Saved Queries

by Manacés Dos Santos-Becerril, Francisca Sánchez-Ayllón, Isabel Morales-Moreno, Flavia Barreto-Tavares-Chiavone, Isabelle Campos-de Acevedo, Ana Luisa Petersen-Cogo, Marcos Antônio Ferreira-Junior and Viviane Euzebia Pereira Santos

Healthcare 2025, 13(15), 1939; https://doi.org/10.3390/healthcare13151939 (registering DOI) - 7 Aug 2025

Abstract

Introduction: With the emergence of the expert patient and the expansion of health literacy, the importance of planning and building health technologies aimed at teaching and training health professionals, especially nurses, due to their activities with patients in Primary Health Care, with the aim of meeting the real and constant demands of the expert patient, is evident. Methods: Methodological study with a quantitative approach. The course was constructed based on a scope review, scientific reference, and observational visits during the months of September 2021 and August 2022. For validation, an organized electronic form was used with general information about the research and items of the course constructed for later evaluation by the judges with the three-point Likert scale and with the application of the Delphi Technique between the months of September and October 2022; for the agreement of the judges, the Content Validation Coefficient > 0.8 was considered. Results: Based on the content selected in the scope review, the reference contribution, and the observational visits, the course was constructed. Nine judges participated in the validation stage in Delphi I with a total Content Validation Coefficient above 0.90 and with some suggestions for modifications and improvements pointed out by them. In Delphi II, six judges evaluated the course, resulting in a total Content Validation Coefficient of 0.99. Conclusions: The course developed was considered valid to support the training of Primary Health Care nurses in the formation of the expert patient, with a view to promoting patient autonomy in self-care management, optimizing Primary Health Care, and reducing unnecessary hospital admissions. Full article

(This article belongs to the Special Issue Nursing, Coaching, Oncology and Chronic Diseases: Future Insights and Perspectives)

26 pages, 6679 KiB

Open AccessArticle

Cotton Leaf Disease Detection Using LLM-Synthetic Data and DEMM-YOLO Model

by Lijun Gao, Tiantian Ran, Hua Zou and Huanhuan Wu

Agriculture 2025, 15(15), 1712; https://doi.org/10.3390/agriculture15151712 (registering DOI) - 7 Aug 2025

Abstract

Cotton leaf disease detection is essential for accurate identification and timely management of diseases. It plays a crucial role in enhancing cotton yield and quality while promoting the advancement of intelligent agriculture and efficient crop harvesting. This study proposes a novel method for detecting cotton leaf diseases based on large language model (LLM)-generated image synthesis and an improved DEMM-YOLO model, which is enhanced from the YOLOv11 model. To address the issue of insufficient sample data for certain disease categories, we utilize OpenAI’s DALL-E image generation model to synthesize images for low-frequency diseases, which effectively improves the model’s recognition ability and generalization performance for underrepresented classes. To tackle the challenges of large-scale variations and irregular lesion distribution, we design a multi-scale feature aggregation module (MFAM). This module integrates multi-scale semantic information through a lightweight, multi-branch convolutional structure, enhancing the model’s ability to detect small-scale lesions. To further overcome the receptive field limitations of traditional convolution, we propose incorporating a deformable attention transformer (DAT) into the C2PSA module. This allows the model to flexibly focus on lesion areas amidst complex backgrounds, improving feature extraction and robustness. Moreover, we introduce an enhanced efficient multi-dimensional attention mechanism (EEMA), which leverages feature grouping, multi-scale parallel learning, and cross-space interactive learning strategies to further boost the model’s feature expression capabilities. Lastly, we replace the traditional regression loss with the MPDIoU loss function, enhancing bounding box accuracy and accelerating model convergence. Experimental results demonstrate that the proposed DEMM-YOLO model achieves 94.8% precision, 93.1% recall, and 96.7% mAP@0.5 in cotton leaf disease detection, highlighting its strong performance and promising potential for real-world agricultural applications. Full article

(This article belongs to the Section Artificial Intelligence and Digital Agriculture)

22 pages, 1972 KiB

Open AccessArticle

Novel Adaptive Intelligent Control System Design

by Worrawat Duanyai, Weon Keun Song, Min-Ho Ka, Dong-Wook Lee and Supun Dissanayaka

Electronics 2025, 14(15), 3157; https://doi.org/10.3390/electronics14153157 (registering DOI) - 7 Aug 2025

Abstract

A novel adaptive intelligent control system (AICS) with learning-while-controlling capability is developed for a highly nonlinear single-input single-output plant by redesigning the conventional model reference adaptive control (MRAC) framework, originally based on first-order Lyapunov stability, and employing customized neural networks. The AICS is designed with a simple structure, consisting of two main subsystems: a meta-learning-triggered mechanism-based physics-informed neural network (MLTM-PINN) for plant identification and a self-tuning neural network controller (STNNC). This structure, featuring the triggered mechanism, facilitates a balance between high controllability and control efficiency. The MLTM-PINN incorporates the following: (I) a single self-supervised physics-informed neural network (PINN) without the need for labelled data, enabling online learning in control; (II) a meta-learning-triggered mechanism to ensure consistent control performance; (III) transfer learning combined with meta-learning for finely tailored initialization and quick adaptation to input changes. To resolve the conflict between streamlining the AICS’s structure and enhancing its controllability, the STNNC functionally integrates the nonlinear controller and adaptation laws from the MRAC system. Three STNNC design scenarios are tested with transfer learning and/or hyperparameter optimization (HPO) using a Gaussian process tailored for Bayesian optimization (GP-BO): (scenario 1) applying transfer learning in the absence of the HPO; (scenario 2) optimizing a learning rate in combination with transfer learning; and (scenario 3) optimizing both a learning rate and the number of neurons in hidden layers without applying transfer learning. Unlike scenario 1, no quick adaptation effect in the MLTM-PINN is observed in the other scenarios, as these struggle with the issue of dynamic input evolution due to the HPO-based STNNC design. Scenario 2 demonstrates the best synergy in controllability (best control response) and efficiency (minimal activation frequency of meta-learning and fewer trials for the HPO) in control. Full article

(This article belongs to the Special Issue Nonlinear Intelligent Control: Theory, Models, and Applications)

12 pages, 5808 KiB

Open AccessArticle

A High-Precision Hydrogen Sensor Array Based on Pt-Modified SnO₂ for Suppressing Humidity and Oxygen Interference

by Meile Wu, Zhixin Wu, Hefei Chen, Zhanyu Wu, Peng Zhang, Lin Qi, He Zhang and Xiaoshi Jin

Chemosensors 2025, 13(8), 294; https://doi.org/10.3390/chemosensors13080294 (registering DOI) - 7 Aug 2025

Abstract

Humidity and oxygen have significant impacts on the accuracy of hydrogen detection, especially for metal oxide semiconductor sensors at room temperature. Addressing this challenge, this study employs a screen-printed 1 × 2 resistive sensor array made from an identical 1 wt.% platinum-modified tin oxide nanoparticle material. Fabrication variability between the two sensing elements was intentionally leveraged to enhance array output differentiation and information content. Systematic hydrogen-sensing tests were conducted on the array under diverse oxygen and moisture conditions. Three distinct feature types—the steady-state value, resistance change, and area under the curve—were extracted from the output of each array element. These features, integrated with their quotient, formed a nine-feature vector matrix. A multiple linear regression model based on this array output was developed and validated for hydrogen prediction, achieving a coefficient of determination of 0.95, a mean absolute error of 125 ppm, and a mean relative standard deviation of 7.07%. The combined information of the array provided significantly more stable and precise hydrogen concentration predictions than linear or nonlinear models based on individual sensor features. This approach offers a promising path for mass-producing highly interference-resistant, precise, and stable room-temperature hydrogen sensor arrays. Full article

(This article belongs to the Section Materials for Chemical Sensing)

►▼ Show Figures

Figure 1

17 pages, 1275 KiB

Open AccessTechnical Note

Agronomic Information Extraction from UAV-Based Thermal Photogrammetry Using MATLAB

by Francesco Paciolla, Giovanni Popeo, Alessia Farella and Simone Pascuzzi

Remote Sens. 2025, 17(15), 2746; https://doi.org/10.3390/rs17152746 (registering DOI) - 7 Aug 2025

Abstract

Thermal cameras are becoming popular in several applications of precision agriculture, including crop and soil monitoring, for efficient irrigation scheduling, crop maturity, and yield mapping. Nowadays, these sensors can be integrated as payloads on unmanned aerial vehicles, providing high spatial and temporal resolution, to deeply understand the variability of crop and soil conditions. However, few commercial software programs, such as PIX4D Mapper, can process thermal images, and their functionalities are very limited. This paper reports on the implementation of a custom MATLAB^® R2024a script to extract agronomic information from thermal orthomosaics obtained from images acquired by the DJI Mavic 3T drone. This approach enables us to evaluate the temperature at each point of an orthomosaic, create regions of interest, calculate basic statistics of spatial temperature distribution, and compute the Crop Water Stress Index. In the authors’ opinion, the reported approach can be easily replicated and can serve as a valuable tool for scientists who work with thermal images in the agricultural sector. Full article

12 pages, 707 KiB

Open AccessArticle

Characteristics of Varicella Breakthrough Cases in Jinhua City, 2016–2024

by Zhi-ping Du, Zhi-ping Long, Meng-an Chen, Wei Sheng, Yao He, Guang-ming Zhang, Xiao-hong Wu and Zhi-feng Pang

Vaccines 2025, 13(8), 842; https://doi.org/10.3390/vaccines13080842 (registering DOI) - 7 Aug 2025

Abstract

Background: Varicella remains a prevalent vaccine-preventable disease, but breakthrough infections are increasingly reported. However, long-term, population-based studies investigating the temporal and demographic characteristics of breakthrough varicella remain limited. Methods: This retrospective study analyzed surveillance data from Jinhua City, China, from 2016 to 2024. Varicella case records were obtained from the China Information System for Disease Control and Prevention (CISDCP), while vaccination data were retrieved from the Zhejiang Provincial Immunization Program Information System (ISIS). Breakthrough cases were defined as infections occurring more than 42 days after administration of the varicella vaccine. Differences in breakthrough interval were analyzed across subgroups defined by dose, sex, age, population category, and vaccination type. A bivariate cubic regression model was used to assess the combined effect of initial vaccination age and dose interval on the breakthrough interval. Results: Among 28,778 reported varicella cases, 7373 (25.62%) were classified as breakthrough infections, with a significant upward trend over the 9-year period (p < 0.001). Most cases occurred in school-aged children, especially those aged 6–15 years. One-dose recipients consistently showed shorter breakthrough intervals than two-dose recipients (M = 62.10 vs. 120.10 months, p < 0.001). Breakthrough intervals also differed significantly by sex, age group, population category, and vaccination type (p < 0.05). Regression analysis revealed a negative correlation between the initial vaccination age, the dose interval, and the breakthrough interval (R² = 0.964, p < 0.001), with earlier and closely spaced vaccinations associated with longer protection. Conclusions: The present study demonstrates that a two-dose varicella vaccination schedule, when initiated at an earlier age and administered with a shorter interval between doses, provides more robust and longer-lasting protection. These results offer strong support for incorporating varicella vaccination into China’s National Immunization Program to enhance vaccine coverage and reduce the public health burden associated with breakthrough infections. Full article

(This article belongs to the Section Epidemiology and Vaccination)

29 pages, 21276 KiB

Open AccessArticle

Study on the Spatio-Temporal Differentiation and Driving Mechanism of Ecological Security in Dongping Lake Basin, Shandong Province, China

by Yibing Wang, Ge Gao, Mingming Li, Kuanzhen Mao, Shitao Geng, Hongliang Song, Tong Zhang, Xinfeng Wang and Hongyan An

Water 2025, 17(15), 2355; https://doi.org/10.3390/w17152355 (registering DOI) - 7 Aug 2025

Abstract

Ecological security evaluation serves as the cornerstone for ecological management decision-making and spatial optimization. This study focuses on the Dongping Lake Basin. Based on the Pressure–State–Response (PSR) model framework, it integrates ecological risk, ecosystem health, and ecosystem service indicators. Utilizing methods including Local Indicators of Spatial Association (LISA), Transition Matrix, and GeoDetector, it analyzes the spatio-temporal evolution characteristics and driving mechanisms of watershed ecological security from 2000 to 2020. The findings reveal that the Watershed Ecological Security Index (WESI) exhibited a trend of “fluctuating upward followed by periodic decline”. In 2000, the status was “relatively unsafe”. It peaked in 2015 (index 0.332, moderately safe) and experienced a slight decline by 2020. Spatially, a significantly clustered pattern of “higher in the north and lower in the south, higher in the east and lower in the west” was observed. In 2020, “High-High” clusters of ecological security aligned closely with Shandong Province’s ecological conservation red line, concentrating in core protected areas such as the foothills of the Taihang Mountains and Dongping Lake Wetland. Level transitions were characterized by “predominant continuous improvement in low levels alongside localized reverse fluctuations in middle and high levels,” with the “relatively unsafe” and “moderately safe” levels experiencing the largest transfer areas. Geographical detector analysis indicates that the Human Interference Index (HI), Ecosystem Service Value (ESV), and Annual Afforestation Area (AAA) were key drivers of watershed ecological security change, influenced by dynamic interactive effects among multiple factors. This study advances watershed-scale ecological security assessment methodologies. The revealed spatio-temporal patterns and driving mechanisms provide valuable insights for protecting the ecological barrier in the lower Yellow River and informing ecological security strategies within the Dongping Lake Watershed. Full article

(This article belongs to the Section Biodiversity and Functionality of Aquatic Ecosystems)

18 pages, 567 KiB

Open AccessReview

Mephedrone and Its Metabolites: A Narrative Review

by Ordak Michal, Tkacz Daria, Juzwiuk Izabela, Wiktoria Gorecka, Nasierowski Tadeusz, Muszynska Elzbieta and Bujalska-Zadrozny Magdanena

Int. J. Mol. Sci. 2025, 26(15), 7656; https://doi.org/10.3390/ijms26157656 (registering DOI) - 7 Aug 2025

Abstract

New psychoactive substances (NPSs) have emerged as a significant global public health challenge due to their ability to mimic traditional drugs. Among these, mephedrone has gained attention because of its widespread use and associated toxicities. This review provides a comprehensive analysis of the structure, pharmacokinetic properties, and metabolic pathways of mephedrone, highlighting its phase I and phase II metabolites as potential biomarkers for detection and forensic applications. A comprehensive literature search was performed without date restrictions. The search employed key terms such as “mephedrone metabolites”, “pharmacokinetics of mephedrone”, “phase I metabolites of mephedrone”, and “phase II metabolites of mephedrone”. Additionally, the reference lists of selected studies were screened to ensure a thorough review of the literature. Mephedrone is a chiral compound existing in two enantiomeric forms, exhibiting different affinities for monoamine transporters and distinct pharmacological profiles. In vivo animal studies indicate rapid absorption, significant tissue distribution, and the formation of multiple phase I metabolites (e.g., normephedrone, dihydromephedrone, 4-carboxymephedrone) that influence its neurochemical effects. Phase II metabolism involves conjugation reactions leading to metabolites such as N-succinyl-normephedrone and N-glutaryl-normephedrone, further complicating its metabolic profile. These findings underscore the importance of elucidating mephedrone’s metabolic pathways to improve detection methods, enhance our understanding of its toxicological risks, and inform future therapeutic strategies. Full article

(This article belongs to the Section Molecular Toxicology)

►▼ Show Figures

Figure 1

25 pages, 6742 KiB

Open AccessArticle

Reservoir Computing with a Single Oscillating Gas Bubble: Emphasizing the Chaotic Regime

by Hend Abdel-Ghani, A. H. Abbas and Ivan S. Maksymov

AppliedMath 2025, 5(3), 101; https://doi.org/10.3390/appliedmath5030101 (registering DOI) - 7 Aug 2025

Abstract

The rising computational and energy demands of artificial intelligence systems urge the exploration of alternative software and hardware solutions that exploit physical effects for computation. According to machine learning theory, a neural network-based computational system must exhibit nonlinearity to effectively model complex patterns and relationships. This requirement has driven extensive research into various nonlinear physical systems to enhance the performance of neural networks. In this paper, we propose and theoretically validate a reservoir-computing system based on a single bubble trapped within a bulk of liquid. By applying an external acoustic pressure wave to both encode input information and excite the complex nonlinear dynamics, we showcase the ability of this single-bubble reservoir-computing system to forecast a Hénon benchmarking time series and undertake classification tasks with high accuracy. Specifically, we demonstrate that a chaotic physical regime of bubble oscillation—where tiny differences in initial conditions lead to wildly different outcomes, making the system unpredictable despite following clear rules, yet still suitable for accurate computations—proves to be the most effective for such tasks. Full article

(This article belongs to the Topic A Real-World Application of Chaos Theory)

►▼ Show Figures

Figure 1

22 pages, 3381 KiB

Open AccessArticle

Improving Urban Resilience Through a Scalable Multi-Criteria Planning Approach

by Carmine Massarelli and Maria Silvia Binetti

Urban Sci. 2025, 9(8), 309; https://doi.org/10.3390/urbansci9080309 (registering DOI) - 7 Aug 2025

Abstract

In highly urbanised and industrialised settings, managing environmental pressures and enhancing urban resilience demand integrated, spatially explicit approaches. This study presents a methodological framework that integrates topographic data, land cover information, and open geodata to produce a high-resolution vulnerability map. A multi-criteria analysis was performed using indicators such as land use, population density, proximity to emission sources, vegetation cover, and sensitive services (e.g., schools and hospitals). The result is a high-resolution vulnerability map that classifies the urban, peri-urban, and coastal zones into five levels of environmental risk. These evaluation levels are derived from geospatial analyses combining pollutant dispersion modelling with land-use classification, enabling the identification of the most vulnerable urban zones. These findings support evidence-based planning and can guide local governments and environmental agencies in prioritising Nature-based Solutions (NBSs), enhancing ecological connectivity, and reducing exposure for vulnerable populations. Full article

(This article belongs to the Special Issue Innovations in Land Resource and Environmental Governance for Future Cities)

23 pages, 3155 KiB

Open AccessArticle

Construction of a Machining Process Knowledge Graph and Its Application in Process Route Recommendation

by Liang Li, Jiaxing Liang, Chunlei Li, Zhe Liu, Yingying Wei and Zeyu Ji

Electronics 2025, 14(15), 3156; https://doi.org/10.3390/electronics14153156 (registering DOI) - 7 Aug 2025

Abstract

This paper proposes a knowledge graph (KG) construction method for a part machining process in response to the low degree of structuring of historical process data association relationships within the enterprise in the field of part machining, which makes it difficult to reuse effectively. The part types are mainly shafts, gears, boxes and other common parts. First, the schema layer of the process knowledge graph was constructed using a top-down approach. Second, deep learning techniques were employed for entity extraction, while knowledge fusion and ontology relationship establishment methods were combined to build the data layer of the process knowledge graph (PKG) from the bottom up. Third, the mapping between the schema layer and data layer was implemented in the Neo4j graph database. Based on the constructed process KG, process route recommendation and rapid retrieval of process information were thus accomplished. Finally, a shaft part was used as the target part to verify the effectiveness of the proposed method. In over 300 trials, the similarity-based recommendation model achieved a hit rate of 91.7% (the target part’s route appeared in the recommended list in 91.7% of cases). These results indicate that the proposed machining PKG construction is feasible and can assist in process planning, potentially improving the efficiency of retrieving and reusing machining knowledge. Full article

(This article belongs to the Special Issue Human Robot Interaction: Techniques, Applications, and Future Trends)

►▼ Show Figures

Figure 1

45 pages, 3787 KiB

Open AccessReview

Electromigration Failures in Integrated Circuits: A Review of Physics-Based Models and Analytical Methods

by Ping Cheng, Ling-Feng Mao, Wen-Hao Shen and Yu-Ling Yan

Electronics 2025, 14(15), 3151; https://doi.org/10.3390/electronics14153151 (registering DOI) - 7 Aug 2025

Abstract

Electromigration (EM), current-driven atomic diffusion in interconnect metals, critically threatens integrated circuit (IC) reliability via void-induced open circuits and hillock-induced short circuits. This review examines EM’s physical mechanisms, influencing factors, and advanced models, synthesizing seven primary determinants: current density, temperature, material properties, microstructure, geometry, pulsed current, and mechanical stress. It dissects the coupled contributions of electron wind force (dominant EM driver), thermomigration (TM), and stress migration (SM). The review assesses four foundational modeling frameworks: Black’s model, Blech’s criterion, atomic flux divergence (AFD), and Korhonen’s theory. Despite advances in multi-physics simulation and statistical EM analysis, achieving predictive full-chip assessment remains computationally challenging. Emerging research prioritizes the following: (i) model order reduction methods and machine-learning solvers for verification of EM in billion-scale interconnect networks; and (ii) physics-informed routing optimization to inherently eliminate EM violations during physical design. Both are crucial for addressing reliability barriers in IC technologies and 3D heterogeneous integration. Full article

(This article belongs to the Section Electronic Materials, Devices and Applications)

►▼ Show Figures

Figure 1

24 pages, 2789 KiB

Open AccessArticle

CLIP-BCA-Gated: A Dynamic Multimodal Framework for Real-Time Humanitarian Crisis Classification with Bi-Cross-Attention and Adaptive Gating

by Shanshan Li, Qingjie Liu, Zhian Pan and Xucheng Wu

Appl. Sci. 2025, 15(15), 8758; https://doi.org/10.3390/app15158758 (registering DOI) - 7 Aug 2025

Abstract

During humanitarian crises, social media generates over 30 million multimodal tweets daily, but 20% textual noise, 40% cross-modal misalignment, and severe class imbalance (4.1% rare classes) hinder effective classification. This study presents CLIP-BCA-Gated, a dynamic multimodal framework that integrates bidirectional cross-attention (Bi-Cross-Attention) and adaptive gating within the CLIP architecture to address these challenges. The Bi-Cross-Attention module enables fine-grained cross-modal semantic alignment, while the adaptive gating mechanism dynamically weights modalities to suppress noise. Hierarchical learning rate scheduling and multidimensional data augmentation further optimize feature fusion for real-time multiclass classification. On the CrisisMMD benchmark, CLIP-BCA-Gated achieves 91.77% classification accuracy (1.55% higher than baseline CLIP and 2.33% over state-of-the-art ALIGN), with exceptional recall for critical categories: infrastructure damage (93.42%) and rescue efforts (92.15%). The model processes tweets at 0.083 s per instance, meeting real-time deployment requirements for emergency response systems. Ablation studies show Bi-Cross-Attention contributes 2.54% accuracy improvement, and adaptive gating contributes 1.12%. This work demonstrates that dynamic multimodal fusion enhances resilience to noisy social media data, directly supporting SDG 11 through scalable real-time disaster information triage. The framework’s noise-robust design and sub-second inference make it a practical solution for humanitarian organizations requiring rapid crisis categorization. Full article

17 pages, 4935 KiB

Open AccessArticle

Steel Surface Defect Detection Algorithm Based on Improved YOLOv8 Modeling

by Miao Peng, Sue Bai and Yang Lu

Appl. Sci. 2025, 15(15), 8759; https://doi.org/10.3390/app15158759 (registering DOI) - 7 Aug 2025

Abstract

Detecting steel defects is a vital process in industrial production, but traditional methods suffer from poor feature extraction and low detection accuracy. To address these issues, this research introduces an improved model, EB-YOLOv8, based on YOLOv8. First, the multi-scale attention mechanism EMA is integrated into the backbone and neck sections to reduce noise during gradient descent and enhance model stability by encoding global information and weighting model parameters. Second, the weighted fusion splicing module, Concat_BiFPN, is used in the neck network to facilitate multi-scale feature detection and fusion. This improves detection precision. The results show that the EB-YOLOv8 model increases detection accuracy on the NEU-DET dataset by 3.1%, reaching 80.2%, compared to YOLOv8. Additionally, the average precision on the Severstal steel defect dataset improves from 65.4% to 66.1%. Overall, the proposed model demonstrates superior recognition performance. Full article

►▼ Show Figures

Figure 1

27 pages, 31042 KiB

Open AccessArticle

Diagnostic System for Early In Situ Melanoma Detection Using Acoustic Microscopy and Infrared Spectroscopic Mapping Imaging

by Georgios th Karagiannis, Ioannis Grivas, Anastasia Tsingotjidou, Georgios Apostolidis, Eirini Tsardaka, Ioanna Dori, Kyriaki-Nefeli Poulatsidou, Ioannis Tsougos, Stefan Wesarg, Argyrios Doumas and Panagiotis Georgoulias

Cancers 2025, 17(15), 2599; https://doi.org/10.3390/cancers17152599 (registering DOI) - 7 Aug 2025

Abstract

This study proposes a novel diagnostic system for the early detection of cutaneous melanoma based on morphological and biochemical changes during tumor formation. The methods used in this system are acoustic microscopy and infrared (IR) spectroscopy. The former identifies the anatomical parameters of the developing tumor, whilst the latter identifies its biochemical features, both at the micron scale. To implement this diagnostic method, an animal model that mimics human melanoma was developed. The results of this investigation show that using high-frequency (>20

MHz

) acoustic microscopy in conjunction with spectroscopic images provides useful information about distinct features of melanoma tumors’ 3D structures. The structures and cytoarchitecture of the tumors were assessed using conventional histology, and their malignant nature was confirmed using histological and immumohistochemical analysis. The proposed approach may provide an invaluable tool in diagnostic dermatology, as it is noninvasive and produces highly detailed and accurate data about the early appearance and development of melanoma tumors. Full article

(This article belongs to the Special Issue Advanced Diagnostics and Optical Imaging Technologies in Cancer Research)

Show export options Show export options

Select all

Export citation of selected articles as:

Error

Oops... you haven't selected anything for export.

Displaying article 1-50 on page 1 of 2000.

Go to page 1 2 3 4 5

Search Results (181,714)

Further Information

Guidelines

MDPI Initiatives

Follow MDPI