The London College

Setting up a psychedelic study can be a long, arduous, and Kafkaesque process. Researchers are faced with a host of challenges in this rapidly evolving field, necessitating a range of considerations that remain largely unstandardised. Many of the complexities inherent to psychedelic research also challenge existing assumptions around, for example, approaches to psychiatric prescribing, the conceptual framing of the placebo effect, and definitions of selfhood. This review paper aims to formalise these unique considerations by addressing the sociocultural, political, legal, pharmacological, safety, study design and experiential facets inherent to a psychedelic study. We bring together several of the major psychedelic research teams across the United Kingdom, identify continuing areas of debate, and provide a practical, comprehensive, experience-based guide, with recommendations for policymakers and future researchers intending to set up a psychedelic research study or clinical trial.

Hearing our own voice is commonly assumed to be an unpleasant experience. In contrast, some studies of voice perception have reported that we actually find our own voice to be more attractive than other voices. It remains however unclear whether previously observed enhancements of self-voice attractiveness were mediated by the recognition of the self-voice during the task, because recognition was never explicitly and rigorously examined in previous studies. We therefore conducted a voice attractiveness rating task under conditions that limited the likelihood of self-voice recognition, in order to directly compare the perceived attractiveness of one's own voice when recognised versus when not. We found that both participants who did (N=59) and did not (N=80) recognise their own voice rated their own voice differently from other voices. However, crucially, the direction of this difference depended on whether the self-voice was recognised during the task or not: listeners who spontaneously recognised their voice rated it as more attractive than other voices, whereas listeners who did not recognise their voice rated it as less attractive. The findings help to explain one source of contradictory perspectives in the voice perception literature and beyond, highlighting a possible interplay between self-perception and voice identity recognition.

Synthesised evidence on the formal recognition of advanced community pharmacists (ACPs) and their practice is limited. This review aims to explore global evidence on the practice of formally recognised ACPs. This scoping review followed the JBI Manual for Evidence Synthesis and PRISMA guidelines. Advanced practice was determined using the International Pharmaceutical Federation framework encompassing four pillars: professional practice, leadership, research, and education. Formal recognition referred to post-registration acknowledgement by authorised bodies. Twelve databases and Google Scholar were searched between 2004 and 2024, without country or language restrictions. Findings were thematically analysed using NVivo 14. Sixty-three studies from six high-income countries were included, predominantly from Canada (n=18) and the United States (n=16). Formally recognised ACPs provided clinical services including prescribing (n=27), vaccination (n=22), and medication review (n=20). Few studies reported ACPs practice within the research and education pillars. There was variation in the definition and formal recognition mechanisms of ACPs between studies. In addition, many studies reported practice corresponding to advanced services but did not describe prerequisite training or formal recognition. Reported challenges included inadequate remuneration, limited access to patient records, and low stakeholder awareness. Key facilitators were availability of formal recognition, interprofessional relationships, and appropriate training. ACPs have expanded public access to clinical services. However, formal recognition and practice of ACPs from low-middle-income-countries are underrepresented in the literature. More work is needed to recognise ACPs' practice in education and research. Future research should clearly operationalise advanced practice within existing frameworks, explicitly report recognition mechanisms, and engage broader stakeholders in the implementation, evaluation, and role development of ACPs.

The ability to substitute some resource or tool for another is a common and important human ability. For example, in cooking, we often lack an ingredient for a recipe and we solve this problem by finding a substitute ingredient. There are various ways that we may reason about this. Often we need to draw on commonsense reasoning to find a substitute. For instance, we can think of the properties of the missing item, and try to find similar items with similar properties. Despite the importance of substitution in human intelligence, there is a lack of a theoretical understanding of the faculty. To address this shortcoming, we propose a commonsense reasoning framework for conceptualizing and harnessing substitution. In order to ground our proposal, we focus on cooking. Though we believe the proposal can be straightforwardly adapted to other applications that require formalization of substitution. Our approach is to produce a general framework based on distance measures for determining similarity (e.g. between ingredients, or between processing steps), and on identifying inconsistencies between the logical representation of recipes and integrity constraints that we use to flag the need for mitigation (e.g. after substituting one kind of pasta for another in a recipe, we may identify an inconsistency in the cooking time, and this is resolved by updating the cooking time).

Abstract Cauda equina syndrome (CES) is a neurological emergency requiring timely surgical decompression within 24 h of MRI confirmation. Limited out-of-hours (OOH) MRI access often necessitates emergency patient transfers to acute spinal centres. This study assesses the justification for such transfers. This retrospective cohort study analysis from April 2019 to March 2025, utilised electronic health records and referral data to UCLH (NHNN) which totalled 5525 OOH referrals for suspected CES. 706 patients were accepted for transfer. 285 were referred with local MRIs before referral, leading to 135 surgeries for CES. 116 were transferred after awaiting a local MRI, leading to 61 surgeries for CES. 305 patients were transferred for an urgent MRI with 37 requiring surgeries for CES. There was a significant difference in time from MRI to surgery for patients who awaited a local MRI (21.74 ± 1.67 h) versus those transferred for an MRI (9.25 ± 2.11 h) although both groups met the guideline target of undergoing surgery within 24 h of a scan (MWUT = 309.0, P = 2.76e-9). OOH MRI sensitivity was 9.8%. Patients with a negative scan result remained hospitalised for a mean of 69.76 ± 8.37 h until discharge, with an average admission cost of £3014.55. OOH referrals predominantly originated from doctors below ST4, with diagnostic accuracy of 4.2% compared to 5.2% for those above ST4 (χ² = 0.031, P = 0.86). OOH MRI transfers yield limited surgical benefit, accrue significant costs, and align poorly with GIRFT guidelines advocating for local MRIs to reduce unnecessary referrals. These findings support minimising OOH transfers for suspected CES.

Low-field magnetic resonance imaging (MRI) offers affordable access to diagnostic imaging but faces challenges such as prolonged acquisition times and reduced image quality. Although accelerated imaging via k-space undersampling helps reduce scan time, image quality enhancement methods often rely on spatial-domain postprocessing. Deep learning achieved state-of-the-art results in both domains. However, most models are trained and evaluated using in-distribution (InD) data, creating a significant gap in understanding model performance when tested using out-of-distribution (OOD) data. To address these issues, we propose a novel framework that reconstructs high-field-like MR images directly from undersampled low-field MRI k-space, quantifies the impact of reduced sampling, and evaluates the generalisability of the model using OOD. Our approach utilises a k-space dual channel U-Net to jointly process the real and imaginary components of undersampled k-space, restoring missing frequency content, and incorporates an ensemble strategy to generate uncertainty maps. Experiments on low-field brain MRI demonstrate that our k-space-driven image quality enhancement outperforms the counterpart spatial-domain and other state-of-the-art baselines, achieving image quality comparable to full high-field k-space acquisitions using OOD data. To the best of our knowledge, this work is among the first to combine low-field MR image reconstruction, quality enhancement using undersampled k-space, and uncertainty quantification within a unified framework.

When water drops slide over a flat hydrophobic solid surface, negativecharges are spontaneously deposited at the rear of the drop, and the drops gain a positive charge. This phenomenon is known as slide electrification. Here, we demonstrate an alternative mechanism of spontaneous charge separation in sliding drops. Therefore, arrays of Janus micropillars were fabricated by photolithography. Each micropillar has a hydrophilic top created by reactive ion etching (RIE) and aminopropyltriethoxysilane (APTES) deposition, while the walls and substrate were hydrophobized by 1H,1H,2H,2H-perfluorooctyltrichlorosilane (PFOTS). Drops on these arrays show high apparent contact angles, remain in the Cassie state, and still contact the hydrophilic top faces. Sliding drops acquire charge, with polarity and magnitude governed by top-surface chemistry, including NH3 +/NH4 + formation and PFOTS removal after the RIE plasma exposure, and dense NH2 groups formation from thermally activated APTES vapor deposition. Unlike flat surfaces, high-speed reflection microscopy reveals tiny satellite droplets left on each pillar that evaporate within one second. Consequently, charge separation occurs within the liquid phase between the primary drop and deposited satellites, rather than directly at the contact line.

We aimed to assess prescribing accuracy, error reduction, usability, and clinician confidence of Ask Eolas (a retrieval-augmented generation-enhanced AI-CDSS) compared to existing antimicrobial guidance tools. We conducted a structured simulation single-site study evaluating Ask Eolas across 45 prescribing cases with healthcare professionals to assess prescribing accuracy. Among 45 participants, Ask Eolas achieved zero prescribing errors versus six and eight documented errors in the two comparator groups (Eolas App and PDF Guidelines), respectively (p < 0.001). The number needed to treat was 1.9 for Ask Eolas versus traditional guidelines, indicating one additional error-free prescription for every two clinicians switching to Ask Eolas. Ask Eolas significantly improved prescribing accuracy while enhancing usability, clinician confidence, and system transparency compared to existing tools. These findings align with TRUST-AI framework principles for safe AI-CDSS deployment, supporting further investigation through real-world implementation studies incorporating live data integration, confidence calibration systems, and comprehensive auditability features in antimicrobial stewardship programmes.