Karl McCreadie

Objectives: The prevalence of stoma formation in the United Kingdom has risen markedly over the past decade, yet comparatively little is known about national prescribing patterns for ostomy products. A retrospective cross-sectional observational study was conducted in which the UK Prescription Cost Analysis (PCA) data for 2023 were examined to assess what information can be extracted from these datasets, to evaluate inter-nation variation in prescribing, and identify areas warranting further health economics and policy investigation. Methods: PCA data for calendar year 2023 were collated from the four devolved UK health services. Data on product type, manufacturer, quantities dispensed, and cost were compared across nations and product categories. Results: Total UK prescription spend on ostomy products in 2023 was £457 million, 3.4% of overall NHS prescription expenditure. Pouches accounted for 72% (£329 million) of spend, with the remainder supporting peristomal skin health and appliance adhesion. Estimated average per-patient spend was modelled and varied substantially by nation, from £1073 in Wales to £2354 in England, against a UK average of £2243. Persistent prescribing of ancillary items no longer recommended for routine use (filters, deodorants, bag covers) accounted for an estimated £ 8 million (>2% of spend). Conclusions: PCA data represent an underutilized resource for population-level analysis of ostomy care. Marked inter-nation variation in expenditure, uncertainty surrounding ostomate population estimates, and the continued prescribing of ancillary items not recommended for routine use identify important evidence gaps and generate hypotheses for future research with direct relevance to prescribing policy, resource allocation, and equitable access to care across the United Kingdom.

Background: Accurate assessment of residual awareness in patients with Prolonged Disorders of Consciousness (PDoC) remains a major clinical challenge, as conventional behavioural tools can underestimate covert cognition. This study evaluates whether a structured, multi-phase motor imagery Brain–Computer Interface (MI-BCI) protocol provides objective electroencephalography (EEG)-based indicators of awareness that complement behavioural assessments. Methods: Forty-four participants ( N = 44) completed repeated imagined-movement tasks using wearable EEG (PDoC: Unresponsive Wakefulness Syndrome (UWS, n = 14), Minimally Conscious State (MCS, n = 17), Locked-In Syndrome (LIS, n = 11); two able-bodied participants as benchmarks; ClinicalTrials.gov: NCT03827187; 30-01-2019). The protocol assessed sensorimotor rhythm modulation, training with and without neurofeedback, and binary question answering across phases. Standard behavioural assessments (CRS-R and WHIM) were administered at each session. Results: Significant MI-BCI decoding accuracy (DA) is achieved by 73.8% of patients, of whom 90% progress to Q&A testing and frequently exceed the 70% usability threshold, revealing marked inter-individual heterogeneity. For significant MI-BCI runs, LIS outperform MCS ( p = 0.007) and UWS ( p = 0.048), while UWS exceed MCS during Q&A ( p = 0.049), driven by familiar-voice stimuli. Using leave-one-subject-out cross-validation, combining predictions from DA and behavioural assessments improves balanced diagnostic accuracy to 62% (from 55%), increasing sensitivity to MCS (39% to 69%), with a modest reduction in LIS sensitivity (78% to 67%). Task-related activity over sensorimotor and parietal cortices differentiate diagnostic groups. Conclusions: The structured MI-BCI protocol demonstrates potential as a movement-independent, EEG-based tool for distinguishing UWS, MCS and LIS. Integrating DA and spatial patterns yields diagnostic information that may augment behavioural assessment and advance objective tools for evaluating awareness in PDoC.

This paper presents a hybrid framework that combines machine learning and heuristic optimisation to minimise the imposed Delta Cycle Time in a wafer fabrication plant. With real manufacturing data from Seagate Technology, the framework integrates statistical analysis and optimisation. First, multiple Machine Learning models were used to develop a continuous regression model for Key Performance Indicators with Delta Cycle Time as the target. We then applied Genetic Algorithm, Particle Swarm Optimisation, and Sequential Quadratic Programming techniques on this regression function to minimise Delta Cycle Time. The Random Forest model demonstrated greater robustness than the other Machine Learning models, achieving a root mean square error of 0.029. The Random Forest with the Genetic algorithm model achieved the most improvement by minimising the average imposed Delta Cycle Time from +0.276 days to -0.392 days, representing a mean absolute reduction of 0.668 days (median: 0.358 days) and 331.05% relative improvement over baseline, with 100% targeted tools improvement (96/96). Particle Swarm Optimisation achieved similar results, with an average Delta Cycle Time of -0.347 days, corresponding to a mean absolute reduction of 0.623 days (median: 0.341 days) and a 307.45% improvement over baseline, also improving all anticipated 96 tools. Sequential Quadratic Programming yielded more modest improvements with an average Delta Cycle Time of 0.091 days, a mean absolute reduction of 0.184 days (median: 0.081 days), and 78.78% improvement over baseline, successfully optimising 80.2% of the targeted tools (77/96). Reducing cycle time will improve production capacity and ease supply chain pressure.

Unrefined psyllium husk derived from Plantago ovata constitutes a complex mixture of water-soluble and insoluble polymeric chains that form an interpenetrating network capable of entrapping carbon nanoparticles. While the resulting composite was found to swell in aqueous electrolyte, it exhibited hydrogel-like properties where the electrochemical activity was retained and found to be stable upon repetitive voltammetric cycling. Planar film systems were characterized by electron microscopy, Raman spectroscopy, tensile testing, gravimetric analysis, contact angle and cyclic voltammetry. A key advantage of the composite lies in its ability to be cast in 3D geometric forms such as pyramidal microneedle arrays (700 μm high × 200 μm base × 500 μm pitch) that could serve as viable electrode sensors. In contrast to conventional composite electrode materials that rely on non-aqueous solvents, the psyllium mixture is processed entirely from an aqueous solution. This, along with its plant-based origins and simple processing requirements, provides a versatile matrix for the design of biodegradable electrode structures that can be manufactured from more sustainable sources.

Stoma surgery can be critical in helping to restore the well-being of patients suffering from gastrointestinal disease or injury but it inevitably comes with numerous psychological and physiological complications. Disposable pouch systems which enable the collection of bowel waste have revolutionized stoma care but providing robust, discreet devices that can efficiently meet the requirements of the patient can be challenging. Pouches must securely store a microbially active waste whilst preventing leakage, protecting the underlying skin from inflammation and minimizing odor. All of this needs to be achieved within the design constraints of a pouch that is easy to manage and yet still maintains a discreet body contour. Stoma collection has moved from the waste being collected in butyl rubber pouches to much more elaborate systems incorporating assemblies of polyvinylidene chloride, ethylene vinyl acetate, ethyl vinyl alcohol and polyvinyl alcohol with new, skin-friendly adhesive such as hydrocolloid and silicones impregnated with ceramides and aloe. Moreover, 3D printing has emerged as a means of providing personalized stoma pouches that can potentially address the age-old issue of leakage. Despite such advances, stoma pouches have evolved slowly over the past 70 or so years. A survey of the literature reveals an abundance of quality-of-life studies but a dearth of reports addressing the key technological challenges. Consequently, this narrative review considers current stoma pouch technology and highlights the issues that continue to afflict stoma patients. Research and patent literature is critically appraised in terms of current pouch technology and the potential opportunities for new composite materials are identified.

The development of a nanostructured copper–laser-induced graphene (LIG) composite that can catalyze the reduction of nitrate is described. The system was characterized using a range of surface analytical methods (SEM, Raman, DekTak profilometry). The electrochemical performance of the copper mesh in reducing nitrate was investigated, the nature of the catalytic response was elucidated, and the influence of potential interferences was critically appraised. The adaptation of the system as the basis of an electrochemical sensor for nitrate was assessed, which displayed a limit of detection of 4.7 μM nitrate. The analytical applicability in authentic media was evaluated through the analysis of two surface water samples and validated by standard spectroscopic (nitrate reductase–Griess methods). The LIG substrate offers a simple, scalable route towards the reduction of nitrate with a construction simplicity and sensitivity that is competitive with much more complex nanomaterials.

Brain-computer interface (BCI) performance is often impacted due to the inherent non-stationarity in the recorded EEG signals coupled with a high variability across subjects. This study proposes a novel method using Logistic Regression with Tangent Space-based Transfer Learning (LR-TSTL) for motor imagery (MI)-based BCI classification problems. The single-trial covariance matrix (CM) features computed from the EEG signals are transformed into a Riemannian geometry frame and tangent space features are computed by considering the lower triangular matrix. These are then further classified using the logistic regression model to improve classification accuracy. The performance of LR-TSTL is tested on healthy subjects’ dataset as well as on stroke patients’ dataset. As compared to existing within-subject learning approaches the proposed method gave an equivalent or better performance in terms of average classification accuracy (78.95±11.68%), while applied as leave-one-out cross-subject learning for healthy subjects. Interestingly, for the patient dataset LR-TSTL significantly (p<0.05) outperformed the current benchmark performance by achieving an average classification accuracy of 81.75±6.88%. The results show that the proposed method for cross-subject learning has the potential to realize the next generation of calibration-free BCI technologies with enhanced practical usability especially in the case of neurorehabilitative BCI designs for stroke patients.B.

Accurate binary classification of electroencephalography (EEG) signals is a challenging task for the development of motor imagery (MI) brain-computer interface (BCI) systems. In this study, two sliding window techniques are proposed to enhance the binary classification of MI. The first one calculates the longest consecutive repetition (LCR) of the sequence of prediction of all the sliding windows and is named SW-LCR. The second calculates the mode of the sequence of prediction of all the sliding windows and is named SW-Mode. Common spatial pattern (CSP) is used for extracting features with linear discriminant analysis (LDA) used for classification of each time window. Both the SW-LCR and SW-Mode are applied on publicly available BCI Competition IV-2a data set of healthy individuals and on a stroke patients' data set. Compared with the existing state of the art, the SW-LCR performed better in the case of healthy individuals and SW-Mode performed better on stroke patients' data set for left- versus right-hand MI with lower standard deviation. For both the data sets, the classification accuracy (CA) was approximately 80% and kappa (κ) was 0.6. The results show that the sliding window-based prediction of MI using SW-LCR and SW-Mode is robust against intertrial and intersession inconsistencies in the time of activation within a trial and thus can lead to a reliable performance in a neurorehabilitative BCI setting.

Objective. Magnetoencephalography (MEG) based brain–computer interface (BCI) involves a large number of sensors allowing better spatiotemporal resolution for assessing brain activity patterns. There have been many efforts to develop BCI using MEG with high accuracy, though an increase in the number of channels (NoC) means an increase in computational complexity. However, not all sensors necessarily contribute significantly to an increase in classification accuracy (CA) and specifically in the case of MEG-based BCI no channel selection methodology has been performed. Therefore, this study investigates the effect of channel selection on the performance of MEG-based BCI. Approach. MEG data were recorded for two sessions from 15 healthy participants performing motor imagery, cognitive imagery and a mixed imagery task pair using a unique paradigm. Performance of four state-of-the-art channel selection methods (i.e. Class-Correlation, ReliefF, Random Forest, and Infinite Latent Feature Selection were applied across six binary tasks in three different frequency bands) were evaluated in this study on two state-of-the-art features, i.e. bandpower and common spatial pattern (CSP). Main results. All four methods provided a statistically significant increase in CA compared to a baseline method using all gradiometer sensors, i.e. 204 channels with band-power features from alpha (8–12 Hz), beta (13–30 Hz), or broadband (α + β) (8–30 Hz). It is also observed that the alpha frequency band performed better than the beta and broadband frequency bands. The performance of the beta band gave the lowest CA compared with the other two bands. Channel selection improved accuracy irrespective of feature types. Moreover, all the methods reduced the NoC significantly, from 204 to a range of 1–25, using bandpower as a feature and from 15 to 105 for CSP. The optimal channel number also varied not only in each session but also for each participant. Reducing the NoC will help to decrease the computational cost and maintain numerical stability in cases of low trial numbers. Significance. The study showed significant improvement in performance of MEG-BCI with channel selection irrespective of feature type and hence can be successfully applied for BCI applications.