Search Results (7,934)

Colorectal liver metastases (CRLM) management remains a complex conundrum in the context of potential curable disease. The combination of systemic therapy and surgery, with overall survival outcomes up to 58% at five years, has become the gold standard. Locoregional therapies have gained evidence in complementing surgery or even substituting it in selected cases. Adequate patient selection is paramount, but prognostic models have certain limitations that prevent their full implementation in clinical practice. A plethora of prognostic factors exists, with variable evidence supporting their definitive role. Thus, CRLM management decisions frequently vary depending on multidisciplinary team experience and hospital access to systemic and locoregional treatments. Definition of resectability has evolved in recent years due to technical developments in surgical and non-surgical approaches. Complexity is added when trying to fully understand the integration between local and systemic treatment. Whereas evidence in the context of resectable disease has been attempted in several phase III trials, definitive conclusions regarding the best approach to potentially resectable disease cannot be drawn. In addition, liver transplantation has gained evidence and is proposed in selected patients, raising a challenge regarding its integration and wider implementation. In this review, current standards in the management of CRLM regarding patient selection, resectability, surgical and non-surgical locoregional strategies, as well as the best systemic approach are covered. Full article

Antibody-free nucleic acid lateral flow assays (AF-NALFA) are an established approach for rapid detection of amplified pathogens DNA but can yield inconsistent signals across targets. Since AF-NALFA depends on dual hybridization of probes to single-stranded amplicons (ssDNA), site-specific thermodynamic (Gibbs free energy-ΔG) at probe-binding regions may be crucial for performance. This study investigated how site-specific-ΔG and sequence complementarity at probe-binding regions determine Test-line signal generation, comparing native and synthetic amplicons and assessing the effects of local secondary structures and mismatches. Asymmetric PCR-generated ssDNA amplicons of Listeria monocytogenes, Mycobacterium leprae, and Leishmania amazonensis were analyzed in silico and tested in AF-NALFA prototypes with gold-labeled thiol probes and biotinylated capture probes. T-line signals were photographed, quantified (ImageJ version 1.4k), and statistically correlated with site-specific-ΔG. While native ssDNA from M. leprae and L. amazonensis failed to produce AF-NALFA T-line signals, L. monocytogenes yielded strong detection. Site-specific-ΔG below −10 kcal/mol correlated with reduced hybridization. Synthetic oligos preserved signals despite structural constraints, whereas ~3–4 mismatches, especially at capture probe regions, markedly impaired T-line intensity. The performance of AF-NALFA depends on the synergism between thermodynamic accessibility, site-specific-ΔG-induced site constraints, and sequence complementarity. Because genomic context affects hybridization, target-specific thermodynamic in silico evaluation is necessary for reliable pathogen DNA detection. Full article

Smartphone overdependence among South Korean adolescents, affecting nearly 40%, poses a growing public health concern, with usage patterns varying by regional context. Leveraging conceptually informed AI/ML models, this study (1) develops a high-performing low-risk screening tool to monitor disease burden, (2) leverages AI/ML to explore psychologically meaningful constructs, and (3) provides place-based policy implication profiles to inform public health policy. This study uses data from 1873 adolescents in the 2023 Smartphone Overdependence Survey by the National Information Society Agency (NISA) in South Korea. Across the sample, the adolescents were about 14 years old (SD = 2.4) and equally distributed by sex (48.1% male). We then conceptually selected 131 features across two domains and 10 identified constructs. A nested modeling approach identified a low-risk screening tool using 59 features that achieved strong predictive accuracy (AUC = 81.5%), with Smartphone Use Case features contributing approximately 20% to performance. Construct-specific models confirmed the importance of Smartphone Use Cases, Perceived Digital Competence and Risk, and Consequences and Dependence (AUC range: 80.6–89.1%) and uncovered cognitive patterns warranting further study. Place-stratified analysis revealed substantial regional variation in model performance (AUC range: 71.4–91.1%) and distinct local feature importance. Overall, this study demonstrated the value of integrating conceptual frameworks with AI/ML to detect adolescent smartphone overdependence, offering novel approaches to monitoring disease burden, advancing construct-level insights, and providing targeted place-based public health policy recommendations within the South Korean context. Full article

Electrocardiogram (ECG) waveform delineation is fundamental to cardiac disease diagnosis. This task requires precise localization of key fiducial points, specifically the onset, peak, and offset positions of P-waves, QRS complexes, and T-waves. Current methods exhibit significant performance degradation in noisy clinical environments (baseline drift, electromyographic interference, powerline interference, etc.), compromising diagnostic reliability. To address this limitation, we introduce ECG-SCFNet: a novel dual-stream architecture employing selective context fusion. Our framework is further enhanced by a consistency training paradigm, enabling it to maintain robust waveform delineation accuracy under challenging noise conditions.The network employs a dual-stream architecture: (1) A temporal stream captures dynamic rhythmic features through sequential multi-branch convolution and temporal attention mechanisms; (2) A morphology stream combines parallel multi-scale convolution with feature pyramid integration to extract multi-scale waveform structural features through morphological attention; (3) The Selective Context Fusion (SCF) module adaptively integrates features from the temporal and morphology streams using a dual attention mechanism, which operates across both channel and spatial dimensions to selectively emphasize informative features from each stream, thereby enhancing the representation learning for accurate ECG segmentation. On the LUDB and QT datasets, ECG-SCFNet achieves high performance, with F1-scores of 97.83% and 97.80%, respectively. Crucially, it maintains robust performance under challenging noise conditions on these datasets, with 88.49% and 86.25% F1-scores, showing significantly improved noise robustness compared to other methods and demonstrating exceptional robustness and precise boundary localization for clinical ECG analysis. Full article

In the context of low carbon, achieving accurate forecasting of electrical energy is critical for power management with the continuous development of power systems. For the sake of improving the performance of load forecasting, an improved MNet-Atten electric vehicle charging load forecasting based on composite decomposition and the evolutionary predator–prey and strategy model is proposed. In this light, through the data decomposition theory, each subsequence is processed using complementary ensemble empirical mode decomposition and filters out high-frequency white noise by using singular value decomposition based on matrix operation, which improves the anti-interference ability and computational efficiency of the model. In the model construction stage, the MNet-Atten prediction model is developed and constructed. The convolution module is used to mine the local dependencies of the sequences, and the long term and short-term features of the data are extracted through the loop and loop skip modules to improve the predictability of the data itself. Furthermore, the evolutionary predator and prey strategy is used to iteratively optimize the learning rate of the MNet-Atten for improving the forecasting performance and convergence speed of the model. The autoregressive module is used to enhance the ability of the neural network to identify linear features and improve the prediction performance of the model. Increasing temporal attention to give more weight to important features for global and local linkage capture. Additionally, the electric vehicle charging load data in a certain region, as an example, is verified, and the average value of 30 running times of the combined model proposed is 117.3231 s, and the correlation coefficient PCC of the CEEMD-SVD-EPPS-MNet-Atten model is closer to 1. Furthermore, the CEEMD-SVD-EPPS-MNet-Atten model has the lowest MAPE, RMSE, and PCC. The results show that the model in this paper can better extract the characteristics of the data, improve the modeling efficiency, and have a high data prediction accuracy. Full article

With accelerating urbanization and global climate warming, Urban Heat Islands (UHIs) pose serious threats to urban development. Existing UHI research mainly focuses on inland regions, lacking systematic understanding of coastal city heat island mechanisms. We selected eight Chinese coastal cities with different backgrounds, quantitatively assessed urban heat island intensity based on summer 2023 Landsat 8 remote sensing data, established block-LCZ spatial analysis units, and employed a combination of machine learning models and causal inference methods to systematically analyze the regional differentiation characteristics of Urban Heat Island Intensity (UHII) and the influence mechanisms of multi-dimensional driving factors within land–sea interaction contexts. The results revealed the following: (1) UHII in the study area presents obvious spatial differentiation, with the highest value occurring in Hong Kong (2.63 °C). Northern cities generally had higher values than southern ones. (2) Different Local Climate Zone (LCZ) types show significant differences in thermal contributions, with LCZ2 (compact midrise) blocks presenting the highest UHII values in most cities, while LCZ G (water) and LCZ A (dense trees) blocks exhibit stable cooling effects. Nighttime light (NTL) and distance to sea (DS) are dominant factors affecting UHII, with NTL marginal effect curves generally presenting hump-shaped characteristics, while DS shows different response patterns across cities. (3) Causal inference reveals true causal driving mechanisms beyond correlations, finding that causal effects of key factors exhibit significant spatial heterogeneity. The research findings provide a new cognitive framework for understanding the formation mechanisms of thermal environments in Chinese coastal cities and offer a quantitative basis for formulating regionalized UHI mitigation strategies. Full article

The success and impact of biological invasions depend on adaptations to novel abiotic and biotic selective pressures. However, the genetic mechanisms underlying adaptations in invasive species are inadequately understood. Taenioides sp. is an invasive worm goby, originally endemic to brackish waters in the estuaries of Southeastern China, and now colonizes multiple inland freshwaters of North China within decades as a byproduct of the East Route of South-to-North Water Transfer (ESNT) project. However, the molecular mechanisms underlying their adaptations to the climate of North China, especially the temperature regime, are unknown. Here, we performed genomic resequencing analysis to assess genetic diversity and population genetic structure, and further investigated the genomic signatures of local adaptation in the invasive population of Taenioides sp. during their northward invasion. We revealed that all invasive populations exhibited no genetic differentiation but low gene flow and an obvious signal of population bottleneck. Yangtze River estuary may serve as the source population, while Gaoyou Lake serves as a potential bridgehead of the invasion. Selective sweep analyses revealed 117 genomic regions, containing 673 candidate genes, under positive selection in populations at the invasive front. Redundancy analysis suggested that local temperature variables, particularly the monthly minimum temperature, represent critical evolutionary forces in driving adaptive divergence. Functional enrichment analyses revealed that multiple biological processes, including metabolism and energy production, substance transmembrane transport, and neural development and synaptic transmission, may play important roles in adaptation to regional temperature conditions. Our findings revealed a scenario of adaptive evolution in teleost species that underpins their regional climate adaptation and successful establishment of invasive populations in a human-facilitated invasion context. Proper management strategies should be established to manage Taenioides sp invasion as soon as possible. Full article

Microgrids are becoming an innovative piece of modern energy systems as they provide locally sourced and resilient energy opportunities and enable efficient energy sourcing. However, microgrid operations can be greatly affected by sudden environmental changes, deviating demand, and unexpected outages. In particular, extreme climatic events expose the vulnerability of microgrid infrastructure and resilience, often leading to increased risk of system-wide outages. Thus, successful microgrid operation relies on timely and accurate outage predictions. This research proposes a data-driven machine learning framework for the optimized operation of a microgrid and predictive outage detection using a Recurrent Neural Network–Long Short-Term Memory (RNN-LSTM) architecture that reflects inherent temporal modeling methods. A time-aware embedding and masking strategy is employed to handle categorical and sparse temporal features, while mutual information-based feature selection ensures only the most relevant and interpretable inputs are retained for prediction. Moreover, the model addresses the challenges of experiencing rapid power fluctuations by looking at long-term learning dependency aspects within historical and real-time data observation streams. Two datasets are utilized: a locally developed real-time dataset collected from a 5 MW microgrid of Maple Cement Factory in Mianwali and a 15-year national power outage dataset obtained from Kaggle. Both datasets went through intensive preprocessing, normalization, and tokenization to transform raw readings into machine-readable sequences. The suggested approach attained an accuracy of 86.52% on the real-time dataset and 84.19% on the Kaggle dataset, outperforming conventional models in detecting sequential outage patterns. It also achieved a precision of 86%, a recall of 86.20%, and an F1-score of 86.12%, surpassing the performance of other models such as CNN, XGBoost, SVM, and various static classifiers. In contrast to these traditional approaches, the RNN-LSTM’s ability to leverage temporal context makes it a more effective and intelligent choice for real-time outage prediction and microgrid optimization. Full article

In recent years, chrono-urbanism has ushered in the x-minute city concept. Effectively combined with the life unit concept, it introduced a new perspective—the neighborhood life circle. This emerging urban decision-making and planning paradigm represents China’s attempt to address the “urban disease” arising from rapid urbanization recently, attracting global attention for its implementation of sustainability. This study aims to reveal the driving factors behind the livable neighborhood life circle amid rapid urbanization by conducting a systematic review of relevant empirical research within China’s context. We used Scopus and WoS as search databases, identifying and extracting a literature review of 67 publications from 2010 to 2025. The findings indicate that the driving factors of a livable neighborhood life circle are a structure constructed comprising social well-being, management and regulation, the built environment, and economic vitality, which are interconnected in multiple ways. This study has advanced discussions on the livable neighborhood life circle and expanded the existing knowledge and literature. It has also deepened insights into how sustainability concepts impact livable neighborhood life circles in China. The study offers insights into four aspects: the systematization of concepts and driving factors related to the neighborhood life circle in China, the development of assessment tools, the establishment of new planning paradigms, and the localization of implementation frameworks. Additionally, it further enriches the global application of the x-minute city and the neighborhood life circle. Full article

In contemporary spatial planning, territorial resilience is rapidly gaining relevance, referring to a territory’s capacity to withstand, adapt to, recover from, and transform in response to environmental, social, and economic pressures. However, several constraints limit its operationalisation in planning. A key element to addressing this gap is to investigate where and which interventions are most urgently needed to tackle the impact of hazards on territories. This can be achieved by understanding and localising the vulnerabilities of territorial systems, thereby enabling the definition of appropriate mitigation and adaptation measures. This paper presents the application of R3C-GeoResilience, an open-source GIS tool and its methodological framework, which allows mapping territorial vulnerabilities across different geographical contexts and spatial scales. The methodology is applied to the Italian case of the Union of Bassa Romagna (UBR), aiming to build capacity for local practitioners to implement resilience thinking in decision-making processes. Findings underscore the potential of R3C-GeoResilience to enhance evidence-based planning and policymaking, supporting adaptive and transformative strategies to address territorial vulnerabilities. The application of the research demonstrates the replicability and adaptability of the methodological framework for integrating participatory vulnerability mapping into local governance and urban planning strategies, thereby enhancing the resilience of territories. Full article

Cultural heritage sites play a crucial role in safeguarding identity, fostering cultural exchange, and generating sustainable tourism. Within this context, the Palace Museum in Beijing, which attracts 19 million annual visitors, offers a compelling case for examining the dynamics that shape revisit intention. This study explores the relationships among perceived authenticity, place attachment, destination satisfaction, visitor engagement, and revisit intention within the context of heritage tourism. Using Partial Least Squares–Structural Equation Modeling (PLS-SEM), data were collected from local visitors to the Palace Museum to analyze both the direct and mediating effects of these constructs. Findings indicate that perceived authenticity significantly enhances both destination satisfaction and visitor engagement, while place attachment makes a strong contribution to visitor engagement. Moreover, visitor engagement emerged as a more influential mediator than destination satisfaction in linking perceived authenticity to revisit intention, showing the importance of immersive and meaningful participation in shaping tourists’ behavioral intentions. These results suggest that while satisfaction remains a relevant concept, strategies that emphasize authenticity-driven experiences and fostering of deeper emotional and participatory bonds are more effective in sustaining revisits. This study advances the understanding of heritage tourism and provides practical insights for managing iconic heritage sites such as the Palace Museum. Full article

Detecting small split pins on transmission lines poses significant challenges, including low accuracy in complex backgrounds and slow inference speeds. To address these limitations, this study proposes a novel two-stage collaborative detection framework. The first stage utilizes a Yolo11x-based model to localize and crop components containing split pins from high-resolution images. This procedure transforms the difficult small-object detection problem into a more manageable, conventional detection task on a simplified background. For the second stage, a new high-performance detector, Mamba-YOLO-SPDC, is introduced. This model enhances the Yolo11 backbone by incorporating a Vision State Space (VSS) block, which leverages Mamba—a State Space Model (SSM) with linear computational complexity—to efficiently capture global context. Furthermore, a Space-to-Depth Convolution (SPD-Conv) module is integrated into the neck to mitigate the loss of fine-grained feature information during downsampling. Experimental results confirm the efficacy of the two-stage strategy. On the cropped dataset, the Mamba-YOLO-SPDC model achieves a mean Average Precision (mAP) of 61.9%, a 238% improvement over the 18.3% mAP obtained by the baseline Yolo11s on the original images. Compared to the conventional SAHI framework, the proposed method provides superior accuracy with a substantial increase in inference speed. This work demonstrates that the ‘localize first, then detect’ strategy, powered by the Mamba-YOLO-SPDC model, offers an effective balance between accuracy and efficiency for small object detection. Full article

As with other non-coding RNAs (ncRNAs), the aberrant expression of long non-coding RNAs (lncRNAs) can be associated with different forms of cancers, including breast cancer (BC). Various lncRNAs may either promote or suppress cell proliferation, metastasis, and other related cancer signaling pathways by interacting with other cellular machinery, thus affecting the expression of BC-related genes. However, lncRNAs are characterized by features that are unlike protein-coding genes, which pose unique challenges when it comes to their study and utility. They are highly diverse and may display contradictory functions depending on factors like the BC subtype, isoform diversity, epigenetic regulation, subcellular localization, interactions with various molecular partners, and the tumor microenvironment (TME), which contributes to the intratumoral heterogeneity and phenotypic plasticity. While lncRNAs have potential clinical utility, their functional heterogeneity coupled with a current paucity of knowledge of their functions present challenges for clinical translation. Strategies to address this heterogeneity include improving classification systems, employing CRISPR/Cas tools for functional studies, utilizing single-cell and spatial sequencing technologies, and prioritizing robust targets for therapeutic development. A comprehensive understanding of the lncRNA functional heterogeneity and context-dependent behavior is crucial for advancing BC research and precision medicine. This review discusses the sources of lncRNA heterogeneity, their implications in BC biology, and approaches to resolve knowledge gaps in order to harness lncRNAs for clinical applications. Full article

Global food systems face growing pressure from population expansion and climate change, making the identification of resilient crops a priority. The nopal cactus (Opuntia spp.) stands out for its capacity to thrive in arid environments and for its cultural and economic importance in Mexico. This study analyzes worldwide research trends and evaluates evidence from Mexico to identify opportunities and strategies for closing production cycles through residue valorization. Scientific output over the past decade shows steady growth and a thematic transition from basic agronomic and compositional studies toward sustainability, bioactive compounds, and circular economy approaches. In the Mexican context, applied studies demonstrate that Opuntia spp. cladodes residues can be transformed into composts with C/N ratios between 12 and 26, improving soil organic matter and nutrient availability. Biofertilizers produced through anaerobic fermentation enhanced phosphorus solubility in alkaline soils, while direct residue incorporation increased carrot and tomato yields up to threefold. Farmers recognize these practices as low-cost and compatible with local systems. Nevertheless, the lack of standardized protocols and scalable models limits widespread adoption. Strengthening research collaboration, policy incentives, and technology transfer could position Mexico as a leader in sustainable Opuntia value chains and advance circular economy practices in smallholder farming systems. Full article

Background/Objectives: Omentin, also known as intelectin-1, is a secreted adipokine with anti-inflammatory, insulin-sensitizing, and immune-modulatory functions, primarily expressed in visceral adipose tissue. While omentin has been associated with favorable metabolic outcomes, its role in cancer pathogenesis appears context-dependent and remains poorly understood. This review investigates the biological functions, expression patterns, and clinical relevance of omentin across gastrointestinal malignancies. Methods: A comprehensive review of the literature was conducted using PubMed, Scopus, and Web of Science up to August 2025 to evaluate the role of omentin in gastrointestinal cancers. Both preclinical and clinical studies evaluating omentin, its analogues and omentin-enhancing agents in gastric, colorectal, hepatic, pancreatic, and esophageal cancers were included. Results: Omentin exhibits anti-proliferative, anti-inflammatory, and anti-angiogenic effects within the tumor microenvironment in several GI malignancies. However, evidence also indicates a dual role. High intratumoral omentin expression correlates with improved prognosis in colorectal, gastric, and hepatic cancers; in contrast, elevated circulating levels–particularly in colorectal and pancreatic cancers–have been paradoxically associated with increased cancer risk and poor outcomes. Mechanistically, omentin modulates PI3K/Akt, NF-κB, AMPK, and oxidative stress pathways, and interacts with TMEM207. However, most available studies are small-scale and heterogeneous, with methodological inconsistencies and limited multi-omics integration, leaving major knowledge gaps. Conclusions: This review highlights omentin’s distinct systemic and local roles across GI cancers, underscoring its translational implications. Omentin emerges as a promising but context-dependent biomarker and therapeutic target, with future research needed to address heterogeneity, standardize assays, and validate its clinical utility in large-scale prospective studies. Full article