (13.2%,14.3%), 12.8% (11.9%,13.6%), and 16.3% (14.4%,18.2%) at 6, 12, and 18 months respectively. Following adjustment for potential confounders, these figures were 6.6% (6.3%, 6.9%), 6.5% (6.0%, 6.9%) and 10.4% (9.1%, 11.6%) respectively. Long-COVID is characterised by a wide range of symptoms that, apart from altered taste and smell, are non-specific. Care should be taken in attributing symptoms to previous SARS-CoV-2 infection.

Posterior cortical atrophy (PCA) and dementia with Lewy bodies (DLB) show distinct atrophy and overlapping hypometabolism profiles, but it is unknown how disruptions in structural and functional connectivity compare between these disorders and whether breakdowns in connectivity relate to either atrophy or hypometabolism.Thirty amyloid-positive PCA patients, 24 amyloid-negative DLB patients and 30 amyloid-negative cognitively unimpaired (CU) healthy individuals were recruited at Mayo Clinic, Rochester, MN, and underwent a 3T head MRI, including structural MRI, resting state functional MRI (rsfMRI) and diffusion tensor imaging (DTI) sequences, as well as [18F] fluorodeoxyglucose (FDG) PET. We assessed functional connectivity within and between 12 brain networks using rsfMRI and the CONN functional connectivity toolbox and calculated regional DTI metrics using the Johns Hopkins atlas. Multivariate linear-regression models corrected for multiple comparisons and adjusted for age and sex compared DTI metrics and within-network and between-network functional connectivity across groups. Regional gray-matter volumes and FDG-PET standard uptake value ratios (SUVRs) were calculated and analyzed at the voxel-level using SPM12. We used univariate linear-regression models to investigate the relationship between connectivity measures, gray-matter volume, and FDG-PET SUVR.On DTI, PCA showed degeneration in occipito-parietal white matter, posterior thalamic radiations, splenium of the corpus collosum and sagittal stratum compared to DLB and CU, with greater degeneration in the temporal white matter and the fornix compared to CU. We observed no white-matter degeneration in DLB compared to CU. On rsfMRI, reduced within-network connectivity was present in dorsal and ventral default mode networks (DMN) and the dorsal-attention network in PCA compared to DLB and CU, with reduced within-network connectivity in the visual and sensorimotor networks compared to CU. DLB showed reduced connectivity in the cerebellar network compared to CU. Between-network analysis showed increased connectivity in both cerebellar-to-sensorimotor and cerebellar-to-dorsal attention network connectivity in PCA and DLB. PCA showed reduced anterior DMN-to-cerebellar and dorsal attention-to-sensorimotor connectivity, while DLB showed reduced posterior DMN-to-sensorimotor connectivity compared to CU. PCA showed reduced dorsal DMN-to-visual connectivity compared to DLB. The multimodal analysis revealed weak associations between functional connectivity and volume in PCA, and between functional connectivity and metabolism in DLB.These findings suggest that PCA and DLB have unique connectivity alterations, with PCA showing more widespread disruptions in both structural and functional connectivity; yet some overlap was observed with both disorders showing increased connectivity from the cerebellum.

Abstract Previous studies on the natural history of long-COVID have been few and selective. Without comparison groups, disease progression cannot be differentiated from symptoms originating from other causes. The Long-COVID in Scotland Study (Long-CISS) is a Scotland-wide, general population cohort of adults who had laboratory-confirmed SARS-CoV-2 infection matched to PCR-negative adults. Serial, self-completed, online questionnaires collected information on pre-existing health conditions and current health six, 12 and 18 months after index test. Of those with previous symptomatic infection, 35% reported persistent incomplete/no recovery, 12% improvement and 12% deterioration. At six and 12 months, one or more symptom was reported by 71.5% and 70.7% respectively of those previously infected, compared with 53.5% and 56.5% of those never infected. Altered taste, smell and confusion improved over time compared to the never infected group and adjusted for confounders. Conversely, late onset dry and productive cough, and hearing problems were more likely following SARS-CoV-2 infection.

Purpose: The pattern of flortaucipir tau PET uptake is topographically similar to the pattern of magnetic susceptibility in progressive supranuclear palsy (PSP); both with increased signal in subcortical structures such as the basal ganglia and midbrain, suggesting that they may be closely related. However, their relationship remains unknown since no studies have directly compared these two modalities in the same PSP cohort. We hypothesized that some flortaucipir uptake in PSP is associated with magnetic susceptibility, and hence iron deposition. The aim of this study was to evaluate the regional relationship between flortaucipir uptake and magnetic susceptibility and to examine the effects of susceptibility on flortaucipir uptake in PSP. Methods: Fifty PSP patients and 67 cognitively normal controls were prospectively recruited and underwent three Tesla MRI and flortaucipir tau PET scans. Quantitative susceptibility maps were reconstructed from multi-echo gradient-echo MRI images. Region of interest (ROI) analysis was performed to obtain flortaucipir and susceptibility values in the subcortical regions. Relationships between flortaucipir and susceptibility signals were evaluated using partial correlation analysis in the subcortical ROIs and voxel-based analysis in the whole brain. The effects of susceptibility on flortaucipir uptake were examined by using the framework of mediation analysis. Results: Both flortaucipir and susceptibility were greater in PSP compared to controls in the putamen, pallidum, subthalamic nucleus, red nucleus, and cerebellar dentate (p

Introduction A non-contrast CT head scan (NCCTH) is the most common cross-sectional imaging investigation requested in the emergency department. Advances in computer vision have led to development of several artificial intelligence (AI) tools to detect abnormalities on NCCTH. These tools are intended to provide clinical decision support for clinicians, rather than stand-alone diagnostic devices. However, validation studies mostly compare AI performance against radiologists, and there is relative paucity of evidence on the impact of AI assistance on other healthcare staff who review NCCTH in their daily clinical practice.Methods and analysis A retrospective data set of 150 NCCTH will be compiled, to include 60 control cases and 90 cases with intracranial haemorrhage, hypodensities suggestive of infarct, midline shift, mass effect or skull fracture. The intracranial haemorrhage cases will be subclassified into extradural, subdural, subarachnoid, intraparenchymal and intraventricular. 30 readers will be recruited across four National Health Service (NHS) trusts including 10 general radiologists, 15 emergency medicine clinicians and 5 CT radiographers of varying experience. Readers will interpret each scan first without, then with, the assistance of the qER EU 2.0 AI tool, with an intervening 2-week washout period. Using a panel of neuroradiologists as ground truth, the stand-alone performance of qER will be assessed, and its impact on the readers’ performance will be analysed as change in accuracy (area under the curve), median review time per scan and self-reported diagnostic confidence. Subgroup analyses will be performed by reader professional group, reader seniority, pathological finding, and neuroradiologist-rated difficulty.Ethics and dissemination The study has been approved by the UK Healthcare Research Authority (IRAS 310995, approved 13 December 2022). The use of anonymised retrospective NCCTH has been authorised by Oxford University Hospitals. The results will be presented at relevant conferences and published in a peer-reviewed journal.Trial registration number NCT06018545.

Heart Failure (HF) is common, with worldwide prevalence of 1%-3% and a lifetime risk of 20% for individuals 40 years or older. Despite its considerable health economic burden, techniques for early detection of HF in the general population are sparse. In this work we tested the hypothesis that a simple Transformer neural network, trained on comprehensive collection of secondary care data across the general population, can be used to prospectively (three-year predictive window) identify patients at an increased risk of first hospitalisation due to HF (HHF). The model was trained using routinely-collected, secondary care health data, including patient demographics, A&E attendances, hospitalisations, outpatient data, medications, blood tests, and vital sign measurements obtained across five years of longitudinal electronic health records (EHRs). The training cohort consisted of n = 183,894 individuals (n = 161,658 age/sex-matched controls and n = 22,236 of first hospitalisation due to HF after a three-year predictive window). Model performance was validated in an independent testing set of n = 8,977 patients (n = 945 HHF patients). Testing set probabilities were well-calibrated and achieved good discriminatory power with Area Under Receiver Operating Characteristic Curve (AUROC]) of 0.86, sensitivity of 36.4% (95% CI: 33.33%-39.56%), specificity of 98.26% (95% CI: 97.95%-98.53%), and PPV of 69.88% (95% CI: 65.86%-73.62%). At Probability of HHF ≥ 90% the model achieved 100% PPV (95% CI: 96.73%-100%) and sensitivity of 11.7% (95% CI: 9.72%-13.91%). Performance was not affected by patient sex or socioeconomic deprivation deciles. Performance was significantly better in Asian, Black, and Mixed ethnicities (AUROC 0.932-0.945) and in the 79-86 age group (AUROC 0.889). We present the first evidence that routinely collected secondary care health record data can be used in the general population to stratify patients at risk of first HHF.

During the COVID-19 pandemic, meatpacking workers were disproportionately affected by disease. Large outbreaks at meatpacking facilities resulted in loss of life and threatened the well-being of workers across the globe. Much work was done throughout the pandemic to understand and prevent these outbreaks. This study combined ventilation system evaluation and measurement of human-generated respiratory aerosol to investigate and identify areas of highest risk for disease transmission. These findings confirm that improved ventilation reduces exposure to human-generated aerosols in meatpacking facilities, including those that may contain infectious agents, such as SARS-CoV-2. This study suggests areas of greatest risk are likely areas where workers break from work, such as cafeterias and locker rooms, where ventilation is poorer, use of face masks is reduced, and people congregate. Furthermore, these findings also suggest that ventilation of production areas of the plant, which have been designed for food safety, is sufficient to reduce exposures and likely contributes to reduced transmission in those spaces. Based on these findings, two controls should be prioritized to minimize the likelihood of exposure to potentially infectious aerosols: (1) improving mechanical ventilation and/or adding mitigation strategies such as media filters, germicidal ultraviolet, and other air cleaning technology and (2) applying administrative practices that minimize large congregations of people in poorly ventilated spaces. Importantly, this work demonstrates a method for in situ measurements of human-generated particles that can be used more broadly to understand exposure and risk in various occupied spaces.

The fossil record of the U.S. Pacific Northwest preserves many Middle Miocene floras with potential for revealing long-term climate-vegetation dynamics during the Miocene Climatic Optimum. However, the possibility of strong, eccentricity-paced climate oscillations and concurrent, intense volcanism may obscure the signature of prevailing, long-term Miocene climate change. To test the hypothesis that volcanic disturbance drove Middle Miocene vegetation dynamics, high-resolution, stratigraphic pollen records and other paleobotanical data from nine localities of the Sucker Creek Formation were combined with sedimentological and geochemical evidence of disturbance within an updated chronostratigraphic framework based on new U-Pb zircon ages from tuffs. The new ages establish a refined, minimum temporal extent of the Sucker Creek Formation, ~15.8 to ~14.8 Ma, and greatly revise the local and regional chronostratigraphic correlations of its dispersed outcrop belt. Our paleoecological analysis at one ~15.52 Ma locality reveals two abrupt shifts in pollen spectra coinciding with the deposition of thick ash-flow tuffs, wherein vegetation dominated by Cupressaceae/Taxaceae, probably representing a Glyptostrobus oregonensis swamp, and upland conifers was supplanted by early-successional forests with abundant Alnus and Betula. Another ephemeral shift from Cupressaceae/Taxaceae swamp taxa in favor of upland conifers Pinus and Tsuga correlates with a shift from low-Ti shale to high-Ti claystone, suggesting a link between altered surface hydrology and vegetation. In total, three rapid vegetation shifts coincide with ash-flow tuffs and are attributed to volcanic disturbance. Longer-term variability between localities, spanning ~1 Myr of the Miocene Climatic Optimum, is chiefly attributed to eccentricity-paced climate change. Overall, Succor Creek plant associations changed frequently over ≤105 years timespans, reminiscent of Quaternary vegetation records. Succor Creek stratigraphic palynology suggests that numerous and extensive collection of stratigraphically controlled samples is necessary to understand broader vegetation trends through time.

Genetic mutations causative of frontotemporal lobar degeneration (FTLD) are highly predictive of a specific proteinopathy, but there exists substantial inter-individual variability in their patterns of network degeneration and clinical manifestations. We collected clinical and 18Fluorodeoxyglucose-positron emission tomography (FDG-PET) data from 39 patients with genetic FTLD, including 11 carrying the C9orf72 hexanucleotide expansion, 16 carrying a MAPT mutation and 12 carrying a GRN mutation. We performed a spectral covariance decomposition analysis between FDG-PET images to yield unbiased latent patterns reflective of whole brain patterns of metabolism (“eigenbrains” or EBs). We then conducted linear discriminant analyses (LDAs) to perform EB-based predictions of genetic mutation and predominant clinical phenotype (i.e., behavior/personality, language, asymptomatic). Five EBs were significant and explained 58.52 % of the covariance between FDG-PET images. EBs indicative of hypometabolism in left frontotemporal and temporo-parietal areas distinguished GRN mutation carriers from other genetic mutations and were associated with predominant language phenotypes. EBs indicative of hypometabolism in prefrontal and temporopolar areas with a right hemispheric predominance were mostly associated with predominant behavioral phenotypes and distinguished MAPT mutation carriers from other genetic mutations. The LDAs yielded accuracies of 79.5 % and 76.9 % in predicting genetic status and predominant clinical phenotype, respectively. A small number of EBs explained a high proportion of covariance in patterns of network degeneration across FTLD-related genetic mutations. These EBs contained biological information relevant to the variability in the pathophysiological and clinical aspects of genetic FTLD, and for offering valuable guidance in complex clinical decision-making, such as decisions related to genetic testing.

Abstract INTRODUCTION This study evaluated the performance of the Lumipulse plasma beta‐amyloid (Aβ) 42/40 and pTau181 compared to other assays to detect an abnormal amyloid‐positron emission tomography (PET). METHODS Plasma samples from cognitively unimpaired (N = 179) and MCI/AD dementia (N = 36) individuals were retrospectively evaluated. Plasma Aβ42/40 and pTau181 were measured using the Lumipulse and Simoa immunoassays. An immunoprecipitation mass spectrometry (IP‐MS) assay for plasma Aβ42/40 was also evaluated. Amyloid‐PET status was the outcome measure. RESULTS Lumipulse and IP‐MS Aβ42/40 exhibited the highest diagnostic accuracy for detecting an abnormal amyloid‐PET (areas under the curve [AUCs] of 0.81 and 0.84, respectively). The Lumipulse and Simoa pTau181 assays exhibited lower performance (AUCs of 0.74 and 0.72, respectively). The Simoa Aβ42/40 assay demonstrated the lowest diagnostic accuracy (AUC 0.57). Combining Aβ42/40 and pTau181 did not significantly improve performance over Aβ42/40 alone for Lumipulse (AUC 0.83) or over pTau181 alone for Simoa (AUC 0.71) DISCUSSION The Lumipulse Aβ42/40 assay showed similar performance to the IP‐MS Aβ42/40 assay for detection of an abnormal amyloid‐PET; and both assays performed better than the two p‐tau181 immunoassays. The Simoa Aβ42/Aβ40 assay was the least accurate at predicting an abnormal amyloid‐PET status. Highlights Lumipulse plasma Aβ42/Aβ40 AUC for abnormal amyloid‐PET detection was 0.81. This performance was comparable to previously reported IP‐MS and higher than Simoa. Performance of Alzheimer's disease blood biomarkers varies between assays.

Primary progressive aphasia (PPA) variants present with distinct disruptions in speech-language functions with little known about the interplay between affected and spared regions within the speech-language network and their interaction with other functional networks.The Neurodegenerative Research Group, Mayo Clinic, recruited 123 patients with PPA (55 logopenic (lvPPA), 44 non-fluent (nfvPPA) and 24 semantic (svPPA)) who were matched to 60 healthy controls. We investigated functional connectivity disruptions between regions within the left-speech-language network (Broca, Wernicke, anterior middle temporal gyrus (aMTG), supplementary motor area (SMA), planum temporale (PT) and parietal operculum (PO)), and disruptions to other networks (visual association, dorsal-attention, frontoparietal and default mode networks (DMN)).Within the speech-language network, multivariate linear regression models showed reduced aMTG-Broca connectivity in all variants, with lvPPA and nfvPPA findings remaining significant after Bonferroni correction. Additional loss in Wernicke-Broca connectivity in nfvPPA, Wernicke-PT connectivity in lvPPA and greater aMTG-PT connectivity in svPPA were also noted. Between-network connectivity findings in all variants showed reduced aMTG-DMN and increased aMTG-dorsal-attention connectivity, with additional disruptions between aMTG-visual association in both lvPPA and svPPA, aMTG-frontoparietal in lvPPA, and Wernicke-DMN breakdown in svPPA.These findings suggest that aMTG connectivity breakdown is a shared feature in all PPA variants, with lvPPA showing more extensive connectivity disruptions with other networks.

Introduction: AD and CVD, which frequently co-occur, are leading causes of age-related cognitive decline. We assessed how demographic factors, socioeconomic status (SES) as indicated by education and occupation, vascular risk factors, and a range of biomarkers associated with both CVD (including white matter hyperintensities [WMH], diffusion MRI abnormalities, infarctions, and microbleeds) and AD (comprising amyloid-PET and tau-PET) collectively influence cognitive function. Methods: In this cross-sectional population study, structural equation models were utilized to understand these associations in 449 participants (mean age (SD) = 74.5 (8.4) years; 56% male; 7.5% cognitively impaired). Results: (1) Higher SES had a protective effect on cognition with mediation through the vascular pathway. (2) The effect of amyloid directly on cognition and through tau was 11-fold larger than the indirect effect of amyloid on cognition through WMH. (3) There is a significant effect of vascular risk on tau deposition. Discussion: The utilized biomarkers captured the impact of CVD and AD on cognition. The overall effect of vascular risk and SES on these biomarkers are complex and need further investigation.

Societal Impact Statement Sandalwood and other high value tree species are under significant threat from illegal harvest. Illegal logging is an increasing problem contributing to deforestation, biodiversity loss, human rights abuses and funding transnational crime. Successful prosecution of illegal logging is hindered by a lack of methods to provide evidence of the origin of timber. New analytical techniques have been developed to trace timber back to its source. These methods, together with the establishment of sustainable sources of forest resources, can help protect vulnerable species by providing evidence to prosecute illegal harvest and ensure that commercially available forest products come from sustainable sources. Summary Sandalwood is highly valued for its fragrant oil and has a long history of cultural and economic importance in many regions of the world. Historical overharvest and poor management have depleted natural populations of sandalwood, which are slow to regenerate. The increasing establishment of plantation sandalwood creates an alternative resource for the sandalwood industry while potentially relieving harvesting pressure on natural stands. Due to the high demand for sandalwood, remaining wild populations are still under threat from illegal logging and methods to identify the source of harvested sandalwood are needed. Laws and regulations aimed at preventing illegal harvest and possession of sandalwood have been put in place but cannot be enforced without the forensic tools to independently verify claimed origin or product quality. The high value of sandalwood combined with the difficulties in enforcing illegal logging laws makes these species particularly vulnerable to poaching. There is an immediate need to develop tools that can identify illegally sourced and adulterated sandalwood products. This paper reviews the current and developing scientific tools that can help identify and control illegal activity in sandalwood supply chains and provides recommendations for future research. Topics include isotope and DNA analysis for tracing illegally harvested sandalwood, chemical profiling for quality control of sandalwood oils, network and policy development to establish a framework for future regulation of the sandalwood trade.

Objective To examine interactions between Neuropsychiatric symptoms (NPS) with Pittsburgh Compound B (PiB) and fluorodeoxyglucose positron emission tomography (FDG‐PET) in predicting cognitive trajectories. Methods We conducted a longitudinal study in the setting of the population‐based Mayo Clinic Study of Aging in Olmsted County, MN, involving 1581 cognitively unimpaired (CU) persons aged ≥50 years (median age 71.83 years, 54.0% males, 27.5% APOE ɛ4 carriers). NPS at baseline were assessed using the Neuropsychiatric Inventory Questionnaire (NPI‐Q). Brain glucose hypometabolism was defined as a SUVR ≤ 1.47 (measured by FDG‐PET) in regions typically affected in Alzheimer's disease. Abnormal cortical amyloid deposition was measured using PiB‐PET (SUVR ≥ 1.48). Neuropsychological testing was done approximately every 15 months, and we calculated global and domain‐specific (memory, language, attention, and visuospatial skills) cognitive z‐scores. We ran linear mixed‐effect models to examine the associations and interactions between NPS at baseline and z‐scored PiB‐ and FDG‐PET SUVRs in predicting cognitive z‐scores adjusted for age, sex, education, and previous cognitive testing. Results Individuals at the average PiB and without NPS at baseline declined over time on cognitive z‐scores. Those with increased PiB at baseline declined faster (two‐way interaction), and those with increased PiB and NPS declined even faster (three‐way interaction). We observed interactions between time, increased PiB and anxiety or irritability indicating accelerated decline on global z‐scores, and between time, increased PiB and several NPS (e.g., agitation) showing faster domain‐specific decline, especially on the attention domain. Conclusions NPS and increased brain amyloid deposition synergistically interact in accelerating global and domain‐specific cognitive decline among CU persons at baseline.

Abstract Dementia with Lewy bodies (DLB) is a neurodegenerative disorder with a wide heterogeneity of symptoms, which suggests the existence of different subtypes. We used data-driven analysis of magnetic resonance imaging (MRI) data to investigate DLB subtypes. We included 165 DLB from the Mayo Clinic and 3 centers from the European DLB consortium and performed a hierarchical cluster analysis to identify subtypes based on gray matter (GM) volumes. To characterize the subtypes, we used demographic and clinical data, as well as β-amyloid, tau, and cerebrovascular biomarkers at baseline, and cognitive decline over three years. We identified 3 subtypes: an older subtype with reduced cortical GM volumes, worse cognition, and faster cognitive decline (n = 49, 30%); a subtype with low GM volumes in fronto-occipital regions (n = 76, 46%); and a subtype of younger patients with the highest cortical GM volumes, proportionally lower GM volumes in basal ganglia and the highest frequency of cognitive fluctuations (n = 40, 24%). This study shows the existence of MRI subtypes in DLB, which may have implications for clinical workout, research, and therapeutic decisions.

Background: The multisystem associations between baseline mental health status and coronavirus disease-19 (COVID)-19 illness trajectory are uncertain. Objectives: This article will investigate the associations between baseline mental health status and disease trajectory following COVID-19 hospitalization, which may have implications for practice and future research. Methods: The Chief Scientist Office Cardiovascular and Pulmonary Imaging in severe acute respiratory syndrome (SARS) COVID-19 study is a prospective, observational, multicenter, longitudinal, secondary care cohort study that assessed the time-course of multi-organ injury in posthospital survivors of COVID-19. Patients were assessed in-hospital, at 28–60 days after discharge and in the longer term using electronic health record linkage. Results: One hundred and fifty-two patients (mean ± standard deviation [SD] age 54.3 ± 11.8 years, 43% female, 40% most socio-economically deprived quintile, 33% history of mental health history) were enrolled and had mental health serially assessed using the Patient Health Questionnaire-4 (PHQ-4) questionnaire. Fifty-three (35%) had PHQ-4 score of 6–12 consistent with moderate-severe symptoms of anxiety or depression and this was associated with diagnostic criteria for myocarditis (P = 0.0498). Moderate-severe symptoms of anxiety or depression were positively associated with higher perception of illness, lower health-related quality of life (HRQoL), and poorer physical function. The mean (SD) duration of follow-up after hospital discharge was 428 (86) days (range, 290–627 days). PHQ-4 score was not associated with clinical outcomes at follow-up. Conclusions: In patients who have been hospitalized with COVID-19, moderate-severe symptoms of anxiety or depression were associated with myocarditis, worse HRQoL, higher perception of illness, and lower levels of physical function. Public Registration: ClinicalTrials.gov identifier is NCT04403607.

Abstract Background As digital tools are increasingly used to support COVID‐19 contact tracing, the equity implications must be considered. As part of a study to understand the public's views of digital contact tracing tools developed for the national ‘Test and Protect’ programme in Scotland, we aimed to explore the views of groups often excluded from such discussions. This paper reports on their views about the potential for contact tracing to exacerbate inequalities. Methods A qualitative study was carried out; interviews were conducted with key informants from organizations supporting people in marginalized situations, followed by interviews and focus groups with people recruited from these groups. Participants included, or represented, minority ethnic groups, asylum seekers and refugees and those experiencing multiple disadvantage including severe and enduring poverty. Results A total of 42 people participated: 13 key informants and 29 members of the public. While public participants were supportive of contact tracing, key informants raised concerns. Both sets of participants spoke about how contact tracing, and its associated digital tools, might increase inequalities. Barriers included finances (inability to afford smartphones or the data to ensure access to the internet); language (digital tools were available only in English and required a degree of literacy, even for English speakers); and trust (many marginalized groups distrusted statutory organizations and there were concerns that data may be passed to other organizations). One strength was that NHS Scotland, the data guardian, is seen as a generally trustworthy organization. Poverty was recognized as a barrier to people's ability to self‐isolate. Some participants were concerned about giving contact details of individuals who might struggle to self‐isolate for financial reasons. Conclusions The impact of contact tracing and associated digital tools on marginalized populations needs careful monitoring. This should include the contact tracing process and the ability of people to self‐isolate. Regular clear messaging from trusted groups and community members could help maintain trust and participation in the programme. Patient and Public Contribution Our patient and public involvement coapplicant, L. L., was involved in all aspects of the study including coauthorship. Interim results were presented to our local Public and Patient Involvement and Engagement Group, who commented on interpretation and made suggestions about further recruitment.

Abstract Background Advances in ultrasensitive detection of phosphorylated tau (p-tau) in plasma has enabled the use of blood tests to measure Alzheimer’s disease (AD) biomarker changes. Examination of postmortem brains of participants with antemortem plasma p-tau levels remains critical to understanding comorbid and AD-specific contribution to these biomarker changes. Methods We analyzed 35 population-based Mayo Clinic Study of Aging participants with plasma p-tau at threonine 181 and threonine 217 (p-tau181, p-tau217) available within 3 years of death. Autopsied participants included cognitively unimpaired, mild cognitive impairment, AD dementia, and non-AD neurodegenerative disorders. Global neuropathologic scales of tau, amyloid-β, TDP-43, and cerebrovascular disease were examined. Regional digital pathology measures of tau (phosphorylated threonine 181 and 217 [pT181, pT217]) and amyloid-β (6F/3D) were quantified in hippocampus and parietal cortex. Neurotransmitter hubs reported to influence development of tangles (nucleus basalis of Meynert) and amyloid-β plaques (locus coeruleus) were evaluated. Results The strongest regional associations were with parietal cortex for tau burden (p-tau181 R = 0.55, p = 0.003; p-tau217 R = 0.66, p

Positron emission tomography (PET) imaging in prostate cancer has advanced significantly in the past decade with prostate cancer targeted radiopharmaceuticals now playing a growing role in diagnosis, staging, and treatment. This narrative review focuses on the most commonly used PET radiopharmaceuticals in the USA: prostate-specific membrane antigen (PSMA), fluciclovine, and choline. 18F-fluorodeoxyglucose (FDG) is used in many other malignancies, but rarely in prostate cancer. Previous literature is discussed regarding each radiopharmaceutical’s utility in the settings of screening/diagnosis, initial staging, biochemical recurrence, advanced disease, and evaluation prior to targeted radiopharmaceutical therapy and radiation therapy. PET imaging has demonstrated utility over traditional imaging in various scenarios; however, there are few head-to-head studies comparing PET radiopharmaceuticals. PSMA radiopharmaceuticals are the newest tracers developed and have unique properties and uses, especially at low prostate-specific antigen (PSA) levels. However, each PET radiopharmaceutical has different properties which can affect image interpretation. Choline and fluciclovine have minimal urinary activity, whereas PSMA agents can have high urinary activity which may affect locoregional disease evaluation. Of the three radiopharmaceuticals, only PSMA is approved for both diagnostic and therapeutic indications with 177 Lu-PSMA. A variety of diagnostic PET radiotracers for prostate cancer allows for increased flexibility, especially in the setting of supply chain and medication shortages. For the time being, keeping a diverse group of PET radiopharmaceuticals for prostate cancer is justifiable.

Hemichordates are an important group for investigating the evolution of bilaterian nervous systems. As the closest chordate outgroup with a bilaterally symmetric adult body plan, hemichordates are particularly informative for exploring the origins of chordates. Despite the importance of hemichordate neuroanatomy for testing hypotheses on deuterostome and chordate evolution, adult hemichordate nervous systems have not been comprehensively described using molecular techniques, and classic histological descriptions disagree on basic aspects of nervous system organization. A molecular description of hemichordate nervous system organization is important for both anatomical comparisons across phyla and for attempts to understand how conserved gene regulatory programs for ectodermal patterning relate to morphological evolution in deep time. Here, we describe the basic organization of the adult hemichordate Saccoglossus kowalevskii nervous system using immunofluorescence, in situ hybridization, and transgenic reporters to visualize neurons, neuropil, and key neuronal cell types. Consistent with previous descriptions, we found the S. kowalevskii nervous system consists of a pervasive nerve plexus concentrated in the anterior, along with nerve cords on both the dorsal and ventral side. Neuronal cell types exhibited clear anteroposterior and dorsoventral regionalization in multiple areas of the body. We observed spatially demarcated expression patterns for many genes involved in synthesis or transport of neurotransmitters and neuropeptides but did not observe clear distinctions between putatively centralized and decentralized portions of the nervous system. The plexus shows regionalized structure and is consistent with the proboscis base as a major site for information processing rather than the dorsal nerve cord. In the trunk, there is a clear division of cell types between the dorsal and ventral cords, suggesting differences in function. The absence of neural processes crossing the basement membrane into muscle and extensive axonal varicosities suggest that volume transmission may play an important role in neural function. These data now facilitate more informed neural comparisons between hemichordates and other groups, contributing to broader debates on the origins and evolution of bilaterian nervous systems.

Tidal flats are some of the most dynamic coastal environments in the world, where traditional coastal mapping and monitoring provide insufficient temporal resolution to reliably map channels and sand flats. Satellite-based Synthetic Aperture Radar (SAR) enables regular cloud-penetrating detection of water flowing through channels within the tidal flats, referred to as tidal channels. This paper presents a method for detecting a path through tidal channels, using satellite imagery, that supports our understanding and safe exploitation of this valuable coastal environment. This approach is the first proposed to identify navigable paths in all conditions, with SAR images susceptible to variation due to weather and tidal conditions. Tidal channels are known to vary in SAR presentation, and we find that tidal flat presentation is also influenced by conditions. The most influential factor is the wind, with high winds causing an inversion in how both tidal flats and tidal channels present in SAR images. The presented method for the automatic detection of tidal channels accounts for this variability by using previous channel paths as a reference to reliably correct imagery and detect the latest path. The final algorithm produces paths with minor errors in 17.6% of images; the error rate increases to 71.7%, with an almost tenfold increase in errors, when the SAR image and paths are not adjusted to account for conditions. This capability has been used to support the Nith Inshore Rescue in attending call-outs from their base in Glencaple, UK, while the insights from monitoring tidal channels for a year demonstrate how periods of high river flow preceded major changes in the channel path.

Background: The red imported fire ant (RIFA) is one of the world’s most destructive invasive species. RIFA stings are painful and can lead to allergic reactions, including life-threatening anaphylaxis, yet health impacts remain inadequately defined. Methods: We searched MEDLINE (Ovid) and Google Scholar (grey literature) from inception until 20 September 2023 for articles in English using search terms related to red imported fire ants and allergies, including anaphylaxis. Results: Approximately a third of the population in RIFA-infested areas are stung each year. The most frequent reaction is a sterile 1–2 mm pseudo pustule on the skin. Approximately 20% of stings cause a large local reaction and between about 0.5% and 2% stings cause a systemic allergic reaction which can range from skin symptoms to life-threatening anaphylaxis. Local biodiversity is also significantly disrupted by invading RIFA and may lead to complex adverse effects on human health, from agriculture losses to expanded ranges for pathogen vectors. Conclusions: The potential for red imported fire ants to establish themselves as an invasive species in the Western Pacific presents a substantial and costly health issue. Successful eradication and surveillance programs, to identify and eradicate new incursions, would avoid substantial health impacts and costs.

In this population-based cohort study from Scotland, the authors investigate the prevalence of symptoms in the post-acute phase of COVID-19 infection compared to matched uninfected controls. They identify persistent symptoms associated with infection and identify factors associated with failure to recover.