Gabriel Gomes: Gabriel Gomes built an agent that turns plain English into physical experiments, enabling research that humans alone could never sustain.

Multi-model databases handles the structured, semi-structured, and highly connected data of various applications such as Healthcare, Library Information System and many more. This research paper put focus on two multi-model databases, one is native multi-model database, ArangoDB, and a Hybrid multi-model database integrating PostgreSQL, Couch DB, and Neo4j. The objective of this paper is to measure the performance of these multi-model using various evaluation criteria as such as execution time, throughput, indexing efficiency, and latency. Also, further it highlights on query designing and data retrieval efficiency showing better approach for library management environment. A real-world college library system which handle big data workloads was chosen to evaluate the performance of these multi-models. The results shows that hybrid multi-model can be adapted in cases where a stronger transactional reliability is required. In contrast, ArangoDB, performs more efficiently in cross-model queries, especially a single AQL query unified document, graph, and relational data retrieval, minimizing query orchestration and communication overhead. ArangoDB performs 47% better in execution time and 42% in throughput than Hybrid Model. Natural Language Processing (NLP) was used for query translation that enabled users to submit queries in plain English, which automatically transformed into structured database commands, improving accessibility and user experience. This research will help developers and researchers to design a better multi-model which is efficient for providing faster and more organized academic resources to students and faculties.

Code-reading ability has traditionally been under-emphasized in assessments as it is difficult to assess at scale. Prior research has shown that code-reading and code-writing are closely related skills; thus being able to assess and train code reading skills may be necessary for student learning. One way to assess code-reading ability is using Explain in Plain English (EiPE) questions, which ask students to describe what a piece of code does with natural language. Previous research deployed a binary (correct/incorrect) autograder using bigram models that performed comparably with human teaching assistants on student responses. With a dataset of 3,064 student responses from 17 EiPE questions, we investigated multiple autograders for EiPE questions. We evaluated methods as simple as logistic regression trained on bigram features, to more complicated Support Vector Machines (SVMs) trained on embeddings from Large Language Models (LLMs) to GPT-4. We found multiple useful autograders, most with accuracies in the \(86\!\!-\!\!88\%\) range, with different advantages. SVMs trained on LLM embeddings had the highest accuracy; few-shot chat completion with GPT-4 required minimal human effort; pipelines with multiple autograders for specific dimensions (what we call 3D autograders) can provide fine-grained feedback; and code generation with GPT-4 to leverage automatic code testing as a grading mechanism in exchange for slightly more lenient grading standards. While piloting these autograders in a non-major introductory Python course, students had largely similar views of all autograders, although they more often found the GPT-based grader and code-generation graders more helpful and liked the code-generation grader the most.

This paper presents a proof-of-concept for a natural-language-based closed-loop controller that regulates the temperature of a simple single-input single-output (SISO) thermal process. The key idea is to express a relay-with-hysteresis policy in plain English and let a local large language model (LLM) interpret sensor readings and output a binary actuation command at each sampling step. Beyond interface convenience, we demonstrate that natural language can serve as a valid medium for modeling physical reality and executing deterministic reasoning in control loops. We implement a compact plant model and compare two controllers: a conventional coded relay and an LLM-driven controller prompted with the same logic and constrained to a single-token output. The workflow integrates schema validation, retries, and a safe fallback, while a stepwise evaluator checks agreement with the baseline. In a long-horizon (1000-step) simulation, the language controller reproduces the hysteresis behavior with matching switching patterns. Furthermore, sensitivity and ablation studies demonstrate the system’s robustness to measurement noise and the LLM’s ability to correctly execute the hysteresis policy, thereby preserving the theoretical robustness inherent to this control law. This work demonstrates that, for slow thermal dynamics, natural-language policies can achieve comparable performance to classical relay systems while providing a transparent, human-readable interface and facilitating rapid iteration.

BackgroundSchizophrenia is a severe mental illness with a significant disease, economic and social burden. Persecutory delusions (beliefs that one will be harmed or mistreated) are a common symptom of schizophrenia with associated high levels of depression. International clinical guidelines recommend individual cognitive behaviour therapy for schizophrenia, but there are currently no group psychological therapies recommended in NICE guidelines. We conducted a pilot randomised controlled trial, comparing group mindfulness therapy (GMT) to treatment as usual (TAU) for people with schizophrenia and persecutory delusions where we demonstrated feasibility and acceptability. It is now timely to progress to conduct a definitive randomised controlled trial (RCT).MethodsA parallel group RCT with single blind assessment comparing GMT + TAU (intervention condition) with TAU alone (control condition). The PHQ-9 and additional patient-reported psychometric measures will be collected at three time points: at baseline (prior to randomisation), end of therapy (approx 4 months post randomisation), and follow up (approx 8 months post-randomisation). Participants will be 144 adults with a schizophrenia spectrum diagnosis with current persecutory delusions or attending an early intervention in psychosis service, recruited across 3 NHS sites (Hampshire and Isle of Wight Healthcare NHS Foundation Trust, Greater Manchester Mental Health NHS Foundation Trust, and Pennine Care NHS Foundation Trust), randomised 1:1 to intervention or control groups. The therapy will be delivered according to our published manualised protocol. We will also monitor and evaluate a range of safety indices throughout the trial, conduct mediation and moderation analyses to understand how, and for whom, the therapy is most effective and assess cost effectiveness.DiscussionIf shown to be effective and cost-effective, GMT will positively impact the lives of people with schizophrenia, by providing an evidence-based group therapy. This is particularly important as there are no group psychological therapies for schizophrenia in the NICE guidelines and group therapies offer the potential for cost savings for service providers. The findings will be disseminated to a range of stakeholders, including service users/carers, academic researchers, clinicians, research participants and the general public, via academic publications, conferences, plain English summary (written and video versions) and public engagement events.Trial registrationISRCTN: ISRCTN16318074, registered on 07/02/2025.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12888-025-07470-3.

The proposed to is designed to Retrieving information from relational databases typically requires proficiency in Structured Query Language (SQL). However, non-technical users often lack the necessary expertise to construct accurate SQL queries, creating a barrier to effective data access. To address this challenge, this paper proposes an LLM-based Query Generation System that translates natural language prompts into executable SQL queries. The system leverages pretrained large language models (LLMs), such as those developed by OpenAI, enhanced with schema-aware prompt engineering to ensure the generated queries align with the underlying database schema. Unlike traditional methods based on keyword matching, template filling, or handcrafted rules, the proposed system adapts flexibly across diverse domains and query structures. A prototype implementation demonstrates how non-technical users can interact with databases by formulating requests in plain English. The system will automatically generate the corresponding SQL query with correct joins, filtering, and ordering clauses. This approach reduces dependency on database experts, enhances accessibility, and improves productivity in organizations where timely data retrieval is essential.

Writing Science in Plain English, Second Edition

ABSTRACT Background Fibromyalgia is a chronic pain condition affecting many people worldwide. There is no curative treatment and symptom management is the main focus. When considering psychological interventions for chronic pain, NICE (2021) suggests Acceptance and Commitment Therapy and Cognitive Behavioural Therapy. This gives service users limited options. Context Chronic pain can affect lives detrimentally through social isolation, depression and anxiety. Social and peer support can relieve this and offer protective factors. Art therapy studies have identified promising outcomes with pain management and relational connections. Approach Using a biopsychosocial framework and brief therapy approach, three online art therapy group cohorts for fibromyalgia (N = 16) were piloted in a rheumatology department. The model provided eight weekly sessions using body maps, psychoeducation and art responses. Additional 6 monthly sessions were offered to encourage peer support. Feedback forms were used after each session and at the end of the weekly programme. Outcomes The online groups were ethnically diverse, had high attendance and low drop-out rates. Feedback indicated that psychoeducation was valued, as was the online group format that supported connections and relieved isolation. Body maps and artmaking supported the exploration of feelings and experiences related to fibromyalgia although some participants found engaging in both initially difficult. Conclusion Online art therapy groups could be considered as a resource for fibromyalgia. Implications for research. This pilot indicates that this model has promise. An ‘expert-by-experience’ advisory group of participants who completed the programme is meeting to review and improve its format and develop a manual for future research. Plain English Summary Fibromyalgia is a chronic pain condition that affects the whole body. Pain is defined as chronic if experienced constantly or intermittently for three months or longer, and is a growing worldwide concern. There is no known cause nor cure for Fibromyalgia and treatment focuses on helping sufferers manage their pain. Chronic pain can affect life detrimentally though reduced mobility, insomnia, impact on employment, etc. This can lead to isolation, depression and anxiety. An approach integrating the physical, psychological and social effects of pain is more effective at addressing this condition than interventions focusing on any single aspects. Social and peer support can help relieve isolation and provide some protection against depression and anxiety. Art therapy studies indicate encouraging results in helping people manage their experiences of chronic pain, especially when delivered in a group setting where participants benefit from artmaking and connection with peers. Online art therapy groups for people living with fibromyalgia were piloted within a rheumatology department. This model was delivered online to support accessibility. Three groups were provided to a total of 16 participants and included weekly sessions (6 for the first cohort increased to 8 following participants’ feedback) and 6 optional follow-up monthly sessions to encourage peer support. Participants used body maps to represent their pain and the art therapist did a short presentation on a chronic pain-related topic (psychoeducation) and art responses and discussions followed. Feedback forms were used weekly and at the end of the 8 sessions. The groups were well attended and participants found learning about their condition, exploring this through art and sharing their experiences with peers helpful. Although a few participants found body maps and artmaking challenging, the model shows promise and is being reviewed with a group of participants who completed the programme.

Abstract The paradoxical nature of ADHD in entrepreneurship presents a unique challenge: traits that spark venture creation often impede business growth. Through person–environment fit theory (P-E-Fit) and the underdog theory of entrepreneurship, we examine how perceived mental health and ecosystem support influence ADHD entrepreneurs’ growth aspirations. The underdog theory suggests that systemic adversities foster adaptive capabilities, while P-E-Fit shows how environmental support enhances alignment between personal characteristics and entrepreneurial demands. In a study of 1160 Quebec entrepreneurs, including 167 with high ADHD levels, findings reveal that while ADHD initially constrains growth aspirations, this relationship shifts through the interaction of mental health perceptions and financial support. Financial support emerges as the crucial mechanism for translating positive mental health perceptions into growth aspirations. These findings demonstrate how underdog attributes can become entrepreneurial advantages through proper environmental fit, while showing how targeted financial support can transform ADHD-related challenges into sources of innovation. Plain English Summary Grit emerged as the missing catalyst that transforms psychological resources cinto entrepreneurial performance during polycrisis. This longitudinal study compared entrepreneurs across two distinct crisis periods to understand how internal mechanisms sustain business performance when external support fails. While entrepreneurs possessed psychological capital and community belonging throughout both periods, grit functioned as an amplifier that activated these resources differently over time. As crises persisted without recovery intervals, high-performing entrepreneurs demonstrated that grit strengthened relationships between psychological strengths and three key outcomes: crisis management capabilities, innovation and technology, and funding acquisition. This study challenges conventional theory by showing that internal resources require emotional activation through grit to translate into sustained performance. Thus, the principal implication is that policymakers must incentivize and educational institutions must redesign entrepreneurship programs to cultivate grit as a core competency in our perpetually crisis-driven landscape.

Abstract Introduction: Colorectal cancer (CRC) remains a leading cause of preventable cancer mortality, with persistent screening disparities in underserved communities. This qualitative study aimed to identify community-specific barriers and facilitators to CRC screening among residents of Baltimore public housing to inform the development of tailored educational materials. Methods: We conducted three in-person focus groups with 16 adults residing in two Baltimore City public housing communities. Semi-structured guides explored screening knowledge, perceived barriers, trusted information sources, and preferences for educational content and delivery. Sessions investigated participants' understanding of CRC risk factors, screening options, and procedural concerns. Discussions were audio-recorded, transcribed, and analyzed by two researchers using thematic analysis to identify major themes and inform material development. Results: Key barriers included deep-seated medical mistrust stemming from historical and personal negative healthcare experiences, stigma surrounding colorectal procedures (particularly among men), confusion about screening options and procedures, and significant logistical challenges. Participants expressed frustration with scheduling delays ("I had to wait 3 months for my first one...a lot could happen in 3 months"), transportation difficulties ("You can't go on your own. You have to have somebody to go with you"), and communication barriers including medical jargon ("Put it in...plain English so we can understand"). Many participants felt their concerns were dismissed by providers, creating additional barriers to care. Facilitators to screening uptake included family history awareness, clear provider communication, peer support, and culturally resonant messaging from trusted community sources. Participants emphasized the importance of plain language, visual aids, and community-specific references. These included outreach strategies including door-to-door flyers with varied levels of medical detail, community information sessions that include meals, and practical guidance on CRC prevention through dietary changes and healthy eating patterns. Conclusions: This study elucidates the value of community-engaged approaches in developing CRC screening education. Tailoring materials to reflect local knowledge, concerns, and communication preferences may improve screening uptake and reduce disparities. These findings provide a foundation for future interventions and health education in similar underserved settings. Citation Format: Celia Marion, Robyn N. Jordan, Tonya Rosebrough, Alison P. Klein, Zachariah Foda. Community-informed development of educational materials to improve colorectal cancer screening uptake in underserved Baltimore populations [abstract]. In: Proceedings of the 18th AACR Conference on the Science of Cancer Health Disparities; 2025 Sep 18-21; Baltimore, MD. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2025;34(9 Suppl):Abstract nr B060.

Abstract While Equity Crowdfunding (ECF) platforms are a virtual space for raising funds, geography remains relevant. To determine how location matters for entrepreneurs using equity crowdfunding (ECF), we analyze the spatial distribution of successful ECF campaigns and the spatial relationship between ECF campaigns and traditional investors, such as banks and venture capitalists (VCs). Using data from the two leading German platforms – Companisto and Seedmacht – we employ spatial eigenvalue filtering and negative binomial estimations. In addition, we introduce an event study based on the implementation of the Small Investor Protection Act in Germany allowing us to obtain causal evidence. Our combined analysis reveals a significant geographic concentration of successful ECF campaigns in some, but not all, dense areas. ECF campaigns tend to cluster in dense areas with VC activity, while they are less prevalent in dense areas with high banking activity and are rarely found in rural areas. Thus, rather than closing the so-called regional funding gap, our results suggest that, from a spatial perspective, ECF fills the gap when firms in dense areas seek external financing below the minimum equity threshold offered by VCs and when there are few banks offering loans. Plain English Summary Equity crowdfunding is not closing the regional funding gap — it thrives where venture capital already flows. We study where successful equity crowdfunding campaigns happen in Germany and how their locations relate to those of traditional investors like banks and venture capitalists. Using data from the country’s two main crowdfunding platforms, we find that geography still matters, although equity is offered through digital marketplaces: crowdfunding campaigns cluster in urban areas with strong venture capital activity but are less common in rural regions or in places with many banks. Our findings imply that equity crowdfunding complements venture capital more than it replaces it — especially in cities — and is unlikely to solve funding challenges in under-served regions. This has important implications for policy, suggesting that additional measures are needed if crowdfunding is to help bridge regional finance gaps for small businesses.

Explain-in-Plain-English (EiPE) questions are used by some researchers and educators to assess code reading skills. EiPE questions require students to briefly explain (in plain English) the purpose of a given piece of code, without restating what the code does line-by-line. The premise is that novices who can explain the purpose of a piece of code have higher code reading skills than those who can trace the code but cannot see its high-level purpose. However, using natural language in EiPE questions poses challenges. Students (especially those whose first language is not English) may struggle to convey their understanding of the code unambiguously. Also, grading responses written in natural language is time-consuming, requires the design of a rubric, and is difficult to automate. We propose a new code reading question type that addresses these issues with EiPE questions. Given a piece of code involving repetition (in the form of iteration or recursion), the student is asked to provide the output for a set of inputs, where the output for some of these inputs cannot be determined using code tracing alone and requires higher-level code comprehension. In empirical evaluations, using CS1 exams, think-aloud interviews with students, and interviews with instructors, we found that assessments of code reading skills using the new question type are highly consistent with the assessments using EiPE questions, yet are more reliable. These results put forward the proposed question type as another way to assess high-level code reading skills without the issues associated with expressing in natural language or grading responses expressed in natural language.

Social determinants of health (SDOH) and clinical severity factors are known to shape substance use disorder (SUD) treatment outcomes, yet limited research has explored how these influences differ by sex. Understanding these differences is important to improving treatment equity and outcomes in publicly funded treatment systems. This study analyzed data from the 2018-2022 Treatment Episode Data Set-Discharges (TEDS-D), a national dataset of adults discharged from publicly funded SUD treatment programs. Sex-stratified binary logistic regressions were used to examine predictors of two outcomes: treatment non-completion and substance use at discharge. Predictors included SDOH (i.e., employment, education level, housing status, criminal justice involvement, prior treatment history, marital status, health insurance coverage and treatment duration) and indicators of SUD severity (e.g., age at first use, polysubstance use, and co-occurring psychiatric disorders). Both SDOH and clinical severity indicators were significantly associated with poorer treatment outcomes, with distinct patterns by sex. Women showed more consistent risk for poor treatment outcomes across predictors, including unemployment, psychiatric comorbidities, and polysubstance use, while lack of prior treatment history was the strongest predictor of substance use at discharge and dropout for men. Other predictors, such as housing instability, criminal justice involvement, and later-onset substance use, were also associated with increased risk of non-abstinence and dropout, with notable sex differences. Health insurance coverage was associated with better outcomes for both sexes, with the protective effect more consistent in women. These findings emphasize the need for sex-informed treatment approaches that address both social determinants of health and clinical complexity. Tailoring care to the unique risks and contexts of men and women may improve retention and reduce substance use at discharge, particularly in publicly funded systems. Highlights We examined social determinants of health (SDOH), and substance use disorder (SUD) severity-related predictors of substance use and treatment completion in a national sample of approximately 7 million adults. Women demonstrated more consistent vulnerability across predictors, including unemployment, co-occurring psychiatric disorders, and polysubstance use. For men, lack of prior treatment for SUD was the most consistent predictor for substance use at discharge and treatment dropout. Housing instability, access to healthcare, and financial barriers showed sex-specific effects, with women generally experiencing great risk of unsuccessful treatment. Findings highlight the importance of improving SUD care to address sex-specific risks and structural barriers, especially in publicly funded systems. Plain English Summary Substance use treatment is not a one-size-fits-all process. Recovery is shaped by both structural challenges, such as housing instability or limited access to care, and the clinical severity of substance use. These factors influence whether someone completes treatment and stays abstinent, and they often affect men and women in different ways. In this study, we analyzed data from approximately 7 million publicly funded substance use treatment episodes across the United States. We looked at how social determinants of health (e.g. employment status, education, housing, access to treatment) and clinical factors (e.g. age of substance use onset, psychiatric comorbidities and polysubstance use), were associated with two key outcomes: whether a person completed treatment and whether they reported use of their primary substance at the end of care. We found that women often faced greater challenges, especially regarding unemployment, co-occurring mental health conditions and using more than one type of substance. For men, being new to treatment was a strong predictor of poorer treatment success. These findings demonstrate the need for treatment programs to offer support that meets men and women where they are, considering the different barriers and challenges each group may face along the path to sustained recovery.

Background/Objectives: The Janus kinase-signal transducer and activator of transcription (JAK-STAT) signaling pathway is a critical mediator of immune regulation, inflammation, and cancer progression. Although implicated in colorectal cancer (CRC) pathogenesis, its molecular heterogeneity and clinical significance remain insufficiently characterized-particularly within early-onset CRC (EOCRC) and across diverse treatment and demographic contexts. We present AI-HOPE-JAK-STAT, a novel conversational artificial intelligence platform built to enable the real-time, natural language-driven exploration of JAK/STAT pathway alterations in CRC. The platform integrates clinical, genomic, and treatment data to support dynamic, hypothesis-generating analyses for precision oncology. Methods: AI-HOPE-JAK-STAT combines large language models (LLMs), a natural language-to-code engine, and harmonized public CRC datasets from cBioPortal. Users define analytical queries in plain English, which are translated into executable code for cohort selection, survival analysis, odds ratio testing, and mutation profiling. To validate the platform, we replicated known associations involving JAK1, JAK3, and STAT3 mutations. Additional exploratory analyses examined age, treatment exposure, tumor stage, and anatomical site. Results: The platform recapitulated established trends, including improved survival among EOCRC patients with JAK/STAT pathway alterations. In FOLFOX-treated CRC cohorts, JAK/STAT-altered tumors were associated with significantly enhanced overall survival (p < 0.0001). Stratification by age revealed survival advantages in younger (age < 50) patients with JAK/STAT mutations (p = 0.0379). STAT5B mutations were enriched in colon adenocarcinoma and correlated with significantly more favorable trends (p = 0.0000). Conversely, JAK1 mutations in microsatellite-stable tumors did not affect survival, emphasizing the value of molecular context. Finally, JAK3-mutated tumors diagnosed at Stage I-III showed superior survival compared to Stage IV cases (p = 0.00001), reinforcing stage as a dominant clinical determinant. Conclusions: AI-HOPE-JAK-STAT establishes a new standard for pathway-level interrogation in CRC by empowering users to generate and test clinically meaningful hypotheses without coding expertise. This system enhances access to precision oncology analyses and supports the scalable, real-time discovery of survival trends, mutational associations, and treatment-response patterns across stratified patient cohorts.

Abstract This article reviews renewable energy programs and policies as a result of the resurgence in demand for fossil fuels. Australia and selected countries are considered through the lens of energy justice. The range of countries evidence a resurgence in demand for fossil fuels, such as coal and gas, in the wake of disruptive global events. For example, the war in Ukraine, Middle East conflicts and pandemics such as COVID, can be seen as major global disruptors of renewable energy policies and projects. While Australia’s renewable energy in contrast to non-renewable energy is the focus, a mix of selected countries are chosen as comparators. The selected countries capture how governments are navigating the fiscal/economic, political and environmental tensions between renewable and non-renewable energy sources, policies, programs and laws. The two research questions ask ‘What current and proposed policy and laws address the energy justice economic, environmental and political aspects of the climate-related transition plans to renewable energy?’ as well as ‘Can the mix of non-renewable and renewable energy resources be quantitively ranked against economic, political and environmental pressures?’ The first question adopts the method of desktop research, conducted to produce policy and legislation data that are linked together with the qualitative method of narrative. For instance, the Australian legislative focus will be taxation law. For the second question, a quantitative method using the ‘energy justice metric’ is adopted. In particular, the research builds and adapts the parameters of the energy justice metric for all comparator countries. The results are plotted on a ternary phase diagram. The highlights of this article include the raising of awareness of energy policy distractions to renewable programs as a result of the resurgence in demand for fossil fuels, such as coal and gas, in the wake of disruptive global events. The essence of the article points towards how energy justice principles can enable resilience in policy decisions despite these disruptor issues and countries can continue to move towards a just transition to a low carbon economy. Plain English summary There are major dilemmas facing countries today in the shift towards sustainable energy policies. Issues include funding for renewable energy programs and policies, alongside the resurgence in demand for fossil fuels, such as coal and gas, due to disruptive global events (such as military conflicts, COVID-19 and extreme weather). This research considers the energy market tensions for the supply of fossil fuels, and the impact on renewable energy policies and laws. Through qualitative and metric-based questions, the trajectory of Australia, a fossil fuel rich country, is evaluated, and then its progress is compared with a range of countries including France, Trinidad-Tobago, Guyana, French Guiana, Iran, Malaysia, Kenya and Uganda. Thus, energy policy success and failures are explored from across the world. Qualitative and quantitative analysis of data using the energy justice metric shows the progress of their just energy transitions. The findings indicate positive steps in the journey of a just transition to a low-carbon economy. The modelling supports the research outcomes on key dilemmas arising from energy resource policies in these selected countries. Achieving the 2015 Paris Agreement emission targets remains elusive, but a justice-framed energy policy transition is the first step for many of these fossil fuel intense nations.

BackgroundInterest in using digital interventions to provide pre-operative total knee replacement (TKR) education and prehabilitation (health/wellbeing optimization) support is growing. Patient engagement with digital interventions tends to be poor; therefore, exploring the intended users’ perspectives during digital intervention development is vital. This study was part of a project focused on developing a pre-operative TKR education and prehabilitation digital intervention, the ‘Virtual Knee School’ (VKS), and aimed to explore patients’ perspectives of potential barriers/facilitators to engagement with the VKS to inform its development.MethodsThis United Kingdom-based, qualitative descriptive study involved 14 purposively selected patients who were awaiting/had undergone TKR. Three online focus groups were conducted to explore patients’ perspectives of barriers and facilitators to engagement with the behaviors targeted by the VKS and digital features that could address the barriers/facilitators. The focus groups were audio-recorded, professionally transcribed, and analyzed inductively using reflexive thematic analysis. Three Patient and Public Involvement representatives were involved in aspects such as reviewing the recruitment materials and/or plain English summary of the study findings.ResultsTwo intersecting themes were developed. Theme 1, ‘Accounting for individual differences’, suggests pre-operative TKR digital interventions should account for the impact of individual differences on engagement with digital technologies, pre-operative education and prehabilitation. Most participants felt a pre-operative TKR digital intervention would be valuable; however, a couple of older participants appeared reluctant to use digital technologies. Participants’ perspectives of specific digital features and pre-operative TKR education and prehabilitation also varied widely. Theme 2, ‘Tailoring to the pre-operative context’ highlights the importance of tailoring pre-operative TKR digital interventions to pre-operative contextual features, including physiological/psychological factors, social/occupational factors and limitations in pre-operative TKR care provision. Various digital features that could address these factors were identified.ConclusionsThis study’s findings suggest pre-operative TKR digital interventions should account for individual differences and be tailored to the pre-operative TKR context. Given that some patients are reluctant to use digital technologies, also offering pre-operative TKR support in non-digital formats is essential. The findings have been used to inform a VKS prototype and could also be used to inform the development of other pre-operative TKR digital interventions.

Patient and Public Involvement (PPI) is well-established in applied health research but remains under utilised in statistical methodology research due to perceived irrelevance and communication challenges. This paper summarises a one-day workshop held in February 2024 in Leicester, organised by the University of Leicester and the NIHR Statistics Group, aimed at addressing barriers to meaningful PPI in statistical methodology. The workshop brought together statisticians and experienced public contributors to discuss strategies, share case studies, and offer practical guidance on conducting effective PPI. Key barriers identified included: (1) uncertainty about the relevance of PPI in methodology-focused research; (2) public contributors' anxiety over mathematical complexity; and (3) mismatched expectations due to different backgrounds in applied versus methodological research. Case studies showcased how PPI led to improved model structures, identification of data issues, and enhanced study materials. The importance of communication was a recurrent theme, with recommendations including use of plain English, regular updates, and visual storytelling tools. Feedback from attendees indicated increased confidence and motivation to engage in PPI. Public contributors emphasised the need for respectful, non-patronising interactions and flexible roles within projects. Recommendations include managing expectations, enhancing accessibility, co-developing materials, and fostering diversity among contributors. This paper highlights the need for tailored strategies to integrate PPI into statistical methodology, including the development of resources (e.g., glossaries, animations) and further case study collection. Future work will focus on expanding these resources, addressing challenges of equity and inclusion, and supporting PPI in complex methodological areas like simulation and model development.

Artificial intelligence is revolutionizing healthcare across the world. This study introduces the development of a smart medical chatbot that can understand symptom descriptions in casual everyday language and accurately maps them to medical conditions. It can even interpret slang, spelling errors, typographical errors and non-medical terms. Built on a huge database covering with over 444 different symptoms and 837 diseases, it is capable of predicting multiple possible diseases at once just like how real doctors think about overlapping symptoms. The chatbot also consider user specific factors and tailor responses based on age and health history. It can also answer general health questions in plain English using advanced language models. Designed with a user-friendly web interface, it is compatible with both smartphones and computers. Evaluation shows 91.6% accuracy in disease prediction and an 83.1% success rate in answering general health questions, all with an average response time under 200 milliseconds., making it feel like a real conversation. This tool has a strong potential to provide preliminary health guidance, especially in areas and circumstances where consulting a doctor is not easy.

Abstract Aim Patients with abdominal wall hernia (AWH) face significant psychological, and emotional challenges that impact their quality of life (QoL). The lived experiences of patients reveal substantial psychosocial burdens, including issues related to body image, interpersonal relationships, and mental health (Smith et al 2022). This project aimed to develop an evidence-based, patient-centred educational leaflet to address these challenges. Method The leaflet was informed by a qualitative study conducted by York Abdominal Wall Unit, using Interpretative Phenomenological Analysis (IPA) to explore the lived experiences of patients with AWH. Themes included body image, mental health, interpersonal relationships, symptoms, and employment. A collaborative approach between surgeons and the hospital’s psychology team integrated psychological insights with surgical expertise. Patient representatives from the British Hernia Society reviewed drafts. Plain English guidelines were followed to ensure readability (Flesch-Reading-Ease Score: 60–70). Results The leaflet included the following sections:Body Image and Self-Esteem: Summarizes key challenges, such as changes in self-perception and fears about others’ judgments.Interpersonal Relationships: Explores difficulties with social connections and intimacy.Emotional Responses: Highlights common emotions such as anxiety and depression, with relatable examples.Guilt and Self-Blame: Addresses feelings of responsibility and their emotional impact, emphasizing normalization.Coping Strategies: Provides practical advice grounded in psychological theory, including reframing negative thoughts, mindfulness, and seeking support. Conclusions This leaflet represents a multidisciplinary approach to addressing the psychosocial aspects of AWH. By combining insights from qualitative research, patient-centred design, and psychological expertise, it offers a low-resource, yet comprehensive support for individuals living with AWH.

Introducing the Data Analysis Web Application, a cutting-edge, full-stack platform meticulously engineered to revolutionize how users interact with their data by seamlessly integrating the power of Large Language Models (LLMs). This innovative application stands out by empowering users to query, analyze, and visualize complex datasets directly from a local database using intuitive, natural language inputs rather than complex code or commands. One of the primary goals of this application is to dramatically lower the barrier to entry for data exploration. By enabling users to simply ask questions in plain English or describe the analysis they need, the necessity for advanced technical skills, such as SQL programming or scripting, is significantly reduced. This approach effectively democratizes access to data-driven insights for a much wider audience within any organization. The backend serves as the sophisticated engine driving this capability. It strategically utilizes DSPy as a framework to effectively orchestrate complex interactions with the integrated LLMs. These powerful models are leveraged for critical tasks, including translating diverse natural language requests into precise SQL queries executable against the database, performing detailed trend analysis directly on the data, and interpreting intricate data patterns to synthesize clear, understandable, and actionable insights. Connectivity to the local PostgreSQL database is handled efficiently and reliably via Psycopg2, ensuring real-time data access essential for dynamic analysis and quick turnaround on queries. On the user-facing side, the application is built using the modern Streamlit framework, providing an interactive and highly user-friendly interface. This frontend design makes the process of exploring data, visualizing findings, and interacting with the analytical outputs generated by the LLMs remarkably seamless and efficient, allowing users to focus purely on understanding their data and its implications. Ultimately, by combining robust modern web development principles with the transformative capabilities of LLMs and a reliable local database setup (serving as a foundational proof of concept), this application fundamentally transforms data interaction. It equips teams and individuals with the tools needed to uncover valuable insights quickly and effectively, fostering a truly data-driven environment without the traditional technical overhead.