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Here, we put forward a numerical method for solving the (1+1)- and (2+1)-dimensional nonlinear generalized Benjamin-Bona-Mahony-Burgers (GBBMB) equation using a collocation approach based on shifted Vieta-Lucas polynomials (SVLPs). The proposed technique involves expressing the approximate solution as a finite series expansion in terms of SVLPs and enforcing the governing equation at specific collocation points. This process leads to a nonlinear algebraic system amenable to efficient solution. The method offers high accuracy and fast convergence. An error analysis and convergence proof of the proposed algorithm are established, thereby ensuring its theoretical soundness. Several numerical experiments were conducted to validate the effectiveness of the approach, and the results demonstrate its superiority over existing methods in terms of accuracy and simplicity. This makes the proposed SVLP-based collocation method a versatile and stable technique for tackling nonlinear partial differential equations in applied sciences and engineering.
Neck pain has a prevalence of 45.7 % in Germany and leads to days of incapacity for work and rehabilitation treatment. The limited access to physiotherapy due to a shortage of specialists makes evidence-based and resource-conserving care necessary. An S3-level guideline for non-specific neck pain has been developed to improve the quality of care. Part of this process is a practical test to evaluate direct clinical implementation with physiotherapists and patients. Using a mixed-methods approach, the guideline and its short version as well as the patient information were evaluated from the perspective of physiotherapists and patients in the practical test. During and after a six-week trial phase, questionnaires were completed by both physiotherapists and patients; the physiotherapists were also invited to take part in interviews. The quantitative analysis was descriptive, and the qualitative analysis followed a deductive-inductive approach using structured content analysis. 20 questionnaires and 11 interviews of physiotherapists and 37 patient questionnaires were included in the analysis. The quantitative data show, for the most part, a high level of consensus with the recommendations. The qualitative analysis revealed the following main physiotherapy categories: (1) Previous experience and comparisons with other guidelines, (2) Useful aspects, (3) New aspects, (4) Conflict situations, and (5) Integration into everyday practice. The physiotherapists identified supported communication, promoted interprofessionalisation, and confirmation of one's own actions as useful aspects. Conflict situations arose, for example, from deviating patient preferences. Five main categories were also identified among the patients. Patients expressed heterogeneous expectations regarding passive measures. There was a need for adaptation with regard to the information content of the patient information and the exclusion of diagnostic imaging. The results confirm the relevance of interprofessional guidelines for promoting patient-centred care. Conflicts such as divergent patient preferences with regard to guideline recommendations are an indication of structural and communicative challenges, not primarily of deficiencies in the guideline design. With regard to patient information, the results indicate a need for differentiated education in order to promote trust and therapy acceptance. Both physiotherapists and patients rated the S3-level guideline for non-specific neck pain and the respective patient information as comprehensible and practical. Despite their limited generalizability, the results provide important starting points for the further development and practical implementation of the S3-level guideline for non-specific neck pain.
A stable cathode-electrolyte interphase (CEI) significantly enhances the durability of lithium-ion batteries; however, the intricate chemistry underlying its formation makes predesign exceedingly challenging. This work demonstrates that surface oxygen vacancy (OV) concentration dually regulates CEI thickness and composition. We develop an in situ strategy where Li2C4O4 incorporation into LiCoO2 (LCO) spontaneously decomposes during cycling, generating surface OVs. Combined experimental and theoretical calculations reveal these OVs enhance interfacial electron/Li⁺ migration rates while stabilizing the CEI. Notably, through 18O isotope labeling with time-of-flight secondary ion mass spectrometry, we innovatively provide direct experimental evidence that surface lattice oxygen serves as the predominant oxygen source for CEI oxygen-containing decomposition products, establishing the mechanism for OV-mediated CEI modulation. Based on this theory, the linear correlation and causal relationship among Li2C2O4 content, surface OV concentration, and LiF/LixPOyFz ratio in CEI are revealed. This strategy endows the OV-rich LCO cathode with 71.1% capacity retention after 600 cycles at 1 C within 3-4.4 V (23.9% for bare LCO) and achieves universal validation at 4.5 V and in LiNi0.8Co0.1Mn0.1O2 (NCM811). This study elucidates the critical function of surface OVs in prolonging cycle life and establishes a new design principle for tailored cathode interfaces and CEI chemistry.
Eupentacta fraudatrix is the only species of sea cucumbers for which transdifferentiation has been described. This type of cell type switching occurs during gut regeneration after evisceration, which makes this species a scientifically valuable model for studying regeneration mechanisms. Moreover, chromosome-level genomes for the family Sclerodactylidae have not been available until now. In this study, we employed the MGI short-read, Oxford Nanopore, and Hi-C technologies to assemble and annotate two chromosome-level, high-quality haplotypes of E. fraudatrix. The estimated heterozygosity was 5.1%, which is much higher than the known values for sea cucumber genomes. Both haplotypes are nearly equivalent, containing 23 chromosomes with a total length of approximately 1.6 gigabase pairs, and 99% of assembled bases anchored to chromosome-level scaffolds. The annotation predicted 26,352 protein-coding genes for one haplotype and 25,238 genes for the other, with BUSCO assessment revealing 98.1 and 97.9% complete metazoa_odb12 core genes. The chromosome-level assembly and annotation of E. fraudatrix genome will provide a genomic basis for further phylogenetic, comparative, and molecular biological studies of echinoderm regeneration.
T-cell acute lymphoblastic leukemia (T-ALL) and T-lymphoblastic lymphoma (T-LBL) are aggressive hematologic malignancies with limited targeted treatment options and poor clinical outcomes across all age groups. CD147 is a multifunctional transmembrane glycoprotein that is highly expressed in various cancers, including T-cell leukemia and lymphoma. Its involvement in tumor progression and immune evasion makes CD147 a promising target for cancer immunotherapy. Monoclonal antibody-based immunotherapy can eliminate tumor cells not only by blocking oncogenic signaling pathways but also by engaging immune effector cells through antibody-dependent cellular cytotoxicity (ADCC). Enhancing ADCC has therefore emerged as a crucial strategy to enhance the therapeutic efficacy of anti-cancer antibodies. This study aimed to evaluate the functional activity of a fully humanized Fc-engineered anti-CD147 monoclonal antibody, HuM6-1B9-5M, which was designed to enhance ADCC by introducing five Fc mutations (L235V/F243L/R292P/Y300L/P396L). HuM6-1B9-5M was successfully expressed in HEK293T cells and retained CD147 binding activity and specificity comparable to those of the parental antibody. Biolayer interferometry against CD147 confirmed that Fc substitutions did not adversely affect antigen-binding affinity. Functional assays using PBMC effector cells demonstrated enhanced ADCC activity of HuM6-1B9-5M relative to the wild-type antibody (HuM6-1B9-WT) against Jurkat and SupT1 cell lines, representing T-ALL and T-LBL models, respectively.
Epilepsy is one of the most prevalent chronic neurological disorders worldwide, affecting approximately 70 million people globally and imposing substantial burdens on patients, families, and healthcare systems. Its multifaceted treatment landscape spanning antiepileptic drug (AED) therapy, epilepsy surgery, ketogenic dietary therapy, and neuromodulation makes accurate health information critical for patient decision-making and treatment adherence. Short-video platforms such as TikTok (Douyin) and Bilibili have emerged as primary channels through which the public accesses health-related content, yet the quality and reliability of epilepsy-related content on these platforms remain largely unexamined. A cross-sectional content analysis was conducted. We systematically retrieved videos via keyword search on TikTok (Douyin) and Bilibili, using the terms "dianxian" (epilepsy) and "jingfeng" (seizure/convulsion). For each platform, we collected the top 100 unique videos ranked by the platform's default relevance algorithm, with duplicate results from the two search terms removed. After applying pre-specified inclusion and exclusion criteria, 182 videos were included in the final analysis. Two physicians independently assessed the videos using a multi-instrument framework with clear applicable boundaries: Global Quality Score (GQS, for overall educational quality across all content types), modified DISCERN (mDISCERN, exclusively for treatment information reliability), JAMA benchmark criteria (for source transparency, not direct clinical accuracy), and a novel Treatment Misinformation Risk Scale (TMRS, specifically for epilepsy treatment-related content). Inter-rater reliability was assessed using the intraclass correlation coefficient (ICC). Engagement metrics and uploader characteristics were also recorded, with sensitivity analyses performed to control for confounding from uneven content theme distribution between platforms. A total of 182 videos were analyzed (96 from TikTok, 86 from Bilibili). The overall educational quality was suboptimal (mean GQS: 2.65 ± 0.93; mDISCERN: 2.12 ± 0.89 for treatment-containing videos). Bilibili videos demonstrated significantly higher performance across all instruments: overall educational quality (GQS: 3.11 ± 0.87 vs. 2.24 ± 0.84, P < 0.001), treatment information reliability (mDISCERN: 2.56 ± 0.81 vs. 1.74 ± 0.76, P < 0.001), and source transparency (JAMA: 2.18 ± 0.72 vs. 1.42 ± 0.68, P < 0.001). The mean normalized TMRS score was 1.15 ± 0.62, with TikTok showing significantly higher treatment misinformation risk (1.41 ± 0.54) than Bilibili (0.86 ± 0.53, P < 0.001). TMRS scores were positively correlated with likes (rho = 0.46, P < 0.001), shares (rho = 0.43, P < 0.001), and comments (rho = 0.39, P < 0.001), while quality scores showed no significant correlation with engagement. Sensitivity analyses confirmed that the observed platform differences were not confounded by differences in content theme distribution. Epilepsy-related content on China's major short-video platforms is of concerningly poor quality, with treatment misinformation receiving disproportionately higher user engagement. These findings highlight the urgent need for collaborative efforts among neurologists, platform operators, and health authorities to improve the quality of epilepsy health information in the digital environment.
The proposed study puts forward an artificial intelligence-based framework to predict the needs of the urban public services and aid resource allocation based on data in the current social governance systems. A hybrid deep learning model is designed by combining a Graph Neural Network (GNN) based on spatial-relational reasoning with a Transformer network to model time-dependent connections, textual complaint semantics and structural relations between service requests, agencies, and locations, and via the joint learning of time-dependent patterns, textual complaint semantics, and structural relationships among service requests, agencies, and locations. In order to deal with the high-dimensional and non-convex problem of hyperparameter tuning on hybrid architectures, an Improved Heap-Based Optimizer (IHBO) is used, using opposition-based learning and chaotic search strategies to improve convergence and global search. The suggested model is tested using the Official Website of the City of New York (NYC 311) Service Requests large-scale data of nearly 12 million records that have mixed temporal, geographic, and categorical variables. It is experimentally proven that the IHBO-optimized Transformer-GNN has an overwhelming performance in comparison to the state-of-the-art baselines with a classification accuracy of 0.938 and lower prediction error by resolution time with Root Mean Square Error equal to 2.18 days, and it is also robust to novel temporal variations and noisy labels. In addition to predictive performance, the suggested model can deliver policy implications to urban governance by making allocation of public service resources more adaptive, equitable, and efficient, which do attest to the utility of hybrid artificial intelligent models in citizen-focused government of any kind.
The integration of Real-World Evidence (RWE) into regulatory frameworks has gained global interest to complement traditional clinical data, particularly where randomized controlled trials may be limited. RWE refers to clinical evidence derived from the analysis of Real-World Data (RWD), including information routinely collected from sources such as electronic health records, claims databases and registries. The increasing reliance on large-scale digital data also raises challenges related to privacy, inclusivity and equity, highlighting the need for transparent and socially responsible approaches. This study developed a consensus-based framework for incorporating RWE into regulatory decision-making in Colombia, with potential applications in similar Global South contexts. A two-phase design was applied. Phase I was a scoping review following Joanna Briggs Institute guidelines to identify RWE frameworks and guidelines. Searches in databases and regulatory agencies yielded 61 relevant documents. In Phase II, an expert panel of ten professionals in RWE, RWD, and regulation refined preliminary recommendations through a modified Delphi approach, reaching consensus on their applicability. The scoping review revealed a high concentration of RWE studies originating from the United States and Europe, highlighting contributions by agencies such as the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA) in guiding RWE for regulatory decisions, particularly in oncology and rare diseases. The expert panel validated 25 recommendations, grouped into six clusters addressing selection, transparency, stakeholder engagement, analytical validation, accessibility and capacity. This framework guides Colombia and the Global South in strengthening evidence-based regulation. It emphasizes governance, transparency, inclusivity and privacy safeguards, supporting equitable decision-making aligned with international standards.
Objective: To develop an ultra-low-frequency pressure reactivity index (PRx) (EL-PRx) based on hourly data (0.000 28 Hz) as an alternative to the conventional PRx, which requires high-frequency sampling, and provide a prognostic tool for traumatic brain injury (TBI) in resource-limited settings. Methods: This multicenter retrospective cohort study included 473 participants, including patients with TBI who were identified from the MIMIC-Ⅳ and eICU-CRD databases and those admitted to Fujian Provincial Hospital affiliated with Fuzhou University between April 2018 and April 2025. They were divided into survival (n=360) and non-survival (n=113) groups based on in-hospital all-cause mortality data. EL-PRx was calculated using 9-23 h moving windows. Propensity score matching (1∶1, caliper width 0.1×logit standard deviation) was performed to adjust for confounders, including demographics, vital signs, laboratory findings, comorbidities, supportive therapies, and IMPACT model variables. Restricted cubic spline analysis, univariate logistic regression, and receiver operating characteristic curve analysis were used to evaluate the association between EL-PRx and outcomes and its predictive performance. The optimal clinical threshold was determined using sequential Chi-squared testing. Results: A total of 473 patients were included (360 survivors and 113 non-survivors). EL-PRx was significantly higher for non-survivors than for survivors. The values were 0.14 (0.00, 0.30) and 0.07 (-0.10, 0.16) (P=0.003), respectively, within the 9-h window, and consistent differences were observed across other time windows (all P<0.05). Restricted cubic spline analysis demonstrated a non-linear positive association between EL-PRx and mortality risk. Univariate logistic regression showed that EL-PRx was significantly associated with mortality across different time windows, with odds ratios ranging from 3.825 to 8.073 (all P<0.05); the strongest effect was observed within the 17-h window (OR=8.073, 95%CI 2.053-35.697). Receiver operating characteristic curve analysis indicated that predictive performance was optimal on days 5-6 with a maximum AUC of 0.667. Sequential Chi-squared testing identified 0.15 as the optimal clinical threshold for EL-PRx, which became stable when monitoring duration was≥3-4 days. Conclusion: EL-PRx, which is derived from routinely collected hourly data, can effectively predict in-hospital mortality for patients with TBI. Its threshold is comparable to that of conventional PRx, making it suitable for intensive care settings with limited monitoring resources. 目的： 开发基于极低频数据（0.000 28 Hz，每小时1次）的压力反应性指数（EL-PRx），以替代需高频采样的传统压力反应指数（PRx），为资源有限的医疗中心提供创伤性脑损伤（TBI）预后评估工具。 方法： 本研究为多中心回顾性队列研究。纳入MIMIC-Ⅳ、eICU-CRD及2018年4月—2025年4月收住入福州大学附属省立医院住院患者共473例TBI，根据住院期间全因死亡情况，将患者分为存活组（n=360）和死亡组（n=113），并在9~23 h移动窗口计算EL-PRx。采用倾向评分匹配（1∶1，卡钳宽度0.1×logit标准差）校正包括人口学特征、生命体征、实验室指标、合并症、支持治疗及IMPACT模型指标在内的混杂因素，采用限制性立方样条、单因素logistic回归及受试者工作特征曲线（ROC）分析EL-PRx与预后的关联及预测性能。通过序贯卡方检验确定最佳临床阈值。 结果： 共纳入473例TBI患者，其中存活组360例、死亡组113例。死亡组的EL-PRx均显著高于存活组，其中9 h时间窗EL-PRx的对比结果为0.14（0.00，0.30）比 0.07（-0.10，0.16）（P=0.003），其他时间窗的EL-PRx对比差异亦有统计学意义（均P<0.05）。限制性立方样条分析显示，EL-PRx与死亡风险呈非线性相关。单因素logistic回归分析显示，不同时间窗EL-PRx均为死亡的危险因素，OR值为3.825~8.073（均P<0.05），其中17 h时间窗下EL-PRx预测效应最强（OR=8.073，95%CI 2.053~35.697）。ROC分析显示，第5~6天数据预测性能最佳，AUC最高为0.667。序贯卡方检验确定EL-PRx最佳临床阈值为0.15，且当监测时间≥3~4 d时阈值趋于稳定。 结论： EL-PRx仅需每小时常规监测数据即可有效预测TBI住院病死率，其阈值与传统PRx相当，适用于医疗条件受限的重症监护病房。.
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Tumour genetic profiling has the potential to significantly improve cancer care by informing targeted treatments and improving patient outcomes. As use increases worldwide, greater attention should be paid to consumer experiences, need and priorities. This study is consumer-led and aims to inform an equitable and ethical roll out of future services by exploring consumer: 1) awareness of tumour genetic profiling, 2) experiences with tumour genetic profiling, and 3) priorities for improving access to and delivery of tumour genetic profiling within Victoria, Australia. A consumer reference group was formed and supported by experienced researchers and professional staff of a comprehensive cancer centre alliance to develop and conduct the research study. A cross-sectional survey was conducted between January and May 2024, capturing demographic and disease characteristics, along with questions relating to each aim. Both quantitative and qualitative data were collected. Eligible participants were patients diagnosed with cancer whose treatment teams were based in Victoria, Australia, or caregivers of such patients. Of the 181 respondents (n = 36 carers, n = 145 patients), 23% (n = 44) reported that they (or the person they cared for) had undergone tumour genetic profiling. The majority reported a positive impact, including increased knowledge/understanding (n = 30, 68%) and personalised treatment options (n = 23, 52%), with very low decisional regret (mean: 3/100). However, 14% reported no understanding of the results at all, and confusion was reported as a drawback of testing. Higher education and greater shared decision making were associated with better understanding of results (p = 0.02 and p = 0.04, respectively) and higher education was also associated with greater awareness of genetic tumour profiling (p = 0.008). The primary barriers to uptake were lack of awareness (n = 88, 83%) and lack of perceived benefit from the treatment team (n = 19, 18%). Key strategies for improvement identified by participants included government-subsidised testing and improved patient and clinician education. This study highlighted gaps in consumer awareness and access to tumour genetic profiling, as well as the benefits of shared decision making. Overall, consumer-led insights emphasise the need for equitable funding, education, and systemic improvements. These findings can inform policies and practices aimed at delivering person-centred cancer care in Victoria and beyond. Future longitudinal research is needed to comprehensively explore these associations and track progress.
Left ventricular ejection fraction (LVEF) remains central to heart failure phenotyping and device-based decision-making, yet the degree to which apical four-chamber (A4C) and biplane Simpson measurements diverge at clinically actionable thresholds is not well defined. We analysed 1,022 unique algorithmically derived echocardiographic studies from 784 patients in the credentialed MIMIC-IV-ECHO-Ext-LVVOLUMES-A4C-ROI resource. Each study contained paired A4C and biplane volumetric labels derived from the same annotated DICOM sequence. Discordance was defined primarily at the HFrEF threshold (LVEF < 40%). Agreement was assessed with Bland-Altman analysis, and independent predictors were evaluated using multivariable logistic regression with cluster-robust standard errors. LVEF discordance at the HFrEF threshold occurred in 48 of 1,022 studies (4.7%, 95% CI 3.5-6.2%). At the ICD threshold (LVEF < 35%), discordance was present in 32 studies (3.1%). In the prespecified borderline zone (A4C LVEF 35-45%; n = 81), discordance rose to 30.9% (95% CI 21.9-41.6%). Mean bias was 0.11%, but the 95% limits of agreement were wide (- 13.5% to + 13.7%). LV end-diastolic volume was the only independent predictor of discordance (OR 1.61 per SD, 95% CI 1.27-2.05; p = 0.0001), and this association persisted after adjustment for acquisition variables. Discordance between A4C and biplane Simpson LVEF is uncommon across an unselected cohort but becomes frequent near therapeutic cut-offs. LV dilatation is the dominant driver. These findings support continued preference for biplane quantification when the ventricle is enlarged or the measured LVEF falls near a treatment threshold.
Growth differentiation factor-15 (GDF15), a stress-responsive cytokine of the transforming growth factor-β superfamily, is elevated in cancer cachexia, chemotherapy-induced nausea, and hyperemesis gravidarum, making it both a biomarker and a therapeutic target. Here, we developed high-affinity GDF15 binders using an artificial intelligence-driven protein design framework. To achieve this, we systematically explored three complementary scaffold-generation strategies: scaffold grafting, diffusion-based de novo design, and scaffold-search and grafting, identifying distinct advantages - scaffold grafting rapidly optimized receptor-derived motifs to sub-nanomolar affinity; de novo diffusion produced topologically novel binders; and scaffold-search and grafting enabled access to concave site B of GDF15 by repurposing evolutionary structural analogs from natural complexes. The designed GDF15 binders were translated into two functional modalities. First, a one-step, wash-free luminescent biosensor was created by coupling a de novo binder to split-luciferase fragments, enabling the rapid and sensitive quantification of GDF15. Second, the highest-affinity binder was engineered as an Fc-fusion decoy receptor, thereby effectively neutralizing GDF15 signaling in cell-based assays (IC50 = 7.2 nM), demonstrating comparable in vitro potency to ponsegromab, a monoclonal antibody currently undergoing phase II clinical trials. Together, this work establishes a versatile artificial intelligence-driven binder design pipeline with broad potential for next-generation diagnostics and therapeutics in cancer cachexia and other GDF15-mediated diseases.
A central challenge in consciousness research is determining whether observers have a conscious experience of a stimulus. However, present/absent detection judgments are often biased by contextual factors, making it difficult to isolate conscious perception from non-perceptual influences. Traditional psychophysical methods struggle to disentangle these components. To address this, we conducted in-person experiments (N = 505) in which participants detected and reproduced dim and absent contrast-defined Gabor stimuli under three contextual manipulations: attentional cues, asymmetrical base rates, and payoff schemes. Using a reproduction task together with a Hurdle-Gaussian model, we quantitatively decomposed reproduction responses into a perceptual continuous contrast component and a non-perceptual "hurdle" component. We found that statistical priors (base rate) and reward structures (payoff) induced non-perceptual shifts in the reproduction hurdle, whereas attentional cues selectively shifted the continuous contrast component, consistent with changes in conscious experience. Critically, comparing conditions with and without intermixed detection trials revealed that the presence of a detection task contaminates reproduction reports with non-perceptual criterion effects. This highlights the need for caution in using and interpreting results that rely on detection judgments, even when combined with subjective measures like reproduction, especially given the central role that detection tasks play in consciousness research.
Acute kidney injury (AKI) is prevalent among hospitalized patients and is frequently complicated by hyperkalemia (HyperK), kidney replacement therapy (KRT), and major adverse kidney events (MAKE). Early prediction of these outcomes remains a clinical priority. To develop and internally validate nomograms using routinely collected clinical variables to predict the risk of HyperK, KRT, and MAKE, including death and ≥25mL/min/1.73m2 reduction in eGFR in hospitalized AKI patients. This retrospective cohort study included 753 adult AKI patients without initial HyperK, evaluated at a tertiary referral center from 2020 to 2024. Logistic regression models identified predictors of HyperK and MAKE, stratified by sex. Model performance was assessed via AUC, calibration, and predictive metrics. Nomograms were constructed based on final multivariate models. During follow-up, 24% of patients developed HyperK. Independent predictors included vasopressor use, shock, urinary obstruction, low hemoglobin, and higher baseline potassium. The HyperK model demonstrated moderate discrimination (AUC 0.68) but a high negative predictive value (97%). Sex-stratified nomograms for MAKE, KRT, and mortality showed strong performance (AUCs 0.74-0.98), with highest accuracy observed in KRT models for both sexes (AUC 0.96). Predictors varied by sex but commonly included volume overload, acid-base disorders, uremia, and elevated creatinine. We developed pragmatic and accessible nomograms capable of predicting HyperK, KRT, and MAKE in AKI patients using standard clinical data. These tools offer timely, personalized risk stratification and may support clinical decision-making in diverse hospital settings.
Shared decision making (SDM) is a process to actively involve both patients and clinicians to weigh the benefits and risks of a healthcare decision, based on clinical guidelines and the patients' preferences, needs and values. Despite the ethical foundation of SDM, its implementation remains limited. Possible physician-reported barriers for this limited uptake include insufficient level of SDM training. Training physicians in SDM could be a part of the puzzle. A recent systematic review showed that there is a shift towards blended training more than live or online learning. We therefore developed and pilot-tested a blended training program for general practitioners (GPs) in SDM in Belgium. Acquired skills were evaluated by three viewpoint - observer, patient and physician. In a pre-post study, GPs participated in the blended training program consisting of an e-learning and a face-to-face session with simulation patients (SPs) GPs and SPs completed surveys before (T0) and after (T1) the blended training. Consultations were recorded for analysis by observer reported scales (OPTION12 and 4SDM scale). Secondary outcomes were SDM-Q9-patient, satisfaction with consultation, knowledge and intentions towards SDM. Ten GPs were included. There was a significant increase in both OPTION12 (mean (SD) from 19·37 before to 37·70 after training, p = 0·0010, 95% CI [9·65 - 27·02]) and 4SDM scale (mean (SD) from 9·2 (4·66) before to 17·00 (5·08) after training, p = 0·0001, 95% CI [5·47 - 10·13]) with a moderate-large effect after training (Cohen's D = 2·39, 95% CI [1·13 - 3·63]. The SDM blended training for GPs improved their skills, knowledge and intentions in SDM in simulated consultations on the short term.
Peer support workers (PSWs) provide support to others through their personal lived experiences of mental health. However, their work is often undervalued by their colleagues, and they frequently face challenges in the workplace, resulting in occupational stigma. Currently, there are limited insights into how PSWs experience and manage the stigma they face. Therefore, this study examines how PSWs in the UK National Health Service experience and navigate occupational stigma in their roles. Seventy semi-structured interviews were conducted with PSWs and their colleagues. Interviews explored their experiences in the role, workplace interactions, and subsequently perceptions and experiences of stigma, and how they dealt with stigmatising experiences. The data were analysed using thematic analysis to identify how stigma manifested and how they navigated it. PSWs reported experiencing stigma both covertly and explicitly. Covert stigma included subtle devaluation of their knowledge and exclusion from decision-making, while explicit stigma involved direct questioning of competence and disrespectful behaviour from colleagues. In response, PSWs navigated stigma through three main strategies. First, they demonstrated commitment to their role via reliability, dedication, and consistent performance, reinforcing the value of their work. Second, PSWs leveraged experiential knowledge as expertise, emphasising practical skills and lived experience in patient care. Third, they used their roles to create reciprocal benefits, where they supported service-users, which in turn helped their own mental health and recovery. Occupational stigma towards PSWs is pervasive, manifesting in both subtle and overt ways that can undermine their role. PSWs actively counter stigma through commitment, expertise, and reciprocal relationships, highlighting their resilience and adaptability. Addressing stigma in healthcare settings is critical for improving team dynamics and ensuring high-quality care. Going forward to support the role, policymakers and organisations that employ PSWs should focus on improving organisational culture, recognition of the role, and collaborative practices to reduce stigma, strengthen workforce sustainability and recognise the value of lived experience in the workforce.
Patient outcomes after robotic surgery vary widely, often reflecting differences in surgical performance. Artificial intelligence (AI) offers new ways to address this variability, with applications spanning automated skills assessment and feedback, intraoperative guidance and autonomous surgery. The most credible short-range advances of AI in this space consist in generating assistive systems that enhance perception, anticipate risks and standardize feedback while remaining under surgeon control. Results from early studies suggest that AI can influence decision-making, reduce errors and shorten learning curves, particularly in areas such as augmented navigation, anatomy recognition, error detection and telesurgery support. Long-term directions include emerging vision-language-action interfaces capable of programming task-specific support through natural language. In addition to technical performance, translation of AI into clinical practice will require robust datasets, systems designed around human users, regulatory alignment and clear accountability. Ultimately, the measure of surgical AI will be patient outcomes, including reduced complications, fast proficiency acquisition and improved outcome consistency across diverse settings.
Magnetic resonance imaging (MRI) coupled with Prostate Imaging-Reporting and Data System (PI-RADS) provides standardization for assessing the clinical significance of prostate cancer (PCa). The association between PI-RADS and clinical endpoints has remained underexplored due to limited follow-up data. Association of PI-RADS with prostate cancer-specific mortality (PCSM), overall survival (OS), metastasis-free survival (MFS), and biochemical recurrence (BCR) was investigated across three retrospective cohorts focusing on the PI-RADS v2 era. The Helsinki University Hospital included 4674 men with clinical suspicion of PCa (MRI during 2015-2019, median follow-up [mFU] 5.0 yr), Tampere University Hospital (N = 1159; 2016-2021, mFU 2.3 yr) with men diagnosed with PCa, and Lille University Hospital (N = 301; 2016-2024, mFU 6.2 yr) with radical prostatectomy-treated men with PCa. In Helsinki cohort, multivariable Cox regression found PI-RADS score 5 to be significantly associated with PCSM (hazard ratio 18.4, 95% confidence interval [6.62-51.1]), along with biopsy GG 5 (5.45 [1.82-16.3]), Charlson's Comorbidity Index (1.53 [1.42-1.7]), and prostate-specific antigen (1.28 [1.11-1.5]). PI-RADS score 5 associated with OS in the Helsinki and Tampere cohorts, and MFS and BCR in the Lille cohort. Main limitation of the work is that all data is retrospective. PI-RADS score 5 is associated with elevated risk of adverse outcomes. The presented clinically strong endpoints have potential to affect decision-making in MRI-based PCa diagnosis and prognosis.