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17 pages, 1306 KiB  
Article
Rapid Salmonella Serovar Classification Using AI-Enabled Hyperspectral Microscopy with Enhanced Data Preprocessing and Multimodal Fusion
by MeiLi Papa, Siddhartha Bhattacharya, Bosoon Park and Jiyoon Yi
Foods 2025, 14(15), 2737; https://doi.org/10.3390/foods14152737 - 5 Aug 2025
Abstract
Salmonella serovar identification typically requires multiple enrichment steps using selective media, consuming considerable time and resources. This study presents a rapid, culture-independent method leveraging artificial intelligence (AI) to classify Salmonella serovars from rich hyperspectral microscopy data. Five serovars (Enteritidis, Infantis, Kentucky, Johannesburg, 4,[5],12:i:-) [...] Read more.
Salmonella serovar identification typically requires multiple enrichment steps using selective media, consuming considerable time and resources. This study presents a rapid, culture-independent method leveraging artificial intelligence (AI) to classify Salmonella serovars from rich hyperspectral microscopy data. Five serovars (Enteritidis, Infantis, Kentucky, Johannesburg, 4,[5],12:i:-) were analyzed from samples prepared using only sterilized de-ionized water. Hyperspectral data cubes were collected to generate single-cell spectra and RGB composite images representing the full microscopy field. Data analysis involved two parallel branches followed by multimodal fusion. The spectral branch compared manual feature selection with data-driven feature extraction via principal component analysis (PCA), followed by classification using conventional machine learning models (i.e., k-nearest neighbors, support vector machine, random forest, and multilayer perceptron). The image branch employed a convolutional neural network (CNN) to extract spatial features directly from images without predefined morphological descriptors. Using PCA-derived spectral features, the highest performing machine learning model achieved 81.1% accuracy, outperforming manual feature selection. CNN-based classification using image features alone yielded lower accuracy (57.3%) in this serovar-level discrimination. In contrast, a multimodal fusion model combining spectral and image features improved accuracy to 82.4% on the unseen test set while reducing overfitting on the train set. This study demonstrates that AI-enabled hyperspectral microscopy with multimodal fusion can streamline Salmonella serovar identification workflows. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning for Foods)
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22 pages, 4169 KiB  
Article
Multi-Scale Differentiated Network with Spatial–Spectral Co-Operative Attention for Hyperspectral Image Denoising
by Xueli Chang, Xiaodong Wang, Xiaoyu Huang, Meng Yan and Luxiao Cheng
Appl. Sci. 2025, 15(15), 8648; https://doi.org/10.3390/app15158648 (registering DOI) - 5 Aug 2025
Abstract
Hyperspectral image (HSI) denoising is a crucial step in image preprocessing as its effectiveness has a direct impact on the accuracy of subsequent tasks such as land cover classification, target recognition, and change detection. However, existing methods suffer from limitations in effectively integrating [...] Read more.
Hyperspectral image (HSI) denoising is a crucial step in image preprocessing as its effectiveness has a direct impact on the accuracy of subsequent tasks such as land cover classification, target recognition, and change detection. However, existing methods suffer from limitations in effectively integrating multi-scale features and adaptively modeling complex noise distributions, making it difficult to construct effective spatial–spectral joint representations. This often leads to issues like detail loss and spectral distortion, especially when dealing with complex mixed noise. To address these challenges, this paper proposes a multi-scale differentiated denoising network based on spatial–spectral cooperative attention (MDSSANet). The network first constructs a multi-scale image pyramid using three downsampling operations and independently models the features at each scale to better capture noise characteristics at different levels. Additionally, a spatial–spectral cooperative attention module (SSCA) and a differentiated multi-scale feature fusion module (DMF) are introduced. The SSCA module effectively captures cross-spectral dependencies and spatial feature interactions through parallel spectral channel and spatial attention mechanisms. The DMF module adopts a multi-branch parallel structure with differentiated processing to dynamically fuse multi-scale spatial–spectral features and incorporates a cross-scale feature compensation strategy to improve feature representation and mitigate information loss. The experimental results show that the proposed method outperforms state-of-the-art methods across several public datasets, exhibiting greater robustness and superior visual performance in tasks such as handling complex noise and recovering small targets. Full article
(This article belongs to the Special Issue Remote Sensing Image Processing and Application, 2nd Edition)
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16 pages, 1827 KiB  
Article
Mixed Candida albicansStaphylococcus aureus Biofilm Is Reduced by Light-Activated Nanocomposite with Phloxine B
by Jarmila Czucz Varga, Juraj Bujdák and Helena Bujdáková
J. Fungi 2025, 11(8), 582; https://doi.org/10.3390/jof11080582 (registering DOI) - 5 Aug 2025
Abstract
Candida albicans and Staphylococcus aureus are opportunistic pathogens that cause life-threatening infections. This study focused on using photodynamic inactivation (PDI) to eliminate mixed biofilms of C. albicans–S. aureus formed on poly (urethane) (PU) discs functionalized with a nanocomposite layer containing phloxine B (PhB). [...] Read more.
Candida albicans and Staphylococcus aureus are opportunistic pathogens that cause life-threatening infections. This study focused on using photodynamic inactivation (PDI) to eliminate mixed biofilms of C. albicans–S. aureus formed on poly (urethane) (PU) discs functionalized with a nanocomposite layer containing phloxine B (PhB). Additionally, the effect of PDI on the ALS3 and HWP1 genes of C. albicans was examined in mixed biofilms. Spectral analysis showed a continuous release of PhB from the nanocomposite in Mueller–Hinton broth within 48 h, with a released amount of PhB < 5% of the total amount. The anti-biofilm effectiveness of the light-activated nanocomposite with PhB showed a reduction in the survival rate of biofilm cells to 0.35% and 31.79% for S. aureus and C. albicans, respectively, compared to the control biofilm on PU alone. Scanning electron microscopy images showed that the nanocomposite effectively reduced the colonization and growth of the mixed biofilm. While PDI reduced the regulation of the ALS3 gene, the HWP1 gene was upregulated. Nevertheless, the cell survival of the C. albicansS. aureus biofilm was significantly reduced, showing great potential for the elimination of mixed biofilms. Full article
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26 pages, 9773 KiB  
Review
A Narrative Review of the Clinical Applications of Echocardiography in Right Heart Failure
by North J. Noelck, Heather A. Perry, Phyllis L. Talley and D. Elizabeth Le
J. Clin. Med. 2025, 14(15), 5505; https://doi.org/10.3390/jcm14155505 - 5 Aug 2025
Abstract
Background/Objectives: Historically, echocardiographic imaging of the right heart has been challenging because its abnormal geometry is not conducive to reproducible anatomical and functional assessment. With the development of advanced echocardiographic techniques, it is now possible to complete an integrated assessment of the right [...] Read more.
Background/Objectives: Historically, echocardiographic imaging of the right heart has been challenging because its abnormal geometry is not conducive to reproducible anatomical and functional assessment. With the development of advanced echocardiographic techniques, it is now possible to complete an integrated assessment of the right heart that has fewer assumptions, resulting in increased accuracy and precision. Echocardiography continues to be the first-line imaging modality for diagnostic analysis and the management of acute and chronic right heart failure because of its portability, versatility, and affordability compared to cardiac computed tomography, magnetic resonance imaging, nuclear scintigraphy, and positron emission tomography. Virtually all echocardiographic parameters have been well-validated and have demonstrated prognostic significance. The goal of this narrative review of the echocardiographic parameters of the right heart chambers and hemodynamic alterations associated with right ventricular dysfunction is to present information that must be acquired during each examination to deliver a comprehensive assessment of the right heart and to discuss their clinical significance in right heart failure. Methods: Using a literature search in the PubMed database from 1985 to 2025 and the Cochrane database, which included but was not limited to terminology that are descriptive of right heart anatomy and function, disease states involving acute and chronic right heart failure and pulmonary hypertension, and the application of conventional and advanced echocardiographic modalities that strive to elucidate the pathophysiology of right heart failure, we reviewed randomized control trials, observational retrospective and prospective cohort studies, societal guidelines, and systematic review articles. Conclusions: In addition to the conventional 2-dimensional echocardiography and color, spectral, and tissue Doppler measurements, a contemporary echocardiographic assessment of a patient with suspected or proven right heart failure must include 3-dimensional echocardiographic-derived measurements, speckle-tracking echocardiography strain analysis, and hemodynamics parameters to not only characterize the right heart anatomy but to also determine the underlying pathophysiology of right heart failure. Complete and point-of-care echocardiography is available in virtually all clinical settings for routine care, but this imaging tool is particularly indispensable in the emergency department, intensive care units, and operating room, where it can provide an immediate assessment of right ventricular function and associated hemodynamic changes to assist with real-time management decisions. Full article
(This article belongs to the Special Issue Cardiac Imaging in the Diagnosis and Management of Heart Failure)
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34 pages, 4124 KiB  
Article
Prompt-Gated Transformer with Spatial–Spectral Enhancement for Hyperspectral Image Classification
by Ruimin Han, Shuli Cheng, Shuoshuo Li and Tingjie Liu
Remote Sens. 2025, 17(15), 2705; https://doi.org/10.3390/rs17152705 - 4 Aug 2025
Abstract
Hyperspectral image (HSI) classification is an important task in the field of remote sensing, with far-reaching practical significance. Most Convolutional Neural Networks (CNNs) only focus on local spatial features and ignore global spectral dependencies, making it difficult to completely extract spectral information in [...] Read more.
Hyperspectral image (HSI) classification is an important task in the field of remote sensing, with far-reaching practical significance. Most Convolutional Neural Networks (CNNs) only focus on local spatial features and ignore global spectral dependencies, making it difficult to completely extract spectral information in HSI. In contrast, Vision Transformers (ViTs) are widely used in HSI due to their superior feature extraction capabilities. However, existing Transformer models have challenges in achieving spectral–spatial feature fusion and maintaining local structural consistency, making it difficult to strike a balance between global modeling capabilities and local representation. To this end, we propose a Prompt-Gated Transformer with a Spatial–Spectral Enhancement (PGTSEFormer) network, which includes a Channel Hybrid Positional Attention Module (CHPA) and Prompt Cross-Former (PCFormer). The CHPA module adopts a dual-branch architecture to concurrently capture spectral and spatial positional attention, thereby enhancing the model’s discriminative capacity for complex feature categories through adaptive weight fusion. PCFormer introduces a Prompt-Gated mechanism and grouping strategy to effectively model cross-regional contextual information, while maintaining local consistency, which significantly enhances the ability for long-distance dependent modeling. Experiments were conducted on five HSI datasets and the results showed that overall accuracies of 97.91%, 98.74%, 99.48%, 99.18%, and 92.57% were obtained on the Indian pines, Salians, Botswana, WHU-Hi-LongKou, and WHU-Hi-HongHu datasets. The experimental results show the effectiveness of our proposed approach. Full article
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21 pages, 4468 KiB  
Article
A Matrix Effect Calibration Method of Laser-Induced Breakdown Spectroscopy Based on Laser Ablation Morphology
by Hongliang Pei, Qingwen Fan, Yixiang Duan and Mingtao Zhang
Appl. Sci. 2025, 15(15), 8640; https://doi.org/10.3390/app15158640 (registering DOI) - 4 Aug 2025
Abstract
To improve the accuracy of three-dimensional (3D) reconstruction under microscopic conditions for laser-induced breakdown spectroscopy (LIBS), this study developed a novel visual platform by integrating an industrial CCD camera with a microscope. A customized microscale calibration target was designed to calibrate intrinsic and [...] Read more.
To improve the accuracy of three-dimensional (3D) reconstruction under microscopic conditions for laser-induced breakdown spectroscopy (LIBS), this study developed a novel visual platform by integrating an industrial CCD camera with a microscope. A customized microscale calibration target was designed to calibrate intrinsic and extrinsic camera parameters accurately. Based on the pinhole imaging model, disparity maps were obtained via pixel matching to reconstruct high-precision 3D ablation morphology. A mathematical model was established to analyze how key imaging parameters—baseline distance, focal length, and depth of field—affect reconstruction accuracy in micro-imaging environments. Focusing on trace element detection in WC-Co alloy samples, the reconstructed ablation craters enabled the precise calculation of ablation volumes and revealed their correlations with laser parameters (energy, wavelength, pulse duration) and the physical-chemical properties of the samples. Multivariate regression analysis was employed to investigate how ablation morphology and plasma evolution jointly influence LIBS quantification. A nonlinear calibration model was proposed, significantly suppressing matrix effects, achieving R2 = 0.987, and reducing RMSE to 0.1. This approach enhances micro-scale LIBS accuracy and provides a methodological reference for high-precision spectral analysis in environmental and materials applications. Full article
(This article belongs to the Special Issue Novel Laser-Based Spectroscopic Techniques and Applications)
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18 pages, 28832 KiB  
Article
Mars-On-Orbit Color Image Spectrum Model and Color Restoration
by Hongfeng Long, Sainan Liu, Yuebo Ma, Junzhe Zeng, Kaili Lu and Rujin Zhao
Aerospace 2025, 12(8), 696; https://doi.org/10.3390/aerospace12080696 - 4 Aug 2025
Abstract
Deep space Color Remote Sensing Images (DCRSIs) are of great significance in reconstructing the three-dimensional appearance of celestial bodies. Among them, deep space color restoration, as a means to ensure the authenticity of deep space image colors, has significant research value. The existing [...] Read more.
Deep space Color Remote Sensing Images (DCRSIs) are of great significance in reconstructing the three-dimensional appearance of celestial bodies. Among them, deep space color restoration, as a means to ensure the authenticity of deep space image colors, has significant research value. The existing deep space color restoration methods have gradually evolved into a joint restoration mode that integrates color images and spectrometers to overcome the limitations of on-orbit calibration plates; however, there is limited research on theoretical models for this type of method. Therefore, this article begins with the physical process of deep space color imaging, gradually establishes a color imaging spectral model, and proposes a new color restoration method for the color restoration of Mars remote sensing images. The experiment verifies that our proposed method can significantly reduce color deviation, achieving an average of 8.43 CIE DE 2000 color deviation units, a decrease of 2.63 (23.78%) compared to the least squares method. The color deviation decreased by 21.47 (71.81%) compared to before restoration. Hence, our method can improve the accuracy of color restoration of DCRSIs in space orbit. Full article
(This article belongs to the Section Astronautics & Space Science)
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21 pages, 7677 KiB  
Article
Hyperspectral Imaging Combined with a Dual-Channel Feature Fusion Model for Hierarchical Detection of Rice Blast
by Yuan Qi, Tan Liu, Songlin Guo, Peiyan Wu, Jun Ma, Qingyun Yuan, Weixiang Yao and Tongyu Xu
Agriculture 2025, 15(15), 1673; https://doi.org/10.3390/agriculture15151673 - 2 Aug 2025
Viewed by 194
Abstract
Rice blast caused by Magnaporthe oryzae is a major cause of yield reductions and quality deterioration in rice. Therefore, early detection of the disease is necessary for controlling the spread of rice blast. This study proposed a dual-channel feature fusion model (DCFM) to [...] Read more.
Rice blast caused by Magnaporthe oryzae is a major cause of yield reductions and quality deterioration in rice. Therefore, early detection of the disease is necessary for controlling the spread of rice blast. This study proposed a dual-channel feature fusion model (DCFM) to achieve effective identification of rice blast. The DCFM model extracted spectral features using successive projection algorithm (SPA), random frog (RFrog), and competitive adaptive reweighted sampling (CARS), and extracted spatial features from spectral images using MobileNetV2 combined with the convolutional block attention module (CBAM). Then, these features were fused using the feature fusion adaptive conditioning module in DCFM and input into the fully connected layer for disease identification. The results show that the model combining spectral and spatial features was superior to the classification models based on single features for rice blast detection, with OA and Kappa higher than 90% and 88%, respectively. The DCFM model based on SPA screening obtained the best results, with an OA of 96.72% and a Kappa of 95.97%. Overall, this study enables the early and accurate identification of rice blast, providing a rapid and reliable method for rice disease monitoring and management. It also offers a valuable reference for the detection of other crop diseases. Full article
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27 pages, 1382 KiB  
Review
Application of Non-Destructive Technology in Plant Disease Detection: Review
by Yanping Wang, Jun Sun, Zhaoqi Wu, Yilin Jia and Chunxia Dai
Agriculture 2025, 15(15), 1670; https://doi.org/10.3390/agriculture15151670 - 1 Aug 2025
Viewed by 303
Abstract
In recent years, research on plant disease detection has combined artificial intelligence, hyperspectral imaging, unmanned aerial vehicle remote sensing, and other technologies, promoting the transformation of pest and disease control in smart agriculture towards digitalization and artificial intelligence. This review systematically elaborates on [...] Read more.
In recent years, research on plant disease detection has combined artificial intelligence, hyperspectral imaging, unmanned aerial vehicle remote sensing, and other technologies, promoting the transformation of pest and disease control in smart agriculture towards digitalization and artificial intelligence. This review systematically elaborates on the research status of non-destructive detection techniques used for plant disease identification and detection, mainly introducing the following two types of methods: spectral technology and imaging technology. It also elaborates, in detail, on the principles and application examples of each technology and summarizes the advantages and disadvantages of these technologies. This review clearly indicates that non-destructive detection techniques can achieve plant disease and pest detection quickly, accurately, and without damage. In the future, integrating multiple non-destructive detection technologies, developing portable detection devices, and combining more efficient data processing methods will become the core development directions of this field. Full article
(This article belongs to the Section Crop Protection, Diseases, Pests and Weeds)
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24 pages, 5018 KiB  
Article
Machine Learning for the Photonic Evaluation of Cranial and Extracranial Sites in Healthy Individuals and in Patients with Multiple Sclerosis
by Antonio Currà, Riccardo Gasbarrone, Davide Gattabria, Nicola Luigi Bragazzi, Giuseppe Bonifazi, Silvia Serranti, Paolo Missori, Francesco Fattapposta, Carlotta Manfredi, Andrea Maffucci, Luca Puce, Lucio Marinelli and Carlo Trompetto
Appl. Sci. 2025, 15(15), 8534; https://doi.org/10.3390/app15158534 (registering DOI) - 31 Jul 2025
Viewed by 167
Abstract
This study aims to characterize short-wave infrared (SWIR) reflectance spectra at cranial (at the scalp overlying the frontal cortex and the temporal bone window) and extracranial (biceps and triceps) sites in patients with multiple sclerosis (MS) and age-/sex-matched controls. We sought to identify [...] Read more.
This study aims to characterize short-wave infrared (SWIR) reflectance spectra at cranial (at the scalp overlying the frontal cortex and the temporal bone window) and extracranial (biceps and triceps) sites in patients with multiple sclerosis (MS) and age-/sex-matched controls. We sought to identify the diagnostic accuracy of wavelength-specific patterns in distinguishing MS from normal controls and spectral markers associated with disability (e.g., Expanded Disability Status Scale scores). To achieve these objectives, we employed a multi-site SWIR spectroscopy acquisition protocol that included measurements from traditional cranial locations as well as extracranial reference sites. Advanced spectral analysis techniques, including wavelength-dependent absorption modeling and machine learning-based classification, were applied to differentiate MS-related hemodynamic changes from normal physiological variability. Classification models achieved perfect performance (accuracy = 1.00), and cortical site regression models showed strong predictive power (EDSS: R2CV = 0.980; FSS: R2CV = 0.939). Variable Importance in Projection (VIP) analysis highlighted key wavelengths as potential spectral biomarkers. This approach allowed us to explore novel biomarkers of neural and systemic impairment in MS, paving the way for potential clinical applications of SWIR spectroscopy in disease monitoring and management. In conclusion, spectral analysis revealed distinct wavelength-specific patterns collected from cranial and extracranial sites reflecting biochemical and structural differences between patients with MS and normal subjects. These differences are driven by underlying physiological changes, including myelin integrity, neuronal density, oxidative stress, and water content fluctuations in the brain or muscles. This study shows that portable spectral devices may contribute to bedside individuation and monitoring of neural diseases, offering a cost-effective alternative to repeated imaging. Full article
(This article belongs to the Special Issue Artificial Intelligence in Medical Diagnostics: Second Edition)
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22 pages, 2809 KiB  
Article
Evaluation of Baby Leaf Products Using Hyperspectral Imaging Techniques
by Antonietta Eliana Barrasso, Claudio Perone and Roberto Romaniello
Appl. Sci. 2025, 15(15), 8532; https://doi.org/10.3390/app15158532 (registering DOI) - 31 Jul 2025
Viewed by 100
Abstract
The transition to efficient production requires innovative water control techniques to maximize irrigation efficiency and minimize waste. Analyzing and optimizing irrigation practices is essential to improve water use and reduce environmental impact. The aim of the research was to identify a discrimination method [...] Read more.
The transition to efficient production requires innovative water control techniques to maximize irrigation efficiency and minimize waste. Analyzing and optimizing irrigation practices is essential to improve water use and reduce environmental impact. The aim of the research was to identify a discrimination method to analyze the different hydration levels in baby-leaf products. The species being researched was spinach, harvested at the baby leaf stage. Utilizing a large dataset of 261 wavelengths from the hyperspectral imaging system, the feature selection minimum redundancy maximum relevance (FS-MRMR) algorithm was applied, leading to the development of a neural network-based prediction model. Finally, a mathematical classification model K-NN (k-nearest neighbors type) was developed in order to identify a transfer function capable of discriminating the hyperspectral data based on a threshold value of absolute leaf humidity. Five significant wavelengths were identified for estimating the moisture content of baby leaves. The resulting model demonstrated a high generalization capability and excellent correlation between predicted and measured data, further confirmed by the successful training, validation, and testing of a K-NN-based statistical classifier. The construction phase of the statistical classifier involved the use of the experimental dataset and the critical humidity threshold value of 0.83 (83% of leaf humidity) was considered, below which the baby-leaf crop requires the irrigation intervention. High percentages of correct classification were achieved for data within two humidity classes. Specifically, the statistical classifier demonstrated excellent performance, with 81.3% correct classification for samples below the threshold and 99.4% for those above it. The application of advanced spectral analysis and artificial intelligence methods has led to significant progress in leaf moisture analysis and prediction, yielding substantial implications for both agriculture and biological research. Full article
(This article belongs to the Special Issue Advances in Automation and Controls of Agri-Food Systems)
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21 pages, 10439 KiB  
Article
Camera-Based Vital Sign Estimation Techniques and Mobile App Development
by Tae Wuk Bae, Young Choon Kim, In Ho Sohng and Kee Koo Kwon
Appl. Sci. 2025, 15(15), 8509; https://doi.org/10.3390/app15158509 (registering DOI) - 31 Jul 2025
Viewed by 100
Abstract
In this paper, we propose noncontact heart rate (HR), oxygen saturation (SpO2), and respiratory rate (RR) detection methods using a smartphone camera. HR frequency is detected through filtering after obtaining a remote PPG (rPPG) signal and its power spectral density (PSD) is detected [...] Read more.
In this paper, we propose noncontact heart rate (HR), oxygen saturation (SpO2), and respiratory rate (RR) detection methods using a smartphone camera. HR frequency is detected through filtering after obtaining a remote PPG (rPPG) signal and its power spectral density (PSD) is detected using color difference signal amplification and the plane-orthogonal-to-the-skin method. Additionally, the SpO2 is detected using the HR frequency and the absorption ratio of the G and B color channels based on oxyhemoglobin absorption and reflectance theory. After this, the respiratory frequency is detected using the PSD of rPPG through respiratory frequency band filtering. For the image sequences recorded under various imaging conditions, the proposed method demonstrated superior HR detection accuracy compared to existing methods. The confidence intervals for HR and SpO2 detection were analyzed using Bland–Altman plots. Furthermore, the proposed RR detection method was also verified to be reliable. Full article
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10 pages, 1468 KiB  
Article
Noninvasive Mapping of Extracellular Potassium in Breast Tumors via Multi-Wavelength Photoacoustic Imaging
by Jeff Folz, Ahmad Eido, Maria E. Gonzalez, Roberta Caruso, Xueding Wang, Celina G. Kleer and Janggun Jo
Sensors 2025, 25(15), 4724; https://doi.org/10.3390/s25154724 - 31 Jul 2025
Viewed by 189
Abstract
Elevated extracellular potassium (K+) in the tumor microenvironment (TME) of breast and other cancers is increasingly recognized as a critical factor influencing tumor progression and immune suppression. Current methods for noninvasive mapping of the potassium distribution in tumors are limited. Here, [...] Read more.
Elevated extracellular potassium (K+) in the tumor microenvironment (TME) of breast and other cancers is increasingly recognized as a critical factor influencing tumor progression and immune suppression. Current methods for noninvasive mapping of the potassium distribution in tumors are limited. Here, we employed photoacoustic chemical imaging (PACI) with a solvatochromic dye-based, potassium-sensitive nanoprobe (SDKNP) to quantitatively visualize extracellular potassium levels in an orthotopic metaplastic breast cancer mouse model, Ccn6-KO. Tumors of three distinct sizes (5 mm, 10 mm, and 20 mm) were imaged using multi-wavelength photoacoustic imaging at five laser wavelengths (560, 576, 584, 605, and 625 nm). Potassium concentration maps derived from spectral unmixing of the photoacoustic images at the five laser wavelengths revealed significantly increased potassium levels in larger tumors, confirmed independently by inductively coupled plasma mass spectrometry (ICP-MS). The PACI results matched ICP-MS measurements, validating PACI as a robust, noninvasive imaging modality for potassium mapping in tumors in vivo. This work establishes PACI as a promising tool for studying the chemical properties of the TME and provides a foundation for future studies evaluating the immunotherapy response through ionic biomarker imaging. Full article
(This article belongs to the Special Issue Advances in Photoacoustic Resonators and Sensors)
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17 pages, 4557 KiB  
Article
Potential of LiDAR and Hyperspectral Sensing for Overcoming Challenges in Current Maritime Ballast Tank Corrosion Inspection
by Sergio Pallas Enguita, Jiajun Jiang, Chung-Hao Chen, Samuel Kovacic and Richard Lebel
Electronics 2025, 14(15), 3065; https://doi.org/10.3390/electronics14153065 - 31 Jul 2025
Viewed by 176
Abstract
Corrosion in maritime ballast tanks is a major driver of maintenance costs and operational risks for maritime assets. Inspections are hampered by complex geometries, hazardous conditions, and the limitations of conventional methods, particularly visual assessment, which struggles with subjectivity, accessibility, and early detection, [...] Read more.
Corrosion in maritime ballast tanks is a major driver of maintenance costs and operational risks for maritime assets. Inspections are hampered by complex geometries, hazardous conditions, and the limitations of conventional methods, particularly visual assessment, which struggles with subjectivity, accessibility, and early detection, especially under coatings. This paper critically examines these challenges and explores the potential of Light Detection and Ranging (LiDAR) and Hyperspectral Imaging (HSI) to form the basis of improved inspection approaches. We discuss LiDAR’s utility for accurate 3D mapping and providing a spatial framework and HSI’s potential for objective material identification and surface characterization based on spectral signatures along a wavelength range of 400-1000nm (visible and near infrared). Preliminary findings from laboratory tests are presented, demonstrating the basic feasibility of HSI for differentiating surface conditions (corrosion, coatings, bare metal) and relative coating thickness, alongside LiDAR’s capability for detailed geometric capture. Although these results do not represent a deployable system, they highlight how LiDAR and HSI could address key limitations of current practices and suggest promising directions for future research into integrated sensor-based corrosion assessment strategies. Full article
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12 pages, 1159 KiB  
Review
Neuroinflammation in Radiation Maculopathy: A Pathophysiologic and Imaging Perspective
by Giulia Midena, Raffaele Parrozzani, Marisa Bruno, Elisabetta Pilotto and Edoardo Midena
Cancers 2025, 17(15), 2528; https://doi.org/10.3390/cancers17152528 - 31 Jul 2025
Viewed by 209
Abstract
Background: Radiation maculopathy (RM) is a delayed, sight-threatening complication of ocular radiotherapy. Traditionally regarded as a pure microvascular disease, emerging evidence points to the central role played by retinal neuroinflammation, driven by microglial activation and cytokine dysregulation affecting both the retina and the [...] Read more.
Background: Radiation maculopathy (RM) is a delayed, sight-threatening complication of ocular radiotherapy. Traditionally regarded as a pure microvascular disease, emerging evidence points to the central role played by retinal neuroinflammation, driven by microglial activation and cytokine dysregulation affecting both the retina and the choroid. Hyperreflective retinal foci, neuroinflammatory in origin (I-HRF), visualized through advanced imaging modalities such as spectral domain optical coherence tomography (OCT), have been identified as early and critical biomarkers of both preclinical and clinical retinal neuroinflammation. Materials and Methods: This review synthesizes findings from experimental and clinical studies to explore the pathophysiology of neuroinflammation and the associated imaging parameters in RM. Results: The integration of experimental and clinical evidence specifically underscores the significance of I-HRF as an early indicator of neuroinflammation in RM. OCT enables the identification and quantification of these biomarkers, which are linked to microglial activation and cytokine dysregulation. Conclusions: The pathophysiology of RM has evolved from a predominantly vascular condition to one strongly secondary to neuroinflammatory mechanisms involving the retina and choroid. In particular, I-HRF, as early biomarkers, offers the potential for preclinical diagnosis and therapeutic intervention, paving the way for improved management of this sight-threatening complication. Full article
(This article belongs to the Special Issue Advances in Choroidal Melanoma: From Treatment to Prognosis)
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