Subashini Suresh

Background: Diabetes mellitus (DM) is a common chronic condition among surgical patients. It is associated with increased perioperative morbidity and mortality. Realworld data on the clinical predictors of postoperative complications in diabetic patients undergoing major surgery are limited. This study aimed to characterise perioperative outcomes and identify independent predictors of 28day postoperative complications. Methods: A retrospective cohort study at a single UK tertiary hospital. Adult patients with a known diagnosis of type 1 or type 2 diabetes mellitus who underwent major procedures under general anaesthesia between January 2020 and September 2025 were identified through electronic patient records (EPR). The primary outcome was any postoperative complication within 28 days. We used multivariable logistic regression to identify independent predictors of complications, adjusting for age, sex, ASA grade, surgical category, admission urgency, operative duration, diabetes type, HbA1c, and socioeconomic deprivation (Index of Multiple Deprivation [IMD]). Results: We screened 2623 patients and included 267; we analysed 267 patients (260 with outcome data). The 28day postoperative complication rate was 23.1% (46/199) among those with outcome data. Infection was the most common complication. In multivariate analysis, surgical duration was the only independent predictor of complications (adjusted odds ratio [aOR], 1.71; 95% CI, 1.16 to 2.51; p=0.007). Other variables were not associated with complications. Conclusions: In this study of patients with DM undergoing major surgery, operative duration was the only modifiable independent predictor of 28-day postoperative complications. Socioeconomic deprivation and preoperative HbA1c were not independently associated with outcomes, suggesting that standardised perioperative care pathways may attenuate these effects.

Autoimmune encephalitis (AE) is characterized by immune‐mediated inflammation of the brain parenchyma, presenting with various neurological syndromes, including but not limited to seizures, altered consciousness, neuropsychiatric symptoms, and movement disorders. A significant proportion of patients with AE develop life‐threatening complications that require hospital and potentially ICU admission. These patients present unique diagnostic and therapeutic challenges, presenting with a range of neurological emergencies such as refractory status epilepticus, severe dysautonomia, coma, and respiratory failure. Diagnosis relies on a combination of clinical criteria, detection of autoantibodies in serum and cerebrospinal fluid, neuroimaging, and electroencephalography, though antibody‐negative AE poses considerable diagnostic difficulty. Management is centered on prompt initiation of first‐line immunotherapies (corticosteroids, plasma exchange, and intravenous immunoglobulin therapy) and escalation to second‐line or emerging targeted therapies (e.g., rituximab, cyclophosphamide, IL‐6 inhibitors, proteasome inhibitors) in refractory cases, alongside aggressive supportive care for neurological and systemic complications. The prognosis is variable and influenced by factors such as the specific autoantibody, the timeliness of treatment, and the severity of complications. Long‐term sequelae, including cognitive and psychiatric impairments, are common among survivors. This review provides a synthesis of current knowledge on AE in acute and intensive care. Given these diagnostic challenges and the potential for severe symptom presentation, the authors believe that this review is an essential addition to the discourse. This review covers pathophysiology, epidemiology, clinical manifestations, diagnostic approaches, immunotherapeutic strategies, management of critical complications, syndrome‐specific considerations, prognostication, intensive care unit‐related complications, and future research priorities to support optimal care.

Context: Neglected Tropical Diseases (NTDs) are infectious illnesses prevalent in tropical and subtropical regions, disproportionately affecting vulnerable populations with limited access to healthcare. Understanding the spatial and temporal dynamics of these diseases in specific regions, such as northern Brazil, is crucial for guiding control strategies. The state of Pará, characterised by low human development indices, is a significant hotspot for NTDs. Development: This cross-sectional study analysed the spatio-temporal distribution of major autochthonous NTDs in Pará between 2016 and 2022, using data from Brazil’s Notifiable Diseases Information System (SINAN). Incidence rates were calculated for dengue, Chagas disease, leishmaniasis (cutaneous and visceral), leprosy, tuberculosis, and schistosomiasis, and used to generate spatial cluster maps. Results: The analysis revealed patterns of clustering and dispersion, with high-risk areas identified in the Southeast (Carajás), Marajó, and Belém Metropolitan regions. Cutaneous leishmaniasis was widespread in Marajó and southwestern Pará, while visceral leishmaniasis was concentrated in the southeast. Leprosy remained prevalent in urban and peri-urban zones, and schistosomiasis persisted in riverside communities. The Southeast region showed a high burden of four NTDs. Conclusion: Spatio-temporal analysis of NTDs in Pará revealed geographic clusters, socio-environmental influences, and diagnostic gaps, guiding targeted, adaptive public health strategies and future research priorities.

Purpose: Today’s business environment is complex; organisations are increasingly forced to adjust. By innovating their business model, organisations have become increasingly capable of creating positive social and environmental value. This paper aims to expand knowledge on driving forces for sustainable business model innovation (SBMI). Design/methodology/approach: This research employed Total Interpretive Structural Modelling (TISM) and Fuzzy MICMAC analysis to examine the contextual relationships among SBMI drivers. Data was collected through semi-structured interviews with experts in sustainable business innovation across various sectors. Thematic analysis was conducted to identify driving forces, followed by TISM to establish a hierarchical structure of interrelationships. Fuzzy MICMAC analysis further classified the drivers based on their influence and dependence levels. Findings: A total of 12 driving forces, internal and external to organisations, were identified. A hierarchical structure of the system composed of the drivers and their contextual relationships was developed. The resulting model provides an improved articulation of the interpreted contextual links between the drivers. Then, the study presents the level of influence and dependency of each driver for SBMI within the system. Originality/value: The study provides a rich insight into the understanding and awareness of how innovative sustainable business model implementation unfolds in new and established organisations which guides organisations to tailor processes based on their needs. Findings help empower top-level management and policy makers since they help differentiate between the drivers and their contextual relations, allowing them to better focus effort and resources on the most relevant.

Community Engagement (CE) is crucial for building flood resilience and empowering communities to participate actively in disaster preparedness, response, and recovery. However, many existing CE approaches neglect key social dimensions, such as inclusivity, social vulnerabilities, and community-driven strategies. This oversight often limits community ownership and reduces effectiveness. This research critically examines the social drivers of community engagement in flood resilience, emphasising the need for a Social Network approach to strengthen social connections, enhance participation, and foster sustained resilience. A review and synthesis of existing literature, spanning disaster risk management, flood resilience, social networks, behavioural science, and community engagement, identifies key internal and external drivers. The main internal drivers include social cohesion, leadership, education and local knowledge. Key external drivers include government support, partnerships, and technological resources. The findings highlight the importance of integrating internal and external drivers through Social Network Analysis (SNA) to foster foster cohesion, adaptability, and sustainability in community engagement. The research provides valuable theoretical insights for policymakers, practitioners, and researchers to improve collaboration, resource allocation, and community-led engagement. It advances knowledge by analysing key social drivers and introducing SNA as a framework to integrate these drivers for more effective community engagement. Future research will develop a theoretical framework and test the applicability of SNA in various contexts.

Existing Community Flood Resilience (CFR) frameworks often integrate a range of dimensions to measure flood resilience, but social indicators are often overlooked or neglected due to the challenges involved in gathering information and quantifying these aspects. This research presents a novel methodology towards the development of a more comprehensive CFR framework. The methodology presented combines quantitative and qualitative insights through a participatory mixed-methods approach. Initially, the Delphi process is used to co-develop resilience indicators with professional experts drawn from key stakeholders including the Environment Agency, Local Authorities and National Flood Forum, as well as a range of community representatives. This new methodology argues for initiating earlier stakeholder input and sustaining this to enhance relevance and applicability of the framework. Grounding the framework in multiple stakeholder perspectives creates an opportunity to capture local and culturally relevant resilience indicators which thus far have received scant treatment in earlier frameworks. The intention is to apply the new methodology in vulnerable UK communities, using GIS mapping and regression analysis to visualise and quantify resilience levels. This new approach can bridge the gap in traditional flood resilience measurement by providing a flexible tool for policymakers to identify specific resilience needs. It contributes to the growing field of CFR assessment, provides practical solutions for adaptive flood management and supports United Nations Sustainable Development Goals (SDGs) Goal 16 by enabling local stakeholder engagement in resilience planning.

Safe, accessible and sustainable transportation systems should be an accepted expectation for everyone. Unfortunately, vulnerable road users (VRUs), which include cyclists, e-scooters, motorcyclists, pedestrians and people with disabilities, are often involved and severely injured or killed in road traffic accidents. A wide range of factors (including infrastructural, environmental and technical) influence the safety of VRUs. To date, limited studies have reviewed the psycho-behavioural aspects or the methodologies adopted that can assist in understanding the safety issues affecting VRUs. The aim of this work was to conduct a systematic review to reveal psycho-behavioural aspects that can influence the safety of VRUs and then use this information to identify cutting-edge methodologies to assess crucial factors influencing critical behavioural determinants that can support future road safety studies. Following a ‘preferred reporting items for systematic reviews and meta-analyses’ process, many articles were screened, selected (N = 91) and reviewed. These articles highlight key psycho-behavioural determinants, behavioural outcomes and research methodologies as themes that need consideration. Amalgamating this evidence can be used to identify gaps in research and practice and, in doing so, also emphasise the necessity for concerted actions that prioritise VRUs in urban planning and policy endeavours.

The UK Government’s Building Information Modelling (BIM) mandate has encouraged the utilisation of BIM within the Transportation Sector (TS), with research demonstrating positive effects of BIM. However, BIM processes are incipient to TS project implementation across the UK. This paper is carried out to understand the current BIM usage within the UK’s TS and how BIM practises and workflows contribute towards the government’s NZC approach. We used research questions derived from the population, intervention, comparison and outcome (PICO) system and inclusion and exclusion criteria to screen irrelevant information from a Systematic Literature review with 18 pieces of literature. We identified six key drivers: carbon reduction and BIM, BIM in transportation design, BIM uptake and usage in transportation, BIM in transportation construction and Digital Twins and BIM. It was identified that, with the integration of the Carbon Calculator Tool into Civil 3D, structural and material data can be obtained and areas of Embodied Carbon hotspots can be identified to contribute to reduce overall carbon across a project, which requires further collaboration between software providers and industry leaders for further streamlining the process. A limitation of this research is the requirement for wider study of differing disciplines within the TS, more qualitative research and a lack of information regarding other Carbon Calculator Tools and how compatible they are with Civil 3D.

Purpose: The COVID-19 pandemic has affected around 216 countries and territories worldwide and more than 2000 cities in India, alone. The smart cities mission (SCM) in India started in 2015 and 100 smart cities were selected to be initiated with a total project cost of INR 2031.72 billion. Smart city strategies play an important role in implementing the measures adopted by the government such as the issuance of social distancing regulations and other COVID-19 mitigation strategies. However, there is no research reported on the role of smart cities strategies in managing the COVID-19 outbreak in developing countries. Design/methodology/approach: This paper aims to address the research gap in smart cities, technology and healthcare management through a review of the literature and primary data collected using semi-structured interviews. Findings: Each city is unique and has different challenges, the study revealed six key findings on how smart cities in India managed the COVID-19 outbreak. They used: Integrated Command and Control Centres, Artificial Intelligence and Innovative Application-based Solutions, Smart Waste Management Solutions, Smart Healthcare Management, Smart Data Management and Smart Surveillance. Originality/value: This paper contributes to informing policymakers of key lessons learnt from the management of COVID-19 in developing countries like India from a smart cities’ perspective. This paper draws on the six Cs for the implications directed to leaders and decision-makers to rethink and act on COVID-19. The six Cs are: Crisis management leadership, Credible communication, Collaboration, Creative governance, Capturing knowledge and Capacity building.