Search Results (4,155)

Holy basil (Ocimum tenuiflorum L.) is an extensively utilized herb, encompassing numerous bioactive compounds that hold significant interest in the food and pharmaceutical industries. High-throughput phenotyping is a rapid and non-invasive technique, providing diverse phenotypic trait observation and measurement. However, basic knowledge regarding the diversity among varieties beneficial for large-scale production in terms of yield and secondary metabolites under a controlled greenhouse environment is limited. Hence, we assessed and classified 12 Thai accessions and two commercial cultivars by evaluating growth, yield, and secondary metabolites at each harvesting time using an advanced NSTDA-Plant Phenomics platform. Notably, accessions OC130, OC141, OC072, and OC059 demonstrated stable metabolite production and antioxidant activity, highlighting their potential as superior accessions for further cultivation and utilization. These findings underscore the potential for tailored cultivation practices to manipulate secondary metabolite synthesis, thereby enhancing the medicinal properties and market value of Thai holy basil. The implications of this study extend to farmers, providing valuable insights into the phenotypic variation and practical avenues under consistent environmental conditions. Breeders can observe genetic diversity to improve basil varieties with desirable traits for specific environmental niches. Moreover, modern agricultural practices can benefit from understanding the impact of controlled environments on secondary metabolite synthesis. Full article

While the Blood–Brain Barrier (BBB) is essential for the protection and function of the Central Nervous System (CNS), it also represents a challenge for drug delivery in the treatment of CNS disorders due to its limited permeability and high expression of efflux transporters. Crossing the BBB becomes even more difficult when dealing with biomolecular therapeutics (e.g., monoclonal antibodies and Antisense Oligonucleotides) due to their hydrophilic nature and high molecular weight. Over the years, different strategies have been developed in order to maximize the ability of biopharmaceuticals to cross the BBB and be delivered to the CNS. Both non-invasive techniques, mainly consisting of developing innovative vectors or using non-conventional routes of administration (e.g., intranasal delivery), and invasive methods, such as intracerebroventricular/intrathecal administration, have been tested individually and in combination. Given the improvements achieved nowadays with both approaches, here, we plan to compare the advances in invasive techniques, such as those based on the use of device-assisted strategies, and the employment of the intranasal route of administration. We are also interested in reporting the applicability of both strategies in the treatment of aggressive forms of cancer, such as glioblastoma, as well as neurodegenerative diseases, in order to determine which technique can be considered a better choice in each specific case. Full article

Multiple sclerosis (MS) is an inflammatory and neurodegenerative disease. Optical coherence tomography (OCT) is a non-invasive imaging technique of the retina. The thickness of the ganglion cell–inner plexiform layer (GCIPL) obtained from an OCT image is a valuable biomarker for monitoring MS. Since the functional shape (F-shape)-based technique has proven to be an effective platform for detecting glaucoma using OCT images, in this paper, we develop an F-shape-based framework to distinguish MS subjects from healthy ones using the thickness of GCIPL. The thickness of the GCIPL layers in the macula region of OCT images in a selected region of interest (ROI) for a set of healthy and MS subjects is represented as F-shape objects, which are registered to a common template using atlas registration. The residual F-shapes, defined as the difference between the F-shape of this common template and the individual registered F-shapes, are used to train an support vector machine (SVM) classifier and subsequently to detect MS. Accuracy, sensitivity, specificity, and area under the curve (AUC) are used to evaluate and compare the classification performance of the proposed F-shape-based scheme and those of sectoral-based schemes. The proposed F-shape-based scheme is shown to significantly outperform the sectoral-based schemes. The superior performance of the proposed F-shape-based scheme can be attributed to the use of (i) a highly dense mesh formed on the ROI in the macula region, (ii) atlas registration that puts the F-shapes of all the subjects on a common platform, and (iii) residual thicknesses as input features for the classification. Full article

This review focuses on electromagnetic imaging methods widely used in urban geophysics and civil engineering. The rapid growth of the urban population and the increase in the frequency of extreme events related to climate change make novel approaches to the geophysical monitoring of urban areas and civil infrastructures essential in the context of programs for the sustainability and resilience of cities. In this scenario, there is a growing interest in using ground-based electromagnetic methods to investigate strategic infrastructures such as bridges, tunnels, dam embankments, power plants, energy plants and pipelines in a non-invasive way. The development of cost-effective, user-friendly sensor arrays, robust methodologies for tomographic data inversion, and AI-based and machine learning techniques has rapidly transformed these methods. This review critically analyzes the results relating to the application of ground-based electromagnetic methods in infrastructure monitoring and surveillance over the past 20 years by presenting a selection of best practice examples and studies planned to support programs for the resilience and maintenance of engineering infrastructures. The analysis reveals that these methods are highly effective in addressing a broad spectrum of monitoring issues in view of effective maintenance of civil infrastructures. In fact, these methods are essential for detecting the geometry of buried objects (e.g., bars and voids), enabling the early detection of degradation phenomena, and mapping water infiltration processes inside structures, as well as many other challenging applications. Finally, prospectives for development are identified in terms of using soft robot technologies, miniaturized sensors, and AI-based methods to acquire, process and interpret data as well as to design smart operational guidelines for infrastructure management. Full article

Prostate cancer is a major cause of cancer-related deaths among men in Sub-Saharan Africa, where late-stage diagnoses are common due to limited access to affordable and sensitive diagnostic tools. Early detection is essential to improve survival and reduce the disease burden. This review explores the integration of epigenetic biomarkers and CRISPR-Cas12a technology as a transformative approach for early, non-invasive prostate cancer detection in resource-limited settings. Among the many complexities of cancer development, molecular dysregulation plays a critical role. Epigenetic modifications including DNA methylation, histone changes, and non-coding RNA expression have emerged as stable and specific biomarkers with significant potential for the early detection and characterisation of prostate carcinogenesis. However, their low concentration in body fluids poses a significant challenge for detection. CRISPR-Cas12a, renowned for its high specificity and sensitivity, offers a promising solution. When integrated with isothermal amplification and liquid biopsy techniques, it enables rapid, point-of-care diagnostics. This review proposes a CRISPR-Cas12a-based diagnostic pipeline for the detection of specific epigenetic markers in liquid biopsies that could be associated with prostate cancer. The adoption of this technology in Sub-Saharan Africa has the potential to significantly improve early diagnosis, reduce mortality, and promote health equity. Full article

Sleep plays a fundamental role in human health and cognitive functioning, motivating the development of reliable and scalable methodologies for sleep stage classification (SSC). Recent advances in non-invasive and economically sustainable sensing technologies enable continuous sleep monitoring beyond laboratory settings. However, SSC remains a challenging data analytics task due to the intrinsic class imbalance among sleep stages. This study investigates the effectiveness of different imbalanced data management strategies within a machine learning framework for non-invasive SSC. The proposed approach relies exclusively on heart rate and motion signals, which can be acquired through wearable devices or contactless under-mattress sensors, making it suitable for longitudinal monitoring scenarios. Using the PhysioNet DREAMT dataset, 32 experimental scenarios are defined by combining data-level techniques (ADASYN oversampling with different balancing weights), algorithm-level strategies (cost-sensitive learning), and hybrid solutions. Four model families are evaluated—Decision Tree, k-Nearest Neighbors, Ensemble Classifiers, and Artificial Neural Networks—across classification tasks involving 2, 3, 4, and 5 sleep stages. The experimental results show that ensemble-based models provide robust and consistent performance under severe class imbalance, achieving macro accuracies of 82% for sleep–wake detection, 73% for 3-stage classification, 72% for 4-stage classification, and 64% for 5-stage classification. These findings confirm the relevance of imbalance-aware analytics and demonstrate the feasibility of accurate, minimally invasive SSC within big data and cognitive computing paradigms. Full article

According to the International Diabetes Federation, 589 million adults worldwide live with diabetes in 2025 (approximately 1 in 9 adults). The development of convenient noninvasive blood glucose monitoring systems has been a central focus in diabetes management. Optical spectroscopy has advanced significantly among all noninvasive glucose detection techniques. A photoacoustic system has been developed using a single-wavelength near-infrared laser, operating at 1625 nm, where glucose exhibits an overtone absorption band with relatively low water interference. The noninvasive system has been evaluated using artificial skin phantoms, with different glucose concentrations, covering both normoglycemic and hyperglycemic blood glucose levels. The detection sensitivity of the developed system has been enhanced to ±15 mg/dL across the entire clinically relevant glucose range. K-nearest neighbours and wide neural network machine learning models were developed for noninvasive glucose classification. The models achieved prediction accuracies of 80.0% and 81.5%, respectively, with 100% of the predicted data located within zones A and B of Clarke’s error grid analysis. These findings satisfy the regulatory requirements for glucose monitors established by Health Canada and the U.S. Food and Drug Administration. Full article

Background/Objectives: This study aimed to describe and quantify facial soft tissue changes in two patients who underwent radiotherapy (RT) for head and neck cancers, using three-dimensional (3D) stereophotogrammetry and surface deviation analysis. The aims were (i) to assess the progression of morphological alterations over time (ii) and to evaluate the clinical potential of 3D surface mapping in documenting RT-related aesthetic changes. Methods: Two patients with head and neck cancer undergoing RT were analyzed using three-dimensional stereophotogrammetry (3dMD Trio-system, Atlanta, GA, USA) at three timepoints: before RT (T0), 45 days after the start of RT (T1), and 6 months after the start of RT (T2). Facial 3D scans were processed using Geomagic Control 2014 software (v.3D Systems, Morrisville, NC, USA) to perform standardized alignments and calculate volumetric deviations, create colorimetric deviation maps, and conduct Root Mean Square (RMS) analysis. Results: Between T0 and T1, both patients showed soft tissue volume reduction, primarily in the mandibular and submental regions, likely reflecting acute treatment effects and weight loss. Between T0 and T2, an increase in soft tissue volume was observed, especially in the lower face and neck, consistent with late radiation effects such as lymphedema and post-treatment weight gain. RMS values ranged from 5.53 mm to 6.87 mm across patients and time points, indicating measurable morphological changes. The upper third of the face remained stable and served as a reliable reference region for alignment. Conclusions: RT may be associated with significant, region-specific changes in facial and cervical soft tissues in HNC patients, but these preliminary observations must always be correlated with weight loss and confirmed by further studies. 3D stereophotogrammetry is a reliable, non-invasive method for detecting and quantifying these alterations over time. This technique can offer valuable insights for clinical monitoring and could promote better patient counseling and potentially mitigate the psychological burden associated with facial changes. Full article

Forensic age estimation is a fundamental component of biological profiling for unidentified skeletal remains, particularly in mass casualty incidents where specimens are frequently fragmented or incomplete. This review evaluates the diagnostic utility of craniofacial suture closure—specifically across four facial regions—as a non-invasive methodology for age determination in adults. By analyzing the predictable fusion patterns of ectocranial and endocranial sutures, forensic practitioners can derive approximate age ranges when postcranial indicators are absent or unreliable. Despite its utility, the reliability of suture-based estimation remains a subject of academic debate. The rate of closure is influenced by a complex interplay of environmental and biological factors, including nutritional status, hormonal influences, and mechanical loading. Historically, the method has faced criticism due to significant inter-individual variability and limited sample sizes in cadaveric studies. To improve precision and novel detail, this review explores the integration of emerging technologies such as artificial intelligence (AI) and machine learning (ML). These tools can process extensive cranial datasets to identify subtle morphological patterns that may elude human observation. While craniofacial suture analysis remains an essential resource in the forensic toolkit, its accuracy is contingent upon accounting for multi-factorial biological factors. The authors emphasize the necessity for further external validation across diverse global populations to ensure the generalizability and refinement of the technique in forensic medicine and osteology. Full article

Monitoring the condition of rolling bearings is critical for industrial reliability, yet traditional contact-based accelerometers can be impractical in confined or hazardous environments. This study investigates the use of microphones as a non-invasive diagnostic alternative, focusing on the impact of sensor distance and spatial placement on fault detection sensitivity across various rotational speeds and load conditions. Using an accelerometer mounted directly on the bearing as a benchmark, acoustic data were acquired on a test bench under different speed and load conditions. The experimental setup evaluated three distinct microphone positions and five distances relative to the source to assess spatial influence. Analysis was conducted comparing scalar indicators, such as Root Mean Square (RMS), kurtosis and Crest Factor (CF) values, with advanced diagnostic techniques, specifically the High-Frequency Resonance Technique (HFRT) for envelope spectrum extraction. Results indicate that while the signal-to-noise ratio (SNR) predictably decreases with distance, diagnostic performance is significantly compromised by acoustic shielding effects caused by bearing housing. Moreover, while simple statistical factors (RMS, kurtosis, CF) show limited reliability across varying distances and noise floors, HFRT-based envelope analysis yields robust fault identification even at the maximum sensor distance. The study concludes that optimal microphone placement is essential for reliable remote monitoring. Particularly, these findings suggest that a preliminary spatial characterization of the acoustic field can significantly enhance the effectiveness of non-contact diagnostic systems in industrial applications. Full article

This review provides a comprehensive overview of the most adopted 3D surveying techniques in Cultural Heritage, offering practical guidance for the selection of appropriate methods when three-dimensional documentation of artworks is required. The analysis focuses on the most effective technologies for the 3D documentation of sites and objects of artistic value, with selection criteria primarily centred on non-invasiveness, given the uniqueness and cultural significance of the case studies, and the instrument flexibility, a crucial requirement for non-transportable items. A broad spectrum of 3D techniques is currently available for the multiscale diagnostic investigation of artworks, providing information at both macroscopic and microscopic levels. This review reports on the state of the art of such systems and evaluates the main characteristics of each technology in relation to its applicability in the heritage field. Particular attention is given to highlighting advantages and limitations, and to assessing performance in terms of resolution, gauge volume/area, acquisition time, and cost. In addition, the review discusses exemplary cases in which 3D methods are integrated with other analytical techniques to enable a more comprehensive understanding of the object under investigation. Finally, recent studies are examined to identify the most suitable approaches and the specific requirements for the digitization of real-world heritage assets. Full article

Objective: Ultrasound Resolution Microscopy (URM) is an emerging technique that provides superior delineation of tumor microvasculature. This prospective study aimed to evaluate the diagnostic value of URM in differentiating benign from malignant breast lesions. Methods: From September 2024 to October 2025, 55 patients with 57 breast masses underwent conventional ultrasound and contrast-enhanced URM. Microvascular parameters were quantitatively analyzed and cross-referenced with histopathology. To mitigate overfitting, LASSO regression was employed to screen 14 URM indices. A combined predictive model integrating core URM features with BI-RADS categorization (dichotomized at 4A) was developed and evaluated using ROC and decision curve analysis (DCA). Results: Thirty-four malignant and 23 benign masses were confirmed. Malignant lesions exhibited comprehensively elevated microvascular abundance and architectural chaos. LASSO regression distilled these features down to two core independent predictors: Vessel Count and Max Curvature. The BI-RADS-alone model yielded 100% sensitivity but extremely low specificity (30.43%). Crucially, the Combined model significantly outperformed the single-modality approaches, achieving an excellent AUC of 0.896 (vs. 0.652 for BI-RADS alone, p < 0.001). By integrating URM parameters, the Combined model maintained adequate sensitivity (73.53%) while drastically boosting specificity to 91.30%. DCA confirmed superior net clinical benefit for the combined strategy. Conclusions: Quantitative URM imaging effectively characterizes the distinct microvascular features of breast cancers. Integrating URM functional parameters with conventional BI-RADS categorization significantly improves diagnostic specificity. Consequently, this combined approach provides a reliable non-invasive strategy to optimize risk stratification, effectively minimizing false-positive diagnoses and averting unnecessary invasive biopsies in routine clinical practice. Full article

Urbanization of cities demands efficient spatial management. The construction of utility lines significantly alters the spatial landscape. The subsurface space is often neglected, resulting in outdated or absent records of underground utility infrastructure. This clearly underscores the need and importance of maintaining accurate utility records. Modern non-destructive techniques for underground utility detection, such as ground penetrating radar (GPR), can enhance the documentation and mapping of subsurface infrastructure. The subject of this paper is the optimization of GPR survey and processing workflows to improve the accuracy of underground utility detection when using the Leica DS2000. The research comprises both theoretical and experimental analyses, including the application of various GPR data collection methods on test sites. The experimental component of the research was conducted using the Leica DS2000 GPR system. The geospatial data were processed using several software applications, including uNext Advanced, IQMaps, and Geolitix. Based on the multicriteria analysis of these results and an assessment of detection accuracy, an optimal workflow (decision diagram) was defined for the detection of underground utility infrastructure using Leica DS2000 under favorable soil conditions. This study explored the feasibility of efficiently updating the cadastral database of public utility infrastructure through non-invasive technologies, thereby contributing to the improvement of subsurface utility infrastructure management. Full article

Biometric systems are widely used in security and forensic applications. Conventionally, contact-based footprint scanners require physical contact, which presents significant limitations. These devices raise hygiene concerns and are impractical in field identification conditions, such as forensic investigations or disaster victim identification, where quick and non-invasive methods are essential. To address these challenges, a contactless footprint acquisition and identification system was developed using image processing techniques and a Convolutional Neural Network (CNN) based on the Visual Geometry Group–16 layer architecture. The system employs a Raspberry Pi 4, a Logitech C922 camera, and a ring light to capture footprints without direct surface contact. Captured images are processed with Contrast Limited Adaptive Histogram Equalization (CLAHE) to improve contrast and mean thresholding to generate binary images for clearer feature extraction. System performance was evaluated using a multiclass confusion matrix. The CNN correctly classified 158 of 160 test images, achieving an accuracy of 98.75%. This result demonstrates higher accuracy than earlier studies that used older CNN models, such as Alex Krizhevsky’s Network and LeCun’s Network-5, which performed with fewer subjects and lower accuracy rates. The developed system shows potential for biometric security, forensic investigations, and disaster response, where contactless and reliable identification is required. Future research can expand the dataset with more diverse footprints, test performance under varied conditions, and extend the approach to other contactless biometrics such as palmprints or ears. Full article

Dry-type electrical transformers are essential components in commercial, industrial, and residential power distribution systems, as they adapt voltage levels required by a broad range of load types. Although they are robustly constructed, they are exposed to adverse operational and environmental conditions such as dust, humidity, and electrical disturbances that may cause premature winding damage, such as inter-turn short circuits. This study focuses on the detection of inter-turn short-circuit faults in a 15 kVA commercial dry-type transformer, where a fault equivalent to 11.54% of short-circuited turns was induced in the tap changers. Axial, radial, and rotational leakage magnetic flux signals were captured using a low-cost, non-invasive triaxial Hall-effect magnetic flux sensor. During data processing, Fisher Score feature selection was applied to identify the most relevant indicators. Subsequently, feature extraction techniques, including Linear Discriminant Analysis, Principal Component Analysis (PCA), Uniform Manifold Approximation and Projection, and Isometric Mapping, were evaluated. The technique that best preserved global and local data structures was selected using Trustworthiness, Spearman’s correlation, and Kruskal’s stress metrics. PCA was selected as the optimal technique based on these quality metrics, achieving the highest classification performance. The resulting subspace data were classified using support vector machines and applying K-fold cross-validation. The proposed system achieved classification accuracies above 95%, with high recall and F1-score values, for inter-turn fault detection in each winding, confirming its effectiveness for reliable inter-turn fault detection in each transformer winding. Full article