Search Results (269)

Phenolic compounds are the main components of green propolis, including its chemical marker Artepellin C. These compounds are well known for their antioxidant, antimicrobial, anti-inflammatory, and anticancer properties, which have prompted numerous studies investigating both their chemical composition and biological activities. In this study, ultrasound-assisted extraction (UAE) and response surface methodology (RSM) were applied to optimize the process of extracting phenolic compounds in Brazilian green propolis using methanol and ethanol as extraction solvents. The results showed that the ideal conditions for extracting simple phenolics with ethanol were 50% ethanol/water, 20 °C, 20% amplitude, and a 0.6 s⁻¹ cycle; for complex phenolics, the ideal conditions were 80% ethanol, 60 °C, a 43% amplitude, and a 0.8 s⁻¹ cycle. Using methanol, the ideal conditions were 100% solvent, 37 °C, a 38% amplitude, and a 0.2 s⁻¹ cycles. These findings have significant practical applications in the pharmaceutical, food, and cosmetic industries, enabling the standardized and efficient extraction of bioactive compounds from propolis for the development of functional products, such as dietary supplements, natural preservatives, and therapeutic formulations. This approach also minimizes solvent use and reduces environmental impact. Full article

Wildfire behavior and post-fire effects are strongly modulated by terrain, yet the relative influence of individual topographic factors on burn severity remains incompletely quantified at landscape scales. The Composite Burn Index (CBI) provides a field-calibrated measure of severity, but large-area analyses have been hampered by limited plot density and cumbersome data extraction workflows. In this study, we paired 6150 CBI plots from 234 U.S. wildfire events (1994–2017) with 30 m SRTM DEM, extracting mean elevation, slope, and compass aspect within a 90 m buffer around each plot to minimize geolocation noise. Topographic variables were grouped into ecologically meaningful classes—six elevation belts (≤500 m to >2500 m), six slope bins (≤5° to >25°), and eight aspect octants—and their relationships with CBI were evaluated using Tukey HSD post hoc comparisons. Our findings show that all three factors exerted highly significant influences on severity (p < 0.001): mean CBI peaked in the 1500–2000 m belt (0.42 higher than lowlands), rose almost monotonically with steepness to slopes > 20° (0.37 higher than <5°), and was greatest on east- and northwest-facing slopes (0.19 higher than south-facing aspects). Further analysis revealed that burn severity emerges from strongly context-dependent synergies among elevation, slope, and aspect, rather than from simple additive effects. By demonstrating a rapid, reproducible workflow for terrain-aware severity assessment entirely within GEE, the study provides both methodological guidance and actionable insights for fuel-management planning, risk mapping, and post-fire restoration prioritization. Full article

Olive mill wastewaters (OMWW) generated during olive oil extraction represent a significant environmental challenge due to their high organic matter content, acidic pH, phenolic content, and toxicity. Their composition varies widely depending on the extraction method and remains difficult to treat, particularly for small-scale producers lacking access to complex infrastructure. This study evaluates the combined effect of the extraction system (traditional vs. three-phase continuous) and filtration level (single vs. double) on the physicochemical and biological properties of OMWW. The methodologies employed included the analysis of water content, density, fatty acid composition, acidity, pH, total solids, chemical and biochemical oxygen demand, and biodegradability. The results indicate that traditional systems consistently produced OMWW with higher organic matter and phenolic loads, while filtration moderately reduced antioxidant potential and acidity, especially in traditional systems. The use of simple, low-cost filtration materials proved effective in improving effluent clarity and could serve as a practical pre-treatment option. This approach offers an accessible strategy for small producers aiming to valorise OMWW or reduce environmental impact. However, the study was conducted at the laboratory scale, and the long-term behaviour of filtered OMWW under real operating conditions remains to be evaluated. Full article

The aim of this study article is to better understand patient outcomes following simple nasal fracture realignment, comparing outcomes when performed under LA versus GA. A systematic search of the evidence base is conducted. Data extraction and documentation are performed in keeping with PRISMA guidance. Critical appraisal tools are applied to aid quality assessment and assessment of bias. Twelve articles were selected for inclusion in this review, accumulating 2405 participants in total. No significant difference in patient outcomes between the LA and GA groups was observed. There was high variation in article quality, with some assessed as having a high risk of bias. Although some methodological limitations and outcome heterogeneity between studies hamper our ability for direct comparison, it seems likely that patient outcomes after nasal fracture correction under LA versus GA are comparable. Further large-scale studies with an agreed set of outcome measures are required to understand this relationship more fully. Full article

The pods from Neltuma ruscifolia (vinal), an underutilized species, are rich in bioactive functional compounds. However, the extraction procedures to obtain the highest proportion of these compounds, considering sustainability aspects, have not been optimized. This study aimed to optimize and compare three affordable extraction methods—dynamic maceration (DME), ultrasound-assisted extraction (UE), and microwave-assisted extraction (ME)—to obtain enriched extracts. The effects of temperature, ethanol-to-water ratio in the solvent, extraction time, and frequency (for ME) were evaluated using a Box–Behnken design and response surface methodology to optimize total polyphenolic content (TPC), total flavonoids (TF), and antioxidant capacity (DPPH). Energy consumption and carbon footprints were also assessed, and phenolic compounds in the optimized extracts were identified by HPLC. The ethanol-to-water ratio emerged as the most influential factor, showing synergistic effects with both time and temperature, enabling optimal yields at intermediate ethanol concentrations. Gallic acid, rutin, and theobromine were found to be the most abundant components, followed by cinnamic, caffeic, and chlorogenic acids. Although UE exhibited the lowest energy consumption (0.64 ± 0.03 Wh/mg of TPC), the simple and easily implementable DME—optimized at 40 min, 50 °C, and 42% ethanol—proved to be the most efficient method, combining high extractive performance (TPC 1432 mg GAE/100 g Dw), reduced solvent use, and intermediate energy efficiency (1.84 Wh/mg of TPC). These findings highlight the potential of vinal as a natural source of bioactive ingredients obtained through simple and cost-effective techniques adaptable to small producers while underscoring the value of experimental design in optimizing sustainable extraction technologies and elucidating the interactions between key processing factors. Full article

The objective of this work was to analyse the genetic diversity of a population of Citrus spp. in the south of the State of Espírito Santo, Brazil, for pre-breeding studies. For that, a total of sixty genotypes were analysed, including ten citrus varieties from four species of the Citrus genus. The methodology involved DNA extraction, amplification via polymerase chain reaction, and the use of a set of 16 Simple Sequence Repeat markers. These markers identified 42 alleles, with a variation of one to four alleles per locus, an average heterozygosity value of 0.53, and an average polymorphic information content of up to 0.29 per species. After the analysis, a dissimilarity matrix was generated using Jaccard distance and a dendrogram, revealing the formation of two groups: Group I, comprising Citrus sinensis varieties, and Group II, comprising varieties of Citrus latifolia, Citrus aurantifolia, and Citrus reticulata. Our study demonstrated that the combination of these markers allowed for the differentiation of genotypes within the collection. The results obtained are valuable for the future management of the collection and the efficient use of genetic diversity estimation in Citrus spp. Full article

The temperature-dependent polarization performance of super-twisted nematic liquid crystals (STN-LCs) when used as polarizers has garnered considerable scholarly attention. In this study, the transmittance of an STN-LC cell was measured under incident light wavelengths of 650 nm, 532 nm, and 405 nm over the temperature range of 30 °C to 100 °C. The STN-LC cell was employed both as the sample under test and as an analyzer in a rotational measurement setup to investigate how its polarization properties vary with temperature. The results indicate that the LC cell exhibits the characteristics of a linear polarizer under red light (650 nm) and violet laser (405 nm) across the full temperature range. However, under green laser (532 nm), when the temperature exceeds 60 °C, its extinction ratio is poor, suggesting its unsuitability for polarization applications under such conditions. A birefringence inversion formula was derived using the transmittance difference method, which effectively eliminates the influence of the glass substrates on the measured transmittance of the LC layer. Utilizing this method, a simple optical setup consisting of a polarizer and photodetector was constructed to accurately extract the birefringence of the LC. The birefringence of super-twisted nematic liquid crystal can be obtained by the transmittance difference method, which is low-cost, has a simple optical path, and is convenient for temperature-controlled experimental measurements of the liquid crystal cell. The findings of this study provide methodological support for the precise determination of birefringence in LCs exhibiting linear polarization characteristics. Full article

Molecular electronics studies have advanced from early, simple single-molecule experiments at cryogenic temperatures to complex and multifunctional molecules under ambient conditions. However, room-temperature environments increase the risk of contamination, making it essential to identify and quantify clean and contaminated rupture traces (i.e., conductance versus relative electrode displacement) within large datasets. Given the high throughput of measurements, manual analysis becomes unfeasible. Clustering algorithms offer an effective solution by enabling the automatic classification and quantification of contamination levels. Despite the rapid development of machine learning, its application in molecular electronics remains limited. In this work, we present a methodology based on the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm to extract representative traces from both clean and contaminated regimes, providing a scalable and objective tool to evaluate environmental contamination in molecular junction experiments. Full article

Background: There are a few very specific inflammation biomarkers in blood, namely lipoprotein NMe⁺ signals of protein clusters (GlycA and GlycB) and a composite resonance of phospholipids (SPC). The relative integrals of these resonances provide clear indication of the unique metabolic changes associated with disease, specifically inflammatory conditions, often related to serious diseases such as cancer or COVID-19 infection. Relatively complicated, yet very efficient experimental methods have been introduced recently (DIRE, JEDI) to suppress the rest of the spectrum, thus allowing measurement of these integrals of interest. Methods: In this study, we introduce a simple alternative processing method using CRAFT (Complete Reduction to Amplitude-Frequency Table), a time-domain (FID) analysis tool which can highlight selected subsets of the spectrum by choice for quantitative analysis. The output of this approach is a direct, spreadsheet-based representation of the required peak amplitude (integral) values, ready for comparative analysis, completely avoiding all the convectional data processing and manipulation steps. The significant advantage of this alternative method is that it only needs a simple water-suppressed 1D spectrum with no further experimental manipulation whatsoever. In addition, there are no pre/post processing steps (such as baseline and/or phase), further minimizing potential dependency on subjective decisions by the user and providing an opportunity to automate the entire process. Results: We applied this methodology to horse serum samples to follow the presence of inflammation for cohorts with or without OCD (Osteochondritis Dissecans) conditions and find diagnostic separation of the of the cohorts through statistical methods. Conclusions: The powerful and simple CRAFT-based approach is suitable to extract selected biomarker information from complex NMR spectra and can be similarly applied to any other biofluid from any source or sample, also retrospectively. There is a potential to extend such a simple analysis to other, previously identified relevant markers as well. Full article

Despite their broad pharmacological potential, the clinical application of stilbenes derived from Cajanus cajan (L.) Millsp. leaves (CCMLs) is limited by their poor aqueous solubility, resulting in low oral bioavailability. In this study, an inclusion complex of stilbenes extracted from CCMLs and 2-hydroxypropyl-β-cyclodextrin (HP-β-CD) was successfully developed to improve their solubility and oral bioavailability. The preparation conditions were optimized using the response surface methodology, with an optimum mass ratio of CCML extract (CCMLE) to HP-β-CD of 1.0:8.2 (g/g) and an optimal temperature of 33 °C for 2 h. The maximum inclusion efficiency of stilbenes was 82%, and the physicochemical properties of the inclusion complex were characterized. Both in vitro dissolution studies and in vivo pharmacokinetic evaluation demonstrated that HP-β-CD encapsulation significantly improved the solubility and dissolution rate of stilbenes compared to those of unformulated CCMLE. Notably, the relative bioavailability of cajaninstilbene acid (CSA), longistyle C (LLC) and 4-O-methylpinosylvic acid (MPA) increased to 198%, 177% and 131%, respectively. This work provides a simple and effective strategy to enhance the solubility and bioavailability of naturally derived stilbenes, offering promising potential for the development of other insoluble natural components for both functional food and pharmaceuticals. Full article

Autonomous vehicles are one of the key components of smart mobility that leverage innovative technology to navigate and operate safely in urban environments. Traffic light detection systems, as a key part of autonomous vehicles, play a key role in navigation during challenging traffic scenarios. Nighttime driving poses significant challenges for autonomous vehicle navigation, particularly in regard to the accuracy of traffic lights detection (TLD) systems. Existing TLD methodologies frequently encounter difficulties under low-light conditions due to factors such as variable illumination, occlusion, and the presence of distracting light sources. Moreover, most of the recent works only focused on daytime scenarios, often overlooking the significantly increased risk and complexity associated with nighttime driving. To address these critical issues, this paper introduces a novel approach for nighttime traffic light detection using the LNT-YOLO model, which is based on the YOLOv7-tiny framework. LNT-YOLO incorporates enhancements specifically designed to improve the detection of small and poorly illuminated traffic signals. Low-level feature information is utilized to extract the small-object features that have been missing because of the structure of the pyramid structure in the YOLOv7-tiny neck component. A novel SEAM attention module is proposed to refine the features that represent both the spatial and channel information by leveraging the features from the Simple Attention Module (SimAM) and Efficient Channel Attention (ECA) mechanism. The HSM-EIoU loss function is also proposed to accurately detect a small traffic light by amplifying the loss for hard-sample objects. In response to the limited availability of datasets for nighttime traffic light detection, this paper also presents the TN-TLD dataset. This newly curated dataset comprises carefully annotated images from real-world nighttime driving scenarios, featuring both circular and arrow traffic signals. Experimental results demonstrate that the proposed model achieves high accuracy in recognizing traffic lights in the TN-TLD dataset and in the publicly available LISA dataset. The LNT-YOLO model outperforms the original YOLOv7-tiny model and other state-of-the-art object detection models in mAP performance by 13.7% to 26.2% on the TN-TLD dataset and by 9.5% to 24.5% on the LISA dataset. These results underscore the model’s feasibility and robustness compared to other state-of-the-art object detection models. The source code and dataset will be available through the GitHub repository. Full article

Trimethylamine N-oxide (TMAO) is a gut microbial metabolite of dietary precursors, including choline and carnitine. Elevated levels of TMAO in human plasma have been associated with several diseases such as cardiovascular, diabetes mellitus, chronic kidney disease, neurological disorders, and cancer. This has led to an increased interest in the accurate determination of TMAO in human blood, for which a reliable, cost-effective and sensitive analytical method should be established. LC-MS/MS has emerged as a powerful tool for the determination of TMAO due to its high sensitivity, specificity, and ability to handle complex matrices. Herein, we describe the development and validation of an LC-MS/MS method for the determination of TMAO in human blood plasma. Our method involves a simple sample preparation protocol, involving a protein precipitation step along with a non-deuterated IS, followed by a Liquid Chromatography-Mass Spectrometry (LC-MS/MS) analysis using a triple quadrupole mass spectrometer. Additionally, the method was adapted and implemented on an UPLC-QTOF/MS. The method was validated using the guidelines set by the European Medicines Agency (EMA) and the US Food and Drug Administration (FDA) for assay performance and robustness in human plasma and successfully applied to plasma derived from healthy and hyperlipidemic volunteers. The developed method was found to be specific, sensitive, and accurate for the determination of TMAO in human plasma, with a lower limit of quantification of 0.25 µM. The intra- and inter-assay precision and trueness were within acceptable limits. Full article

A simple, fast, and low-cost pre-concentration methodology based on the application of multi-template molecularly imprinted polymers (mt-MIP) in a solid-phase extraction system coupled with capillary electrophoresis was developed for the determination of naproxen, diclofenac, and ibuprofen in environmental water samples. A systematic study of the mt-MIP composition was conducted using a second-order simplex lattice experiment design (fraction of the functional monomer methacrylic acid (MAA), the total moles of functional monomers, and the total moles of the cross-linker agent). The optimal mt-MIP, consisting of 0.025 mmol of each analyte, with 2.40 mmol of methacrylic acid (MAA) and 3.60 mmol of 4-vinylpyridine (4VP) and 23.00 mmol of the cross-linker agent (EGDMA), was coupled to an SPE system under the optimal conditions: pH = 3.5; 20 mg of mt-MIP; and an eluent (MeOH/NaOH [0.001]). This methodology provides limits of detection from 3.00 to 12.00 µg L⁻¹ for the studied NSAIDs. The methodology’s precision was evaluated in terms of inter- and intra-day repeatability, with %RSD < 10% in all cases. Finally, the proposed method can be successfully applied in the analysis of environmental water samples (bottle, tap, cistern, well, and river water samples), which demonstrates the developed method’s robustness. Full article

This work explores the classification of driving behaviors using a hybrid deep learning model that combines Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks (ConvLSTM). Sensor data are collected from a smartphone application and undergo a preprocessing pipeline, including data normalization, labeling, and feature extraction, to enhance the model’s performance. By capturing temporal and spatial dependencies within driving patterns, the proposed ConvLSTM model effectively differentiates between normal and aggressive driving behaviors. The model is trained and evaluated against traditional stacked LSTM and Bidirectional LSTM (BiLSTM) architectures, demonstrating superior accuracy and robustness. Experimental results confirm that the preprocessing techniques improve classification performance, ensuring high reliability in driving behavior recognition. The novelty of this work lies in a simple data preprocessing methodology combined with the specific application scenario. By enhancing data quality before feeding it into the AI model, we improve classification accuracy and robustness. The proposed framework not only optimizes model performance but also demonstrates practical feasibility, making it a strong candidate for real-world deployment. Full article

Green coffee is essential in many tropical economies. Its cultivation often necessitates using pesticides that can leave behind residues harmful to human health. To ensure consumer safety, the European Community has set strict maximum residue limits (ranging from 0.01 to 1.0 mg/kg) for pesticides in green coffee sold within Europe. However, the lack of official testing methods for neonicotinoids (NEOs) is a problem, as laboratories must spend resources and time developing and validating suitable analytical methods. This study developed and validated a method for the simultaneous analysis of seven NEOs frequently used in coffee cultivation: acetamiprid, clothianidin, dinotefuran, imidacloprid, nitenpyram, thiacloprid, and thiamethoxam. The proposed methodology uses Strata^®-X PRO cartridges (solid-phase extraction) to remove interfering compounds present in the food matrix and high-performance liquid chromatography (HPLC), equipped with a diode array detector (DAD), to determine NEOs. The accuracy profile strategy validated the method’s suitability for the intended application. NEO recovery rates above 97%; negligible matrix effects (>93%); the linearity of the quantification method (R² values above 0.99); relative biases and standard deviations below 5% and 6%, respectively; and an expected error rate less than 8% allowed to consider the method reliable for the intended objectives. Because of its low ecological impact and simple execution, this method can be used in routine analyses. Full article