Chronic wounds remain as a major clinical and economic challenge, requiring advanced materials that can both support tissue regeneration and effectively prevent infection. Alginate-based dressings are widely used due to their excellent biocompatibility, high absorbency, and gel-forming ability, which help maintain a moist environment favorable for healing. However, these systems inherently lack strong antioxidant and antimicrobial properties. In this context, the incorporation of lignin, a naturally abundant and phenolic-rich biopolymer, offers a promising strategy to overcome these limitations by introducing intrinsic radical scavenging activity, enhanced antibacterial performance, and improved mechanical strength. In this review, we provide a focused and comprehensive overview of lignin-alginate hydrogels as multifunctional wound dressing materials. Unlike previous reviews that discuss lignin- or alginate-based systems individually, this work specifically emphasizes their synergistic integration and the resulting enhancements in hydrogel performance. We first outline the fundamental properties of hydrogels that make them suitable for wound healing applications, followed by detailed discussions on the individual characteristics of alginate and lignin. Furthermore, recent advances in formulation strategies, crosslinking approaches, and multifunctional design are critically discussed, with a focus on improving mechanical stability, exudate management, and controlled therapeutic release. Finally, key challenges related to reproducibility, large-scale production, and clinical translation are highlighted, along with future perspectives emphasizing sustainability and personalized wound care. According to these data, lignin-alginate hydrogels could be considered as a promising next-generation platform for developing sustainable, effective, and multifunctional wound dressing systems.
Degenerative lumbar spine disorder (DLSD) is a significant public health concern, presenting with low back pain, radicular leg pain, and reduced quality of life. Although conservative management is the first-line treatment, some patients require surgery due to persistent symptoms or progressive neurological deficits. Lumbar instrumented fusion is a commonly performed procedure for DLSD. Adjacent segment degeneration (ASD) is a frequent complication after spinal fusion, occurring at either the cranial or caudal side of the fusion mass. While the role of spinopelvic parameters in ASD is well recognized, radiological predictors for poor outcomes and complications remain less understood. This study aimed to identify preoperative radiological predictors of ASD in patients who underwent short-segment decompression and fusion for DLSD. We cross-sectionally evaluated radiological data of the patients who underwent short-segment decompression and spinal fusion for DLSD at a tertiary spine clinic between 2013 and 2023. In 209 patients, 31.6
This study explores the impact of eco-innovation performance, green energy transition, energy transition, economic growth, and natural resources depletion on ecological footprint in a specific group of countries, the 'eco-innovation leaders', recently identified by the EU for their eco-innovation performances. Due to structural breaks and cross-sectional dependence, the study benefits from novel Fourier-PEGLS and Fourier-CS-ARDL methods with an extended period of 1990-2022. The empirical findings confirm long-run cointegrated relationships among eco-innovations, ENEF, the green energy transition, economic growth, and natural resource depletion in the eco-innovation leaders. The novel Fourier-CS-ARDL and Fourier-PEGLS models further indicate that eco-innovations, energy efficiency, and the green energy transition significantly reduce ecological footprint and, hence, environmental degradation. Convergence to the long-run equilibrium takes approximately 2.7 years, with 37.09% of the deviations from the long-run equilibrium corrected each year. The country-specific duration of convergence ranges from 1.78 to 9.9 years, with Finland experiencing the shortest convergence period and Denmark having the longest. Moreover, with robust methods, the study identifies bidirectional and unidirectional Granger causality between eco-innovation, ENEF, economic growth, natural resources depletion, and green energy transition with feedback effects for a large set of tested directions of causality, which have profound impacts. These findings provide a comprehensive analysis of how eco-innovation, green energy transition, and ENEF contribute to reversing environmental degradation, with implications for achieving carbon neutrality targets in the EU.
Glioblastoma (GBM) represents one of the most lethal and therapy-resistant forms of brain tumors, characterized by high heterogeneity, metabolic reprogramming, and recurrence. In the current study, we aimed to identify novel small molecule inhibitors targeting mutant isocitrate dehydrogenase 1 (IDH1), a crucial enzyme involved in GBM tumor metabolism. For this aim, machine learning-based quantitative structure-activity relationships modeling was combined with structure-based e-Pharmacophore screening to virtually screen ultralarge chemical libraries containing around 157 million compounds. The best hits were selected based on docking score, predicted pIC50 value, and molecular mechanics/generalized Born surface area binding energy calculations. Furthermore, molecular dynamics (MD) simulations were conducted to validate the selected hit compounds. In total, 36 compounds were subjected to short MD simulations (10 ns), and 16 molecules showing low binding free energies (below -90 kcal/mol) were further analyzed through long MD simulations (100 ns). Among these, 11 synthetically available hits were ordered and experimentally tested on human glioblastoma U87 and U251 cell lines. Our experimental results showed that 5 of the tested compounds (hits 1, 4, 5, 6, and 7) reduced spheroid formation by nearly 80%-90% and inhibited cell proliferation. Moreover, these hits decreased the oxygen consumption rate and extracellular acidification rate (ECAR), by up to 62% and 55%, respectively, indicating inhibition of both mitochondrial respiration and glycolysis. Furthermore, Western blot and quantitative real-time polymerase chain reaction analyses revealed downregulation of glycolytic enzymes and stemness markers. Moreover, steered MD and free energy perturbation analyses confirmed the stable interactions of these compounds at the IDH1 mutant active site. This multistage in silico-in vitro approach allowed the identification of metabolically disruptive novel mutant IDH1 inhibitors that suppress glycolysis, mitochondrial respiration, and cancer stemness in glioblastoma cells. These compounds represent promising scaffolds for the development of next-generation GBM therapeutics. SIGNIFICANCE STATEMENT: This study integrate machine learning-guided quantitative structure-activity relationships modeling with structure-based pharmacophore screening to discover small molecule inhibitors of mutant IDH1, a central mediator of metabolic reprogramming in glioblastoma. Lead compounds identified through this pipeline inhibit mutant IDH1 activity, disrupt metabolic pathways required for glioblastoma cell viability, and concomitantly reduce stem-like phenotypes in vitro, consistent with a dual mechanism of action that targets both bulk tumor cells and cancer stem-like populations.
Sustainable tourism performance of cruise ports is a critical determinant of the long-term competitiveness of both ports and destination regions. This study aims to develop ad apply a novel piture fuzzy ses (PFSs)-based decision-support system to sstematically evaluate cruise pots' sustainable tourism performance by integrating qualiative expert judgments wth quanttative operational data. The proposed framewok adopts a ulti-attibute group decisin-making structure, in which PFSs are employed to explicitly capture ucertainty, hesitation, and neutrality in expert evaluations. Criteria weights are determined using an extended PFS-based weighs by envelope and slope (WENSLO) method, while cruisport rankings are obtained through a PFS-integrated multi-ttributive border approximation area comparison (MABAC) approach. The applicability of the proposed PFS-WENSLO-MABAC hybrid model is demonstrated through a case stuy of major cruise ports in Tu & uml;rkiye. The results indite that the number of cruispassengers (0.5601) and the numer of cruise ships (0.4218) are the most influental detrminants of sustainable tourism performance, with Ku & cedil;sadas & imath; Cruise Port (0.9029) achieving te highesoverall ranking. Sensitivity and robustness analyses confirm the stblity of the ranking outcomes under varying weghting scenarios. The findings provide actionable isights for pt authorities and policymkers by identfying priority prformance dimenionad offering a reliable analytical tool to support straegic plannig nd ustinbility-oriented decision-making in cruise tourism.
