WH2 and proline-rich domains of WASP-family proteins collaborate to accelerate actin filament elongation.

WASP-family proteins are known to promote assembly of branched actin networks by stimulating the filament-nucleating activity of the Arp2/3 complex. Here, we show that WASP-family proteins also function as polymerases that accelerate elongation of uncapped actin filaments. When clustered on a surface, WASP-family proteins can drive branched actin networks to grow much faster than they could by direct incorporation of soluble monomers. This polymerase activity arises from the coordinated action of two regulatory sequences: (i) a WASP homology 2 (WH2) domain that binds actin, and (ii) a proline-rich sequence that binds profilin-actin complexes. In the absence of profilin, WH2 domains are sufficient to accelerate filament elongation, but in the presence of profilin, proline-rich sequences are required to support polymerase activity by (i) bringing polymerization-competent actin monomers in proximity to growing filament ends, and (ii) promoting shuttling of actin monomers from profilin-actin complexes onto nearby WH2 domains. Unoccupied WH2 domains transiently associate with free filament ends, preventing their growth and dynamically tethering the branched actin network to the WASP-family proteins that create it. Collaboration between WH2 and proline-rich sequences thus strikes a balance between filament growth and tethering. Our work expands the number of critical roles that WASP-family proteins play in the assembly of branched actin networks to at least three: (i) promoting dendritic nucleation; (ii) linking actin networks to membranes; and (iii) accelerating filament elongation

The value of multimodality imaging for detection, characterisation and management of a wall adhering structure in the right atrium

The case presents a wall adherent structure in the right atrium in a young patient with peripheral tcell lymphoma followed by successful prolonged lysis therapy resulting in the resolution of the thrombus is presented. This case highlights the utility of multimodality imaging in an accurate assessment of the right atrium thrombus and the effectiveness of prolonged lysis therapy.peer-reviewe

Realizing a Deterministic Source of Multipartite-Entangled Photonic Qubits

Sources of entangled electromagnetic radiation are a cornerstone in quantum information processing and offer unique opportunities for the study of quantum many-body physics in a controlled experimental setting. While multi-mode entangled states of radiation have been generated in various platforms, all previous experiments are either probabilistic or restricted to generate specific types of states with a moderate entanglement length. Here, we demonstrate the fully deterministic generation of purely photonic entangled states such as the cluster, GHZ, and W state by sequentially emitting microwave photons from a controlled auxiliary system into a waveguide. We tomographically reconstruct the entire quantum many-body state for up to $N=4$ photonic modes and infer the quantum state for even larger $N$ from process tomography. We estimate that localizable entanglement persists over a distance of approximately ten photonic qubits, outperforming any previous deterministic scheme

Traits associated with central pain augmentation in the Knee Pain in the Community (KPIC) cohort

This study aimed to identify self-report correlates of central pain augmentation in individuals with knee pain. A subset of participants (n=420) in the Knee Pain and related health In the Community (KPIC) baseline survey undertook pressure pain threshold (PPT) assessments. Items measuring specific traits related to central pain mechanisms were selected from the survey based on expert consensus, face validity, item association to underlying constructs measured by originating host questionnaires, adequate targeting and PPT correlations. Pain distribution was reported on a body manikin. A `central pain mechanisms’ factor was sought by factor analysis. Associations of items, the derived factor and originating questionnaires with PPTs were compared. Eight self-report items measuring traits of anxiety, depression, catastrophizing, neuropathic- like pain, fatigue, sleep disturbance, pain distribution and cognitive impact, were identified as likely indices of central pain mechanisms. PPTs were associated with items representing each trait and with their originating scales. Pain distribution classified as “pain below the waist additional to knee pain” was more strongly associated with low PPT than were alternative classifications of pain distribution. A single factor, interpreted as “central pain mechanisms”, was identified across the 8 selected items and explained variation in PPT (R² = 0.17) better than did any originating scale (R² = 0.10 to 0.13). In conclusion, including representative items within a composite self-report tool might help identify people with centrally augmented knee pain

Microcirculatory alterations in critically ill COVID-19 patients analyzed using artificial intelligence

Background: The sublingual microcirculation presumably exhibits disease-specific changes in function and morphology. Algorithm-based quantification of functional microcirculatory hemodynamic variables in handheld vital microscopy (HVM) has recently allowed identification of hemodynamic alterations in the microcirculation associated with COVID-19. In the present study we hypothesized that supervised deep machine learning could be used to identify previously unknown microcirculatory alterations, and combination with algorithmically quantified functional variables increases the model's performance to differentiate critically ill COVID-19 patients from healthy volunteers. Methods: Four international, multi-central cohorts of critically ill COVID-19 patients and healthy volunteers (n = 59/n = 40) were used for neuronal network training and internal validation, alongside quantification of functional microcirculatory hemodynamic variables. Independent verification of the models was performed in a second cohort (n = 25/n = 33). Results: Six thousand ninety-two image sequences in 157 individuals were included. Bootstrapped internal validation yielded AUROC(CI) for detection of COVID-19 status of 0.75 (0.69-0.79), 0.74 (0.69-0.79) and 0.84 (0.80-0.89) for the algorithm-based, deep learning-based and combined models. Individual model performance in external validation was 0.73 (0.71-0.76) and 0.61 (0.58-0.63). Combined neuronal network and algorithm-based identification yielded the highest externally validated AUROC of 0.75 (0.73-0.78) (P < 0.0001 versus internal validation and individual models). Conclusions: We successfully trained a deep learning-based model to differentiate critically ill COVID-19 patients from heathy volunteers in sublingual HVM image sequences. Internally validated, deep learning was superior to the algorithmic approach. However, combining the deep learning method with an algorithm-based approach to quantify the functional state of the microcirculation markedly increased the sensitivity and specificity as compared to either approach alone, and enabled successful external validation of the identification of the presence of microcirculatory alterations associated with COVID-19 status. Keywords: Artificial intelligence; COVID-19; Deep learning; Microcirculation; Neuronal network

Quantitative sensory testing and predicting outcomes for musculoskeletal pain, disability, and negative affect: a systematic review and meta-analysis

Hypersensitivity due to central pain mechanisms can influence recovery and lead to worse clinical outcomes, but the ability of quantitative sensory testing (QST), an index of sensitisation, to predict outcomes in chronic musculoskeletal disorders remains unclear. We systematically reviewed the evidence for ability of QST to predict pain, disability and negative affect using searches of CENTRAL, MEDLINE, EMBASE, AMED, CINAHL and PubMed databases up to April 2018. Title screening, data extraction, and methodological quality assessments were performed independently by 2 reviewers. Associations were reported between baseline QST and outcomes using adjusted (β) and unadjusted (r) correlations. Of the 37 eligible studies (n=3860 participants), 32 were prospective cohort studies and 5 randomised controlled trials. Pain was an outcome in 30 studies, disability in 11 and negative affect in 3. Metaanalysis revealed that baseline QST predicted musculoskeletal pain (mean r=0.31, 95%CI: 0.23 to 0.38, n=1057 participants) and disability (mean r=0.30, 95%CI: 0.19 to 0.40, n=290 participants). Baseline modalities quantifying central mechanisms such as temporal summation (TS) and conditioned pain modulation (CPM) were associated with follow-up pain (TS: mean r=0.37, 95%CI: 0.17 to 0.54; CPM: r=0.36, 95%CI: 0.20 to 0.50), whereas baseline mechanical threshold modalities were predictive of followup disability (mean r=0.25, 95%CI: 0.03 to 0.45). QST indices of pain hypersensitivity might help develop targeted interventions aiming to improve outcomes across a range of musculoskeletal conditions

An observational study of centrally facilitated pain in individuals with chronic low back pain

Central pain facilitation can hinder recovery in people with chronic low back pain (CLBP). The aim of this observational study was to investigate whether indices of centrally facilitated pain are associated with pain outcomes in a hospital-based cohort of individuals with CLBP undertaking a pain management programme. Participants provided self-report and pain sensitivity data at baseline (n=97), and again 3-months (n=87) after a cognitive behavioural therapy-based group intervention including physiotherapy. Indices of centrally facilitated pain were; Pressure Pain detection Threshold (PPT), Temporal Summation (TS) and Conditioned Pain Modulation (CPM) at the forearm, Widespread Pain Index (WPI) classified using a body manikin, and a Central Mechanisms Trait (CMT) factor derived from 8 self-reported characteristics of anxiety, depression, neuropathic pain, fatigue, cognitive dysfunction, pain distribution, catastrophizing and sleep. Pain severity was a composite factor derived from Numerical Rating Scales. Cross-sectional and longitudinal regression models were adjusted for age and sex. Baseline CMT and WPI each was associated with higher pain severity (CMT: r=0.50, p<0.001, WPI: r=0.21, p=0.04) at baseline as well as at 3 months (CMT: r=0.38, p<0.001, WPI: r=0.24, p=0.02). High baseline CMT remained significantly associated with pain at 3 months after additional adjustment for baseline pain (β=2.45, p=0.04, R2=0.25, p<0.0001). QST indices of pain hypersensitivity were not significantly associated with pain outcomes at baseline or at 3 months. In conclusion, central mechanisms beyond those captured by QST are associated with poor CLBP outcome and might be targets for improved therapy

The Central Aspects of Pain in the Knee (CAP-Knee) questionnaire; a mixed-methods study of a self-report instrument for assessing central mechanisms in people with knee pain

OBJECTIVES: Pain is the prevailing symptom of knee osteoarthritis. Central sensitisation creates discordance between pain and joint pathology. We previously reported a central pain mechanisms trait derived from 8 discrete characteristics: neuropathic-like pain, fatigue, cognitive-impact, catastrophising, anxiety, sleep disturbance, depression, and pain distribution. We here validate and show that an 8-item questionnaire, Central Aspects of Pain in the Knee (CAP-Knee) is associated both with sensory and affective components of knee pain severity.METHODS: Participants with knee pain were recruited from the Investigating Musculoskeletal Health and Wellbeing study in the East Midlands, UK. CAP-Knee items were refined following cognitive interviews. Psychometric properties were assessed in 250 participants using Rasch-, and factor-analysis, and Cronbach’s alpha. Intra-class correlation coefficients tested repeatability. Associations between CAP-Knee and McGill Pain questionnaire pain severity scores using linear regression.RESULTS: CAP-Knee targeted the knee pain sample well. Cognitive interviews indicated that participants interpreted CAP-Knee items in diverse ways aligned to their intended meanings. Fit to the Rasch model was optimised by rescoring each item, producing a summated score from 0-16. Internal consistency was acceptable (Cronbach’s alpha=0.74) and test–retest reliability excellent (ICC2,1=0.91). Each CAP-Knee item contributed uniquely to one discrete `Central Mechanisms trait’ factor. High CAP-Knee scores were associated with worse overall knee pain intensity and with each of sensory and affective McGill Pain Questionnaire scores.CONCLUSION: CAP-Knee is a simple and valid self-report questionnaire, which measures a single `Central Mechanisms’ trait, and may help identify and target centrally-acting treatments aiming to reduce the burden of knee pain

Central Aspects of Pain in Rheumatoid Arthritis (CAP-RA): protocol for a prospective observational study

Background: Pain and fatigue are persistent problems in people with rheumatoid arthritis. Central sensitisation (CS) may contribute to pain and fatigue, even when treatment has controlled inflammatory disease. This study aims to validate a self-report 8-item questionnaire, the Central Aspects of Pain in Rheumatoid Arthritis (CAP-RA) questionnaire, developed to measure central pain mechanisms in RA, and to predict patient outcomes and response to treatment. A secondary objective is to explore mechanisms linking CS, pain and fatigue in people with RA. Methods/design: This is a prospective observational cohort study recruiting 250 adults with active RA in secondary care. The CAP-RA questionnaire, demographic data, medical history, and patient reported outcome measures (PROMs) of traits associated with central sensitization will be collected using validated questionnaires. Quantitative sensory testing modalities of pressure pain detection thresholds, temporal summation and conditioned pain modulation will be indices of central sensitization, and blood markers, swollen joints and ultrasound scans will be indices of inflammation. Primary data collection will be at baseline and 12 weeks. The test-retest reliability of CAP-RA questionnaire will be determined 1 week after the baseline visit. Pain and fatigue data will be collected weekly via text messages for 12 weeks. CAP-RA psychometric properties, and predictive validity for outcomes at 3 months will be evaluated. Discussion: This study will validate a simple self-report questionnaire against psychophysical indices of central sensitization and patient reported outcome measures of traits associated with CS in a population of individuals with active RA. The application of this instrument in the clinical environment could provide a mechanism-based stratification tool to facilitate the provision of targeted therapy to individuals with pain and fatigue in RA, alongside treatments that target joint inflammation. Trial registration: Clinicaltrials.gov NCT04515589. Date of registration 17 August 2020