Search Results (179)

Background: Artificial intelligence is emerging as a promising tool in surgical oncology, with growing evidence suggesting potential applications in diagnostic support, intraoperative guidance, and perioperative risk assessment. In gastric cancer surgery, emerging applications range from AI-assisted endoscopic detection to data-driven perioperative risk prediction, while some technological developments, particularly in robotic autonomy, derive from broader surgical or experimental models that may inform future gastric procedures. Methods: A narrative review was conducted following established methodological standards, including the Scale for the Assessment of Narrative Review Articles (SANRA) and the Search–Appraisal–Synthesis–Analysis (SALSA) framework. English-language studies indexed in PubMed, Scopus, Embase, and Web of Science up to October 2025 were included. Evidence was synthesized thematically across five domains: AI-assisted anatomical recognition and lymphadenectomy support, autonomous robotic systems, early cancer detection, perioperative predictive and frailty models, and ethical and regulatory considerations. Results: AI-based computer vision and deep learning algorithms have demonstrated promising capabilities for real-time anatomical recognition, surgical phase classification, and intraoperative guidance, although evidence of direct patient-level benefit remains limited. In diagnostic settings, AI-assisted endoscopy and Raman spectroscopy have been shown to improve early lesion detection and reduce dependence on operator experience. Predictive models, including MySurgeryRisk and AI-driven frailty assessments, may support individualized prehabilitation planning and perioperative risk stratification. Persistent limitations include small and heterogeneous datasets, insufficient external validation, and unresolved concerns related to data privacy, algorithmic interpretability, and medico-legal responsibility. Conclusions: Artificial intelligence is progressively emerging as a promising tool in gastric cancer surgery, integrating automation, advanced analytics, and human clinical reasoning. Its safe and ethical adoption requires robust validation, transparent governance, and continuous surgeon oversight. When developed within human-centered and ethically grounded frameworks, AI can augment, rather than replace, surgical expertise, potentially advancing precision, safety, and equity in oncologic care. Full article

Eye-tracking methodology represents a young but rapidly growing approach in tourism research, offering a direct window into the cognitive processes driving tourism stakeholders’ behaviour. However, a critical gap remains between the rapid adoption of this tool and the methodological rigour required to interpret its neurophysiological data. This critical review synthesizes 23 empirical studies (2020–2025) from the destination marketing and branding domain to diagnose eye-tracking’s state-of-the-art application. Adopting the SALSA framework (Search, Appraisal, Synthesis, Analysis) augmented by PRISMA 2020 guidelines, this study systematically searched Web of Science and Scopus databases. Studies were appraised using an eight-dimensional quality rubric, assessing from theoretical grounding to experimental design to statistical rigour. Findings revealed a “tool-first” exploratory phenomenon, where the majority of studies relied on basic fixation metrics to infer complex psychological states such as “interest”, when they could imply other cognitive states. Furthermore, most reviewed studies failed to control for stimulus-level confounds (e.g., luminance, AOI size) and utilized inappropriate data-handling procedures and methods, such as the absence of data cleaning and treating count and binary data as continuous data. These, coupled with transparency deficits, undermined the validity of their conclusions. Hence, a Checklist for Eye-Tracking Rigour (CETR) and a methodological decision tree were developed to guide researchers towards confirmatory and neurobiologically grounded research. Findings also provided a framework for managers/practitioners to more accurately interpret eye-tracking studies. Full article

Offloading remains the cornerstone of diabetic foot ulcer (DFU) management. This review traces the evolution of mechanical offloading from early plaster casting in South Asian leprosy clinics to modern removable walkers and emerging “SmartBoot” technologies. We examine the historical progression from total contact casting (TCC) through the era of randomized trials and instant TCC (iTCC), up to the current integration of wearable sensors and digital adherence tools. Contemporary evidence—including meta-analyses—is discussed to compare the effectiveness of offloading modalities (non-removable vs. removable devices, knee-high vs. ankle-high boots, therapeutic footwear, and adjunctive surgeries). Current challenges, such as patient adherence, frailty, and balance, are linked to technological responses like smart insoles, remote monitoring, and gamification strategies. Through this historical and evidence-based lens, we highlight how decades-old biomechanical principles are being reimagined with 21st-century innovations, aiming to improve healing rates and patient engagement in DFU care. Full article

Drought stress critically constrains plant development and morphogenesis, representing a substantial challenge to crop production systems. Dehydrins (DHNs), belonging to the late embryogenesis abundant (LEA) protein superfamily, play crucial roles in plant adaptation to environmental stress conditions. Nevertheless, the capacity of Suaeda salsa SsDHN protein to confer drought resistance has not been adequately investigated. In the present study, transgenic tobacco lines with constitutive SsDHN expression (SsDHN-OE) were employed to examine its influence on seedling development under water-limited conditions. Results indicated that constitutive SsDHN expression enhanced biomass accumulation, foliar expansion, root elongation, and root surface dimensions in water-stressed seedlings. Moreover, transformed lines demonstrated elevated proline (Pro) accumulation and abscisic acid (ABA) content, augmented antioxidant enzyme activity, and intensified stomatal regulation under stress conditions. Conversely, photoinhibition intensity, chloroplast structural degradation, malondialdehyde (MDA) accumulation, electrolyte leakage, hydrogen peroxide (H₂O₂), and superoxide radical (O₂⁻) concentrations were diminished. Furthermore, transcript abundance of stress-responsive genes—encompassing NtNCED3, NtSnRK2.2, NtRD26, NtLEA5, NtPOD, NtSOD, NtCAT, and NtAPX1—was markedly increased in SsDHN-OE lines experiencing drought stress. Taken together, these findings establish that SsDHN functions as a positive regulator of drought resilience in plants. Full article

Sensor degradation poses a significant challenge in autonomous driving. During heavy rainfall, interference from raindrops can adversely affect the quality of LiDAR point clouds, resulting in, for instance, inaccurate point measurements. This, in turn, can potentially lead to safety concerns if autonomous driving systems are not weather-aware, i.e., if they are unable to discern such changes. In this study, we release a new, large-scale, multi-modal emulated rain dataset, REHEARSE-3D, to promote research advancements in 3D point cloud de-raining. Distinct from the most relevant competitors, our dataset is unique in several respects. First, it is the largest point-wise annotated dataset (9.2 billion annotated points), and second, it is the only one with high-resolution LiDAR data (LiDAR-256) enriched with 4D RADAR point clouds logged in both daytime and nighttime conditions in a controlled weather environment. Furthermore, REHEARSE-3D involves rain-characteristic information, which is of significant value not only for sensor noise modeling but also for analyzing the impact of weather at the point level. Leveraging REHEARSE-3D, we benchmark raindrop detection and removal in fused LiDAR and 4D RADAR point clouds. Our comprehensive study further evaluates the performance of various statistical and deep learning models, where SalsaNext and 3D-OutDet achieve above $94\%$ IoU for raindrop detection. Full article

Background: Although surfactant replacement therapy has been a cornerstone of respiratory distress syndrome (RDS) management for decades, traditional delivery via endotracheal intubation and mechanical ventilation is associated with procedure-related complications and increased risk of bronchopulmonary dysplasia (BPD). These concerns have driven the development of less invasive surfactant administration strategies. Objective: This review aims to summarize and evaluate the current literature on less invasive surfactant delivery techniques used in preterm infants with RDS, with a focus on their feasibility, efficacy, and short- and long-term neonatal outcomes. Methods: We reviewed the available literature evaluating less invasive surfactant administration methods, including InSurE, Less Invasive Surfactant Therapy/Minimally Invasive Surfactant Therapy (LISA/MIST), surfactant administration via laryngeal mask airway (SALSA/LMA), pharyngeal administration, and nebulized surfactant. We compared major outcomes, namely the need for mechanical ventilation, incidence of BPD, procedural complications and long-term neurodevelopmental outcomes. Results: Non-invasive surfactant administration techniques have been associated with reduced exposure to mechanical ventilation and lower rates of BPD compared with conventional approaches. Studies on LISA/MIST demonstrate the most consistent evidence in reducing the need for mechanical ventilation and BPD, while other techniques such as LMA-assisted delivery and nebulization show promise but remain limited by device constraints, gestational age applicability, and heterogeneous study designs. Long-term neurodevelopmental outcome data remain sparse across all techniques. Conclusions: Non-invasive surfactant administration represents an important advancement in the management of RDS. While several techniques offer potential advantages over traditional intubation-based delivery, further high-quality studies are required to optimize patient selection, standardize techniques, develop safe and effective delivery devices, and evaluate long-term neurodevelopmental outcomes. Full article

Dynamic network conditions in precision agriculture motivate a scalable, privacypreserving federated learning architecture that tightly integrates ground-based edge intelligence with a space-assisted hierarchical aggregation layer. In Phase 1, heterogeneous tractors act as intelligent farm nodes that train local models, form capability- and task-aware clusters, and employ Network Quality Index (NQI)-driven scheduling, similarity-based checkpointing, and compressed transmissions to cope with highly variable 3G/4G/5G connectivity. In Phase 2, cluster drivers synchronize with Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) satellites that perform regional and global aggregation using staleness- and fairness-aware weighting, while end-to-end Salsa20 + MAC encryption preserves the confidentiality and integrity of all model updates. Across two representative tasks—nutrient prediction and crop health assessment—our full hierarchical system matches or exceeds centralized performance (e.g., AUC 0.92 vs. 0.91 for crop health) while reducing uplink traffic by ∼90% relative to vanilla FedAvg and cutting the communication energy proxy by more than 4×. The proposed fairness-aware GEO aggregation substantially narrows regional performance gaps (standard deviation of AUC across regions reduced from 0.058 to 0.017) and delivers the largest gains in low-connectivity areas (AUC 0.74 → 0.88). These results demonstrate that coupling on-farm intelligence with multi-orbit federated aggregation enables near-centralized model quality, strong privacy guarantees, and communication efficiency suitable for large-scale, connectivity-challenged agricultural deployments. Full article

Coastal wetland vegetation exhibits pronounced spectral mixing, complex mosaic spatial patterns, and small target sizes, posing considerable challenges for fine-grained classification in high-resolution UAV imagery. At present, remote sensing classification of ground objects based on deep learning mainly relies on spectral and structural features, while the frequency domain features of ground objects are not fully considered. To address these issues, this study proposes a vegetation classification model that integrates spatial-domain and frequency-domain features. The model enhances global contextual modeling through a large-kernel convolution branch, while a frequency-domain interaction branch separates and fuses low-frequency structural information with high-frequency details. In addition, a shallow auxiliary supervision module is introduced to improve local detail learning and stabilize training. With a compact parameter scale suitable for real-world deployment, the proposed framework effectively adapts to high-resolution remote sensing scenarios. Experiments on typical coastal wetland vegetation including Reeds, Spartina alterniflora, and Suaeda salsa demonstrate that the proposed method consistently outperforms representative segmentation models such as UNet, DeepLabV3, TransUNet, SegFormer, D-LinkNet, and MCCA across multiple metrics including Accuracy, Recall, F1 Score, and mIoU. Overall, the results show that the proposed model effectively addresses the challenges of subtle spectral differences, pervasive species mixture, and intricate structural details, offering a robust and efficient solution for UAV-based wetland vegetation mapping and ecological monitoring. Full article

The nitrate signaling core regulator NLP7 is known to negatively regulate salt tolerance in Arabidopsis thaliana, but the function of the (SsNLP7A) gene in the halophyte Suaeda salsa remains unclear. To investigate whether SsNLP7A participates in salt stress responses, this study heterologously overexpressed the gene in tomato (Solanum lycopersicum) and systematically evaluated its function under salt stress through phenotypic, physiological, and transcriptomic analyses. The results indicate that SsNLP7A overexpression significantly promotes tomato root development and alleviates growth inhibition caused by salt stress. Under salt treatment, transgenic plants exhibited significantly higher chlorophyll content, accumulation of osmotic regulators (proline and soluble sugars), and antioxidant enzyme (POD, CAT, SOD) activity compared to wild-type plants. Transcriptome analysis further revealed that SsNLP7A enhances salt tolerance by regulating carbon metabolism, phytohormone signaling pathway, photosynthesis, and antioxidant pathways. Collectively, this study elucidates the positive regulatory role of SsNLP7A in salt stress response, providing new insights into its molecular mechanisms. Full article

The deployment of solar and other renewable energy technologies (RETs) plays a central role in the global energy transition and the pursuit of sustainable development. Beyond reducing greenhouse gas emissions, these technologies generate far-reaching societal co-benefits that shape environmental quality, social equity, and economic growth. This study systematically reviews peer-reviewed literature published between 2009 and 2025 to identify, integrate, and assess empirical evidence on how RET deployment contributes to societal welfare. Following the SALSA framework and PRISMA guidelines, 147 studies were selected from Scopus and Web of Science. The evidence reveals a consistent welfare triad: environmental gains (emission and pollution reduction, climate mitigation), social gains (improved health, affordability, energy security, and inclusion), and economic gains (employment and income growth, local development). These benefits are, however, heterogeneous and depend on enabling conditions such as policy stability, financial development, grid integration, innovation capacity, and social acceptance. The review highlights that solar energy, in particular, acts as both an environmental and social catalyst in advancing sustainable welfare outcomes. The findings provide a comprehensive basis for policymakers and researchers seeking to design equitable and welfare-enhancing renewable energy transitions. Full article

Moringa oleifera has been recognized for its adaptability, nutritional richness, and multipurpose potential, particularly in resource-limited regions. While most research has focused on its leaves, moringa seeds remain underutilized despite their broad applicability in the environmental, agricultural, and food sectors. This review systematically and critically examines recent scientific literature on the use of M. oleifera seeds across these fields, emphasizing their functional value, applications, and challenges for sustainable use. The review follows the SALSA methodology (Search, Appraisal, Synthesis, and Analysis), a structured and iterative framework designed to identify, evaluate, and integrate scientific evidence from diverse sources. The analysis encompasses three main areas: (i) water treatment, where moringa seed extracts have achieved turbidity removal efficiencies above 90% and effective adsorption of dyes and potentially toxic elements; (ii) agriculture, where seed-derived fertilizers improve soil fertility, nutrient availability, and crop yield compared to conventional inputs; and (iii) the food industry, where moringa seed derivatives enhance the nutritional, functional, and antioxidant properties of bakery, beverage, and oil-based products. Overall, M. oleifera seeds emerge as a versatile and sustainable resource with proven potential as a natural coagulant, biofertilizer, and nutraceutical ingredient. By integrating findings from both English and Spanish language studies, this work highlights their contribution to sustainable water management, agricultural productivity, and food innovation, while emphasizing the need for further safety evaluation and process optimization to support large-scale application. Full article

Soil salinization severely constrains agricultural productivity and ecosystem sustainability. Suaeda salsa L. is a representative halophyte and demonstrates strong adaptability and potential for saline–alkali land restoration. To elucidate its physiological responses to salt stress, pot experiments were conducted under four salinity levels, namely CK (0 mM NaCl), LS (800 mM NaCl), MS (1600 mM NaCl), and HS (2400 mM NaCl), with 20 replicates per treatment, and the dynamics of endogenous hormone were analyzed using targeted metabolomics. The soil salinity levels were prepared by adding NaCl solutions of different molarities to achieve the desired salinity treatments. Results showed that low to moderate salinity (CK-LS: 0–800 mM) promoted growth performance, whereas higher salinity (HS: 2400 mM) significantly inhibited biomass accumulation, plant height, and stem diameter (p < 0.01). Salinity markedly affected nutrient accumulation in Suaeda salsa, with Na increasing up to 222%, K decreasing by 17–33%, Ca by 7–21%, Mg by 35–46%, and S by 45–56% across growth stages, while Fe remained unchanged. Under increasing salinity, stress-related hormones such as abscisic acid, jasmonic acid, salicylic acid, and indole derivatives were upregulated, while gibberellins decreased markedly. Zeatin and its derivatives showed significant increases under MS (p < 0.01). Correlation analysis indicated positive associations of abscisic acid and zeatin with growth traits, and negative correlations for gibberellins (R > 0.6). These findings suggest that Suaeda salsa adapts to saline conditions by modulating hormone-mediated ion balance, osmotic regulation, and defense metabolism, thereby optimizing growth and biomass allocation under salt stress. Full article

Background/Objectives: Genetic abnormalities are critical for the diagnosis, prognosis, and therapeutic management of myelodysplastic syndromes (MDS). This study aims to evaluate the clinical utility of Multiplex Ligation-dependent Probe Amplification (MLPA) as a rapid and cost-effective method, determining its place alongside Next-Generation Sequencing (NGS) for the initial genetic assessment of patients with MDS. Methods: Bone marrow samples from 68 patients newly diagnosed with MDS were analyzed. Genomic DNA was investigated using the SALSA MLPA P414-C1 MDS probe mix to detect common copy number variations (CNVs). Results: MLPA detected genetic variants in 25 patients (36.8%). The most common finding was a single chromosomal abnormality (26.5%). Multiple pathological findings were observed in only 1.5% of patients, and a JAK2 mutation was observed in 8.8% of the cohort. However, the presence of these aberrations did not show a statistically significant association with overall survival (OS) in the cohort. Patient sex was identified as the only variable that was associated with a marginal level of statistical significance regarding OS, indicating a worse prognosis for males. Conclusions: MLPA is a valuable, rapid, and cost-effective tool for initial genetic screening in low-resource settings. This was highlighted by our finding that sex was the sole significant prognostic factor, while the MLPA-detected variants were not found to be significant. The findings suggest that comprehensive risk stratification aligned with international standards requires more advanced molecular technologies. Full article

Accelerating the diffusion of renewable energy requires clear evidence on which determinants enable or hinder deployment across contexts. This study aims to identify the most frequently discussed contemporary determinants of renewable energy deployment. To this end, we conduct a PRISMA-guided systematic review within the SALSA framework, complemented by VOSviewer bibliometric mapping, synthesizing 110 peer-reviewed studies published between 2013 and 2025. We group the most frequently examined determinants into eight domains (economic, environmental, energy, political, regulatory, regional, technological, and social) and summarize the prevalent direction of effect reported in the literature. Economic conditions (e.g., economic growth, financial development, green finance, and trade) and policy/regulation (e.g., institutional quality, instrument stringency, and feed-in and net-billing schemes) emerge as pivotal. Environmental co-benefits (emissions reduction and air quality improvements) and energy system factors (security and energy poverty) are influential, with context-dependent roles for fossil fuel prices and consumption. Regional context (e.g., geopolitical risk) and technological progress (eco-innovation, storage, and grid integration) shape outcomes, while public acceptance, awareness, perceived benefits/costs, and demographics condition uptake. We also document contradictory findings (e.g., foreign direct investment and oil price effects) and gaps (especially social/demographic determinants and causal evaluation of specific policies). Overall, the review offers a coherent synthesis of evidence and an actionable framework of determinants to inform policy design and investment targeting for large-scale diffusion of renewable energy technologies. Full article

Soil microbial communities are vital for saline-alkaline ecosystem functioning; however, their succession during land degradation and their influence on phosphorus (P) transformation remain unclear. To address this gap, this study investigated the dynamics of soil microbial communities and P fractions along a degradation gradient from native grassland to Suaeda salsa vegetation and ultimately to bare land in the Songnen Plain, China. The results revealed that progressive saline-alkaline degradation significantly altered soil properties, increased the proportion of stable P fractions, and reduced microbial alpha diversity. Network analysis revealed that bacterial communities shifted from competition to cooperation along the salinity–alkalinity degradation gradient, indicating a cooperative strategy to cope with environmental stress. Fungal networks exhibit progressively reduced complexity and stability with increasing degradation. Partial least squares path modeling confirmed that soil pH and electrical conductivity directly and indirectly regulated P fractions by reshaping microbial communities, with bacteria exhibiting a stronger total effect than fungi. In conclusion, saline-alkaline degradation drives microbial community succession, which mediates the transformation of soil P into more stable forms and exacerbates P limitation. This study provides a scientific basis for targeted restoration and sustainable management of saline-alkaline ecosystems. Full article