The phenotype of circulating follicular-helper T cells in patients with rheumatoid arthritis defines CD200 as a potential therapeutic target

Rheumatoid arthritis (RA) is a systemic autoimmune disease primarily affecting synovial joints in which the development of autoantibodies represents a failure of normal tolerance mechanisms, suggesting a role for follicular helper T cells (TFH) in the genesis of autoimmunity. To determine whether quantitative or qualitative abnormalities in the circulating TFH cell population exist, we analysed by flow cytometry the number and profile of these cells in 35 patients with RA and 15 matched controls. Results were correlated with patient characteristics, including the presence of autoantibodies, disease activity, and treatment with biologic agents. Circulating TFH cells from patients with RA show significantly increased expression of the immunoglobulin superfamily receptor CD200, with highest levels seen in seropositive patients (P=0.0045) and patients treated with anti-TNFα agents (P=0.0008). This occurs in the absence of any change in TFH numbers or overt bias towards Th1, Th2, or Th17 phenotypes. CD200 levels did not correlate with DAS28 scores (P=0.887). Although the number of circulating TFH cells is not altered in the blood of patients with RA, the TFH cells have a distinct phenotype. These differences associate TFH cells with the pathogenesis of RA and support the relevance of the CD200/CD200R signalling pathway as a potential therapeutic target

(Re)conceptualising physical activity participation as career

Physical activity is increasingly positioned as playing an important role in preventing and mitigating many of the decrements associated with biological ageing. As a result, public health messages encourage older people to remain active in later life. Despite this, physical activity participation rates among older adults are low. This may be in part related to the conventional approach to understanding physical activity participation as a product of motivation. We contend that this approach does not allow for a deeper exploration of the wider structural, historical and discursive contexts in which physical activity participation occurs. Therefore, we propose that physical activity can be reconceptualised as a career. Through a synthesis of findings from four studies exploring physical activity experiences in later life, we demonstrate that beginning and maintaining a physical activity career requires a disposition towards physical activity, the legitimation of physically active practices and dealing with contingencies. In addition, we demonstrate that maintaining a physical activity career requires investment and deliberation to adapt physical activity practices continually within an individual's own personal biography. As such, we conclude that current strategies to promote physical activity to older adults are unlikely to result in increased levels of participation. To promote physical activity to older adults an understanding of how structural, cultural and historical contexts influence participation is needed

A hybrid individual-based mathematical model to study bladder infections

RB was supported by a fellowship funded by the Medical Research Council, MR/P014704/1, and also acknowledges funding from the Academy of Medical Sciences (London), the Wellcome Trust (London), the UK Government Department of Business, Energy and Industrial Strategy (London), the British Heart Foundation (London), and the Global Challenges Research Fund (Swindon, UK; grant number SBF003\1052). TL gratefully acknowledges support from the Italian Ministry of University and Research (MUR) through the grant Dipartimenti di Eccellenza 2018-2022 (Project no. E11G18000350001) and the PRIN 2020 project (No. 2020JLWP23) Integrated Mathematical Approaches to Socio-Epidemiological Dynamics (CUP: E15F21005420006).Introduction: Bladder infections are common, affecting millions each year, and are often recurrent problems. Methods: We have developed a spatial mathematical framework consisting of a hybrid individual-based model to simulate these infections in order to understand more about the bacterial mechanisms and immune dynamics. We integrate a varying bacterial replication rate and model bacterial shedding as an immune mechanism. Results: We investigate the effect that varying the initial bacterial load has on infection outcome, where we find that higher bacterial burden leads to poorer outcomes, but also find that only a single bacterium is needed to establish infection in some cases. We also simulate an immunocompromised environment, confirming the intuitive result that bacterial spread typically progresses at a higher rate. Conclusions: With future model developments, this framework is capable of providing new clinical insight into bladder infections.Publisher PDFPeer reviewe

Exploring the utility of assistive artificial intelligence for ultrasound scanning in regional anesthesia

This work was funded by Intelligent Ultrasound Limited (Cardiff, UK). Data from this study were included in medical device regulatory approval submissions in the USA.Introduction: Ultrasound-guided regional anesthesia (UGRA) involves the acquisition and interpretation of ultrasound images to delineate sonoanatomy. This study explores the utility of a novel artificial intelligence (AI) device designed to assist in this task (ScanNav Anatomy Peripheral Nerve Block; ScanNav), which applies a color overlay on real-time ultrasound to highlight key anatomical structures. Methods: Thirty anesthesiologists, 15 non-experts and 15 experts in UGRA, performed 240 ultrasound scans across nine peripheral nerve block regions. Half were performed with ScanNav. After scanning each block region, participants completed a questionnaire on the utility of the device in relation to training, teaching, and clinical practice in ultrasound scanning for UGRA. Ultrasound and color overlay output were recorded from scans performed with ScanNav. Experts present during the scans (real-time experts) were asked to assess potential for increased risk associated with use of the device (eg, needle trauma to safety structures). This was compared with experts who viewed the AI scans remotely. Results: Non-experts were more likely to provide positive and less likely to provide negative feedback than experts (p=0.001). Positive feedback was provided most frequently by non-experts on the potential role for training (37/60, 61.7%); for experts, it was for its utility in teaching (30/60, 50%). Real-time and remote experts reported a potentially increased risk in 12/254 (4.7%) vs 8/254 (3.1%, p=0.362) scans, respectively. Discussion: ScanNav shows potential to support non-experts in training and clinical practice, and experts in teaching UGRA. Such technology may aid the uptake and generalizability of UGRA. TRIAL REGISTRATION NUMBER: NCT04918693.Publisher PDFPeer reviewe

Artificial intelligence for ultrasound scanning in regional anaesthesia: a scoping review of the evidence from multiple disciplines

Background Artificial intelligence (AI) for ultrasound scanning in regional anaesthesia is a rapidly developing interdisciplinary field. There is a risk that work could be undertaken in parallel by different elements of the community but with a lack of knowledge transfer between disciplines, leading to repetition and diverging methodologies. This scoping review aimed to identify and map the available literature on the accuracy and utility of AI systems for ultrasound scanning in regional anaesthesia. Methods A literature search was conducted using Medline, Embase, CINAHL, IEEE Xplore, and ACM Digital Library. Clinical trial registries, a registry of doctoral theses, regulatory authority databases, and websites of learned societies in the field were searched. Online commercial sources were also reviewed. Results In total, 13,014 sources were identified; 116 were included for full-text review. A marked change in AI techniques was noted in 2016–17, from which point on the predominant technique used was deep learning. Methods of evaluating accuracy are variable, meaning it is impossible to compare the performance of one model with another. Evaluations of utility are more comparable, but predominantly gained from the simulation setting with limited clinical data on efficacy or safety. Study methodology and reporting lack standardisation. Conclusions There is a lack of structure to the evaluation of accuracy and utility of AI for ultrasound scanning in regional anaesthesia, which hinders rigorous appraisal and clinical uptake. A framework for consistent evaluation is needed to inform model evaluation, allow comparison between approaches/models, and facilitate appropriate clinical adoption

A photonic platform for donor spin qubits in silicon

Donor spins in silicon are highly competitive qubits for upcoming quantum technologies, offering complementary metal-oxide semiconductor compatibility, coherence (T2) times of minutes to hours, and simultaneous initialization, manipulation, and readout fidelities near ~99.9%. This allows for many quantum error correction protocols, which will be essential for scale-up. However, a proven method of reliably coupling spatially separated donor qubits has yet to be identified. We present a scalable silicon-based platform using the unique optical properties of “deep” chalcogen donors. For the prototypical 77Se+ donor, we measure lower bounds on the transition dipole moment and excited-state lifetime, enabling access to the strong coupling limit of cavity quantum electrodynamics using known silicon photonic resonator technology and integrated silicon photonics. We also report relatively strong photon emission from this same transition. These results unlock clear pathways for silicon-based quantum computing, spin-to-photon conversion, photonic memories, integrated single-photon sources, and all-optical switches

Variability between human experts and artificial intelligence in identification of anatomical structures by ultrasound in regional anaesthesia: a framework for evaluation of assistive artificial intelligence

Background: ScanNavTM Anatomy Peripheral Nerve Block (ScanNav™) is an artificial intelligence (AI)-based device that produces a colour overlay on real-time B-mode ultrasound to highlight key anatomical structures for regional anaesthesia. This study compares consistency of identification of sono-anatomical structures between expert ultrasonographers and ScanNav™. Methods: Nineteen experts in ultrasound-guided regional anaesthesia (UGRA) annotated 100 structures in 30 ultrasound videos across six anatomical regions. These annotations were compared with each other to produce a quantitative assessment of the level of agreement amongst human experts. The AI colour overlay was then compared with all expert annotations. Differences in human–human and human–AI agreement are presented for each structure class (artery, muscle, nerve, fascia/serosal plane) and structure. Clinical context is provided through subjective assessment data from UGRA experts. Results: For human–human and human–AI annotations, agreement was highest for arteries (mean Dice score 0.88/0.86), then muscles (0.80/0.77), and lowest for nerves (0.48/0.41). Wide discrepancy exists in consistency for different structures, both with human–human and human–AI comparisons; highest for sartorius muscle (0.91/0.92) and lowest for the radial nerve (0.21/0.27). Conclusions: Human experts and the AI system both showed the same pattern of agreement in sono-anatomical structure identification. The clinical significance of the differences presented must be explored; however the perception that human expert opinion is uniform must be challenged. Elements of this assessment framework could be used for other devices to allow consistent evaluations that inform clinical training and practice. Anaesthetists should be actively engaged in the development and adoption of new AI technology

Characterization of the Si:Se+ Spin-Photon Interface

Silicon is the most-developed electronic and photonic technological platform and hosts some of the highest-performance spin and photonic qubits developed to date. A hybrid quantum technology harnessing an efficient spin-photon interface in silicon would unlock considerable potential by enabling ultralong-lived photonic memories, distributed quantum networks, microwave-to-optical photon converters, and spin-based quantum processors, all linked with integrated silicon photonics. However, the indirect band gap of silicon makes identification of efficient spin-photon interfaces nontrivial. Here we build upon the recent identification of chalcogen donors as a promising spin-photon interface in silicon. We determine that the spin-dependent optical degree of freedom has a transition dipole moment stronger than previously thought [here 1.96(8) D], and the spin T1 lifetime in low magnetic fields is longer than previously thought [here longer than 4.6(1.5) h]. We furthermore determine the optical excited-state lifetime [7.7(4) ns], and therefore the natural radiative efficiency [0.80(9)%], and by measuring the phonon sideband determine the zero-phonon emission fraction [16(1)%]. Taken together, these parameters indicate that an integrated quantum optoelectronic platform based on chalcogen-donor qubits in silicon is well within reach of current capabilities