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OP0085 Outcomes of upfront combination vs monotherapy with rituximab or mycophenolate mofetil for systemic sclerosis interstitial lung disease (SSc-ILD): results from an EUSTAR cohort study
No full textOP0082 Evidence-based detection of progression of systemic sclerosis-interstitial lung disease patients: the EUSTAR-PPF study (CP number 148)
No full textPhysics-informed transfer learning for SHM via feature selection
Get PDFData used for training structural health monitoring (SHM) systems are expensive and often impractical to obtain, particularly labelled data. Population-based SHM presents a potential solution to this issue by considering the available data across a population of structures. However, differences between structures will mean the training and testing distributions will differ; thus, conventional machine learning methods cannot be expected to generalise between structures. To address this issue, transfer learning (TL), can be used to leverage information across related domains. An important consideration is that the lack of labels in the target domain limits data-based metrics to quantifying the discrepancy between the marginal distributions. Thus, a prerequisite for the application of typical unsupervised TL methods is to identify suitable source structures (domains), and a set of features, for which the conditional distributions are related to the target structure. Generally, the selection of domains and features is reliant on domain expertise; however, for complex mechanisms, such as the influence of damage on the dynamic response of a structure, this task is not trivial. In this paper, knowledge of physics is leveraged to select more similar features, the modal assurance criterion (MAC) is used to quantify the correspondence between the modes of healthy structures. The MAC is shown to have high correspondence with a supervised metric that measures joint-distribution similarity, which is ultimately the primary indicator of whether a classifier will generalise between domains. The MAC is proposed as a physics-informed measure for selecting a set of features that behave consistently across domains when subjected to damage, i.e. features with invariance in the conditional distributions. When used in conjunction with established methods for aligning marginal distributions, the proposed approach yields transfers with high joint distribution similarities while remaining entirely unsupervised, thereby alleviating the need for costly labels. This approach is demonstrated on numerical and experimental case studies to verify its effectiveness in various applications
Socioeconomic inequalities in disease prevalence by age and sex for 17 common long-term conditions in England:retrospective, observational study of electronic primary care records from Clinical Practice Research Datalink (CPRD) Aurum
Get PDFBackground Evidence on socioeconomic inequalities in the prevalence of common long-term conditions and their variation across the life course is necessary for equitable service design and resource allocation. We used routinely collected electronic primary care records and a unified data extraction and analysis framework to estimate socioeconomic variations in the prevalence of 17 common long-term conditions by age and sex.Methods Electronic records for 2.2 m patients registered with 300 randomly selected primary care practices contributing to the Clinical Practice Research Datalink Aurum database were used to estimate observed, age-sex standardised and age-specific rates of disease prevalence on 31 March 2020 by Index of Multiple Deprivation quintile groups. Inequality in disease burden was expressed as the prevalence rate ratio (RR) between the most and least deprived fifths of the population.Results Age-sex standardised prevalence rates were higher in the most deprived compared with the least deprived fifth of the population for 16 of 17 conditions. The largest relative differences in disease prevalence were observed for chronic obstructive pulmonary disease (RR: 3.29; 95 3.19 to 3.38), severe mental illness (RR: 2.72; 95 2.60 to 2.85) and peripheral arterial disease (RR: 2.58; 95 2.46 to 2.72). For most conditions, the equity gap was largest in middle age and reduced with age thereafter.Conclusions Substantial socioeconomic inequalities in disease prevalence are evident in the English population. A catalogue of disease prevalence by socioeconomic quintile group, age and sex is provided to facilitate further analysis and modelling.Data may be obtained from a third party and are not publicly available. Data were made available to the research team through a data sharing agreement with the Clinical Practice Research Datalink (CPRD) and cannot be shared further. Interested parties can review CPRD’s research data governance process and apply for access at cprd.com
GPR56/ADGRG1 induces biased Rho-ROCK-MLC and JAK-STAT3 signaling to promote amoeboid-like morphology and IL-6 upregulation in melanoma cells
Get PDFBackground: GPR56/ADGRG1 is an adhesion G protein-coupled receptor involved in cell-matrix interactions and metastasis of human melanoma cells. Previously, we demonstrated that GPR56 activation in melanoma cells triggers Gα₁₂/₁₃-RhoA signaling, leading to increased IL-6 production and enhanced cell migration. Yet little is known of the downstream signaling effectors and their specific roles in regulating melanoma cellular phenotypes. Results: In this study, we show that GPR56 activation induces Rho-ROCK-MLC and JAK-STAT3 signaling, which temporally and differentially drive amoeboid-like morphology and IL-6 upregulation. Interestingly, GPR56-induced JAK-STAT3 activation is partially regulated by Rho-ROCK-MLC signaling but not vice versa. Moreover, receptor auto-proteolysis modulates the magnitude of GPR56-mediated signaling, and its unique intracellular regions contribute to the selective regulation of unique signaling pathways and associated cellular phenotypes. Conclusion: Our findings reveal complex GPR56-mediated biased signaling through the Rho-ROCK-MLC and JAK-STAT3 pathways, highlighting these networks as potential therapeutic targets for modulating distinct tumorigenic phenotypes in human melanoma cells
Enhancement of curcumin bioaccessibility: An assessment of possible synergistic effect of γ-cyclodextrin metal–organic frameworks with micelles
Get PDFBiocompatible γ-cyclodextrin metal–organic frameworks (γ-CD-MOFs) have been reported to improve the apparent solubility and bioavailability of encapsulated bioactive compounds, including curcumin, potentially enabling sustained delivery. However, disintegration of γ-CD-MOFs may “release” encapsulated curcumin within the cavity in its insoluble crystallized form. This study evaluated whether the presence of micelles (at a concentration above curcumin's maximum solubility) would be able to take up the “released” curcumin from γ-CD-MOFs to further increase its apparent solubility. Release in the presence of a micellar phase was compared to curcumin encapsulated within γ-CD-MOFs alone. Results demonstrated that Tween 80 solubilized higher curcumin concentrations compared to oleic acid and bile salts. Dispersed in combination with Cur- γ-CD-MOFs, it was found that Tween 80 and oleic acid can improve the apparent solubility of curcumin, 5× and 1.5× higher than in the absence of micelles of these surfactants, respectively, whereas bovine bile salts exhibited a negative effect, due to a displacement of curcumin from within cavity of γ-CD-MOFs. Moreover, it was also found that Tween 80 was less affected by pH and salt concentrations due to its nonionic nature. In vitro digestion (via the INFOGEST protocol) revealed an 8-fold increase in apparent solubility of curcumin with bile salts and a 53-fold increase when combining bile salts and γ-CD-MOFs, achieving bioaccessibility of 2 % and 16 %, respectively. In conclusion, this research revealed the evaluation of γ-CD-MOFs’ functionality for delivery and controlled release, with the possibility of an emulsion system boosting the fraction of curcumin that would potentially be bioavailable
Using artificial intelligence to predict patient outcomes from patient-reported outcome measures: a scoping review
Get PDFPurpose
This scoping review aims to identify and summarise artificial intelligence (AI) methods applied to patient-reported outcome measures (PROMs) for prediction of patient outcomes, such as survival, quality of life, or treatment decisions.
Introduction
AI models have been successfully applied to predict outcomes for patients using mainly clinically focused data. However, systematic guidance for utilising AI and PROMs for patient outcome predictions is lacking. This leads to inconsistency of model development and evaluation, limited practical implications, and poor translation to clinical practice.
Materials and methods
This review was conducted across Web of Science, IEEE Xplore, ACM, Digital Library, Cochrane Central Register of Controlled Trials, Medline and Embase databases. Adapted search terms identified published research using AI models with patient-reported data for outcome predictions. Papers using PROMs data as input variables in AI models for prediction of patient outcomes were included.
Results
Three thousand and seventy-seven records were screened, 94 of which were included in the analysis. AI models applied to PROMs data for outcome predictions are most commonly used in orthopaedics and oncology. Poor reporting of model hyperparameters and inconsistent techniques of handling class imbalance and missingness in data were found. The absence of external model validation, participants’ ethnicity information and stakeholders involvement was common.
Conclusion
The results highlight inconsistencies in conducting and reporting of AI research involving PROMs in patients’ outcomes predictions, which reduces the reproducibility of the studies. Recommendations for external validation and stakeholders’ involvement are given to increase the opportunities for applying AI models in clinical practice
Balancing anticipatory and deliberative governance in public–private partnerships for responsible innovation: The role of corporate innovation capabilities
No full textAccelerating technological change is expanding the role of corporations in public-private partnerships for responsible innovation. While existing research emphasizes the importance of deliberative processes for responsible innovation, little is known about how corporate innovation capabilities impact such processes. Through an in-depth case study of Quayside, a Canadian smart city project, we examine how established corporate innovation capabilities shape public deliberation for responsible innovation. Our findings expose intricate challenges that arise when public entities grant corporations significant authority over innovation processes intended to be deliberative. We critically assess the effectiveness of widely embraced approaches to open innovation and human-centric design, showing that, without reflexivity, these capabilities can give rise to an imbalance between two critical modes of governance for responsible innovation: anticipatory and deliberative. Corporate self-referentiality and business interests drive anticipatory governance, reinforcing corporate expertise and promoting the instrumental use of resources and capabilities to engage citizens as consumers. When corporations lack the reflexivity needed to align this approach with expectations for meaningful public participation in a democratic context, this can derail rather than inform responsible innovation processes
Identification of gene targets for the sprouting inhibitor CIPC
Get PDFSprout suppressants are widely used in industry to ensure year-round availability of potato tubers, significantly decreasing wastage by repressing premature growth of buds on the tuber surface during storage. Despite its ban from 2020 in the EU, isopropyl N-(3-chlorophenyl) carbamate (also known as chlorpropham or CIPC) remains the most widely used suppressant worldwide. However, the mechanism of action of CIPC remains obscure. Here, we report on a combined targeted transcriptomic and genetic approach to identify components in the tuber bud cell-division machinery that might be involved in CIPC's mode of action. This involved RNAseq analysis of dissected, staged tuber buds during in vitro sprouting with and without CIPC to identify lead genes, followed by the development and application of an Arabidopsis root assay to assess cell division response to CIPC in selected mutants. The ease of use of this model plant, coupled with its immense genetic resources, allowed us to test the functionality of lead genes encoding cell-division–associated proteins in the modulation of plant growth response to CIPC. This approach led to the identification of a component of the augmin complex (a core player in mitosis) as a potential target for CIPC
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