Search Results (72)

Background/Objectives: Real-world antidepressant use for depressive disorder varies across clinical settings. This study aimed to characterize prescribing pathways and evaluate the comparative safety of frequently prescribed antidepressants in a large multicenter cohort with newly diagnosed depressive disorder. Methods: We conducted a retrospective multicenter observational study using electronic health record data standardized to the OMOP Common Data Model (1999–2026) in adults initiating antidepressants. Prescribing patterns among 13,381 patients were visualized using sunburst plots and Sankey diagrams. Comparative safety, escitalopram vs. sertraline and trazodone, was evaluated across 18 hospitals (n = 44,542) for nine clinical safety outcomes using propensity score-matched Cox proportional hazards models and empirically calibrated random-effects meta-analysis. Results: Selective serotonin reuptake inhibitors, particularly escitalopram (21.4%, n = 2868), were the preferred antidepressant. Common adjunctive medications included trazodone (14.6%, n = 794) and quetiapine (14.0%, n = 761), though utilization varied across hospitals. No clear differences were detected for most safety outcomes between cohorts. However, empirically calibrated QTc prolongation risk was significantly increased with sertraline relative to escitalopram (HR 1.75, 95% CI 1.10–2.78), a signal requiring further confirmation. Conclusions: This study demonstrates substantial institutional variation exists in real-world antidepressant treatment pathways and combination strategies. While comparative safety profiles were broadly comparable across evaluated outcomes, the isolated QTc prolongation signal for sertraline warrants targeted external confirmation. Furthermore, trazodone and escitalopram cohorts should not be regarded as clinically interchangeable first-line options, supporting individualized, evidence-based antidepressant selection in routine clinical practice. Full article

Background: Outpatient palliative care effectively alleviates symptom burden in advanced cancer patients, yet data from Central–Eastern Europe remain scarce. This retrospective study examined changes in revised Edmonton Symptom Assessment Scale (ESAS) scores from initial outpatient palliative consultation to first follow-up in Hungarian cancer patients, assessing clinically meaningful improvement and inter-symptom associations. Methods: Revised ESAS scores from 119 patients attending an outpatient palliative care clinic (2017–2020) were analyzed using paired baseline and first follow-up assessments (7–30 days). Symptom changes (Time 2–Time 1) were evaluated using Wilcoxon signed-rank tests. Clinically meaningful improvement was assessed with minimal clinically important difference thresholds (0.5× baseline SD). Sankey diagrams visualized symptom transitions, and multivariable linear regression examined inter-symptom associations. Results: Baseline pain was highest (mean 6.29, median 7), followed by fatigue, sleep disorder, and impaired well-being. At follow-up, significant reductions were observed in pain (mean 4.52, p = 0.001), nausea, dyspnea, constipation, sleep disorder, depression, and anxiety (all p < 0.05). Sankey diagrams showed shifts from severe to mild/moderate pain (50% to 24%) and constipation. Clinically meaningful improvement occurred in pain, nausea, and constipation, with 59–65% achieving ≥1-point pain reduction. Regression analyses showed that pain reduction was associated with concurrent improvements in sleep disorder (β = 0.31), depression (β = 0.20), fatigue (β = 0.20), and anxiety (β = 0.14), while dyspnea reduction was associated with concurrent improvements in depression (β = 0.22) and anxiety (β = 0.14). Conclusions: Outpatient palliative care in Hungarian cancer patients resulted in clinically meaningful symptom reductions, particularly pain and dyspnea. Improvements in these core symptoms were associated with concurrent improvements in other symptom domains, underscoring the clinical relevance of inter-symptom associations and supporting early, integrated outpatient palliative care and symptom cluster-based management. Full article

Mapping land cover, monitoring its changes, and simulating future alterations are essential tasks for sustainable land management. These processes enable accurate assessment of environmental impacts, support informed policymaking, and assist in the planning needed to mitigate risks related to urban expansion, deforestation, and climate change. This study proposes a GeoAI-based framework leveraging Multilayer Perceptron (MLP), a class of Artificial Neural Networks (ANNs), to predict land cover changes in the Aosta Valley region (NW Italy). The model uses Copernicus Earth Observation data, specifically Sentinel-1 and Sentinel-2 imagery, and is trained and validated on land cover maps derived from different time periods previously validated with ground truth data. The objective is to provide a predictive tool capable of simulating potential future landscape configurations, supporting proactive regional land use planning including regulatory constraints under the current land use plan. Model performance is evaluated using accuracy metrics. The land cover classification methodology follows established approaches in the scientific literature, adapted to the specific geomorphological characteristics of the Aosta Valley. To explore and visualize potential future land cover transitions, Sankey and chord diagrams are used in combination with zonal statistics and thematic plots. These provide detailed insights into the intensity, direction, and magnitude of landscape dynamics. Training data were stratified-sampled across the study area, covering a diverse set of land cover classes to ensure robustness and generalization of the MLP model. This GeoAI approach offers a scalable and replicable methodology for anticipating land cover dynamics, identifying vulnerable areas, and informing adaptive environmental management strategies at the regional scale, while simultaneously considering the latest urban planning regulations. Full article

Land use and land cover change (LULCC) constitutes a major challenge to sustainability worldwide. This also applies to Japan, where urbanization in coastal lowlands is contrasted with widespread agricultural abandonment in rural landscapes. In this systematic review we synthesized the main LULCC trajectories, their driving forces, and specific effects in Japan from 1994 to 2024. Following PRISMA guidelines, 158 peer-reviewed articles were analyzed using quantitative co-occurrence analyses, Chi-squared tests, and Sankey diagrams to map land-use flows. Two dominant and opposing trajectories were confirmed: urban expansion and agricultural abandonment. The most significant land transition flow involved the conversion of agricultural land to forests/natural vegetation, while the conversion of agricultural land to built-up areas came in second place. These transitions were primarily driven by economic and demographic factors, but reforestation trends were strongly influenced by policy and institutional factors (35.70%), reflecting national regreening initiatives. Ecological and biodiversity impacts of LULCC were the most often documented effects (>40% of records). While the published literature describes trends in land-use transformations, the mechanistic understanding of LULCC remains limited. There is an urgent need to move toward process-based predictive modeling that integrates socio-economic variables. Future policies should balance urban density management with the strategic use of rural abandonment for ecosystem services provision and climate mitigation. Full article

Research on low-carbon transitions often neglects small developing countries due to severe data scarcity. To address this critical gap, this study introduces a novel systematic analysis framework tailored for data-constrained environments. Integrating Sankey diagram-based energy allocation analysis with a six-dimensional diagnostic framework (resources, policy, economy, technology, society, and environment), the methodology leverages publicly available International Energy Agency data to visualize complex energy flows and identify underlying drivers. To verify the framework’s efficacy, a case study of Mongolia (2016–2021) is carried out. The analysis reveals a persistent structural reliance on coal and uncovers a strategic disconnect between macro-level climate goals and micro-level implementation, primarily driven by economic dependence and technological inertia. By transforming limited public data into actionable systemic insights, this study provides a robust and reproducible tool for evidence-based policymaking. The proposed method offers a vital contribution to global decarbonization efforts, enabling other data-scarce developing countries to navigate their energy transitions effectively. Full article

Against the dual backdrop of the rural revitalization strategy and the pursuit of high-quality, balanced urban–rural education, optimizing rural campus spaces has emerged as an important lever for addressing educational resource disparities and improving pedagogical quality. However, conventional evaluation of campus space optimization faces two systemic dilemmas. First, top-down decision-making often neglects the authentic needs of diverse stakeholders and place-based knowledge, resulting in spatial interventions that lose regional distinctiveness. Second, routine public participation is constrained by geographical barriers, time costs, and sample-size limitations, which can amplify professional cognitive bias and impede comprehensive feedback formation. The compounded effect of these challenges contributes to a disconnect between spatial optimization outcomes and perceived needs, thereby constraining the distinctive development of rural educational spaces. To address these constraints, this study proposes a novel method that integrates regional spatial feature recognition with digital media-based public perception assessment. At the data collection and ethical governance level, the study strictly adheres to platform compliance and academic ethics. A total of 12,800 preliminary comments were scraped from major social media platforms (e.g., Douyin, Dianping, and Xiaohongshu) and processed through a three-stage screening workflow—keyword screening–rule-based filtering–manual verification—to yield 8616 valid records covering diverse public groups across China. All user-identifying information was fully anonymized to ensure lawful use and privacy protection. At the analytical modeling level, we develop a Transformer-based deep learning system that leverages multi-head attention mechanisms to capture implicit spatial-sentiment features and metaphorical expressions embedded in review texts. Evaluation on an independent test set indicates a classification accuracy of 89.2%, aligning with balanced and stable scoring performance. Robustness is further strengthened by introducing an equal-weight alternative strategy and conducting stability checks to indicate the consistency of model outputs across weighting assumptions. At the scenario interpretation level, we combine grounded-theory coding with semantic network analysis to establish a three-tier spatial analysis framework—macro (landscape pattern/hydro-topological patterns), meso (architectural interface), and micro (teaching scenes/pedagogical scenarios)—and incorporate an interpretive stakeholder typology (tourists, residents, parents, and professional groups) to systematically identify and quantify key features shaping public spatial perception. Findings show that, at the macro level, naturally integrated scenarios—such as “campus–farmland integration” and “mountain–water embeddedness”—exhibit high affective association, aligning with the “mountain-water-field-village” spatial sequence logic and suggesting broad public endorsement of ecological campus concepts, whereas vernacular settlement-pattern scenarios receive relatively low attention due to cognitive discontinuities. At the meso level, innovative corridor strategies (e.g., framed vistas and expanded corridor spaces) strengthen the building–nature interaction and suggest latent value in stimulating exploratory spatial experience. At the micro level, place-based practice-oriented teaching scenes (e.g., intangible cultural heritage handcraft and creative workshops) achieve higher scores, aligning with the compatibility of vernacular education’s “differential esthetics,” while urban convergence-oriented interdisciplinary curriculum scenes suggest an interpretive gap relative to public expectations. These results indicate an embedded relationship between public perception and regional spatial features, which is further shaped by a multi-actor governance process—characterized by “Government + Influencers + Field Study”—that mediates how rural educational spaces are produced, communicated, and interpreted in digital environments. The study’s innovative value lies in integrating sociological theories (e.g., embeddedness) with deep learning techniques to fill the regional and multi-actor perspective gap in rural campus POE and to promote a methodological shift from “experience-based induction” toward a “data-theory” dual-drive model. The findings provide inferential evidence for rural campus renewal and optimization; the methodological pipeline is transferable to small-scale rural primary schools with media exposure and salient regional ecological characteristics, and it offers a new pathway for incorporating digital media-driven public perception feedback into planning and design practice. The research methodology of this study consists of four sequential stages, which are implemented in a systematic and progressive manner: First, data collection was conducted: Python and the Octopus Collector were used to crawl online comment data related to Fuwen Township Central Primary School, strictly complying with the user agreements of the Douyin, Dianping, and Xiaohongshu platforms. Second, semantic preprocessing was performed: The evaluation content was segmented to generate word frequency statistics and semantic networks; qualitative analysis was conducted using Origin software, and quantitative translation was realized via Sankey diagrams. Third, spatial scene coding was carried out: Combined with a spatial characteristic identification system, a macro–meso–micro three-tier classification system for spatial scene characteristics was constructed to encode and quantitatively express the textual content. Finally, sentiment quantification and correlation analysis was implemented: A deep learning model based on the Transformer framework was employed to perform sentiment quantification scoring for each comment; Sankey diagrams were used to quantitatively correlate spatial scenes with sentiment tendencies, thereby exploring the public’s perceptual associations with the architectural spatial environment of rural campuses. Full article

Background/Objectives: Carbapenem-resistant Acinetobacter baumannii (CRAB) is classified as an urgent-threat pathogen because of its resistance to nearly all available antibiotics, resulting in high morbidity and mortality rates. However, data on the molecular epidemiology of CRAB isolates in southern Thailand are limited. This study aimed to investigate the genomic epidemiology of CRAB isolates within a hospital network in lower southern Thailand. Methods: Whole-genome sequencing data of CRAB clinical isolates (n = 224) were obtained from a previous study. Additional isolates (n = 70) were included, for which genomic DNA was extracted and sequenced. In total, 294 isolates were collected from patients across seven hospitals in southern Thailand between 2019 and 2020. Their genomes were analyzed using several bioinformatic tools. Results: A high proportion of isolates were obtained from sputum samples of patients with CRAB infection or colonization. Sequence type (ST) 2 was the most frequent ST and was classified in the quadrant with high resistance and virulence. The Sankey diagram showed that ST2 was the dominant and most versatile CRAB lineage circulating across major hospitals, commonly associated with pneumonia, and that diverse resistance genes and plasmid combinations were dominated by bla_OXA-23. The core single-nucleotide polymorphism (SNP)-based phylogenetic tree revealed clades A1 (ST215), A2 (multiple STs), and B (ST2). Bloodstream, skin, and soft tissue infections were predominantly observed in clade B. Conclusions: Our analysis revealed widespread circulation of a high-risk ST2 CRAB lineage with enhanced resistance and virulence across hospital networks in the studied region, highlighting the importance of genomics-informed surveillance for controlling CRAB dissemination. Full article

Drought stress severely limits wheat growth, development and yield. Endophytic fungi play a crucial role in plant growth and drought resistance. In agricultural production, they hold significant application potential as biocontrol agents capable of mitigating drought-induced damage. However, the mechanisms underlying changes in endophytic fungal community structure under drought stress remain unclear. Our study employed amplicon sequencing to investigate the structure of endophytic fungal communities in wheat roots under different water treatments, comparing structural and functional changes between different treatments. Results revealed that drought stress led to the greatest accumulation of relative abundance in the phylum Ascomycota (86.4%). At the genus level, Stachybotrys (increase 994.2%), Fusarium (increase 94.6%) and Aspergillus (increase 295.6%) showed the most significant increases in relative abundance. Co-occurrence network and Sankey diagram analysis revealed that wheat roots formed a drought-specific endophytic fungal community centered around Stachybotrys, Fusarium and Aspergillus, which indirectly enhanced crop drought tolerance. Our findings provide a theoretical foundation for future agricultural strategies to improve crop drought resistance through precise regulation of microbial communities. Full article

Driver fatigue detection based on electroencephalography (EEG) signals has gained increasing attention for enhancing road safety. However, existing deep learning models often treat EEG data as generic time-series inputs, neglecting the inherent hierarchical and spatial–temporal structure of brain activity, which limits their interpretability and generalization. To address this, we propose a novel Residual Polynomial Network (RPN) that explicitly models the positive and negative activation patterns in EEG signals through a polarity-aware architecture. The RPN integrates polarity decomposition, residual learning, and hierarchical feature fusion to capture discriminative neurophysiological dynamics while maintaining model transparency. Extensive experiments are conducted on a real-world driving fatigue dataset using a subject-wise 10-fold cross-validation protocol. Results show that the proposed RPN achieves an average classification accuracy of 97.65%, outperforming conventional machine learning and deep learning baselines including SVM, KNN, DT, and LSTM. Ablation studies confirm the effectiveness of each component, and Sankey diagram analysis provides interpretable insights into feature-to-class mappings. This work not only advances the state of the art in EEG-based fatigue detection but also offers a more transparent and physiologically plausible deep learning framework for brain signal analysis. Full article

To satisfy the needs of an ever-growing population, it is imperative to cope with the extended demand for copper. To do so, copper makers mostly rely on pyrometallurgical processes that are characterized by emitting hazardous gases and solid wastes, and by the fact that these processes are energy demanding. Additionally, copper makers face the issue of processing leaner ore bodies or exploiting mineral deposits already overexploited or about to end their productivity cycle. These problems compromise the sustainable production of copper. Because of that, this study focuses on the leading technology in use to assess and identify possible solutions in order to improve the efficiency of energy usage and to decrease the amount of wastes generated in copper pyrometallurgy. To do so, reliable thermodynamic databases and Sankey diagrams were used to determine possible improvements. For example, it is determined that by increasing the mass ratio of Fe/Cu in the mineral feedstock may result in increasing the copper content in the matte, and thus reducing the exergy flows, resulting in improved energy usage. Another positive impact is that using oxygen-enriched air with higher copper concentrations could decrease SO₂ emissions by nearly 25%. Among other detrimental environmental issues, they entail. Full article

Background: The development of later-line drugs for metastatic colorectal cancer (mCRC) has expanded treatment options. However, real-world evidence of treatment sequences and transition rates from early- to later-line treatments are limited. Patients and methods: This was a retrospective study using hospital administrative data from patients in Japan who underwent colorectal cancer surgery or first-line treatment after January 2017. Transition rates were calculated and treatment sequences were summarized using a Sankey diagram. Logistic regression was performed to identify factors associated with the transition from second- to third-line treatment. Results: In total, 27,100 patients (median age: 69 years) were included in the study population. Transition rates to subsequent treatment lines from first to fifth ranged from 66.6% to 71.3% (first to second: 69.4%; second to third: 71.3%; third to fourth: 71.1%; fourth to fifth: 66.6%). Among 9061 patients who received second-line treatment, 6456 continued to third-line treatment, and 2605 received the best supportive care. Longer first- (≥180 days; OR: 1.24; 95% CI: 1.13–1.37) and second-line (≥120 days; OR: 1.70; 95% CI: 1.55–1.86) treatment durations were significant factors for continuing to third-line treatment. Prior therapy with oxaliplatin and irinotecan plus molecular targeted drugs was also associated with a higher likelihood of proceeding to third-line treatment (OR: 1.41; 95% CI: 1.27–1.56). Conclusions: This study describes the current mCRC treatment landscape in Japan. Considering the findings, appropriate early treatments are critical for transition to later-line treatment. Additionally, many later-line options are necessary to provide treatment continuation opportunities for better outcomes. Full article

Background/Objectives: Type 2 diabetes mellitus (T2DM) is a major metabolic disorder and is frequently accompanied by liver steatosis. Apolipoprotein J (ApoJ) is a glucose-regulated molecular chaperone that has been implicated in hepatic lipid deposition under nutrient overload. This study aimed to investigate the role of hepatocyte-specific ApoJ deletion in hepatic metabolism under diabetic conditions. Methods: A T2DM mouse model with hepatocyte-specific ApoJ knockout (HKO) was established through a high-fat diet combined with streptozotocin injection. Hepatic metabolic profiles were analyzed using untargeted metabolomics with UHPLC–MS/MS. Differential metabolites were subjected to KEGG pathway and Sankey diagram analyses to identify biologically relevant pathways. Results: In total, 140 metabolites showed significant differential abundance in HKO mouse liver, primarily encompassing organic acids and derivatives as well as lipids and lipid-like molecules. KEGG analysis revealed that ApoJ deletion enhanced pathways related to vitamin digestion and absorption, thiamine metabolism, amino acid biosynthesis, lysine degradation, and 2-oxocarboxylic acid metabolism. In contrast, pathways associated with galactose metabolism, cysteine and methionine metabolism, purine metabolism, and the pentose phosphate pathway were suppressed. Sankey diagram analysis further demonstrated that ApoJ deletion markedly reshapes hepatic metabolic networks in T2DM. Conclusions: Given the central role of hepatic dysmetabolism in the pathogenesis of diabetes and its complications, targeting ApoJ may represent a promising therapeutic approach for restoring hepatic metabolic homeostasis and preventing diabetes-associated steatosis. Full article

Wood and wood-based products are increasingly recognized for their renewability and carbon storage capacity, supporting sustainable development and circular economy goals in the EU. This paper provides a comprehensive review of 42 material flow analysis (MFA) studies on wood resources conducted in the European Union and Switzerland between 2000 and 2024, introducing a five-level data risk classification. It examines how MFA is applied, including system boundaries, data sources, unit consistency, flow representation, and uncertainty handling. Results show that while volume-based units and Sankey diagrams are widely used, there is substantial variation in terminology, data quality, and methodology. The building stage is frequently excluded, limiting the completeness of wood flow assessments. Key challenges include restricted data access, inconsistent spatial and temporal scales, and varying levels of data processing risk. The study recommends harmonized units and terminology, open-access databases, standardization in visualization practices, and ultimately a wood-specific MFA framework to improve data quality, comparability, and policy relevance. Full article

This study examines the accessibility of tourism facilities in the Banská Bystrica region of Slovakia for visitors with disabilities and explores the attitudes of service providers toward inclusive tourism. Accessibility remains a key challenge in developing equitable tourism services, and this research aims to evaluate the current state of barrier-free infrastructure while identifying opportunities for improvement. A survey of 45 tourism facilities was conducted to assess compliance with accessibility standards, revealing that only 22.22% of the facilities meet the required criteria. To complement these findings, structured interviews with representatives from eight facilities were carried out, with responses analyzed using ATLAS.ti software (Version 24.0.0) to visualize patterns through diagrams and Sankey networks. The results highlight significant shortcomings in physical accessibility as well as mixed attitudes of service providers toward the needs of disabled visitors. The study concludes that while awareness of inclusive practices is growing, substantial efforts are still required to improve infrastructure and foster positive engagement from service providers. These findings provide valuable insights for policymakers, tourism stakeholders, and service operators, offering practical recommendations for enhancing accessibility and promoting a more inclusive tourism environment in the region. Full article

Agriculture is Ireland’s largest sector with agri-food exports amounting to EUR 15.2B in 2021. However, agriculture is also Ireland’s largest contributor to GHGs, accounting for 37.4% of emissions in 2020. Developing indigenous renewable energy sources is a national objective towards reducing GHG emissions. The National Policy Statement on the Bioeconomy of Ireland advises a cascading principle of biomass use, where higher-value applications are derived from biomass before energy generation. This research quantifies and characterises biomass wastes at farms, food production, and forestry settings in counties Monaghan and Tipperary, Ireland. Value chains, along with Sankey diagrams, are presented, which identify biomass that can be exploited for valorisation and show their fates in industry/environment. The quantity of biomass wastes available for valorisation under Business as Usual (BAU) vs. Best-Case Scenario (BCS) models is presented. BCS assumes a co-operative system to increase the feedstock available for valorisation. In Monaghan, 73 t of biomass waste vs. 240 t are available for valorisation under Scenario A vs. Scenario B, respectively. In contrast, in Tipperary, a 7-fold increase in biomass waste is achieved, comparing Scenario A (126 t) against Scenario B (905 t). This highlights the importance of engaging local stakeholders to build co-operative models for biomass valorisation. Not only is this environmentally beneficial, but also socially and economically advantageous. Creating indigenous fertiliser and energy sources is important for the island of Ireland, not only in meeting market demand, but also in reducing greenhouse gas (GHG) emissions and achieving emission reduction targets. Full article