Advancing Open Science

Background/Objectives: Aortic valve therapy increasingly follows a lifetime management concept. As all bioprostheses ultimately degenerate, optimal outcomes rely on the appropriate selection and timing of treatment modality. This study evaluates outcomes of redo surgical aortic valve replacement (redo-SAVR) and valve-in-valve transcatheter aortic valve replacement (ViV-TAVR) in a consecutive, unselected real-world cohort treated for bioprosthetic valve failure (BVF). Methods: A single-center retrospective analysis of all patients undergoing redo-SAVR or ViV-TAVR for BVF between June 2019 and December 2024 was conducted. The primary endpoint was survival at 30 days and at 1, 3, and 5 years; the secondary endpoint was time to reintervention. Cox proportional hazards models were used; proportionality was tested; subgroups were defined by indication and presence of concomitant procedures. Results: Eighty-three patients were included (redo-SAVR n = 42; ViV-TAVR n = 41). All active endocarditis cases were managed surgically. In isolated procedures, 30-day survival was 95.5% after redo-SAVR (100% when excluding endocarditis) and 100% after ViV-TAVR; 5-year survival was 81.3% and 94.1%, respectively (94.4% for isolated redo-SAVR excluding endocarditis). Because hazards were non-proportional and risk sets were sparse beyond 5 years, we fitted a time-split Cox model (0–5 years). In multivariable analysis, endocarditis (HR 4.45, 95% CI 1.16–17.04) and NYHA IV (HR 4.87, 95% CI 0.98–24.17)—not treatment modality—were associated with mortality. Conclusions: In a real-world, all-comers setting, early outcomes for isolated reinterventions were favorable with both pathways. Mortality patterns were case-mix driven—especially by endocarditis and the need for concomitant surgery. Accordingly, ViV-TAVR and redo-SAVR should be viewed not as competing procedures but as complementary, scenario-specific options within a lifetime management strategy.

This research investigates the process of determining and maintaining the Acceptable Level of Safety Performance (ALoSP) at an airport, utilizing a case study conducted at Split Airport. The study illustrates how the ALoSP framework, originally developed for State-level application under ICAO Annex 19, can be systematically adapted and implemented at the organizational level within the Safety Management System (SMS) of an aviation service provider. The aim of the study is to systematically demonstrate the process by which an airport defines, monitors, and maintains its ALoSP through the application of Safety Performance Indicators (SPIs), Safety Performance Targets (SPTs), and alert thresholds within the framework of Safety Performance Management (SPM). Main results show that Split Airport consistently maintained its safety performance at an acceptable level throughout a ten-year monitoring period (2015–2024), with a small number of deviations observed in certain safety performance indicators. The findings highlight the airport’s robust safety culture, strong data-driven monitoring, and proactive use of both leading and lagging SPIs to anticipate and prevent safety issues. The study confirms that the ALoSP framework can successfully support continuous safety improvement and regulatory compliance at the organizational level, offering a practical example for other aviation service providers.

Background/Objectives: Oral squamous cell carcinoma (OSCC) remains a major therapeutic challenge due to treatment-related toxicity and impaired oral tissue regeneration. This study aimed to develop and characterize a novel biocomposite based on bioactive compounds from Ganoderma lucidum incorporated into marine collagen derived from Rhizostoma pulmo and to evaluate its physicochemical properties, antioxidant and antimicrobial activities, and in vitro antitumor potential in the oral cavity. Methods: Hydroalcoholic extracts of G. lucidum and pepsin-soluble collagen peptides from R. pulmo jellyfish were prepared and combined to obtain two hydrogel biocomposites with different component ratios. Chemical and structural characterization was performed using HPLC-DAD, SDS-PAGE, FT-IR, circular dichroism, and spectrophotometric assays. Antioxidant activity was assessed by DPPH radical scavenging and reducing power assays, while antimicrobial activity was evaluated against oral pathogens using diffusion and MIC methods. In vitro biological activity was investigated using MTT viability and scratch migration assays on human OSCC cell lines (SCC-9 and HSC-3). Results: The biocomposites preserved the structural integrity of type I collagen and incorporated polysaccharides and polyphenols from G. lucidum. The combined formulations showed enhanced antioxidant and antimicrobial activities compared with collagen alone. In vitro assays demonstrated dose- and time-dependent reductions in OSCC cell viability and delayed cell migration, with effects comparable to those of G. lucidum extract. Conclusions: The G. lucidum–R. pulmo biocomposite exhibits favorable physicochemical properties and demonstrates antioxidant, antimicrobial, and in vitro antitumor activity. These findings support its potential as a multifunctional biomaterial for further investigation as an adjunct approach in oral cancer-related applications.

The convergence of artificial intelligence (AI), machine learning (ML), blockchain, and big data analytics is transforming the governance, sustainability, and resilience of modern banking ecosystems. This study provides a multivariate bibliometric analysis using Principal Component Analysis (PCA) of research indexed in Scopus and Web of Science to explore how decentralized digital infrastructures and AI-driven analytical capabilities contribute to sustainable financial development, transparent governance, and climate-resilient digital societies. Findings indicate a rapid increase in interdisciplinary work integrating Distributed Ledger Technology (DLT) with large-scale data processing, federated learning, privacy-preserving computation, and intelligent automation—tools that can enhance financial inclusion, regulatory integrity, and environmental risk management. Keyword network analyses reveal blockchain’s growing role in improving data provenance, security, and trust—key governance dimensions for sustainable and resilient financial systems—while AI/ML and big data analytics dominate research on predictive intelligence, ESG-related risk modeling, customer well-being analytics, and real-time decision support for sustainable finance. Comparative analyses show distinct emphases: Web of Science highlights decentralized architectures, consensus mechanisms, and smart contracts relevant to transparent financial governance, whereas Scopus emphasizes customer-centered analytics, natural language processing, and high-throughput data environments supporting inclusive and equitable financial services. Patterns of global collaboration demonstrate strong internationalization, with Europe, China, and the United States emerging as key hubs in shaping sustainable and digitally resilient banking infrastructures. By mapping intellectual, technological, and collaborative structures, this study clarifies how decentralized intelligence—enabled by the fusion of AI/ML, blockchain, and big data—supports secure, scalable, and sustainability-driven financial ecosystems. The results identify critical research pathways for strengthening financial governance, enhancing climate and social resilience, and advancing digital transformation, which contributes to more inclusive, equitable, and sustainable societies.

Background: Lactate Dehydrogenase C (LDHC) is a promising therapeutic target due to its highly tumor-specific expression, immunogenicity, and oncogenic functions. We previously showed that LDHC silencing in triple-negative breast cancer (TNBC) cells enhances treatment response to DNA-damage response-related drugs, supporting its therapeutic potential. However, no selective LDHC inhibitors exist, highlighting the need for innovative targeting strategies. Methods: We assessed the physicochemical properties and evaluated the delivery efficiency, anti-tumor activity, and safety of four cell-penetrating peptides (CPPs)—R10, 10R-RGD, cRGD-10R, and iRGD-10R—for siRNA-mediated LDHC silencing in TNBC. Clonogenic assays were used to evaluate effects on olaparib sensitivity, and TNBC zebrafish xenografts were utilized to study in vivo anti-tumor activity. Results: All CPP:siRNA complexes formed uniform nanocomplexes (129–168 nm) with low polydispersity indices (<0.25) and positive zeta potentials (+6.47 to +29.6 mV). Complexes remained stable in human serum for 24 h and showed no significant cytotoxicity in TNBC and non-cancerous cell lines. The 10R-RGD and cRGD-10R:siLDHC complexes achieved 40% LDHC protein knockdown, reduced TNBC clonogenicity by 30–36%, and enhanced olaparib sensitivity. Treatment of TNBC zebrafish xenografts with 10R-RGD or cRGD-10R:siLDHC complexes significantly reduced tumor growth by approximately 50% without major toxicity. Conclusions: These results demonstrate that CPP-mediated siRNA delivery enables selective LDHC silencing with tumor growth inhibition in triple-negative breast cancer models. This approach represents a novel, effective, and safe proof-of-concept therapeutic strategy to target LDHC, with potential translational relevance as a standalone therapy or in combination with common anti-cancer drugs.

For centuries, plants have been central in human medicine, food/nutrition, and culture [...]

This study addresses the limitations of conventional highway visibility monitoring under rain–fog conditions, where fixed stations and visibility sensors provide limited spatial coverage and unstable accuracy. Considering that drivers’ visual fields are jointly affected by global fog and local spray-induced mist, a dynamic visibility recognition and risk assessment framework is proposed using roadside monocular CCTV (Closed-Circuit Television) imagery. The method integrates the Koschmieder scattering model with the dark channel prior to estimate atmospheric transmittance and derives visibility through lane-line calibration. A Monte Carlo-based coupling model simulates local visibility degradation caused by tire spray, while a safety potential field defines the low-visibility risk field force (LVRFF) combining dynamic visibility, relative speed, and collision distance. Results show that this approach achieves over 86% accuracy under heavy rain, effectively captures real-time visibility variations, and that LVRFF exhibits strong sensitivity to visibility degradation, outperforming traditional safety indicators in identifying high-risk zones. By enabling scalable, infrastructure-based visibility monitoring without additional sensing devices, the proposed framework reduces deployment cost and energy consumption while enhancing the long-term operational resilience of highway systems under adverse weather. From a sustainability perspective, the method supports safer, more reliable, and resource-efficient traffic management, contributing to the development of intelligent and sustainable transportation infrastructure.

This paper investigates the interactive effects of live streaming pre-sale modes and return policies within a manufacturer’s two-stage supply chain. On the one hand, in addition to traditional pre-sales, manufacturers can adopt merchant live streaming or commission influencer live streaming to stimulate demand, enhance consumer engagement, and generate social influence. On the other hand, a “Money-back guarantee (MBG)” policy can be implemented to mitigate consumers’ valuation uncertainty. Using a game-theoretic model, we examine the manufacturer’s optimal joint decisions across six scenarios, determining the optimal conditions for activating each pre-sale mode and the MBG policy. Our findings reveal that the effectiveness of an MBG in promoting pre-sales is not universal but highly contingent on factors such as return costs, product satisfaction, and the social influence generated through live streaming. Specifically, traditional pre-sales tend to adopt an MBG only when return costs are low, whereas merchant live streaming pre-sales consistently adopt it under weak social influence. In contrast, influencer live streaming pre-sales are more likely to adopt MBGs when return costs are low; however, with high return costs, the manufacturer’s profit may fall below that of not conducting pre-sales, leading to the abandonment of this mode. Regarding mode selection, under a no-return policy, manufacturers prefer influencer live streaming when social influence is weak but switch to merchant live streaming when it is strong. When offering an MBG, manufacturers tend to commission influencer live streaming when product satisfaction and return costs are low but revert to traditional pre-sales when satisfaction is high. Conversely, with high return costs, manufacturers prefer merchant live streaming under low satisfaction but favor influencer live streaming when satisfaction is high. These findings offer valuable theoretical insights and practical guidance for manufacturers to optimize their pre-sales and return strategies under diverse market conditions.