Search Results (833)

Background: Clinical diagnostics, food industries, and biotechnological processes typically use an enzyme called alpha-amylase to metabolize carbohydrates. Objective: The aim of this study is to investigate how low-level laser irradiation (LLLI) affects alpha-amylase activity towards determining the usability of LLLI in non-invasive enzymatic modulation. Methods: Enzyme solutions were irradiated at 10, 20, 30, and 40 J/cm² utilizing 589 nm and 532 nm diode-pumped solid-state lasers. The iodine–starch colorimetric method was used to quantify post-irradiation enzymatic activity, with inverse correlations found between absorbance and activity levels. Modulation was determined by the wavelength and dosage. Results: Enzymatic activity significantly improved when utilizing 589 nm irradiation at lower doses, maximizing at 120% at 20 J/cm² (p < 0.01). Neutral or inhibitory effects were revealed when higher doses were applied. Enzymatic activity showed progressive inhibition when 532 nm irradiation was applied, declining to 75% at 40 J/cm² (p < 0.01). Conclusions: These outcomes indicate that conformational flexibility and catalytic efficiency occur when applying lower-energy photons at 589 nm, whilst oxidative stress and impaired enzymatic function are induced by higher-energy photons at 532 nm. This is consistent with the biphasic dose–response characteristic of photobiomodulation. Full article

Although corn germ meal is a rich source of dietary fiber, it contains a relatively low proportion of soluble dietary fiber (SDF) and is frequently contaminated with high levels of zearalenone (ZEN). Solid-state fermentation has the dual effects of modifying dietary fiber (DF) and degrading mycotoxins. This study optimized the solid-state fermentation process of corn germ meal using Bacillus subtilis K6 through response surface methodology (RSM) to enhance SDF yield while efficiently degrading ZEN. Results indicated that fermentation solid-to-liquid ratio and time had greater impacts on SDF yield and ZEN degradation rate than fermentation temperature. The optimal conditions were determined as temperature 36.5 °C, time 65 h, and solid-to-liquid ratio 1:0.82 (w/v). Under these conditions, the ZEN degradation rate reached 96.27 ± 0.53%, while the SDF yield increased from 9.47 ± 0.68% to 20.11 ± 1.87% (optimizing the SDF/DF ratio from 1:7 to 1:3). Scanning electron microscopy (SEM) and confocal laser scanning microscope (CLSM) revealed the structural transformation of dietary fiber from smooth to loose and porous forms. This structural modification resulted in a significant improvement in the physicochemical properties of dietary fiber, with water-holding capacity (WHC), oil-holding capacity (OHC), and water-swelling capacity (WSC) increasing by 34.8%, 16.4%, and 15.2%, respectively. Additionally, the protein and total phenolic contents increased by 23.0% and 82.61%, respectively. This research has achieved efficient detoxification and dietary fiber modification of corn germ meal, significantly enhancing the resource utilization rate of corn by-products and providing technical and theoretical support for industrial production applications. Full article

This study examined the integraftion of airborne Light Detection and Ranging (LiDAR), Simultaneous Localization and Mapping (SLAM)-based handheld LiDAR, and iPhone LiDAR to inspect forest road networks following forest operations. The goal is to overcome the challenges posed by dense canopy cover and ensure accurate and efficient data collection and mapping. Airborne data were collected using the DJI Matrice 300 RTK UAV equipped with a Zenmuse L2 LiDAR sensor, which achieved a high point density of 285 points/m² at an altitude of 80 m. Ground-level data were collected using the BLK2GO handheld laser scanner (HPLS) with SLAM methods (LiDAR SLAM, Visual SLAM, Inertial Measurement Unit) and the iPhone 13 Pro Max LiDAR. Data processing included generating DEMs, DSMs, and True Digital Orthophotos (TDOMs) via DJI Terra, LiDAR360 V8, and Cyclone REGISTER 360 PLUS, with additional processing and merging using CloudCompare V2 and ArcGIS Pro 3.4.0. The pairwise comparison analysis between ALS data and each alternative method revealed notable differences in elevation, highlighting discrepancies between methods. ALS + iPhone demonstrated the smallest deviation from ALS (MAE = 0.011, RMSE = 0.011, RE = 0.003%) and HPLS the larger deviation from ALS (MAE = 0.507, RMSE = 0.542, RE = 0.123%). The findings highlight the potential of fusing point clouds from diverse platforms to enhance forest road mapping accuracy. However, the selection of technology should consider trade-offs among accuracy, cost, and operational constraints. Mobile LiDAR solutions, particularly the iPhone, offer promising low-cost alternatives for certain applications. Future research should explore real-time fusion workflows and strategies to improve the cost-effectiveness and scalability of multisensor approaches for forest road monitoring. Full article

Real-time quality monitoring using molten pool images is a critical focus in researching high-quality, intelligent automated welding. To address interference problems in molten pool images under complex welding scenarios (e.g., reflected laser spots from spatter misclassified as porosity defects) and the limited interpretability of deep learning models, this paper proposes a multi-granularity spatiotemporal representation learning algorithm based on the hybrid enhancement of handcrafted and deep learning features. A MobileNetV2 backbone network integrated with a Temporal Shift Module (TSM) is designed to progressively capture the short-term dynamic features of the molten pool and integrate temporal information across both low-level and high-level features. A multi-granularity attention-based feature aggregation module is developed to select key interference-free frames using cross-frame attention, generate multi-granularity features via grouped pooling, and apply the Convolutional Block Attention Module (CBAM) at each granularity level. Finally, these multi-granularity spatiotemporal features are adaptively fused. Meanwhile, an independent branch utilizes the Histogram of Oriented Gradient (HOG) and Scale-Invariant Feature Transform (SIFT) features to extract long-term spatial structural information from historical edge images, enhancing the model’s interpretability. The proposed method achieves an accuracy of 99.187% on a self-constructed dataset. Additionally, it attains a real-time inference speed of 20.983 ms per sample on a hardware platform equipped with an Intel i9-12900H CPU and an RTX 3060 GPU, thus effectively balancing accuracy, speed, and interpretability. Full article

Photobiomodulation (PBM) has been widely studied for its regenerative and anti-inflammatory properties. Its application, combined with biomaterials, is emerging as a promising strategy for promoting tissue regeneration. Considering the diversity of available evidence, this study conducted an integrative literature review, aiming to critically analyze and synthesize the effects of PBM on bone tissue, particularly its potential role as an adjunct in guided bone regeneration (GBR) procedures. To ensure an integrative approach, studies with different methodological designs were included, encompassing both preclinical and clinical research. The article search was performed in the digital databases PubMed/MEDLINE, Scopus, and Web of Science, using the following search terms: “Photobiomodulation therapy” AND “guided bone regeneration”. The search was conducted from November 2024 to January 2025. A total of 85 articles were found using the presented terms; after checking the results, 11 articles were selected for this study. The remaining articles were excluded because they did not fit the proposed inclusion and exclusion criteria. Studies to date have shown preclinical models that demonstrated increased bone-volume fraction and accelerating healing. Although it has exciting potential in bone regeneration, offering a non-invasive and promising approach to promote healing and repair of damaged bone tissue, the clinical application of PBM faces challenges, such as the lack of consensus on the ideal treatment parameters. Calcium phosphate ceramics were one of the most used biomaterials in the studied associations. Further well-designed studies are necessary to clarify the effectiveness, optimal parameters, and clinical relevance of PBM in bone regeneration, in order to strengthen the current evidence base and guide its potential future use in clinical practice. Full article

Mycelium-bonded composites (MBCs), or myco-composites, represent a novel engineered material that combines natural lignocellulosic substrates with a fungal matrix. As a sustainable alternative to plastics, MBCs are gaining increasing interest; however, their large-scale industrial adoption remains limited, partly due to low social acceptance resulting from their unattractive appearance. Laser engraving provides a promising method for fabricating intricate patterns and functional surfaces on MBCs, minimizing tool wear, material loss, and environmental impact, while enhancing esthetic and engineering properties. This study investigates the influence of CO₂ laser parameters on the material removal rate during the engraving of myco-composites, focusing on the effects of variable laser power, beam defocus, and head feed rate on engraving outcomes. The results demonstrate that laser power and beam focus significantly impact material removal in mycelium-bonded composites. Specifically, increasing the laser power results in greater material removal, which is more pronounced when the beam is focused due to higher energy density. In contrast, a beam defocused by 1 mm produces less intense material removal. These findings highlight the critical role of beam focus—surpassing the influence of power alone—in determining engraving quality, particularly on irregular or uneven surfaces. Moreover, reducing the laser head feed rate at a constant power level increases the material removal rate linearly; however, it also results in excessive charring and localized overheating, revealing the low thermal tolerance of myco-composites. These insights are essential for optimizing laser processing techniques to fully realize the potential of mycelium-bonded composites as sustainable engineering materials, simultaneously maintaining their appearance and functional properties. Full article

Background: Third lower molar (TLM) extraction is one of the most common oral surgical procedures, often accompanied by postoperative pain and inflammation. In order to treat postoperative pain, different methods are used, mainly based on painkillers. PBMT may represent an adjunct to pain management. Objective: This narrative review aims to evaluate the efficacy of PBMT in reducing postoperative pain following TLM extraction. Methods: A comprehensive search was conducted to identify studies examining the use of PBMT for postoperative pain relief after TLM extraction. Four randomized controlled trials (RCTs) met the inclusion criteria and were analyzed qualitatively. Results: Two studies showed statistically significant reductions in pain with PBMT. Kahraman et al. reported lower pain scores in the intraoral PBMT (p = 0.001), with up to a 3.2-point reduction on the Visual Analog Scale (VAS). De Paula et al. found improved pain control using a dual-wavelength (808 + 660 nm) versus a single wavelength protocol (p = 0.031). The remaining studies showed non-significant results toward pain reduction. Conclusions: PBMT shows encouraging results in managing postoperative pain after TLM extraction, specifically with intraoral and multi-wavelength protocols. However, further studies are necessary to confirm its clinical utility. Full article

The present investigation was conducted to observe the effects of different energy densities of a low-level red laser (LLRL) on human embryonic stem cell-derived mesenchymal stem cells (hESC-MSCs). hESC-MSCs were cultured and irradiated with a LLRL from 0.5 to 5.0 J/cm² at a wavelength of 635 nm. Biological parameters such as proliferation, viability, and migration were observed after 72 h of LLRL irradiation. Compared with the control, LLRL irradiation significantly increased the proliferation and viability of hESC-MSCs from 0.5 to 2.5 J/cm² (p < 0.001, p < 0.05). LLRL irradiation from 0.5 to 3.0 J/cm² significantly increased the migration of hESC-MSCs (p < 0.01). These results revealed that LLRL irradiation at lower energy densities significantly increased the proliferation, viability, and migration of hESC-MSCs. However, higher energy densities were ineffective; this was also true when we examined osteogenic differentiation, as low energy densities of LLRL had a positive effect on differentiation, whereas higher energy densities had a negative effect on alkaline phosphatase activity, Alizarin Red staining and gene expression analysis. In addition, not all stem cell markers were affected by the laser, and a slight decrease in the expression of CD146, which is a stemness marker, was detected, indicating improved differentiation. These findings indicate that low energy densities of LLRL irradiation have positive effects on the proliferation, migration, and differentiation of hESC-MSCs. However, higher energy densities showed inhibitory effects. Full article

Low-level laser therapy (LLLT) is gaining attention as an effective adjunct to non-surgical periodontal treatment. This study evaluates the potential of LLLT to reduce bacterial load in a clinically relevant in vitro subgingival biofilm model and its impact on the inflammatory response. A subgingival biofilm model consisting of seven bacterial species was established. Primary human gingival fibroblasts (GFs) and periodontal ligament cells (PDLs) were cultured. Both biofilms and host cells were treated with the DenLase Diode Laser (980 nm) under various clinically relevant settings. The composition and structure of the seven-species biofilms were evaluated using quantitative PCR and fluorescence microscopy, respectively. The inflammatory response in host cells was analyzed by measuring the gene and protein expression levels of various inflammatory mediators. Laser treatment at power outputs ranging from 0.3 to 2 W had no significant effect on biofilm composition or architecture. LLLT, particularly at higher power settings, reduced the viability in both GFs and PDLs up to 70%. Gene expression levels of inflammatory mediators were only minimally influenced by laser treatment. However, LLLT significantly decreased the secretion of all examined cytokines. These findings suggest that LLLT with a 980 nm diode laser, under clinically relevant conditions, exerts anti-inflammatory rather than antimicrobial effects. Full article

Biogenic amines (BA) are simple organic bases of low molecular weight, formed during decarboxylation of amino acids. Ripened cheeses provide suitable conditions for the development of bacteria and production of BAs. The aim of the present study was to investigate the presence of eight BAs in ripened cheese samples (n = 125) using a high-performance liquid chromatography with diode array detector (HPLC-DAD). Furthermore, microbiological analyses towards identification of bacteria using matrix-assisted laser desorption ionisation—time of flight mass spectrometry (MALDI-TOF MS) were performed. Cadaverine and putrescine were detected in 28.0% and 20.8% of cheese samples at concentrations ranging from 6.12 to 2871 mg/kg and 5.74 to 441 mg/kg, respectively. High amounts of putrescine and cadaverine in cheeses were associated with the presence of Hafnia alvei. Tyramine was identified in 28.0% of samples in the concentration range of 5.62–646 mg/kg. High concentrations of this amine was found in cheeses containing Enterococcus faecium and Enterococcus faecalis. Histamine content, the only BA restricted in food according to Regulation 2073/2005, was observed above 100 mg/kg in 11.2% of the cheeses. Ripened cheeses available on the local retail market may contain significant levels of biogenic amines and may pose a potential health hazard to consumers. Full article

Livestock farming is a vital component of global food security, yet it remains a major contributor to greenhouse gas (GHG) emissions, particularly methane (CH₄), which has a global warming potential 28 times greater than carbon dioxide (CO₂). This review provides a comprehensive synthesis of current knowledge surrounding the sources, biological mechanisms, and mitigation strategies related to CH₄ emissions from ruminant livestock. We first explore the process of methanogenesis within the rumen, detailing the role of methanogenic archaea and the environmental factors influencing CH₄ production. A thorough assessment of both direct and indirect methods used to quantify CH₄ emissions is presented, including in vitro techniques (e.g., syringe method, batch culture, RUSITEC), in vivo techniques (e.g., respiration chambers, Greenfeed, laser CH₄ detectors), and statistical modeling approaches. The advantages and limitations of each method are critically analyzed in terms of accuracy, cost, feasibility, and applicability to different farming systems. We then examine a wide range of mitigation strategies, organized into four core pillars: (1) animal and feed management (e.g., genetic selection, pasture quality improvement), (2) diet formulation (e.g., feed additives such as oils, tannins, saponins, and seaweed), (3) rumen manipulation (e.g., probiotics, ionophores, defaunation, vaccination), and (4) manure management practices and policy-level interventions. These strategies are evaluated not only for their environmental impact but also for their economic and practical viability in diverse livestock systems. By integrating technological innovations with sustainable agricultural practices, this review highlights pathways to reduce CH₄ emissions while maintaining animal productivity. It aims to support decision-makers, researchers, and livestock producers in the global effort to transition toward climate-smart, low-emission livestock farming. Full article

The Zhurong rover of the Tianwen-1 mission has detected sulfates in its landing area. The analysis of these sulfates provides scientific evidence for exploring past hydration conditions and atmospheric evolution on Mars. As a non-contact technique with long-range detection capability, Laser-Induced Breakdown Spectroscopy (LIBS) is widely used for elemental identification on Mars. However, quantitative analysis of anionic elements using LIBS remains challenging due to the weak characteristic spectral lines of evaporite salt elements, such as sulfur, in LIBS spectra, which provide limited quantitative information. This study proposes a quantitative analysis method for sulfur in sulfate-containing Martian analogs by leveraging spectral line correlations, full-spectrum information, and prior knowledge, aiming to address the challenges of sulfur identification and quantification in Martian exploration. To enhance the accuracy of sulfur quantification, two analytical models for high and low sulfur concentrations were developed. Samples were classified using infrared spectroscopy based on sulfur content levels. Subsequently, multimodal deep learning models were developed for quantitative analysis by integrating LIBS and infrared spectra, based on varying concentrations. Compared to traditional unimodal models, the multimodal method simultaneously utilizes elemental chemical information from LIBS spectra and molecular structural and vibrational characteristics from infrared spectroscopy. Considering that sulfur exhibits distinct absorption bands in infrared spectra but demonstrates weak characteristic lines in LIBS spectra due to its low ionization energy, the combination of both spectral techniques enables the model to capture complementary sample features, thereby effectively improving prediction accuracy and robustness. To validate the advantages of the multimodal approach, comparative analyses were conducted against unimodal methods. Furthermore, to optimize model performance, different feature selection algorithms were evaluated. Ultimately, an XGBoost-based feature selection method incorporating prior knowledge was employed to identify optimal LIBS spectral features, and the selected feature subsets were utilized in multimodal modeling to enhance stability. Experimental results demonstrate that, compared to the BPNN, SVR, and Inception unimodal methods, the proposed multimodal approach achieves at least a 92.36% reduction in RMSE and a 46.3% improvement in R². Full article

Fish is important for human health due to its high nutritional value. However, it is prone to spoilage due to its structural characteristics. Traditional freshness assessment methods, such as visual inspection, are subjective and prone to inconsistency. This study proposes a novel, cost-effective hybrid methodology for automated three-level fish freshness classification (Day 1, Day 2, Day 3) by integrating single-wavelength laser reflectance data with deep learning-based image features. A comprehensive dataset was created by collecting visual and laser data from 130 mackerel specimens over three consecutive days under controlled conditions. Image features were extracted using four pre-trained CNN architectures and fused with laser features to form a unified representation. The combined features were classified using SVM, MLP, and RF algorithms. The experimental results demonstrated that the proposed multimodal approach significantly outperformed single-modality methods, achieving average classification accuracy of 88.44%. This work presents an original contribution by demonstrating, for the first time, the effectiveness of combining low-cost laser sensing and deep visual features for freshness prediction, with potential for real-time mobile deployment. Full article

Mango, as an important economic crop in tropical and subtropical regions, suffers from chilling injuries caused by postharvest low-temperature storage, which seriously affect its quality and economic benefits. Traditional detection methods have limitations such as low efficiency and strong destructiveness. This study designs and implements a flexible visible light spectral sensing system based on visible light spectral sensing technology and low-cost environmentally friendly flexible circuit technology. The system is structured based on a perception-analysis-warning-processing framework, utilizing laser-induced graphene electroplated copper integrated with laser etching technology for hardware fabrication, and developing corresponding data acquisition and processing functionalities. Taking Yunnan Yumang as the research object, a three-level chilling injury label dataset was established. After Z-Score standardization processing, the prediction accuracy of the SVM (Support Vector Machine) model reached 95.5%. The system has a power consumption of 230 mW at 4.5 V power supply, a battery life of more than 130 days, stable signal transmission, and a monitoring interface integrating multiple functions, which can provide real-time warning and intervention, thus offering an efficient and intelligent solution for chilling injury monitoring in mango cold chain storage. Full article

Wearable laser Doppler devices are susceptible to complex noise interferences, such as Gaussian white noise, baseline drift, and motion artifacts, with motion artifacts significantly impacting clinical diagnostic accuracy. Addressing the limitations of existing denoising methods—including traditional adaptive filtering that relies on prior noise information, modal decomposition techniques that depend on empirical parameter optimization and are prone to modal aliasing, wavelet threshold functions that struggle to balance signal preservation with smoothness, and the high computational complexity of deep learning approaches—this paper proposes an ISSA-VMD-AWPTD denoising algorithm. This innovative approach integrates an improved sparrow search algorithm (ISSA), variational mode decomposition (VMD), and adaptive wavelet packet threshold denoising (AWPTD). The ISSA is enhanced through cubic chaotic mapping, butterfly optimization, and sine–cosine search strategies, targeting the minimization of the envelope entropy of modal components for adaptive optimization of VMD’s decomposition levels and penalty factors. A correlation coefficient-based selection mechanism is employed to separate target and mixed modes effectively, allowing for the efficient removal of noise components. Additionally, an exponential adaptive threshold function is introduced, combining wavelet packet node energy proportion analysis to achieve efficient signal reconstruction. By leveraging the rapid convergence property of ISSA (completing parameter optimization within five iterations), the computational load of traditional VMD is reduced while maintaining the denoising accuracy. Experimental results demonstrate that for a 200 Hz test signal, the proposed algorithm achieves a signal-to-noise ratio (SNR) of 24.47 dB, an improvement of 18.8% over the VMD method (20.63 dB), and a root-mean-square-error (RMSE) of 0.0023, a reduction of 69.3% compared to the VMD method (0.0075). The processing results for measured human blood flow signals achieve an SNR of 24.11 dB, a RMSE of 0.0023, and a correlation coefficient (R) of 0.92, all outperforming other algorithms, such as VMD and WPTD. This study effectively addresses issues related to parameter sensitivity and incomplete noise separation in traditional methods, providing a high-precision and low-complexity real-time signal processing solution for wearable devices. However, the parameter optimization still needs improvement when dealing with large datasets. Full article