Search Results (883)

Background: Precise intraoperative localisation of small colorectal tumours during laparoscopic surgery remains challenging due to absent tactile feedback and subserosal tumour location. Current standard methods, particularly India ink tattooing, demonstrate 15–30% failure rates for lesions less than 10 mm, leading to prolonged operative times, incomplete resections, and re-operations. Multiple emerging technologies promise improved localisation, yet comparative evidence remains fragmented. Objective: To map and characterise the current landscape of intraoperative marking and identification technologies for small colorectal tumour localisation during laparoscopic surgery, with emphasis on radiofrequency-based methods and alternative approaches, and to identify evidence gaps guiding future research. Methods: Following PRISMA-ScR guidelines, we systematically searched PubMed, Web of Science, and Scopus databases from January 2000 through December 2025 for studies evaluating tumour localisation technologies in colorectal cancer surgery, including primary tumour localisation during laparoscopic colectomy and localisation of colorectal liver metastases during hepatic surgery, or transferable anatomical applications with documented translational potential to colorectal surgery. Two independent reviewers screened all records, with discrepancies resolved through discussion and a third senior reviewer consulted for unresolved disagreements; data were extracted on technical performance, safety, feasibility, cost-effectiveness, usability, innovation potential, and evidence quality. Results: We included 89 studies comprising 18 colorectal-specific articles and 71 transferable/GI-adjacent studies. Detection success rates ranged from 71% to 100% across modalities. Near-infrared fluorescence with indocyanine green demonstrated the strongest clinical evidence with 75–100% detection across eight colorectal studies encompassing 2134 procedures and seamless workflow integration. Radiofrequency identification systems achieved 91.9–99% detection in feasibility studies with promising tissue penetration of 15–35 mm but limited colorectal validation. Electromagnetic navigation excelled in rigid organs with 85–98% success but showed degraded performance in mobile bowel at 71–75%. Critical evidence gaps included absent head-to-head comparative trials, non-standardised outcome metrics limiting cross-study comparability, and limited long-term safety data with only 14 studies providing follow-up exceeding six months. Conclusions: ICG fluorescence represents the most clinically mature technology identified, representing a priority candidate for colorectal-specific validation in challenging localisation scenarios. RFID systems demonstrate promising characteristics justifying prioritised research investment through adequately powered comparative trials. Future research must emphasise consortium-based comparative effectiveness studies, standardised outcome metrics, and integration with robotic and AI-assisted surgical platforms to accelerate clinical translation. Full article

Scent marking has been discussed as an important component of communication in brown bears (Ursus arctos Linnaeus, 1758). However, the environmental factors influencing the occurrence of rub trees and their value for non-invasive genetic sampling remain poorly understood. This study examined the patterns of rub tree occurrence in the eastern High Tatra Mountains (Slovakia) at two spatial scales. At the tree scale, paired-design generalized linear mixed models showed that rub trees were more frequently recorded on large-diameter coniferous trees, indicating an association with visually prominent and chemically suitable substrates. At the landscape scale, logistic regression models revealed that the probability of rub tree occurrence increased with elevation and distance from human settlements, identifying high-elevation forests as areas of higher predicted rub tree occurrence. The best-supported model was used to produce a predictive map of rub tree occurrence across the study area. We also evaluated whether rub trees are reliable sources of biological material for non-invasive sampling. Hair collected during repeated field visits provided DNA suitable for genotyping and individual identification. Overall, the results show that rub trees exhibit non-random spatial patterns and represent effective focal points for systematic genetic sampling, linking patterns of rub tree occurrence to the spatial targeting of non-invasive genetic sampling in mountain landscapes. Full article

Early stage mineral exploration requires the reliable identification of anomalous gold zones from drillhole geochemistry in data-limited environments. This study applies a machine learning (ML) classification framework to detect anomalous gold zones (Au ≥ 0.68 ppm; 90th percentile) from bulk XRF multielement drillhole geochemistry in a Paleoproterozoic Birimian greenstone belt sulphide-hosted orogenic gold system, West African Craton. A total of 53,126 one-metre diamond core samples from 301 drillholes were preprocessed within a compositional data analysis (CoDA) framework, with Au being explicitly excluded from the centred log-ratio (CLR) transformation to eliminate target–predictor circularity. After Minimum Covariance Determinant (MCD) outlier filtering, 40,385 samples were retained to construct a 19-feature matrix of 10 CLR-transformed elements, 1 rock-type feature, and 8 sulphide–lithology interaction features. Drillhole-based block cross-validation (DH-block CV), validated by an experimental along-hole variogram (practical autocorrelation range ≈ 20 m), ensured spatially honest performance estimates. Four nonlinear classifiers—Random Forest (RF), XGBoost, LightGBM, and Multi-Layer Perceptron (MLP)—were benchmarked against a Logistic Regression (LR) linear baseline. All nonlinear classifiers achieved validation AUC of 0.936–0.938, outperforming LR (AUC = 0.931) with F1-score improvements of +0.09 to +0.11 and precision gains of up to +35 percentage points—directly reducing wasted drill holes in applied exploration. MLP recorded the highest F1-score (0.666) and precision (0.765), and XGBoost the highest recall (0.787). Permutation importance identified S-Ti (ΔAUC = 0.028), S-Fe (0.021), and S-Al (0.013) as the top-ranked features, confirming that sulphide enrichment relative to lithological background is the primary discriminating signal. Partial dependence analysis revealed a threshold-driven non-monotonic Fe dependence at CLR(Fe) ≈ 3, marking the transition from lithological dilutant to sulphide co-indicator—a nonlinear pattern inaccessible to linear classifiers. Full article

Research on twice-exceptional students, particularly those with co-occurring intellectual giftedness and dyslexia, remains limited and conceptually fragmented. This study examines the reading- and writing-related profiles of these students by comparing three groups: gifted students without dyslexia (G), gifted students with dyslexia (G-D), and dyslexic students without intellectual giftedness (D). The sample consisted of 133 Spanish-speaking primary school students (Grades 3–6). The results revealed a distinct and non-linear performance pattern. G-D students exhibited marked difficulties in lower-level literacy processes, including phonological and lexical processing, with a performance pattern closer to that of dyslexic peers. However, they showed relative strengths in higher-order language abilities, particularly text comprehension, oral comprehension, and written composition. The findings suggest a non-uniform profile of reading- and writing-related abilities in these students, characterized by weaknesses in several lower-level literacy processes and relative strengths in some higher-order language abilities. This pattern may contribute to the underidentification of these students across educational and clinical contexts. By providing empirical evidence from Spanish, a relatively underexplored orthographic context, this study contributes to current models of twice-exceptionality and highlights the need for more sensitive and staged identification procedures, as well as multidimensional assessment and intervention approaches that address both strengths and weaknesses. Full article

To address challenges such as complex background interference, limited long-range modeling capabilities of CNNs, and poor generalization in steel-concrete cross-material scenarios, this study proposes an enhanced detection framework. This framework integrates a TAPFormer with morphological reasoning rules. The method utilizes TAPFormer as the backbone network. It captures global topological features of cracks through a Task-Aware Query mechanism. This approach compensates for the deficiencies of traditional convolutional operators in modeling the continuity of thin and long cracks. Furthermore, a mask reasoning module based on geometric priors is developed to handle unstructured interferences, such as marker pen marks, welds, and concrete holes. This module defines logical criteria, including edge curvature consistency, axial aspect ratios, and endpoint extension directions. These criteria are used to perform topological repair and filter false positives in the initial segmentation masks. A hybrid dataset containing 4500 cross-material damage images was used for validation. The results show that the proposed method achieves a mean IoU of 86.72% and an F1-score of 90.36%. Notably, the method filters over 91.0% of false positives caused by manual marker pen marks in interference-rich scenarios. Compared to mainstream state-of-the-art models, the IoU improves by at least 5.48%. The results show that the proposed framework improves the robustness and logical self-consistency of crack identification in complex engineering environments. Full article

This article presents an integrated approach that combines Virtual Reality (VR), Augmented Reality (AR), and an Intelligent Virtual Assistant (IVA) to support training, on-the-job assistance, and maintenance in a textile manufacturing environment. The solution spans three systems: RioRV, a Unity-based VR platform for immersive, step-by-step procedure rehearsal, instructional videos, and simplified 3D animations; RiAR, a mobile AR application for assisted maintenance and access to real-time and historical machine data using marker-based (VuMark) identification; and Ria, a web-based IVA that delivers document-grounded answers, operational queries over a secure plant API, short-horizon forecasting, and a narrow set of guarded remote actions. The architecture prioritizes human-centered Industry 5.0 principles—safety, usability, and resilience—by enabling operators to learn procedures in VR, execute tasks with AR overlays and maintenance media at the workstation, and obtain concise, source-cited guidance via the IVA without leaving immersion. In the case study with a spinning section at RIOPELE, the convergence of VR, AR, and IVA reduced reliance on bulky manuals, shortened time-to-information for machine status, and established a feedback loop in which training and operational experience continuously enrich the knowledge base. Full article

Research on free-ranging animal species, which extends from the breadth of stock identification and population dynamics to fine-scaled analysis of behavior, depends on identifying individuals. Recognizing wild bottlenose dolphins (Tursiops truncatus) primarily depends on photographs of distinct patterns of notches which eventually develop on the trailing edge of most dorsal fins. Although photo-identification has advanced to include computer-aided fin identification software, there can still be error related to dolphins whose identifying patterns are subtle, obscured, or absent (“clean fins”). Facial pigment patterns on dolphin species were described as bridle marks over a hundred years ago. The purpose of this descriptive study was to evaluate bridle mark presence, distinctiveness, and permanence as a supplement to dorsal fin photo-identification. A 17-year population census of 860 bottlenose dolphins in Palm Beach, Florida, USA, includes 458 dolphins with photographic records showing bridle marks. A subset of 30 dolphins, based on an encounter history spanning at least 7 years, was coded using a 17-point system. The results confirmed bottlenose dolphin bridle mark distinctiveness and permanence on all 30 dolphins. In conclusion, bridle marks can supplement photo-identification as a double-mark system to improve accuracy. This tool is helpful for recognizing calves with clean dorsal fins post weaning and adults without dorsal fin markings. Full article

Gallbladder cancer (GBC) is an aggressive tumor that, together with the cholangiocarcinomas, constitutes the spectrum of biliary tract cancer (BTC). These tumors are characterized by a frequently late diagnosis, marked genomic heterogeneity, variable response to cytotoxic therapies, and poor overall survival in advanced stages. Nevertheless, the characterization of the tumor microenvironment (TME) and the identification of actionable molecular targets have driven the development of biological therapies. This review summarizes current and emerging evidence on monoclonal antibodies, bispecific antibodies, and antibody–drug conjugates (ADCs) in the management of GBC. The analysis addresses the early exploration of autoantibodies as potential diagnostic biomarkers, mechanistic hypotheses of immune evasion, and the clinical translation of targeted agents in the metastatic setting. Additionally, we critically discuss the extrapolation of data from global BTC trials to the specific GBC setting, the integration of population genetics into epidemiological studies such as the EULAT Eradicate GBC initiative, and the preliminary status of immunotherapy in perioperative scenarios. Full article

Background/Objectives: Type 1 diabetes (T1D) is a heterogeneous disease in terms of clinical presentation, treatment requirements, and risk of complications. The identification of biological endotypes and clinical phenotypes has been proposed to support precision medicine approaches. We aimed to assess the prevalence and clinical characteristics of age-at-diagnosis-based endotypes, adult-onset phenotypes, and insulin-resistant phenotypes in a real-world cohort of adults with T1D. Methods: We conducted a single-center, observational, cross-sectional study including adults (≥18 years) with clinically confirmed T1D under active follow-up. Clinical, metabolic, and treatment-related variables were analyzed across predefined age-at-diagnosis-based endotypes and clinical phenotypes. Results: A total of 868 patients were included (median age 49 years; diabetes duration 23 years; age at diagnosis 20 years; 51.5% women). Continuous subcutaneous insulin infusion (CSII) was used by 20.4% of patients, and continuous glucose monitoring (CGM) was used by 95.3%. Mean HbA1c was 7.47%, with a median time in range (TIR) of 63%. The prevalence of age-at-diagnosis-based endotype 1 (ED1) was 11.8%, adult-onset phenotype was 31.3%, and insulin-resistant phenotype was 7.3%. No major differences in glycemic control were observed across age-at-diagnosis-based endotypes. Associations between endotypes and treatment-related variables were largely explained by current age and diabetes duration. In contrast, the adult-onset phenotype was independently associated with lower TIR, higher time above range, lower use of CSII, and greater use of adjunctive therapies. The insulin-resistant phenotype was associated with higher HbA1c, lower TIR, and greater therapeutic complexity. Conclusions: Adult T1D shows marked heterogeneity. In this real-world cohort, age-at-diagnosis-based endotypes were not independently associated with major clinical differences after adjustment for current age and diabetes duration. In contrast, adult-onset and insulin-resistant phenotypes identified subgroups with poorer glycemic control and greater therapeutic complexity. Full article

Background/Objectives: Diabetes mellitus is a chronic metabolic disorder characterized by hyperglycemia, dyslipidemia, and oxidative stress, leading to severe systemic complications. Medicinal plants rich in polyphenolic compounds have gained increasing attention as complementary therapeutic agents. This study aimed to comparatively evaluate the chemical composition, as well as the antidiabetic, hypolipidemic, and antioxidant effects of Polygonum persicaria and Vaccinium myrtillus in a streptozotocin-induced diabetic model. Although Vaccinium myrtillus has been more extensively investigated for its antidiabetic potential, the pharmacological relevance of Polygonum persicaria in diabetes remains insufficiently characterized, particularly in direct comparison with a recognized phytotherapeutic comparator. Methods: Hydroalcoholic tinctures prepared from Polygonum persicaria L. herb and Vaccinium myrtillus L. leaves were subjected to phytochemical analysis using High-Performance Thin-Layer Chromatography (HPTLC) for the identification of flavonoids and phenolcarboxylic acids, alongside spectrophotometric determination of total polyphenol and flavonoid content. Experimental diabetes was induced in CD1 mice by streptozotocin administration. Animals were treated orally for 35 days, and glycemic parameters, lipid profile, body weight, food and water intake, and oxidative stress markers (MDA, SOD, TAC, and GPx) were evaluated. Results: HPTLC/CSS screening indicated the presence of rutin, chlorogenic acid, and caffeic acid in Polygonum persicaria, while Vaccinium myrtillus showed stronger densitometric signals for phenolcarboxylic acid-type compounds, particularly chlorogenic and caffeic acids. Total polyphenol and flavonoid content were also higher in Vaccinium myrtillus (433.89 ± 8.67 mg/L GAE; 154.38 ± 3.08 mg/L QE) compared to Polygonum persicaria (269.28 ± 5.25 mg/L GAE; 132.75 ± 2.65 mg/L QE). Functionally, Vaccinium myrtillus demonstrated a significant antihyperglycemic effect from day 14 (p = 0.009) and improved lipid parameters, while Polygonum persicaria showed a delayed glycemic effect, significant only at day 35 (p = 0.014), without significant hypolipidemic activity. In contrast, Polygonum persicaria exerted a marked antioxidant effect, significantly increasing GPx activity (p = 0.025) and reducing MDA levels (p = 0.053). Conclusions: Vaccinium myrtillus showed stronger antihyperglycemic and hypolipidemic effects, while Polygonum persicaria was mainly associated with antioxidant-related biochemical changes. These differences may be influenced by phytochemical composition, but they cannot be attributed solely to total polyphenol or flavonoid content. Full article

Seasonal variability in feed availability in Mediterranean dairy sheep systems can compromise animal performance and welfare, highlighting the need for reliable, high-frequency monitoring tools. Live weight (LW) is a key indicator of nutritional status, but conventional measurements are labour-intensive and poorly suited to dynamic conditions. Walk-over-weighing (WoW) systems integrated with electronic identification (EID) enable automated, continuous, individual-level LW monitoring. This study assessed the sensitivity of a WoW system to detect nutritionally driven LW changes in Sarda dairy ewes under indoor and grazing conditions. Two experiments were conducted: an indoor short-term nutritional challenge involving 24 non-lactating ewes and a grazing trial with contrasting pasture access times involving 48 lactating ewes. In both experiments, the WoW system detected consistent LW differences between nutritional treatments (p < 0.001), capturing both short-term responses and sustained LW dynamics. Differences were approximately 5%, indicating that the WoW system was sensitive to nutritionally induced LW variation under the experimental conditions of the present study, before marked changes in body condition score (BCS) became detectable. These results demonstrate that WoW systems can reliably capture LW trajectories in response to nutritional variation. However, LW responses should be interpreted cautiously, as short-term variation may also reflect gut fill and hydration dynamics, and intake information was not fully available at the individual level because some feed intake components were measured at the group level or estimated indirectly. Integrating automated LW data with production and management information may support group-level nutritional decisions and early detection of animals deviating from expected LW trajectories in precision dairy sheep systems. Full article

Ship exhaust emissions have become an increasingly prominent global atmospheric environmental issue, triggering a series of ecological disturbances and adverse public health consequences. However, comprehensive analyses of the research progress and evolution trends in this field remain scarce. This study systematically retrieved 1346 scholarly publications in the ship exhaust emissions field for the period 2011–2025 from the Web of Science Core Collection and carried out a bibliometric analysis encompassing publication outputs, contributing countries/regions, and keyword characteristics. The findings reveal a sustained and robust growth trajectory in global research output, with annual publications increasing nearly fivefold over the 15-year study period. Notably, academic interest in this field has increased significantly since 2020 due to the implementation of the global sulfur cap regulation. Core thematic clusters (mean silhouette S = 0.7205) in this field include source apportionment, numerical modeling analysis, atmospheric criteria pollutants, and technological emission reduction strategies. The geographical distribution of research output shows a significant positive correlation with the importance of regional maritime economies. China, the United States, and Germany are the leading contributors in terms of publication outputs, while frequent research collaborations have been observed among European countries. Since 2021, the emergence of Automatic Identification System data as a keyword with high burst strength (intensity = 3.60) marks a paradigm shift toward a “big data-enabled refined management” framework. Concurrently, the sustained burst activity of keywords including nitrogen oxides, volatile organic compounds, and traffic-related emissions from 2023 to 2025 indicates rapidly growing scholarly attention to secondary aerosol precursors from shipping, and the critical need for coordinated multi-pollutant control strategies. Future research directions for ship exhaust emissions are expected to transition from fundamental characterization research to big data-driven monitoring and estimation methods, as well as advanced emission reduction technologies. The bibliometric insights derived from this study provide a valuable reference framework for subsequent in-depth studies on ship exhaust emissions. Full article

Martian periglacial landforms are among the clearest surface clues for investigating ground-ice occurrence, climate evolution, and potential habitability on Mars. Utopia Planitia contains abundant ice-related landforms and is therefore well suited to regional-scale mapping of periglacial features. However, most existing identifications still rely heavily on manual interpretation, which is time-consuming and difficult to keep consistent across large image mosaics. In this paper, using Context Camera (CTX) imagery, a dataset of four representative landform types in Utopia Planitia, namely flat-floored depressions, thermal contraction cracks, scalloped depressions, and brain terrain, was built. A Multi-scale Texture-enhanced U-Net (MTU-Net) was then developed as an automated and standardized mapping solution for semantic segmentation of these landforms. The model incorporates hierarchical attention and multi-scale texture enhancement modules, enabling recognition under complex backgrounds where fine-scale landforms such as thermal contraction cracks and brain terrain exhibit only weak textural details, alongside large scale variations. On the held-out test set, MTU-Net reaches a mean intersection over union (mIoU) of 89.55%, a mean F1-score of 94.71%, and a Kappa coefficient of 91.21%, outperforming the baseline U-Net under the same evaluation protocol. The resulting regional maps show marked spatial heterogeneity in the occurrence of the four landform types across Utopia Planitia. This study provides a methodological basis for automated periglacial landform mapping in Mars. Full article

Background/Objectives: Multimodal large language models (LLMs) are increasingly integrated into dental diagnostics. This study evaluated the ability of ChatGPT-4o and Gemini 3 Flash to visually identify endodontic instruments and assess their explanatory plausibility regarding instrument morphology. Methods: Standardized images of five endodontic file systems (Reciproc R25, Reciproc Blue, WaveOne Gold, MM One Shape, and XP-endo Finisher) were submitted to both models via their free tiers. Each image was evaluated 50 times per model (total n = 500) to assess both classification accuracy and response consistency. Visual recognition performance was measured using recall, precision, and F1-score, while the plausibility of morphological explanations was evaluated using a structured 3-point scale. Results: Gemini 3 Flash demonstrated significantly higher recognition performance compared to ChatGPT-4o (p < 0.001). The overall acceptable response rate was higher for Gemini 3 Flash (94.4%, [95% CI: 91.5–97.3%]) than for ChatGPT-4o (67.2%, [95% CI: 61.4–73.0%]; p < 0.001). Notably, Gemini 3 Flash showed strong performance in identifying complex instrument designs, whereas ChatGPT-4o exhibited marked limitations in recognizing certain non-standard geometries. Reliability analysis indicated higher consistency for Gemini 3 Flash (κ = 0.86, [95% CI: 0.81–0.91]) compared to ChatGPT-4o (κ = 0.51, [95% CI: 0.44–0.58]). Conclusions: Gemini 3 Flash outperformed ChatGPT-4o in both classification accuracy and consistency in this controlled visual identification task. While these findings highlight the potential of multimodal LLMs in endodontic workflows, their current performance variability limits direct, autonomous clinical application. Further validation under clinically realistic conditions is required before such systems can be considered reliable adjunctive tools. Full article

Landslide mapping and susceptibility assessment are essential for hazard identification, infrastructure protection, and risk management. The middle and lower reaches of the Nujiang River have high relief, rapid geomorphic change, and fragile landscape conditions, which increase landslide susceptibility and hinder timely detection. To improve the spatiotemporal characterization of landslide activity, we developed a multi-source Earth observation framework for annual landslide mapping and susceptibility assessment. First, interannual embedding-change intensity maps were generated to guide the visual interpretation of landslide-related surface disturbances. Second, annual landslide and non-landslide samples were collected through field validation and visual interpretation. Third, annual 10 m landslide maps for 2017–2025 were generated using random forest on Google Earth Engine. Finally, 24 multidimensional environmental factors were incorporated into landslide susceptibility modeling. Landslides were concentrated mainly along the Nujiang River corridor and adjacent high-relief canyon slopes, with marked interannual variability but relatively stable hotspot regions. SHAP analysis further identified BSI_mean as the most important predictor, with a mean absolute SHAP value of 0.116, followed by NDVI_mean and terrain-related variables, indicating that bare-surface exposure, vegetation condition, and terrain dissection were strongly associated with mapped landslide occurrence. This study provides annual landslide inventories and susceptibility information for hazard mitigation and infrastructure planning. Full article