University of Dundee

While the National Health Service (NHS) in the United Kingdom (UK) is increasing provisions for psychological interventions in primary care for patients with long-term conditions (LTCs), there is limited knowledge of effective implementation. This study reports on outcomes of an open pilot trial implementing the Accessible Depression and Anxiety Psychological Therapies for Long Term Conditions (ADAPT-LTC) programme in Scottish primary care services. This study entails a pragmatic, real-world implementation evaluation of ADAPT-LTC in primary care using data collected from routine outcome monitoring. Analysis focused on implementation (feasibility, suitability), service (clinical outcomes) and client (care experiences and satisfaction) outcomes. Rates of recovery, reliable improvement, reliable recovery and reliable deterioration were used as indicators of clinical outcomes. Approximately half of referred patients progressed to treatment. Patient engagement in treatment was split into groups of patients who attended few treatment sessions (approximately <4 sessions) or patients who accessed 6-8 sessions or more. Among patients who participated in treatment, CBT was associated with large pre- to post-treatment effects in reducing depression, anxiety, and functional impairment, with 35%-40% of patients no longer meeting criteria for psychiatric diagnosis and 60% reporting clinically reliable improvements. Patients reported positive care experiences and satisfaction. Delivering brief psychological intervention in primary care was effective for most patients with LTCs. However, a subgroup of patients did not start treatment or struggled to attend treatment suggesting the programme may not be suitable for all patients. Methods for enhancing engagement and implementation of ADAPT-LTC are discussed.

Small airways dysfunction (SAD) is an important treatable trait in asthma that is often under-recognised. It can be assessed through spirometry as forced expiratory flow between 25% and 75% of vital capacity however this is limited due to being both effort and volume dependent. Forced oscillometry technique is effort independent and showing to be more sensitive for SAD. We reviewed patients with severe uncontrolled asthma prior to commencing biologic therapy to identify the incidence of abnormal SAD defining values. SAD was identified in 63% of patients. Of the 31 patients with preserved spirometry (normal FEV1 and FEV1/FVC), only 6% had an impaired FEF25-75 while 29% had impaired oscillometry. This result highlights SAD is a common finding in severe uncontrolled asthma and that oscillometry is more sensitive than spirometry at identifying SAD, particularly when spirometry is preserved.

The question of how to distinguish between dialects and languages has plagued linguists for decades. The present investigation uses behavioral observations to objectively distinguish between bilingual and bidialectal translation effects. Using the picture-word interference paradigm, Italian - Neapolitan (Exp 1) bidialectal participants named pictures while ignoring semantically identical, related, or unrelated distractor words presented in their two linguistic varieties. Consistent with prior investigations of dialectal lexical selection studies, we observed translation equivalent interference between dialectal varieties that were mutually non-intelligible and in participants who were active, proficient speakers of both varieties. Experiment 2 assessed the distractor effects in Italian-English bilingual speakers and did not observe similar translation equivalent inference effects. The results suggest that mutual intelligibility and high speaker proficiency are not necessary and sufficient conditions for establishing independent lexical systems.

In this study, we propose and validate a simple agent-based model to study cell-cell interactions and cell migration during in vitro scratch assays in the context of cutaneous fibrosis (keloid). For model parametrization, we collect data from in vitro experiments performed with healthy or keloid fibroblasts treated (or not) with type 1 or 2 macrophages secretome to mimic specific in vivo environments. All experiments were performed with mitomycin to inhibit cell proliferation, and subsequently isolate the sole contribution of migration to wound filling over time. The scratch assays are modeled within the cellular Potts model framework. The calibration process, via Levenberg-Maquart algorithm, gives a mean error of 4.53 ± 0.77% across the four modalities (healthy, control, M1 and M2 secretum) and the evaluation dataset gives a mean error of 10.55 ± 0.77%. With the help of this model, we test whether the hypothesis of contact inhibition of locomotion (CIL) can explain the movement of keloid fibroblasts. The simulation results and their comparison with the experimental data suggest that CIL might not characterize the movement of keloid fibroblasts, which is in contrast to the importance of CIL for the movement of healthy fibroblasts.

Relating billions of proteins across the tree of life remains a challenging task for comparative biosphere genomics and artificial intelligence-driven structure prediction. Here we present DIAMOND DeepClust, a cascaded, ultra-fast clustering method enabling planetary-scale organization of protein space, scaling to trillions of sequences while retaining sensitivity at low identity. Aggregating 19 billion biosphere proteins into 544 million nonsingleton clusters, we show that using our DeepClust database, available for download, can enhance structure prediction with AlphaFold2.

Protein-based molecular biomarkers play an important role in prognostic modeling and risk stratification in precision medicine. However, longitudinal survival studies involving high-dimensional biomarker data are frequently challenged by pervasive missingness and limited sample sizes, which can compromise model stability and interpretability. In this study, we present and evaluate a reproducible analytical pipeline for survival risk prediction that integrates established methods for missing data handling, feature selection, and time-to-event modeling. Missing values are addressed using an unsupervised random forest-based imputation approach that leverages internal covariate structure without incorporating outcome information, thereby reducing the risk of information leakage. Feature dimensionality is subsequently reduced using penalized Cox regression with the least absolute shrinkage and selection operator, followed by refinement and stability assessment using random survival forests to capture nonlinear effects and interactions. The final set of selected biomarkers is examined using univariate and multivariable Cox proportional hazards models to support clinical interpretability and risk stratification. Using a publicly available proteomic dataset from cancer patients, we demonstrate how this sequential modeling strategy can identify stable prognostic biomarkers while highlighting the challenges of overfitting in small-sample, high-dimensional survival settings. The proposed workflow serves as a practical and transparent framework for biomarker-driven survival analysis rather than a new statistical methodology.

This study examines the pedagogical impact of artificial intelligence (AI) tools on student engagement and academic performance in two undergraduate economics and business modules at a UK university. Using a mixed-methods design, the study compares three academic periods: the pre-AI era (2022/23, prior to the widespread adoption of generative AI tools such as ChatGPT), the AI era (2023/24, characterized by unguided AI use), and the AI intervention period (2024/25, involving structured pedagogical guidance). Quantitative data from institutional learning platforms, complemented by qualitative survey data, inform a two-stage mediation analysis assessing engagement as a mediator between AI use and academic outcomes. Results indicate that unguided AI use does not consistently improve performance and may negatively affect problem-solving in summative assessments. In contrast, structured AI interventions significantly increase independent study engagement, enhance critical thinking, communication skills, and problem-solving, and improve overall performance. Engagement emerges as a robust mediator, highlighting the importance of pedagogical framing in effective AI integration. The findings emphasize the need for instructional scaffolding and provide practical guidance for institutions seeking to promote responsible AI use, active learning, and deeper cognitive engagement in AI-enhanced educational environments.

The coloration of vegetative organs in grapevine (Vitis vinifera L.) is an important phenotypic trait associated with environmental responsiveness and stress adaptation; however, its genetic regulatory mechanisms remain poorly understood. To systematically elucidate the genetic basis of vegetative coloration, a genome-wide association study (GWAS) was performed using 151 grapevine accessions, integrating high-depth whole-genome resequencing data with phenotypic evaluations of eight coloration traits across two consecutive years (2023–2024). Using a mixed linear model (MLM), 13 stable and significant SNP loci were identified on chromosomes 3, 5, 6, 13, 14, 15, 16, and 18, explaining 13.28–19.35% of the phenotypic variation. Within ±15 kb of these loci, 16 candidate genes were identified. qRT-PCR analysis of key candidates revealed that VIT_06s0004g03620 (mitogen-activated protein kinase), VIT_16s0039g01900 (MYB-like domain-containing protein), and VIT_14s0083g01050 (MADS-box protein 2) showed significant positive correlations between expression levels and coloration intensity, with distinct expression patterns among accessions exhibiting different coloration grades. These genes are likely involved in the regulation of anthocyanin accumulation. Collectively, this study elucidates the genomic architecture underlying vegetative organ coloration in grapevine and provides valuable candidate genes and SNP markers for functional validation and molecular-assisted breeding.

Hallux valgus (HV) is a painful deformity of the first ray characterised by lateral deviation of the proximal phalanx and medial displacement of the metatarsal head. There are multiple surgical options for its management, including arthrodesis. In our institution, first metatarsophalangeal joint (MTPJ) arthrodesis is preferred over corrective osteotomy for patients with severe HV. This study aimed to investigate the outcomes of patients undergoing first MTPJ arthrodesis for HV. The primary outcome measures were Patient-Reported Outcome Measures (PROMs), the Manchester-Oxford Foot Questionnaire (MOxFQ), and the EuroQol-5 dimensions (EQ5D) score. Secondary outcome measures were complications, including non-union, recurrence, pain, reoperation, and infection. This was a single-center, case series study with retrospective case note review of prospectively collected data. Caldicott approval was granted. Inclusion criterion was all patients undergoing arthrodesis for HV between 2016 and 2021. Exclusion criterion was patients with inadequate operative information. 62 feet were included with an average preoperative HV angle (HVA) of 40.2 degrees (range, 19.6-82.0) and an average preoperative intermetatarsal angle (IMA) of 15.9 degrees (range, 2.7-26.4). All first MTPJ arthrodeses were performed between January 2016 and June 2021. The average improvement in composite MOxFQ scores was 33.0 points at 26 weeks (P < .001), sustained at 52 weeks and 2 years (P = .219 and P = .008, respectively). The average improvement in EQ5D time trade-off score was 0.217 at 26 weeks (P < .001), sustained at 52 weeks and 2 years (P = .918 and P = .975). Complications occurred in 12 of 62 (19.4%) feet, including nonunion (9.7%, n = 6), ongoing pain (11.3%, n = 7), and prominent metalwork requiring removal (4.8%, n = 3). Our results suggest that first MTPJ arthrodesis for HV has satisfactory early term patient-reported outcomes. Improvement in PROMs is apparent by 26 weeks and sustained at 2 years postoperatively. There was a high overall complication rate (19.4%), with a non-union rate of 9.7%.