ccRCC is marked by niches of immune evasion in late-stage disease, that are associated with resistance to immune checkpoint inhibitors (CPI). Intratumoral microbes are emerging as key modulators of the tumor immune microenvironment and may therefore play a yet unrecognised role in ccRCC disease progression and therapy resistance, marking them as potential biomarkers, and novel therapeutic targets. We extracted bacterial reads from three cohorts of treatment-naïve primary tumors and one cohort of pre- and post-CPI treated tumors: Genomics England Renal (GEL) (636 patients, WGS), TRACERx Renal (154 patients, 629 samples, WGS); TCGA Renal (494 patients, RNA) & ADAPTeR (15 patients, 56 samples, RNA). Bespoke denoising, and decontamination were applied. Live presence of intratumoral bacteria was confirmed through culture and RNAscope. Genera associated with survival were identified using ElasticNet feature selection. We observed significant heterogeneity in bacterial abundance within and across tumors, driven by differences in Cutibacterium abundance. Cutibacterium makes up ∼90% of bacteria in each tumor sample and is enriched in tumors compared to adjacent normal tissue (p=7.1e-10). 9 of 11 colonies grown from two positive tumors were genotyped as Cutibacterium acnes, confirming its live presence and relative abundance in ccRCC tumors. Cutibacterium is higher in late-stage tumors (III&IV) than early-stage tumors (I&II)(p=7.8e-3). Its abundance also separates patients by progression free survival (PFS) and overall survival (OS) (p=9.9e-3, p=0.016) and is higher in CPI non-responders than responders (p=0.028). Association with survival is confirmed in the TCGA cohort independently of stage (PFS = 0.031, OS = 7.3e-4). While other genera associate with survival in either TRACERx or TCGA, only Cutibacterium is prognostic in both. Enrichment analyses of bulk RNA (TRACERx & TCGA), show upregulation of leukocyte taxis and innate immune response with Cutibacterium abundance. To further probe this interaction with the immune microenvironment we are performing single cell spatial transcriptomics, and in vitro co-cultures. Results of these ongoing experiments will be presented at the conference. Cutibacterium either creates or exploits a disease- and CPI-resistance-promoting environment in ccRCC tumors and can therefore act as a prognostic and predictive biomarker. The genus is known for causing chronic inflammation in sebaceous skin follicles, induces Nf-kB signalling, and M2-macrophage differentiation in vitro, and associates with myeloid response in ccRCC tumors. Together this supports a compelling hypothesis that Cutibacterium reduces survival in ccRCC by creating immunosuppressive niches, thereby fostering disease progression and CPI-resistance. Alice C. Martin, Anne-Laure Cattin, Irene Lobon, Zayd Tippu, Fiona Byrne, Charlotte Spencer, Clara Becker, Martha Zepeda Rivera, Krupa Thakkar, Hongui Cha, Angel Fernandez-Sanroman, Annika Fendler, Taja Barber, Leo Bickley, Daqi Deng, Scott Shepherd, Parise Lockwood, Maximiliano Gutierrez, Susan Bullman, Kevin Litchfield, Samra Turajlic. Intratumoral bacteria predict survival in clear cell renal cell carcinoma (ccRCC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2207.

Flow cytometry is a well-established method to analyze cell populations using antibody-based fluorescent detection of protein biomarkers. In this study, we demonstrate the ability to generate intact single cells and perform flow cytometry analysis with two types of formalin fixed tissue: FFPE curls and Representative Samples (RS). RS are homogenized, well-mixed tissue samples from formalin fixed tumors dissected from leftover surgical material. We demonstrate biomarker expression results which correlate with IHC scores. This new method for biomarker quantification may be considered alongside other methods (e.g. digital pathology). Intact single cells were dissociated from RS and FFPE curls using a non-enzymatic, mechanical dissociation method. Cells were stained in suspension for Cytokeratin 8&18 (CK8&18), Ki67, Her2, and DNA content was assessed via DAPI staining and analyzed by flow cytometry. Samples were analyzed on a BD FACSMelody or BD LSR II flow cytometer, and analysis was performed using FCS Express 7 software. Immunohistochemistry (IHC) was performed on the Benchmark ULTRA to compare Ki67 expression (n=78) and Her2 expression (n=16) to the flow analysis. Ki67 expression by IHC was assessed using the international Ki67 working group scoring methods to generate a positive percentage. Her2 expression by IHC was assessed by a pathologist and assigned a score ranging 0 to 3+. Formalin fixed tissue such as RS and FFPE curls can be mechanically dissociated into single cells with intact surface biomarkers. These single cells can be stained in suspension, analyzed via flow cytometry and generate correlating data with both weighted IHC-based scores and clinical IHC scores. The flow analysis of Her2 positive percentage correlates with the IHC scores showing an increasing trend and significant difference between scores for both FFPE and RS. Ki67 expression varied by tumor region using IHC analysis, however by flow cytometry showed a strong correlation with a weighted average across multiregional quantification of Ki67 expression. We demonstrate that millions of intact single cells can be generated from RS and FFPE curls for breast tissue, using non-enzymatic mechanical dissociation methods. Staining in suspension and flow cytometry analysis can be performed in a day for rapid biomarker quantification. Flow cytometry has the potential to analyze FFPE samples using workflows and technologies that have existed in the hematopathology space for decades. Samantha M. Hill, Hannah L. Veloz, Brian Hanley, Tracy Davis, Lisa L. Gallegos, Harold Sasano, Samra Turajlic, Nelson R. Alexander. Optimizing fixed flow cytometry for breast cancer biomarker expression in representative samples and FFPE curls [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 670.

The ubiquity of mobile applications has increased dramatically in recent years, opening up new opportunities for cyber attackers and heightening security concerns in the mobile ecosystem. As a result, researchers and practitioners have intensified their research into improving the security and privacy of mobile applications. At the same time, more and more mobile applications have appeared on the market that address the aforementioned security issues. However, both academia and industry currently lack a comprehensive overview of these mobile security applications for Android and iOS platforms, including their respective use cases and the security information they provide. To address this gap, we systematically collected a total of 410 mobile applications from both the App and Play Store. Then, we identified the 20 most widely utilized mobile security applications on both platforms that were analyzed and classified. Our results show six primary use cases and a wide range of security information provided by these applications, thus supporting the core functionalities for ensuring mobile security.

Cyber threats have become increasingly prevalent and sophisticated. Prior work has extracted actionable cyber threat intelligence (CTI), such as indicators of compromise, tactics, techniques, and procedures (TTPs), or threat feeds from various sources: open source data (e.g., social networks), internal intelligence (e.g., log data), and “first-hand” communications from cybercriminals (e.g., underground forums, chats, darknet websites). However, “first-hand” data sources remain underutilized because it is difficult to access or scrape their data. In this work, we analyze (i) 6.6 million posts, (ii) 3.4 million messages, and (iii) 120,000 darknet websites. We combine NLP tools to address several challenges in analyzing such data. First, even on dedicated platforms, only some content is CTI-relevant, requiring effective filtering. Second, “first-hand” data can be CTI-relevant from a technical or strategic viewpoint. We demonstrate how to organize content along this distinction. Third, we describe the topics discussed and how “first-hand” data sources differ from each other. According to our filtering, 20% of our sample is CTI-relevant. Most of the CTI-relevant data focuses on strategic rather than technical discussions. Credit card-related crime is the most prevalent topic on darknet websites. On underground forums and chat channels, account and subscription selling is discussed most. Topic diversity is higher on underground forums and chat channels than on darknet websites. Our analyses suggest that different platforms may be used for activities with varying complexity and risks for criminals.

Ethnic villages are examples of tourism products based on historical representations of the region. Within these villages, tourists participate in various customs and traditions, gaining insights into the heritage of local communities. As heritage should be the basis for improving rural tourism, this research was conducted to investigate the extent to which ethnic villages safeguard their heritage. The examination of cultural heritage was carried out by experts who evaluated the importance of the criteria for assessing heritage and the application of cultural heritage in these ethnic villages. A fuzzy approach was used to assess the criteria and ethnic villages using fuzzy Logarithm Methodology of Additive Weights (LMWA) and fuzzy Additive Ratio Assessment (ARAS). The sampling process included an initial pool of 28 ethnic villages identified through various associations and agencies. The villages included in this research were chosen by randomly selecting eight villages using a random number generator. Through collaboration with experts and thorough literature research, 12 criteria were established for evaluating heritage use degree in these villages. Results highlighted tourist participation as the most significant criterion, with the Lubac Valley ethnic village demonstrating superior performance. As this research has shown, applying heritage in tourism provides a unique experience, which is the base for developing rural tourism, and the Lubac Valley could serve as an example to other ethnic villages on building a tourist offer based on heritage. In addition, this research contributes to understanding the current landscape and strengthening the promotion of heritage in ethnic villages by developing a sustainable tourist offer.

The widespread deployment of AI systems has led to overlapping concerns around technological impact and governance, often resulting in conceptual ambiguities and policy confusion. We propose a structured and context-sensitive framework for addressing the ethical implications of artificial intelligence. We argue that ethical frameworks must distinguish between the intended domain of AI deployment and the scale of its societal effects.To resolve these tensions, we introduce a two-dimensional matrix based on (1) the extent of AI’s impact and (2) the scope of its governance, which together form four distinct ethical contexts. Within each quadrant, we explore specific risks, values, and regulatory considerations. This matrix not only clarifies the conceptual terrain of AI ethics but also offers a practical roadmap for anticipating ethical risks, developing normative guidance, and informing domain-specific governance strategies.Our goal is not to prescribe a single ethical doctrine but to provide decision-makers with a structured lens through which AI systems can be evaluated in context. This approach promotes adaptive and anticipatory governance while remaining responsive to local, institutional, and cultural variations.

Background and aim Public health and social measures (PHSM) are critical aspects of limiting the spread of infections in pandemics. Compliance with PHSM depends on a wide range of factors, including behavioral determinants such as emotional response, trust in institutions or risk perceptions. This study examines self-reported compliance with PHSM during the COVID-19 pandemic in the Federation of Bosnia and Herzegovina (FBIH). Materials and methods We analyze the association between compliance and behavioral determinants, using data from five cross-sectional surveys that were conducted between June 2020 and August 2021 in FBIH. Quota-based sampling ensured that the 1000 people per wave were population representative regarding age, sex, and education level based on the data from the latest census in Bosnia and Herzegovina. One-way analysis of variance (ANOVA) was used to identify significant changes between studies on determinants and PHSM measures. Regression was used to find relations between behavioral determinants and PHSM. Results Participants reported strong emotional responses to the rapid spread of the virus and its proximity to them. Risk perception was spiking in December 2020 when rates of infection and death were particularly high. Trends in policy acceptance were divergent; participants did not rate PHSM as exaggerated, but perceived fairness was low. Trust in institutions was low across all waves and declined for specific institutions such as the health ministry. In five wave-specific regression analyses, emotional response (βmin/max = .11*/.21*), risk perception (βmin/max = .06/.18*), policy acceptance (βmin/max = .09/.20*), and trust in institutions (βmin/max = .06/.21*) emerged as significant predictors of PHSM. Conclusions This study contributes to the body of research on factors influencing compliance with PHSM. It emphasizes the importance of behavioral monitoring through repeated surveys to understand and improve compliance. The study also affirms the impact of public trust on compliance, the risk of eroding compliance over time, and the need for health literacy support to help reinforce protective behaviors.

This article situates itself in the theoretical space between world-systems theory and postcolonial theory, exploring how the state of peripherality and concomitant dependency is reproduced in Bosnia and Herzegovina during the Covid-19 pandemic. The dependent position of the Bosnian protectorate in the world-system, its heritage of colonial rule and peripherality, as well as post-colonial influences of Pax-Americana on state constitution and state capture, have all contributed to the inability of the divided state to adequately respond to the pandemic. This article reveals a multifaceted dependence of Bosnia and Herzegovina on the Western core economies in relation to aid, equipment and vaccines as well as its gradual move towards China as a new opportunity. The pandemic also becomes the stage for competition between the Eastern and Western companies for mining concessions needed to secure the green transition in the respective economies, as a new wave of primitive accumulation ravages the European periphery. As a result of this new scramble for the Balkans, and amidst the global shift towards multipolarity, we see a stable reproduction of peripherality in Bosnia and Herzegovina and the Western Balkans, and re-emergence of ethnic conflict in previously disputed areas, where ethnic groups identify with the interests of their respective hegemons.

This study compares two titrimetric methods for quantifying acetylsalicylic acid (ASA) in aspirin tablets stored under different environmental conditions. ASA stability can be influenced by factors such as temperature, humidity, and light exposure. The two titrimetric methods used are acid-base titration with hydrochloric acid (HCl) and sodium hydroxide (NaOH). Aspirin tablets were stored for 30 days under controlled conditions simulating varying environmental factors, and both methods were evaluated for accuracy, precision, and reliability. The results show a strong  correlation between the two methods, with a Pearson correlation coefficient of 0.937 and a high Intraclass Correlation Coefficient (ICC), indicating consistency and reliability. However, the paired t-test revealed a statistically significant difference (r = 0.937, p = 0.001) between the  methods, suggesting small but meaningful discrepancies in their results. The Bland-Altman   analysis demonstrated that Method I consistently provided higher values than Method II, while the linear regression analysis indicated that Method II slightly underestimates values compared to Method I. Overall, both methods were found to be highly reliable and interchangeable within certain limits, but the small systematic differences between them should be considered when interpreting results. This study provides valuable insights into the performance of titrimetric   methods for ASA quantification, contributing to the optimization of pharmaceutical analysis techniques.

BACKGROUND AND AIMS Adults with congenital heart disease (ACHD) knowledge regarding their heart condition is crucial for optimal long-term outcome. Previous studies from North-Western Europe showed that important gaps in ACHD knowledge still exist. This study evaluates ACHD patients' knowledge in Central and South-eastern Europe (CESEE) and aims to identify opportunities for improving life-long ACHD care and outcomes in this region. METHODS A structured survey regarding the baseline heart condition knowledge was prospectively distributed to stable ACHD patients over a one-year period (2021-2022). Patients' responses were verified by their ACHD physicians to ensure accurate background information. RESULTS Among 1650 patients (age 34.5 ±14) across 14 CESEE countries the majority 1023(62.0%) had simple congenital heart disease with at least one previous heart procedure performed 1201(72.8%); 1060(64.2%) were asymptomatic and 875(53.8%) had secondary school education. Overall, 576(34.9%) did not have basic knowledge regarding their congenital heart disease and 146(12.2%) did not have basic understanding regarding their previous heart procedure/s. Patients considered their life expectancy similar to the general population (p=0.039). Encouragingly, 962(59.5%) expressed a desire to learn more, and 929(58.1%) favoured technological integration in their care. CONCLUSIONS Significant knowledge gaps exist amongst CESEE ACHD patients regarding their heart condition. Better ACHD patient education on current health and prospects is urgently needed. The results of this study should serve for developing congenital heart disease structured transitional and educational programmes in CESEE incorporating technology for their ACHD care and education to enhance patients' health knowledge and healthy life-behaviours to positively influence their life-long prospects.

BackgroundDialysis is a very complex treatment that is received by around 3 million people annually. Around 10% of the death cases in the presence of the dialysis machine were due to the technical errors of dialysis devices. One of the ways to maintain dialysis devices is by using machine learning and predictive maintenance in order to reduce the risk of patient's death, costs of repairs and provide a higher quality treatment.ObjectivePrediction of dialysis machine performance status and errors using regression models.MethodThe methodology includes seven steps: data collection, processing, model selection, training, evaluation, fine-tuning, and prediction. After preprocessing 1034 measurements, twelve machine learning models were trained to predict dialysis machine performance, and temperature and conductivity error values.ResultsEach model was trained 100 times on different splits of the dataset (80% training, 10% testing, 10% evaluation). Logistic regression achieved the highest accuracy in predicting dialysis machine performance. For temperature predictions, Lasso regression had the lowest MSE on training data (0.0058), while Linear regression showed the highest R² (0.59). For conductivity predictions, Lasso regression provided the lowest MSE (0.134), with Decision tree achieving the highest R² (0.2036). SVM attained the lowest MSE on testing dataset, with 0.0055 for temperature and 0.1369 for conductivity.ConclusionThe results of this study demonstrate that clinical engineering (CE) and health technology management (HTM) departments in healthcare institutions can benefit from proposed automated systems for advanced management of dialysis machines.

Background and Objectives Progression independent of relapse activity (PIRA) is associated with worse outcomes in people with multiple sclerosis (pwMS). Although previous research has linked PIRA to accelerated brain and spinal cord atrophy and compartmentalized chronic inflammation, the role of white matter (WM) tract degeneration remains unclear. This study aimed to explore the relationship between PIRA and the integrity of major WM tracts using diffusion tensor imaging (DTI). Methods A cohort of 258 pwMS was stratified based on the presence or absence of PIRA over a 4-year follow-up period. At the end of follow-up, DTI metrics were compared between groups using propensity score–weighted linear regression models to account for potential confounders. Results PwMS with ≥1 PIRA event (n = 39) exhibited significant reductions in fractional anisotropy and increases in radial, axial, and mean diffusivity within the corpus callosum and motor tracts (false discovery rate–adjusted p ≤ 0.04) compared with those without PIRA, indicating more pronounced WM damage. Discussion Our findings highlight an association between PIRA and microstructural damage in key WM tracts. The observed DTI changes likely reflect processes such as Wallerian degeneration and contribute to the growing evidence linking PIRA to neurodegeneration.