Brunel University London

Background Health systems are increasingly adopting Integrated Neighbourhoods (INs) to deliver hyper-local, community-based care that integrates health, social care, and public sector resources to address healthcare costs, improve outcomes, and reduce health inequalities. However, IN models lack a unified definition and standard framework for development and evaluation, limiting their scalability and effectiveness. This systematic review aims to establish a foundational framework for INs, identifying key domains to guide their implementation (including barriers of implementation, evaluation, and potential for future research. Methods A systematic literature search, restricted to the English language, was performed to identify relevant studies with expert librarian support. Study quality was assessed with the Mixed-Methods Appraisal Tool (MMAT). A Braun and Clarke thematic analysis was conducted to identify recurring themes and extract key domains. Results A total of 29 studies met the inclusion criteria, encompassing a diverse range of IN models with varying focus areas and methodologies. Seven key domains emerged as central to effective IN models: integrator host, integrator enablers, integrator partnership principles, core integrated workforce, core areas of work, and services provided. These domains support multidisciplinary collaboration, enhance resource utilisation, and promote community engagement. However, barriers such as funding limitations, digital exclusion, and inconsistent evaluation frameworks present challenges to IN scalability and sustainability. Conclusion This proposed framework provides a starting point for a standardised structure for implementing and evaluating INs, guiding clinicians, academics, and policymakers in developing sustainable, equitable, and adaptable community-based care solutions with the potential to improve access to patients from low-socioeconomic and underserved communities. Trial Registration PROSPERO ID: CRD42024597197; available: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=597197.

Colorectal cancer (CRC) is the third most common form of cancer globally, and arises from the hyperproliferation of epithelial cells in the intestine. The architecture and maintenance of these cells is governed by two major signalling pathways working in a counter‐gradient: the stem cell WNT signalling pathway, and the prodifferentiation bone morphogenetic protein (BMP) pathway. It has long been known that this WNT‐BMP balance is disrupted in CRC, with hyperactive WNT signalling leading to increased proliferation of epithelial cells and tumour progression. BMP signalling, and its prodifferentiation effects, have increasingly become a focus for CRC research. Loss of BMP signalling, and that of its receptors, has been shown to increase WNT signalling and cancer stem cells in CRC. BMP signalling is further modulated through secreted BMP antagonists localised to the intestinal crypts, which create a niche ensuring that sustained WNT signalling can maintain stem‐cell self‐renewal capacity. A number of studies combine to demonstrate the effects of overexpression of these BMP antagonists, showing that hyperactivity of the stem‐cell‐supporting WNT signalling pathway ensues, leading to deregulation of the intestinal epithelium. Cellular hyperproliferation, the emergence of ectopic crypts, and an increase in stem cell numbers and characteristics are common themes, contributing to disrupted epithelial homeostasis, an increase in CRC risk and progression, and resistance to therapy. This review aims to compile the current knowledge on BMP antagonists, their role in CRC development, and how we can utilise this information for biomarker research and novel therapeutics. © 2025 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Time series anomaly detection (TSAD) is a critical task in various research fields such as quantitative trading, cyber attack detection, and semiconductor outlier detection. As a binary classification task, the performance of TSAD is significantly influenced by the data imbalance problem, where the datasets heavily skew towards the normal class due to the extreme scarcity of abnormal data. Furthermore, the limited availability of anomaly data makes it challenging to perform manual labeling, which leads to the development of unsupervised anomaly detection approaches. In this paper, we propose a novel generative adversarial network (GAN) with Multiple-Seasonal-Trend decomposition using Loess (MSTL) data preprocessing algorithm for unsupervised anomaly detection on time series data. With the MSTL data preprocessing algorithm, the network architecture is simplified, thereby alleviating computational burden. A 2D-variation convolution-based method is integrated into the GAN to enhance feature extraction and generalization capabilities. To avoid the model collapse problem caused by data deficiency, multiple generators are employed, and a joint loss function is designed to improve the robustness of the training process. Experiments on several benchmark datasets from various domains demonstrate the efficacy and superiority of our approach compared to existing competitive approaches.

Digital Twin (DT) technology in healthcare is relatively new and faces several challenges, e.g., real-time data processing, secure system integration, and robust cybersecurity. Despite the growing demand for real-time monitoring frameworks, further improvements remain possible. In this study, an architecture has been introduced that utilises cloud computing to create a DT ecosystem. A group of 20 participants has been monitored continuously using high-speed technology to track key physiological parameters, i.e., diabetes risk factors, heart rate (HR), oxygen saturation (SpO2) levels, and body temperature (BT). To strengthen the study and enhance diversity, the dataset was supplemented with 1177 anonymized medical records from the publicly available MIMIC-III Public Health Dataset. The DT model functions as a tool, storing both real-time sensor data and historical records, to effectively identify health risks and anomalies. An MLP model was combined with XGBoost, resulting in a 25% reduction in training time and a 33% reduction in testing time. The model demonstrated reliability with an accuracy of 98.9% and achieved real-time accuracy of 95.4%, alongside an F1 score of 0.984. Meticulous attention has been paid to cybersecurity measures, ensuring system integrity through end-to-end encryption and compliance with health data regulations. The incorporation of DT and AI within the healthcare sector is seen as having the potential to overcome existing limitations in monitoring systems, while workloads are relieved and data-driven diagnostics and decision-making processes are improved, e.g., through enhanced real-time patient monitoring and predictive analysis.

Evaluating the blood smear test images remains the main route of detecting the type of leukaemia, accurate diagnosis is fundamental in providing effective treatment. The changes in the structure of the white blood cells present different morphological characteristics translated into extractable features. This paper explores techniques for manipulating a reduced dataset to increase the classification with CNN (Convolutional neural Network) and feature extraction. Extracting ROI (Regions of Interest) divides the leukaemia images into points of interest respective white blood cells, expanding the dataset an important factor for CNN’s performance. Segmenting the initial dataset into ROI through computation after applying Otsu thresholding results in a new dataset of images. The two datasets are analysed, feature extraction performs better on the initial dataset while CNN’s accuracy is higher for ROI images. Further steps will divide the images into filtered regions of interest where more specific characteristics are extracted to increase the accuracy.

Background Physical rehabilitation is advocated to improve muscle strength and function after critical illness, yet interventional studies have reported inconsistent benefits. A greater insight into patients’ physiological response to exercise may provide an option to prescribe individualised, targeted rehabilitation, yet there is limited data measuring oxygen consumption (VO2) during physical rehabilitation. We aimed to test the feasibility of measuring VO2 during seated and standing exercise using the Beacon Caresystem and quantify within- and between-patient variability of VO2 percentage change. Methods We conducted a prospective observational study on patients mechanically ventilated for ≥72 hours and able to participate in physical rehabilitation in critical care. Oxygen consumption was measured continuously using indirect calorimetry. A total of 29 measurements were taken from ten participants performing active sitting and standing exercise. Results Median (IQR) first session baseline VO2 was 3.54 (2.9–3.9) mL/kg/min, increasing significantly to 4.37 (3.96–5.14) mL/kg/min during exercise (p=0.005). The median (IQR) coefficient of variation of VO2 percentage change in participants (n=7) who completed more than one rehabilitation session (range 2–7 sessions) was 43 (34–61)% in 26 measurements. The median (IQR) coefficient of variation of VO2 percentage change was 46 (26–63)% in participants performing >1 sitting exercise session (six participants, 19 sessions). Conclusions VO2 increases significantly with exercise but is highly variable between participants, and in the same participant on separate occasions, performing the same functional activity. These data suggest that simplified measures of function do not necessarily relate to oxygen consumption. Trial registration number NCT05101850.

Big data present unprecedented opportunities to test long-standing theories regarding patterns and rates of geomorphic river adjustments. Here, we use locational probabilities derived from Landsat imagery (1988-2019) to quantify the dynamics of 600 km² of riverbed in 10 Philippine catchments. Analysis of lateral adjustments reveals spatially non-uniform variability in along-valley patterns of geomorphic river mobility, with zones of relative stability interspersed with zones of relative instability. Hotspots of mobility vary in magnitude, size and location between catchments. We could not identify monotonic relationships between local factors (active channel width, valley floor width and confinement ratio) and mobility. No relation between the channel pattern type and rates of adjustment was evident. We contend that satellite-derived locational probabilities provide a spatially continuous dynamic metric that can help unravel and contextualise forms and rates of geomorphic river adjustment, thereby helping to derive insights into idiosyncrasies of river behaviour in dynamic landscapes.

Background Ethnicity may play a significant role in determining surgical outcomes. This study examines the disease profiles across ethnic groups and investigates whether ethnicity influences the risk of complications following bariatric surgery. Methods Data from the United Kingdom’s National Bariatric Surgery Registry (NBSR) were analysed, encompassing all adult patients undergoing bariatric procedures. Comparative analyses were performed, and a multivariable regression model was developed to identify factors associated with postoperative complications. Results A total of 77,710 (78.8% female) patients were included in the analysis, with a median age of 46 (IQR 37–55) years. Most patients were Caucasian (91.6%), followed by Asian (4.1%), Afro-Caribbean (2.5%), and African (1.7%) groups. Afro-Caribbean patients had the highest median BMI (44.5 kg/m²) and the highest prevalence of hypertension (43.2%), while Asian patients were younger (median age 41 years) and had a higher prevalence of diabetes mellitus (29.1%). African and Afro-Caribbean patients were less likely to self-fund their procedures (14.9% and 10.6%, respectively) compared to Caucasians (25.9%). Complication rates were the highest among Afro-Caribbean patients (5.8 vs 4.8%, p < 0.001) compared to Caucasians. Multivariable regression analysis identified ethnicity as an independent predictor of postoperative complications, with Afro-Caribbean (OR 1.47, 95% CI 1.22–1.87, p < 0.001) and African (OR 1.34, 95% CI 1.05–1.70, p = 0.019) patients demonstrating significantly increased risks. Conclusions This registry analysis identified ethnic disparities in disease profiles and postoperative outcomes among bariatric surgery patients in the UK, underscoring the need for targeted health policies to improve outcomes in these vulnerable populations.

Deepening the understanding of composite and metal joint methodologies applied in the aerospace industry is crucial for minimizing operational expenditures. Current investigations are focusing on innovative joining techniques that incorporate additive manufactured rivet pins. This research aims to analyze the mechanical strength of these joints for the effective optimization of pin profiles. Through extensive study of the impact of pin geometry on joint performance, we derived the optimal pin design, considering various initial parameters with the objective of minimizing stress concentration in the pin structure. The joint configurations of metal to composite interfaces were systematically examined using finite element analysis and lap shear testing, which included a singular pin and an adhesive-bonding layer. Numerical simulations reveal that the maximum shear stress in the pin is located at the junction between the base of the pin and the metal plate. By optimizing the shape and dimensions of the pin, both the shear and axial stresses can be significantly mitigated. Following the numerical optimization process, a series of enhanced pins have been produced via additive manufacturing techniques to facilitate mechanical testing. The experimental data obtained align closely with the simulation results, thereby reinforcing the validity of the optimization. The optimal configuration for a single pin, involving a 60° angle and a total height of 3.43 mm, achieves the minimum shear stress. Based on these findings, further investigations are underway to explore optimized designs utilizing multiple pins. This paper presents the results of the single pin study, whereas the findings pertaining to the ongoing investigation on the multi-pin configuration will be disseminated in subsequent publications.

Background Breastfeeding rates in the UK have remained stubbornly low despite long-term intervention efforts. Social support is a key, theoretically grounded intervention method, yet social support has been inconsistently related to improved breastfeeding. Understanding of the dynamics between infant feeding and social support is currently limited by retrospective collection of quantitative data, which prohibits causal inferences, and by unrepresentative sampling of mothers. In this paper, we present a case-study presenting the development of a data collection methodology designed to address these challenges. Methods In April–May 2022 we co-produced and piloted a mobile health (mHealth) data collection methodology linked to a pre-existing pregnancy and parenting app in the UK (Baby Buddy), prioritising real-time daily data collection about women's postnatal experiences. To explore the potential of mHealth in-app surveys, here we report the iterative design process and the results from a mixed-method (explorative data analysis of usage data and content analysis of interview data) four-week pilot. Results Participants (n = 14) appreciated the feature’s simplicity and its easy integration into their daily routines, particularly valuing the reflective aspect akin to journaling. As a result, participants used the feature regularly and looked forward to doing so. We find no evidence that key sociodemographic metrics were associated with women’s enjoyment or engagement. Based on participant feedback, important next steps are to design in-feature feedback and tracking systems to help maintain motivation. Conclusions Reflecting on future opportunities, this case-study underscores that mHealth in-app surveys may be an effective way to collect prospective real-time data on complex infant feeding behaviours and experiences during the postnatal period, with important implications for public health and social science research.

Following the prime minister’s announcement of the abolition of NHS England, Dr Bryan McIntosh and Mr John Cossar discuss the implications for NHS leadership and development, emphasising that simply removing one national agency will not be enough to eliminate the problems caused by ‘malignant bureaucracy’.

Family business ethics are uniquely shaped by family influence and a strong emphasis on preserving socioemotional wealth. Although research in this area has grown rapidly in recent years, it remains fragmented and underdeveloped. Advancing the field requires a more integrated approach that consolidates existing concepts and dimensions. This paper synthesizes current knowledge and proposes an integrative framework for studying ethical issues in family firms that encompasses ethical determinants, processes, and outcomes. We also examine how existing research contributes to the family business ethics literature and outline directions for future study.