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With this article, I explore the connections between blockchain technology, coloniality, and decolonial practices. Drawing on Sylvia Wynter’s thought on the interdependent systems of colonialism, capitalism, and knowledge, as well as more recent work on the coloniality of digital technologies, I argue that blockchain-based systems reproduce certain dynamics at work in historical colonialism. Additionally, Wynter’s decolonial propositions provide a generative framework to understand countercultural practices with. Inspired by Wynter, Patricia de Vries explores the notion of “plot work as artistic praxis” to ask how artistic work, implicated as it is in capitalist logics, can create space for relating dierently in the context of the exploitations of those dominant logics. I apply this notion to examine how Decentralised Autonomous Organisations (DAOs) in the countercultural blockchain space might contribute to this praxis.

We examine the ideological differences in the debate surrounding large language models (LLMs) and AI regulation, focusing on the contrasting positions of the Future of Life Institute (FLI) and the Distributed AI Research (DAIR) institute. The study employs a humanistic HCI methodology, applying narrative theory to HCI-related topics and analyzing the political differences between FLI and DAIR, as they are brought to bear on research on LLMs. Two conceptual lenses, “existential risk” and “ongoing harm,” are applied to reveal differing perspectives on AI's societal and cultural significance. Adopting a longtermist perspective, FLI prioritizes preventing existential risks, whereas DAIR emphasizes addressing ongoing harm and human rights violations. The analysis further discusses these organizations’ stances on risk priorities, AI regulation, and attribution of responsibility, ultimately revealing the diverse ideological underpinnings of the AI and LLMs debate. Our analysis highlights the need for more studies of longtermism's impact on vulnerable populations, and we urge HCI researchers to consider the subtle yet significant differences in the discourse on LLMs.

Contains fulltext : 288887.pdf (Publisher’s version ) (Open Access)

Item does not contain fulltext, OBJECTIVE: Fibroblast growth factor 21 (FGF21) is a peptide hormone synthesized by several organs and regulates, among others, energy homeostasis. In obesity, insulin resistance and type 2 diabetes (T2D), higher circulating FGF21 concentrations have been found. Temporal analyses in murine studies demonstrate that FGF21 increases before insulin resistance occurs. The current study aims to investigate in time-to-event analyses whether FGF21 may be an early biomarker in the development of T2D. RESEARCH DESIGN AND METHODS: Circulating FGF21 was measured using an immunoassay of the Mesoscale U-PLEX assay platform. The study outcome was incident T2D. Associations of circulating FGF21 concentration with T2D were quantified using Cox proportional hazards models with adjustments for potential confounders. RESULTS: We included 5244 participants aged 52 ± 12 years, of whom 50% were male. Median [interquartile range] circulating FGF21 concentration was 860 [525-1329] pg/mL. During 7.3 [6.1-7.7] years of follow-up, 299 (5.7%) participants developed T2D. In fully adjusted analyses, higher circulating FGF21 concentration was associated with an increased risk of incident T2D (hazard ratio per doubling: 1.26 [95% CI, 1.06-1.51]; P = 0.008), with effect modification by fasting plasma glucose, consistent with strengthening of the association at lower fasting glucose (interaction coefficient: -0.12; P = 0.022). CONCLUSION: Higher circulating FGF21 concentrations are independently associated with an increased risk of incident T2D in participants with a low fasting plasma glucose, making circulating FGF21 concentration a potential early biomarker for type 2 diabetes.

Contains fulltext : 296018.pdf (Publisher’s version ) (Open Access), Mechanisms of infection and pathogenesis have predominantly been studied based on differential gene or protein expression. Less is known about posttranslational modifications, which are essential for protein functional diversity. We applied an innovative glycoproteomics method to study the systemic proteome-wide glycosylation in response to infection. The protein site-specific glycosylation was characterized in plasma derived from well-defined controls and patients. We found 3862 unique features, of which we identified 463 distinct intact glycopeptides, that could be mapped to more than 30 different proteins. Statistical analyses were used to derive a glycopeptide signature that enabled significant differentiation between patients with a bacterial or viral infection. Furthermore, supported by a machine learning algorithm, we demonstrated the ability to identify the causative pathogens based on the distinctive host blood plasma glycopeptide signatures. These results illustrate that glycoproteomics holds enormous potential as an innovative approach to improve the interpretation of relevant biological changes in response to infection.

Contains fulltext : 294860.pdf (Publisher’s version ) (Open Access)

BACKGROUND: Differentiating between self-resolving viral infections and bacterial infections in children who are febrile is a common challenge, causing difficulties in identifying which individuals require antibiotics. Studying the host response to infection can provide useful insights and can lead to the identification of biomarkers of infection with diagnostic potential. This study aimed to identify host protein biomarkers for future development into an accurate, rapid point-of-care test that can distinguish between bacterial and viral infections, by recruiting children presenting to health-care settings with fever or a history of fever in the previous 72 h. METHODS: In this multi-cohort machine learning study, patient data were taken from EUCLIDS, the Swiss Pediatric Sepsis study, the GENDRES study, and the PERFORM study, which were all based in Europe. We generated three high-dimensional proteomic datasets (SomaScan and two via liquid chromatography tandem mass spectrometry, referred to as MS-A and MS-B) using targeted and untargeted platforms (SomaScan and liquid chromatography mass spectrometry). Protein biomarkers were then shortlisted using differential abundance analysis, feature selection using forward selection-partial least squares (FS-PLS; 100 iterations), along with a literature search. Identified proteins were tested with Luminex and ELISA and iterative FS-PLS was done again (25 iterations) on the Luminex results alone, and the Luminex and ELISA results together. A sparse protein signature for distinguishing between bacterial and viral infections was identified from the selected proteins. The performance of this signature was finally tested using Luminex assays and by calculating disease risk scores. FINDINGS:376 children provided serum or plasma samples for use in the discovery of protein biomarkers. 79 serum samples were collected for the generation of the SomaScan dataset, 147 plasma sample

Background: Poor prognosis in colon cancer is associated with a high content of cancer-associated fibroblasts (CAFs) and an immunosuppressive tumor microenvironment. The relationship between these two features is incompletely understood. Here, we aimed to generate a model system for studying the interaction between cancer cells and CAFs and their effect on immune-related cytokines and T cell proliferation. Methods: CAFs were isolated from colon cancer liver metastases and were immortalized to prolong lifespan and improve robustness and reproducibility. Established medium and matrix compositions that support the growth of patient-derived organoids were adapted to also support CAF growth. Changes in growth pattern and cellular re-organization were assessed by confocal microscopy, live cell imaging, and immunofluorescence. Single cell RNA sequencing was used to study CAF/organoid co-culture-induced phenotypic changes in both cell types. Conditioned media were used to quantify the production of immunosuppressive factors and to assess their effect on T cell proliferation. Results: We developed a co-culture system in which colon cancer organoids and CAFs spontaneously organize into superstructures with a high capacity to contract and stiffen the extracellular matrix (ECM). CAF-produced collagen IV provided a basement membrane supporting cancer cell organization into glandular structures, reminiscent of human cancer histology. Single cell RNA sequencing analysis showed that CAFs induced a partial epithelial-to-mesenchymal-transition in a subpopulation of cancer cells, similar to what is observed in the mesenchymal-like consensus molecular subtype 4 (CMS4) colon cancer. CAFs in co-culture were characterized by high expression of ECM components, ECM-remodeling enzymes, glycolysis, hypoxia, and genes involved in immunosuppression. An expression signature derived from CAFs in co-culture identified a subpopulation of glycolytic myofibroblasts specifically residing in CMS1 an

The main cause of cancer lethality is metastasis. Throughout cancer progression cells are affected by mechanical forces. Forces like tension, compression and shear of fluid flow can be generated and sensed by cells (mechanosensing), and can biochemically be transduced (mechanotransduction), leading to alterations in cellular fate and behavior. In tumors, changes can occur in how cells sense forces in the environment, or the environment can change. Tumor cells are affected by changes in extracellular matrix (ECM) composition and mechanical strength, of which these changes can also be induced by the tumor itself or tumor-recruited stromal cells. Mechanical forces and sensing of them are involved in many steps of metastasis, from the local environment of the primary tumor, to dissemination through circulation, re-entry into tissues and colonization of secondary sites. In this review we discuss our current understanding of the contribution of mechanosensing and mechanotransduction at several steps of the metastatic cascade, including mechanical changes prior to colonization caused by cancer, various pathologies and aging.

Background: Differentiating between self-resolving viral infections and bacterial infections in children who are febrile is a common challenge, causing difficulties in identifying which individuals require antibiotics. Studying the host response to infection can provide useful insights and can lead to the identification of biomarkers of infection with diagnostic potential. This study aimed to identify host protein biomarkers for future development into an accurate, rapid point-of-care test that can distinguish between bacterial and viral infections, by recruiting children presenting to health-care settings with fever or a history of fever in the previous 72 h. Methods: In this multi-cohort machine learning study, patient data were taken from EUCLIDS, the Swiss Pediatric Sepsis study, the GENDRES study, and the PERFORM study, which were all based in Europe. We generated three high-dimensional proteomic datasets (SomaScan and two via liquid chromatography tandem mass spectrometry, referred to as MS-A and MS-B) using targeted and untargeted platforms (SomaScan and liquid chromatography mass spectrometry). Protein biomarkers were then shortlisted using differential abundance analysis, feature selection using forward selection-partial least squares (FS-PLS; 100 iterations), along with a literature search. Identified proteins were tested with Luminex and ELISA and iterative FS-PLS was done again (25 iterations) on the Luminex results alone, and the Luminex and ELISA results together. A sparse protein signature for distinguishing between bacterial and viral infections was identified from the selected proteins. The performance of this signature was finally tested using Luminex assays and by calculating disease risk scores. Findings: 376 children provided serum or plasma samples for use in the discovery of protein biomarkers. 79 serum samples were collected for the generation of the SomaScan dataset, 147 plasma samples for the MS-A dataset, and 150 plasma samples for the MS

Mechanisms of infection and pathogenesis have predominantly been studied based on differential gene or protein expression. Less is known about posttranslational modifications, which are essential for protein functional diversity. We applied an innovative glycoproteomics method to study the systemic proteome-wide glycosylation in response to infection. The protein site-specific glycosylation was characterized in plasma derived from well-defined controls and patients. We found 3862 unique features, of which we identified 463 distinct intact glycopeptides, that could be mapped to more than 30 different proteins. Statistical analyses were used to derive a glycopeptide signature that enabled significant differentiation between patients with a bacterial or viral infection. Furthermore, supported by a machine learning algorithm, we demonstrated the ability to identify the causative pathogens based on the distinctive host blood plasma glycopeptide signatures. These results illustrate that glycoproteomics holds enormous potential as an innovative approach to improve the interpretation of relevant biological changes in response to infection.