Search Results (2,742)

Background/Objectives: The rapid emergence of multidrug-resistant bacterial infections demands innovative non-antibiotic therapeutic strategies. Dual-modal photoresponse therapy integrating photodynamic (PDT) and photothermal (PTT) effects offers a promising rapid antibacterial approach, yet designing single-material systems with synergistic enhancement remains challenging. This study aims to develop uniform Cu-based metal–organic framework micrometer cubes (Cu-BN) for efficient PDT/PTT synergy. Methods: Cu-BN cubes were synthesized via a one-step hydrothermal method using Cu(NO₃)₂ and 2-amino-p-benzoic acid. The material’s dual-mode responsiveness to visible light (420 nm) and near-infrared light (808 nm) was characterized through UV–Vis spectroscopy, photothermal profiling, and reactive oxygen species (ROS) generation assays. Antibacterial efficacy against multidrug-resistant Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) was quantified via colony counting under dual-light irradiation. Results: Under synergistic 420 + 808 nm irradiation for 15 min, Cu-BN (200 μg/mL) achieved rapid eradication of multidrug-resistant E. coli (99.94%) and S. aureus (99.83%). The material reached 58.6 °C under dual-light exposure, significantly exceeding single-light performance. Photodynamic analysis confirmed a 78.7% singlet oxygen (¹O₂) conversion rate. This enhancement stems from PTT-induced membrane permeabilization accelerating ROS diffusion, while PDT-generated ROS sensitized bacteria to thermal damage. Conclusions: This integrated design enables spatiotemporal PDT/PTT synergy within a single Cu-BN system, establishing a new paradigm for rapid-acting, broad-spectrum non-antibiotic antimicrobials. The work provides critical insights for developing light-responsive biomaterials against drug-resistant infections. Full article

Soil testing has long been used to optimize fertilization and crop production. More recently, soil health testing has emerged to reflect the growing interest in soil multifunctionality and ecosystem services. Soil health encompasses physical, chemical, and biological properties that support ecosystem functions and sustainable agriculture. Despite its relevance to several United Nations Sustainable Development Goals (SDGs 1, 2, 3, 6, 12, 13, and 15), comprehensive soil health testing is not widely practiced due to complexity and cost. The aim of the study presented here was to contribute to the further development, implementation, and testing of an integrated procedure for soil health assessment in practice. We developed and tested a rapid, standardized soil health assessment tool that combines near-infrared spectroscopy (NIRS) and multi-nutrient 0.01 M CaCl₂ extraction with Inductive Coupled Plasma Mass Spectroscopy analysis. The tool evaluates a wide range of soil characteristics with high accuracy (R² ≥ 0.88 for most parameters) and has been evaluated across more than 15 countries, including those in Europe, China, New Zealand, and Vietnam. The results are compiled into a soil health indicator report with tailored management advice and a five-level ABCDE score. In a Dutch test set, 6% of soils scored A (optimal), while 2% scored E (degraded). This scalable tool supports land users, agrifood industries, and policymakers in advancing sustainable soil management and evidence-based environmental policy. Full article

Agricultural nondestructive testing technology is pivotal in safeguarding food quality assurance, safety monitoring, and supply chain transparency. While conventional optical methods such as near-infrared spectroscopy and hyperspectral imaging demonstrate proficiency in surface composition analysis, their constrained penetration depth and environmental sensitivity limit effectiveness in dynamic agricultural inspections. This review highlights the transformative potential of microwave technologies, systematically examining their operational principles, current implementations, and developmental trajectories for agricultural quality control. Microwave technology leverages dielectric response mechanisms to overcome traditional limitations, such as low-frequency penetration for grain silo moisture testing and high-frequency multi-parameter analysis, enabling simultaneous detection of moisture gradients, density variations, and foreign contaminants. Established applications span moisture quantification in cereal grains, oilseed crops, and plant tissues, while emerging implementations address storage condition monitoring, mycotoxin detection, and adulteration screening. The high-frequency branch of the microwave–millimeter wave systems enhances analytical precision through molecular resonance effects and sub-millimeter spatial resolution, achieving trace-level contaminant identification. Current challenges focus on three areas: excessive absorption of low-frequency microwaves by high-moisture agricultural products, significant path loss of microwave high-frequency signals in complex environments, and the lack of a standardized dielectric database. In the future, it is essential to develop low-cost, highly sensitive, and portable systems based on solid-state microelectronics and metamaterials, and to utilize IoT and 6G communications to enable dynamic monitoring. This review not only consolidates the state-of-the-art but also identifies future innovation pathways, providing a roadmap for scalable deployment of next-generation agricultural NDT systems. Full article

Objectives: To synthesize evidence on structural and functional neuroplasticity in patients after anterior cruciate ligament reconstruction (ACLR) and its clinical implications. Methods: Adhering to the PRISMA guidelines for systematic reviews and meta-analyses, a literature search was conducted using PubMed, Embase, Web of Science, Scopus, and Cochrane CENTRAL (2018–2025) using specific keyword combinations, screening the results based on predetermined inclusion and exclusion criteria. Results: Among the 27 included studies were the following: (1) sensory cortex reorganization with compensatory visual dependence (5 EEG/fMRI studies); (2) reduced motor cortex efficiency evidenced by elevated AMT (TMS, 8 studies) and decreased γ-CMC (EEG, 3 studies); (3) progressive corticospinal tract degeneration (increased radial diffusivity correlating with postoperative duration); (4) enhanced sensory-visual integration correlated with functional recovery. Conclusions: This review provides a novel synthesis of evidence from transcranial magnetic stimulation (TMS), electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), diffusion tensor imaging (DTI), and functional magnetic resonance imaging (fMRI) studies. It delineates characteristic patterns of post-ACLR structural and functional neural reorganization. Targeting visual–cognitive integration and corticospinal facilitation may optimize rehabilitation. Full article

Rising volumes of textile waste necessitate the development of more efficient recycling systems, with a primary focus on the optimization of sorting technologies. Near-infrared (NIR) spectroscopy is a state-of-the-art method for fiber identification; however, its accuracy in quantifying textile blends, particularly common polyester/cotton blend textiles, still requires refinement. This study explores the potential and limitations of NIR spectroscopy for quantifying cotton content in post-consumer textiles. A lab-scale NIR sorter and a handheld NIR spectrometer in complementary wavelength ranges were applied to a diverse range of post-consumer textile samples to test model accuracies. Results show that the commonly assumed 10% accuracy threshold in industrial sorting can be exceeded, especially when excluding textiles with <35% cotton content. Identifying and excluding the range of non-linearity significantly improved the model’s performance. The final models achieved an RMSEP of 6.6% and bias of −0.9% for the NIR sorter and an RMSEP of 3.1% and bias of −0.6% for the handheld NIR spectrometer. This study also assessed how textile characteristics—such as color, structure, product type, and alkaline treatment—affect spectral behavior and model accuracy, highlighting their importance for refining quantification when high-purity inputs are needed. By identifying current limitations and potential sources of errors, this study provides a foundation for improving NIR-based models. Full article

Coffee beverage quality is determined by a complex interaction of genetic and environmental factors, including specific biochemical characteristics. In this context, the present study aimed to estimate the genetic parameters of elite irrigated Conilon coffee genotypes in the Cerrado over two consecutive years based on the biochemical characteristics of the beans, assessed by near-infrared spectroscopy (NIRS). The research was conducted at the Embrapa Cerrados experimental field, using the unit’s elite collection. Levels of chlorogenic acid (5-ACQ), caffeine, sucrose, citric acid and trigonelline were analyzed in the raw beans of 18 genotypes harvested in two consecutive years. Data were subjected to analysis of variance in a time-subdivided plot design, considering genotypes as plots and years as subplots, with means grouped by the Scott-Knott test at 5% significance. Results showed significant genetic variability for caffeine, sucrose and trigonelline, while chlorogenic and citric acid levels did not differ significantly among genotypes. A significant genotype × year interaction was observed for caffeine, sucrose, and 5-ACQ. Estimated heritabilities were high for caffeine (85.5%), trigonelline (80.1%), sucrose (62%) and citric acid (60%). Selection gains were positive for sucrose (5.58%), citric acid (10.01%) and trigonelline (8.27%), and negative for caffeine (−6.87%) and 5-ACQ (−0.47%). It is concluded that among the compounds evaluated, caffeine shows the greatest potential for selection, enabling effective gains in raw bean composition, while sucrose and trigonelline present moderate potential for genetic improvement. Full article

Background/Objectives: The heterogeneity of cancer disease and the frequent ineffectiveness and resistance observed with currently available treatments highlight the importance of developing new antitumor therapies. The properties of gold nanoparticles, such as their photon-energy heating, are attractive for oncology therapy; this can be effective and localized. The combination of chemotherapy and hyperthermia is promising. Our aim was to evaluate the combination therapy of photon hyperthermia with 5-fluorouracil (5-FU) both in vitro and in vivo. Methods: This study evaluated the antitumor efficacy of a combined chemo-photothermal therapy using 5-fluorouracil (5-FU) and branched gold nanoshells (BGNSs) in a colorectal cancer model. BGNSs were synthesized via a seed-mediated method and characterized by electron microscopy and UV–vis spectroscopy, revealing an average diameter of 126.3 nm and a plasmon resonance peak at 800 nm, suitable for near-infrared (NIR) photothermal applications. In vitro assays using SW620-GFP colon cancer cells demonstrated a ≥90% reduction in cell viability after 24 h of combined treatment with 5-FU and BGNS under NIR irradiation. In vivo, xenograft-bearing nude mice received weekly intratumoral administrations of the combined therapy for four weeks. The group treated with 5-FU + BGNS + NIR exhibited a final tumor volume of 0.4 mm³ on day 28, compared to 1010 mm³ in the control group, corresponding to a tumor growth inhibition (TGI) of 100.74% (p < 0.001), which indicates not only complete inhibition of tumor growth but also regression below the initial tumor volume. Thermographic imaging confirmed that localized hyperthermia reached 45 ± 0.5 °C at the tumor site. Results: These findings suggest that the combination of 5-FU and BGNS-mediated hyperthermia may offer a promising strategy for enhancing therapeutic outcomes in patients with colorectal cancer while potentially minimizing systemic toxicity. Conclusions: This study highlights the potential of integrating nanotechnology with conventional chemotherapy for more effective and targeted cancer treatment. Full article

Grains and their derivatives play a crucial role as staple foods for the global population. Identifying grains in the food chain that are free from mycotoxin contamination is essential. Researchers have explored various traditional detection methods to address this concern. However, as grain consumption becomes increasingly time-sensitive and dynamic, traditional approaches face growing limitations. In recent years, emerging techniques—particularly molecular-based vibrational spectroscopy methods such as visible–near-infrared (Vis–NIR), near-infrared (NIR), Raman, mid-infrared (MIR) spectroscopy, and hyperspectral imaging (HSI)—have been applied to assess fungal contamination in grains and their products. This review summarizes research advances and applications of vibrational spectroscopy in detecting mycotoxins in grains from 2019 to 2025. The fundamentals of their work, information acquisition characteristics and their applicability in food matrices were outlined. The findings indicate that vibrational spectroscopy techniques can serve as valuable tools for identifying fungal contamination risks during the production, transportation, and storage of grains and related products, with each technique suited to specific applications. Given the close link between grain-based foods and humans, future efforts should further enhance the practicality of vibrational spectroscopy by simultaneously optimizing spectral analysis strategies across multiple aspects, including chemometrics, model transfer, and data-driven artificial intelligence. Full article

Background: This study investigated the relationship between problematic media use (PMU) and working memory in preschoolers. Methods: Parents of children aged 3 to 7 (260 boys, 257 girls; M_age = 5.57, SD = 0.73) in Jinan, China, completed questionnaires assessing children’s PMU and working memory. Subsequently, High (n_high = 32, M_age = 4.53, SD = 0.67) and Low (n_low = 30, M_age = 4.67, SD = 0.66) PMU groups, based on the survey data, complete a dual 1-back task during functional near-infrared spectroscopy (fNIRS) recording. Results: Behavioral accuracy and reaction time showed no significant group differences. However, a significant interaction between the PMU group and gender on prefrontal activation was observed, F(1, 60) = 5.88–7.59, ps < 0.05, ηp² = 0.09–0.12. High-PMU boys exhibited greater left prefrontal activation than low-PMU boys, while low-PMU girls showed greater activation in these same areas compared to low-PMU boys. A three-way interaction of group, task condition, and gender on prefrontal activation was also found, F(2, 60) = 5.81–6.42, p < 0.01, ηp² = 0.10–0.19, suggesting that neural responses varied by task and participant characteristics. Conclusions: These findings indicate that PMU may be associated with altered prefrontal activation during working memory tasks in preschoolers, with gender playing a moderating role. Full article

Pesticide residues in fruits and vegetables pose a serious threat to food safety. Traditional detection methods have defects such as complex operation, high cost, and long detection time. Therefore, it is of great significance to develop rapid, non-destructive, and efficient detection technologies and equipment. In recent years, the combination of spectroscopic techniques and imaging technologies with machine learning algorithms has developed rapidly, providing a new attempt to solve this problem. This review focuses on the research progress of the combination of spectroscopic techniques (near-infrared spectroscopy (NIRS), hyperspectral imaging technology (HSI), surface-enhanced Raman scattering (SERS), laser-induced breakdown spectroscopy (LIBS), and imaging techniques (visible light (VIS) imaging, NIRS imaging, HSI technology, terahertz imaging) with machine learning algorithms in the detection of pesticide residues in fruits and vegetables. It also explores the huge challenges faced by the application of spectroscopic and imaging technologies combined with machine learning algorithms in the intelligent perception of pesticide residues in fruits and vegetables: the performance of machine learning models requires further enhancement, the fusion of imaging and spectral data presents technical difficulties, and the commercialization of hardware devices remains underdeveloped. This review has proposed an innovative method that integrates spectral and image data, enhancing the accuracy of pesticide residue detection through the construction of interpretable machine learning algorithms, and providing support for the intelligent sensing and analysis of agricultural and food products. Full article

The illegal sale of mussels is a persistent problem for food safety and public health in the Campania region, where bivalve molluscs are often sold without traceability, evading regulatory controls. In this study, ten batches of mussels seized from unauthorized vendors were analyzed to evaluate their microbiological safety and trace their geographical origin. High loads of Escherichia coli, exceeding European regulatory limits (Regulation (EC) No 2073/2005), were detected in all samples. In addition, Salmonella Infantis strains resistant to trimethoprim-sulfamethoxazole and azithromycin were isolated, raising further concerns about antimicrobial resistance. Of the 93 Vibrio isolates, identified as V. alginolyticus and V. parahaemolyticus, 37.63% showed multidrug resistance. Approximately 68.57% of the isolates were resistant to tetracyclines and cephalosporins. The presence of resistance to last-resort antibiotics such as carbapenems (11.43%) is particularly alarming. Near-infrared spectroscopy, combined with chemometric models, was used to obtain traceability information, attributing a presumed origin to the seized mussel samples. Of the ten samples, seven were attributed to the Phlegraean area. These findings have provided valuable insights, reinforcing the need for continuous and rigorous surveillance and the integration of innovative tools to ensure seafood safety and support One Health approaches. Full article

Functional flours, high in bioactive compounds, have garnered increasing attention, driven by consumer demand for alternative ingredients and the nutritional limitations of wheat flour. This study explores the thermal stability of phenolic compounds in various functional flours using visible, near and shortwave-infrared (Vis–NIR–SWIR) spectroscopy (350–2500 nm), integrated with machine learning (ML) algorithms. Random Forest models were employed to classify samples based on flour type, baking temperature, and phenolic concentration. The full spectral range yielded high classification accuracy (0.98, 0.98, and 0.99, respectively), and an explainability framework revealed the wavelengths most relevant for each class. To address concerns regarding color as a confounding factor, a targeted spectral refinement was implemented by sequentially excluding the visible region. Models trained on the 1000–2500 nm and 1400–2500 nm ranges showed minor reductions in accuracy, suggesting that classification is not solely driven by visible characteristics. Results indicated that legume and wheat flours retain higher total phenolic content (TPC) under mild thermal conditions, whereas grape seed flour (GSF) and olive stone flour (OSF) exhibited notable thermal stability of TPC even at elevated temperatures. These first findings suggest that the proposed non-destructive spectroscopic approach enables rapid classification and quality assessment of functional flours, supporting future applications in precision food formulation and quality control. Full article

To enhance agricultural soil health and soil organic carbon (SOC) sequestration, it is important to accurately measure SOC. The aim of this study was to compare common methods for measuring SOC in soils in order to determine the most effective approach among different agricultural land types. The measurement methods of loss-on-ignition (LOI), automated dry combustion (Dumas), and real-time near-infrared spectroscopy (NIRS) were compared. A total of 95 soil core samples, ranging in clay and calcareous content, were collected across a range of agricultural land types from forty-eight fields across five farms in the Southwest of England. There were similar and positive correlations between all three methods for measuring SOC (ranging from r = 0.549 to 0.579; all p < 0.001). On average, permanent grass fields had higher SOC content (6.6%) than arable and temporary ley fields (4.6% and 4.5%, respectively), with the difference of 2% indicating a higher carbon storage potential in permanent grassland fields. Newly predicted conversion equations of linear regression were developed among the three measurement methods according to all the fields and land types. The correlation of the conversation equations among the three methods in permanent grass fields was strong and significant compared to those in both arable and temporary ley fields. The analysed results could help understand soil carbon management and maximise sequestration. Moreover, the approach of using real-time NIRS analysis with a rechargeable portable NIRS soil device can offer a convenient and cost-saving alternative for monitoring preliminary SOC changes timely on or offsite without personnel risks from the high-temperature furnace and chemical reagent adopted in the LOI and Dumas processes, respectively, at the laboratory. Therefore, the study suggests that faster, lower-cost, and safer methods like NIRS for analysing initial SOC measurements are now available to provide similar SOC results as traditional soil analysis methods of the LOI and Dumas. Further studies on assessing SOC levels in different farm locations, land, and soil types across seasons using NIRS will improve benchmarked SOC data for farm stakeholders in making evidence-informed agricultural practices. Full article

This study evaluated fecal near-infrared spectroscopy (fNIRS) potential to predict three external markers (Yb, Ti, and polyethylene glycol (PEG)) and dry matter digestibility (DMD) calculated from these markers and fiber fractions. A total of 192 fecal samples were collected from 576 Ross 308 male chicks supplemented with TiO₂ (2 g/kg), Yb₂O₃ (50 mg/kg), and PEG (5 g/kg) for 8 d. Reference values for Ti and Yb were obtained using an inductively coupled plasma–optical emission spectrometer, for fiber fractions via ANKOM, and for PEG content using an ad hoc fNIRS model. Prediction models were developed in external validation with 25% of the samples. Good and fair prediction models were built for Ti and Yb, respectively, and considered adequate for rough screening. The DMD models based on Yb and ADF were unreliable, whereas the model based on Ti was suitable for rough screening. The PEG prediction model built during the adaptation period performed exceptionally well; however, the DMD prediction based on PEG highlighted limitations due to diet differences during both the adaptation and experimental periods. In conclusion, fNIRS shows promise for screening Ti and Yb fecal content and DMD using Ti. However, tailored PEG prediction equations need to be developed for each specific diet. Full article

Early and accurate detection of mechanical damage in prunes is crucial for preserving postharvest quality and enabling automated sorting. This study proposes a practical and reproducible method for identifying cumulative bruising in prunes using visible–near-infrared (Vis–NIR) reflectance spectroscopy coupled with machine learning techniques. A self-developed impact simulation device was designed to induce progressive damage under controlled energy levels, simulating realistic postharvest handling conditions. Spectral data were collected from the equatorial region of each fruit and processed using a hybrid modeling framework comprising continuous wavelet transform (CWT) for spectral enhancement, uninformative variable elimination (UVE) for optimal wavelength selection, and support vector machine (SVM) for classification. The proposed CWT-UVE-SVM model achieved an overall classification accuracy of 93.22%, successfully distinguishing intact, mildly bruised, and cumulatively damaged samples. Notably, the results revealed nonlinear reflectance variations in the near-infrared region associated with repeated low-energy impacts, highlighting the capacity of spectral response patterns to capture progressive physiological changes. This research not only advances nondestructive detection methods for prune grading but also provides a scalable modeling strategy for cumulative mechanical damage assessment in soft horticultural products. Full article