Search Results (5,688)

Background: There is a need for intraoperative image guidance in gynecologic oncologic surgery to provide accurate identification of malignant tissue and ensure negative resection margins. Emerging imaging technologies can complement standard histopathology and reshape intraoperative decision-making. Spectral imaging can extract information on tissue composition and physiological status in real time, without the need for tissue contact, contrast agents, staining, or freezing. This systematic review synthesizes its current clinical applications in gynecologic oncology, decision support utility, and diagnostic performance with data processing frameworks for tissue classification. Materials and Methods: This systematic review (PROSPERO: CRD420251032899) adhered to PRISMA guidelines. PubMed, Google Scholar, Embase, ClinicalTrials.gov, and Scopus databases were searched until September 2025. Manuscripts reporting data on spectral imaging in gynecologic oncology were included in the analysis. Results: Twenty-nine studies and two clinical trials met the inclusion criteria. Most of them focused on cervical neoplasia (n = 17, 58.6%) and ovarian cancer (n = 7, 24.1%) detection, followed by assessment of the fallopian tubes (n = 2, 6.9%), endometrium (n = 1, 3.4%), and vulvar skin (n = 2, 6.9%). Using final pathology as the gold standard, overall specificity ranged from 30 to 99%, and overall sensitivity from 75 to 100%, with particularly high sensitivity for cervical lesions (79–100%) and ovarian cancer (81–100%). Among the included studies, thirteen (44.8%) used data interpretation algorithms, of which eleven (84.6%) applied machine learning, one (7.7%) deep learning, and one (7.7%) combined both. Conclusions: Spectral imaging, supported by computational methods, has shown promising results in the diagnostic evaluation of gynecologic disease by providing functional and molecular information beyond the capacities of standard visual assessment. Full article

Background: This study investigated associations between anthropometric characteristics, postural deviations, musculoskeletal and visceral symptoms, and squat movement quality to clarify how individual physical attributes and symptom profiles influence fundamental movement performance. Method(s): A cross-sectional observational study recruited adults aged 18–65 who could ambulate without pain. Anthropometric and body composition measures were collected. Standardized posture images and multi-angle squat videos were obtained, and visual classifications of posture and squat technique were conducted using predefined criteria. Descriptive statistics characterized the sample, and multivariable logistic regression with LASSO regularization examined associations between demographic, postural, and symptom variables and binary squat outcomes. Results: Two hundred participants (57.5% female; median age 26 years) were included. Males showed higher stature, lean mass, and waist circumference, whereas females exhibited higher body fat and reported more neck pain and headaches. Forward head posture was common (62%), while women demonstrated more favorable upper-body alignment. Most participants maintained neutral lumbar posture and grounded heels during squats, with sex differences in foot rotation and knee path. Higher fat mass predicted reduced squat depth (OR = 1.06, 95% CI: 1.00 to 1.11, p = 0.033); heel lift and absent forward knee movement were associated with better spinal neutrality (OR = 0.07 and 0.18, both p ≤ 0.002); and low skeletal muscle mass (OR = 0.87, 95% CI: 0.79 to 0.95, p = 0.004) and heel lift (OR = 7.09, 95% CI: 1.86 to 26.2, p = 0.003) predicted suboptimal knee tracking. Only 8% achieved a fully “perfect” squat. Conclusion(s): Suboptimal squat mechanics were linked to higher fat mass, lower skeletal muscle mass, and compensatory lower-limb strategies, suggesting that squat quality reflects an interaction among body composition, posture, and motor control rather than any single demographic or anthropometric factor. Full article

Instance-level data augmentation methods, exemplified by “copy-paste”, serve as a conventional strategy for improving the performance of small object detectors. The core idea involves leveraging background redundancy by compositing object instances with suitable backgrounds—drawn either from the same image or from different images—to increase both the quantity and diversity of training samples. However, existing methods often struggle with mismatches in background, scale, illumination, and viewpoint between instances and backgrounds. More critically, their predominant reliance on background information, without a joint understanding of instance-background characteristics, results in augmented images lacking visual realism. Empirical studies have demonstrated that such unrealistic images not only fail to improve detection performance but can even be detrimental. To tackle this problem, we propose a scene-understanding-driven approach that systematically addresses these mismatches via joint instance-background understanding. This is achieved through a unified framework that integrates image inpainting, image tagging, open-set object detection, the Segment Anything Model (SAM), and pose estimation to jointly model instance attributes, background semantics, and their interrelationships, thereby abandoning the random operation paradigm of existing methods and synthesizing highly realistic augmented images while preserving data diversity. On the VisDrone dataset, our method improves the mAP@0.5:0.95 and mAP@0.5 of the baseline detector by 1.6% and 2.2%, respectively. Both quantitative gains and qualitative visualizations confirm that the systematic resolution of these mismatches directly translates into significantly higher visual realism and detection performance improvements. Full article

Despite the enormous amounts of waste textiles produced by the world’s textile industry’s explosive growth, resource utilization rates are still poor. Cotton/polyester blended waste fabrics make up a sizable share, and sorting them precisely is essential to increasing recycling value and promoting the circular economy in the textile industry. Traditional mechanical and human sorting techniques are ineffective and inaccurate; current spectral analysis algorithms mainly concentrate on quantitative composition prediction and are insufficiently capable of differentiating between waste fabrics with comparable content gradients. To address these challenges, this paper proposes an improved 1DCNN model (Dual-1DCNN-Residual-SE) integrated with Near-Infrared (NIR) hyperspectral imaging technology. This model takes raw spectral data and Savitzky-Golay (SG) smoothing data as dual-channel inputs, introducing residual connections to capture subtle spectral differences between similar fabric categories, and employs SE attention mechanisms to adaptively enhance key features. Comparative experiments with four traditional algorithms—KNN, RF, SVM, and PLS—demonstrate that the proposed model achieves a classification accuracy of 95.94%, surpassing the best traditional algorithm SVM (88.12%) by 7.82%. Ablation experiments confirm each enhanced module’s efficacy. This study achieves high-precision classification of cotton/polyester blended waste fabrics, providing technical support for intelligent sorting of industrial waste fabrics. Full article

Plant microtechnique involves a sequence of skill-intensive histological procedures that often yield poorly reproducible images and limited quantitative information. Nevertheless, it provides an essential cellular and tissue context needed to understand biological functions. In this work, we present an optimized resin-based microtechnique that replaces paraffin embedding, incorporates a chemically activated adhesive treatment for glass slides, and develop a trichrome stain for resin sections. All these improvements enhanced section stability and image reproducibility, enabled a broader color palette with sharp contrast of tissues, cells and organelles, and selected ultrastructural features using light microscopy. Based on these preparations, a quantitative micrograph analysis workflow was developed based on image segmentation and feature extraction using MATLAB (R2024a) and Adobe Photoshop (CS6). This approach enables the measurement of a wide range of morphometric and compositional features, generating structured histological datasets that we refer to as plant histolomes. As an illustrative application, this workflow was applied to leaves from several model plants species and integrated multiple anatomical traits into a composite feature, the “C₄ Kranz-anatomy level”, enabling quantitative comparison along the C_3-C₄ anatomical transition. The resin-based microtechnique and the histolomics framework developed in this work provide a robust and reproducible basis for the quantitative plant histology, bridging classical microscopy with a data-driven tissue analysis. Full article

Surgical-site infections (SSIs) represent a significant healthcare burden, often complicating wound healing and recovery. To overcome the limitations of systemic antibiotic administration, such as toxicity and poor localization, this study aimed to develop a bioactive dressing utilizing bacterial nanocellulose (BNC) impregnated with gentamicin-loaded chitosan nanoparticles (GNP). Chitosan nanoparticles were synthesized via ionic gelation with sodium tripolyphosphate (TPP) and optimized using a one-factor-at-a-time (OFAT) approach to control particle size, polydispersity index (PDI) and zeta potential. The optimized nanoparticles were impregnated into BNC disks, and the resulting composite was characterized using FTIR and Raman spectroscopy, XRD and SEM. Antimicrobial efficacy was evaluated against Klebsiella pneumoniae, while biocompatibility was assessed using MTT assays and cell-adhesion studies on human fibroblasts. The optimization process yielded stable, monodisperse nanoparticles with a mean size of 80.07 nm and a PDI of 0.192. SEM imaging confirmed the successful integration of nanoparticles into the BNC nanofibrillar network without compromising the membrane’s structural integrity. The BNC-GNP composite demonstrated significant antimicrobial activity against K. pneumoniae, comparable to free gentamicin solution. Furthermore, in vitro studies revealed good biocompatibility, with cell viability exceeding 70% and sustained fibroblast adhesion, although cell-attachment density decreased with higher nanoparticle concentrations. The developed BNC dressing containing gentamicin-loaded chitosan nanoparticles presents a promising multifunctional biomaterial. It effectively combines local infection control with a biocompatible environment suitable for tissue regeneration, offering a novel approach for the postoperative treatment of surgical-site infections. Full article

Lunar dark mantle deposits (DMDs), formed by explosive volcanic activity on the Moon, are typically composed of glass- and iron-rich pyroclastic materials, with slight variations in color, crystallinity, and TiO₂ concentration by region. This paper proposes a method for identifying DMDs using the YOLOv8 deep learning model, enhanced by the introduction of a multi-scale feature extraction (MSFE) module with an attention mechanism, which improves the model’s ability to detect targets at different scales. First, a DMD dataset was constructed using Lunar Reconnaissance Orbiter (LRO) data, with manual annotations of DMD regions and lunar image slicing to optimize computational efficiency. The YOLOv8 architecture, with the incorporated MSFE module, was then used to improve model accuracy in complex terrain. The experimental results showed that the improved DM-YOLO model achieved a precision (P) of 83.9%, a recall (R) of 83.2%, and a mean average precision (mAP@0.5) of 84.2%, representing increases of 15.2%, 14.4%, and 14.0%, respectively, over those obtained with the original YOLOv8 model. The predicted results were preliminarily verified using FeO abundance data and further confirmed by analysis of M³ spectral absorption features, showing strong consistency with known DMDs in terms of both chemical composition and mineralogical characteristics. Observations showed that DMDs were located primarily in the low- and mid-latitude regions of the Moon, with most deposits found in the lunar highlands. The findings suggest that the DM-YOLO model has significant potential for providing technical support for lunar exploration and resource development, particularly for identifying small-scale features that are difficult to annotate. Full article

Background: Sex-related differences in cardiovascular disease outcomes are well recognized. Their impact on short-term outcomes in acute pulmonary embolism (PE) remains unclear. This study aimed to assess the association between sex and in-hospital outcomes in patients with acute PE. Methods: We performed a retrospective observational cohort study including 322 consecutive adult patients with acute PE admitted to a university hospital. Clinical, hemodynamic, laboratory, and imaging data were collected at presentation. The primary outcome was a composite poor outcome defined as intensive care unit (ICU) admission, systemic thrombolysis, or in-hospital mortality. Multivariable logistic regression analysis was used to evaluate whether sex independently predicted adverse outcomes after adjustment for established prognostic factors. Results: This study included 322 patients with acute pulmonary embolism (mean age 64.4 ± 13.1 years), of whom 50.0% were women. The composite poor outcome occurred more frequently in women than in men (34.0% vs. 22.7%, p = 0.032). Female sex was associated with increased odds of poor outcome in univariate analysis (odds ratio (OR) 1.76; 95% confidence interval (CI) 1.08–2.88). This association remained significant after multivariable adjustment (adjusted OR 1.69; 95% CI 1.02–2.82; p = 0.042). No significant sex differences were observed for individual components of the composite endpoint. Conclusions: Female sex was independently associated with a higher risk of adverse in-hospital outcomes in acute PE, suggesting that sex-specific factors may influence early prognosis and should be considered in future risk stratification models. Full article

(1) Background/Objectives: Gap formation contributes to the clinical failure of partial crowns. Therefore, it was analyzed at the interfaces between restoration, luting material, and tooth in partial crowns made of lithium disilicate ceramic (LS2) and nanohybrid composite (RBC) after thermomechanical loading (TCML) using optical coherence tomography (OCT). (2) Materials and Methods: Sixteen human mandibular molars were restored with CAD/CAM partial crowns made of LS2 (IPS e.max^® CAD) or RBC (Tetric^® CAD) using adhesive cementation (Variolink^® Esthetic DC). The restorations were imaged by OCT (1550 nm, 28 kHz) at t0 = 24 h, t1 = 90 days of water, t2 = after TCML with 480,000 loading cycles, and t3 = TCML with 1,200,000 loading cycles. Gap lengths (%) at interface 1 (partial crown-luting material) and interface 2 (luting material–enamel/dentin) were quantified. Groupwise and pairwise comparison of OCT parameters was conducted using the Mann–Whitney U, Friedman, and Conover–Iman tests with Bonferroni correction (α = 0.05). (3) Results: At interface 1, LS2 showed a larger median gap length than RBC (ceramic = 48.4%; composite = 5.2%, p < 0.01). At interface 2, the largest median gap length for LS2 was measured at the dentin (ceramic = 59.7%; composite = 52.5%), while for RBC, the enamel was more affected (ceramic = 26.2%; composite = 36.9%). (4) Conclusions: OCT enables reliable gap detection in partial crowns under functional loading and is therefore suitable for monitoring adhesive interface integrity. Under in vitro conditions, both materials demonstrated stable adhesive performance without debonding, while material-dependent differences in gap formation and distribution were observed. Full article

Urban waterfront walkways are everyday public built environments where people commonly engage in slow walking, yet evidence remains limited that links what pedestrians see to immediate psychophysiological responses under controlled first-person dynamic exposure. To address this gap, we developed a fixed-speed, fixed-duration VR walk-through model using real-world 360° panoramic video and quantified scene visual composition via computer vision-based image analysis. Based on the visible shares of key components (greenery, water, sky, hardscape, and built structures), clips were grouped into four interpretable waterfront typologies: Vegetation-Enclosed, Built-Dominant, Hardscape-Plaza, and Blue-Open. Fifty healthy adults completed within-subject VR exposures to the four typologies (50 s per clip), while multimodal physiological signals and brief affect and landscape ratings were collected before and after exposure. The results showed that scenes with more water and vegetation coverage, along with expansive views, were associated with promoted autonomic nervous system calming responses, whereas scenes with fewer natural elements and higher built structure density were more likely to induce tension responses. Negative emotions decreased significantly across all four scene experiences, though artificial scenes concurrently exhibited emotional improvement alongside physiological tension. Overall, brief first-person dynamic VR exposure can yield immediate emotional benefits, and waterfront designs combining water proximity, abundant greenery, and expansive vistas may maximize short-term restorative potential, offering quantitative targets for health-supportive planning and retrofitting. Full article

Paravalvular leak (PVL) remains a clinically important complication after surgical or transcatheter valve implantation, presenting predominantly with heart failure (HF) and/or high-shear hemolysis. While redo surgery can be definitive, contemporary candidates frequently carry prohibitive operative risk, positioning transcatheter PVL closure as a key therapeutic alternative. However, available outcome data are largely derived from observational series and registries with heterogeneity in PVL mechanisms, prosthesis types, imaging protocols, and endpoint definitions. Standardized frameworks—such as those proposed by the PVL Academic Research Consortium—support harmonized PVL grading and clinically meaningful composite endpoints that integrate imaging/hemodynamic results with patient-centered outcomes. Across datasets, the most consistent determinant of benefit is residual PVL severity: procedural efficacy is most commonly defined as achieving ≤ mild residual regurgitation without prosthetic leaflet interference, device embolization, or major complications. This review provides a step-by-step, phenotype-driven approach to transcatheter PVL closure, emphasizing multimodality imaging (TEE and cardiac CT, with adjunct CMR and PET when appropriate), access and support planning tailored to valve position, and morphology-matched device selection—often requiring modular multi-device strategies for elongated crescentic channels, particularly in hemolysis-predominant presentations. We also synthesize evidence on complications and bailout management, with a focus on preventable high-severity events (leaflet impingement, embolization, stroke/air, vascular injury, tamponade) and standardized pre-release safety checks. Collectively, contemporary practice supports high implant success in experienced programs, with clinical improvement tightly coupled to procedural endpoint quality and careful Heart Team selection. Full article

Air pollution remains a major public-health concern, and exposure to particulate matter (PM), particularly PM₁₀ (with a diameter ≤ 10 µm), is associated with adverse respiratory and cardiovascular outcomes. Most research relies on a singular model for PM₁₀ surface estimation. This study is an assessment of a national-scale, daily PM₁₀ estimation framework for Lithuania (2019–2024), using a hybrid machine-learning method that combines Random Forest (RF) and extreme gradient boosting (XGBoost) algorithms. Hourly PM₁₀ observations were aggregated from 18 monitoring stations to obtain daily means and temporal means. The predictors integrated meteorological factors, such as temperature, wind, humidity, and precipitation, to determine satellite-based atmospheric composition from Sentinel-5P Tropospheric Monitoring Instruments (TROPOMI). Atmospheric components include nitrogen dioxide (NO₂), carbon monoxide (CO), sulfur dioxide (SO₂), ozone (O₃), formaldehyde (HCHO), and the absorbing aerosol index (AI). Moderate-Resolution Imaging Spectroradiometers (MODIS) were used to record land-surface temperature and static spatial descriptors, such as elevation, land cover, Normalized Difference Vegetation Index (NDVI), population, and road proximity. The dataset was partitioned temporally into training (70%), validation (20%), and testing (10%). The hybrid model achieved an improved accuracy, compared with single-model baselines, reaching a coefficient of determination (R²) of 0.739 in validation and R² = 0.75 in the tested dataset. Mean absolute error (MAE) was 3.15 µg/m³, and root mean square error (RMSE) was 3.98 µg/m³. The results indicate a slight tendency to overestimate PM₁₀ concentrations at lower concentration levels. Feature-importance analysis revealed that short-term temporal persistence is the key to daily PM₁₀ prediction, while meteorological variables provide secondary contributions. Temporal evaluation, using consecutive two-year windows, revealed a consistent improvement in predictive performance from 2019–2020 to 2023–2024, while station-level analysis showed moderate-to-strong agreement between the predicted and observed PM₁₀ concentrations across monitoring stations, with R² ranging from 0.455 to 0.760. This provides decision-support capabilities for air-quality management, the evaluation of mitigation measures, and integration of air-pollution considerations into sustainable urban planning strategies assessing public-health protection. Full article

Zhenwu (Perfected Warrior), one of the most influential Daoist martial deities, was historically shaped by the northern celestial emblem Xuanwu and later personified and integrated into the Daoist pantheon. While scholarship on Zhenwu has largely relied on textual sources, cliff sculptures provide a material setting in which doctrine, ritual space, and iconography can be examined together. Taking the Zhenwu niche (No. 1) at Nanshan, Dazu (Chongqing) as a case study, this article first situates the niche within the spatial program of the Nanshan Daoist carvings and describes its architectural design, composition, and inscriptional evidence of worship. It then revisits key motifs associated with Zhenwu—such as the sword, bare feet, and the turtle–snake pair—through Daoist and inner-alchemical (neidan) textual traditions. Rather than positing a direct or exclusive link between the Nanshan sculpture and inner-alchemical practice, the article argues that the niche mobilizes an established iconographic repertoire that could have resonated with late imperial discourses of self-cultivation, and that its northern placement within the Nanshan ensemble reinforces these cosmological associations. By combining site-based analysis with a cautious reading of Daozang and neidan texts, the study contributes to scholarship on Daoist visual culture and offers a framework for comparing Zhenwu images across regions and media. Full article

Landscape quality significantly impacts residents’ well-being through visual perception, particularly among the elderly who exhibit heightened sensitivity to environmental stimuli. Therefore, this study investigates how landscape configurations influence emotional and physiological responses in older adults under controlled visual conditions. This study selected representative outdoor activity sites in northern Chinese cities and designed five landscape scenarios by adjusting the green coverage ratio (GCR) and landscape composition. Participants (mean age 64.8) reported feelings of pleasure, relaxation, and fatigue while viewing screen-based landscape images, with simultaneous recording of attention-to-interest area (AOIA), pupil diameter range (PD), and electroencephalogram (EEG) data. Research findings reveal a non-linear relationship between the GCR and emotional and physiological responses among elderly populations: when the GCR increased from 18.4% to 38.1%, participants reported significantly heightened feelings of pleasure and relaxation, alongside marked reductions in fatigue-related physiological indicators. However, when the GCR further rose to 48.5%, both reported subjective measures and physiological indicators deteriorated among elderly participants. Under equivalent green coverage conditions, water features within natural settings enhance visual focus on natural elements more effectively than purely green landscapes. Women demonstrated greater sensitivity to changes in the GCR. Correlation analysis further indicated that visual attention among the elderly positively correlated with positive emotions and negatively correlated with fatigue-related physiological responses. This research provides valuable guidance for green space design. Full article

Recent exploration has demonstrated significant prospecting potential at the Huayagou Au deposit in Longnan mineral Field, Gansu Province, West Qinling Orogen, Central China. However, the nature and evolution of the auriferous fluids responsible for gold enrichment remain poorly constrained, hindering effective exploration targeting of high-grade ores. In this study, apatite and tourmaline closely associated with gold mineralization are investigated as mineralogical recorders of fluid composition and evolution. Integrated petrographic observations, TIMA phase mapping, cathodoluminescence imaging, electron probe microanalysis, and in situ trace element analyses were used to distinguish magmatic, metamorphic, and syn-ore hydrothermal generations of apatite and tourmaline, together with in situ Nd isotopic analyses of apatite and B isotopic analyses of tourmaline. Syn-ore hydrothermal apatite is characterized by homogeneous blue cathodoluminescence, fluorapatite compositions, strong LREE depletion, and εNd(t) values overlapping those of Triassic magmatic apatite, whereas Early-Devonian magmatic and metamorphic apatites display more distinct signatures. Tourmaline records a systematic evolution from early dravite to late schorl, accompanied by trace element enrichment and a shift toward heavier δ¹¹B values. These mineralogical and isotopic features, together with published sulfur isotope constraints, indicate that gold mineralization at Huayagou was dominantly controlled by structurally focused metamorphic fluids, with localized Triassic magmatic–hydrothermal overprinting enhancing gold enrichment in high-grade ores. The Huayagou Au deposit is, therefore, best interpreted as an atypical orogenic gold system, highlighting enhanced exploration potential in structurally favorable zones at depth, particularly in the western part of the district where Triassic magmatism is inferred. Full article