Search Results (2,417)

The southwestern region of China is one of the major maize (Zea mays L.)-producing areas and a concentrated zone of farmland contaminated by heavy metals (HMs). Selection of maize varieties with low accumulation of HMs under complex HM pollution conditions is one the most feasible and effective ways for safe utilization of HM-polluted farmland. In this study, we conducted field experiments to investigate the differences in biological traits among 28 local maize varieties under combined soil pollution with Cd, Pb, As, Cr, and Hg. We analyzed the absorption, accumulation, and transport characteristics of Cd, Pb, As, and Cr in various parts of the maize plant (Hg was not detected in any part of maize plants) and explored the relationships of HM contents in different parts of maize with soil HM contents through cluster analysis, correlation analysis, and principal component analysis. The results indicated that among different biological traits of maize, root length, root dry weight, and plant height were the most significantly influenced by soil HM content, while stem dry weight was the least affected. The accumulation capacity of various maize parts for HMs followed the order of grains < stems < cobs < leaves < roots, while the transport capacity followed the order of root–grain < root–stem < cob–grain < stem–cob < stem–leaf. In addition, the accumulation capacity of maize grains for HMs followed the order of As < Cr < Pb < Cd. Different HMs exhibited synergistic effects in various maize parts, except for the stem, particularly in the grains. A synchronous transport mechanism was observed for As and other HMs in different parts. The accumulation of HMs in maize was primarily derived from human activities such as the extraction, storage, and smelting of non-ferrous metals, while the HMs in soil parent material and weathering products played a secondary role. The yield of the tested maize varieties ranged from 7377.6 to 11,037.0 kg·hm⁻², with M5 (Haoyu 1511) achieving the highest yield. M2, M4, M5, M9, M10, M21, and M25–28 were identified as suitable varieties with low Cd, Pb, As, and Cr accumulation for popularization in HM-contaminated soils in southwestern China due to their low accumulation of HMs. Full article

Metal oxide semiconductor (MOS) hydrogen sensors offer advantages, such as high sensitivity and fast response, but their challenges remain in achieving low-cost fabrication and stable operation at room temperature. This study investigates Nb-doped TiO₂ (NTO) thin films prepared via a one-step micro-arc oxidation (MAO) with the addition of Nb₂O₅ nanoparticles into the electrolyte for room-temperature hydrogen sensing. The characterization results revealed that the incorporation of Nb₂O₅ altered the film’s morphology and phase composition, increasing the Nb content and forming a homogeneous composite thin film. Hydrogen sensing tests demonstrated that the NTO samples exhibited significantly improved sensitivity, selectivity, and stability compared to undoped TiO₂. Among the fabricated samples, NTO thin film prepared at Nb₂O₅ concentration of 6 g/L (NTO-6) showed the best performance, with a broad detection range, excellent sensitivity, rapid response, and good specificity to hydrogen. A strong linear relationship between response values and hydrogen concentration (10–1000 ppm) highlights its potential for precise hydrogen detection. The enhanced hydrogen sensing mechanism of NTO thin films primarily stems from the influence of Nb₂O₅; nanoparticles doping in the anatase-phase TiO₂ structure on the semiconductor surface depletion layer, as well as the improved charge transfer and additional adsorption sites provided by the Nb/Ti composite metal oxides, such as TiNb₂O₇ and Ti_0.95Nb_0.95O₄. This study demonstrates the potential of MAO-fabricated Nb-doped TiO₂ thin films as efficient and reliable hydrogen sensors operating at room temperature, offering a pathway for novel gas-sensing technologies to support clean energy applications. Full article

The association between microRNAs and various diseases, especially cancer, has been established in recent years, indicating that miRNAs can potentially serve as biomarkers for these diseases. Determining miRNA concentrations in biological samples is crucial for disease diagnosis. Nevertheless, the stem-loop reverse transcription quantitative PCR method, the gold standard for detecting miRNA, has great challenges in terms of high costs and enzyme limitations when applied to clinical biological samples. In this study, an isothermal signal amplification method based on a duplex-specific nuclease (DSN) enzyme-driven DNA walker and an improved catalytic hairpin assembly (CHA) was designed for miRNA detection. First, biotin–triethylene glycol-modified trigger-releasable DNA probes were conjugated to the streptavidin-coated magnetic beads for recognizing the target miRNA. The DSN enzyme specifically hydrolyzes DNA strands when the DNA probe hybridizes with the targeted miRNA. This recycling process converts the input miRNA into short trigger fragments (catalysts). Finally, three hairpins of improved CHA are driven by this catalyst, resulting in the three-armed CHA products and a fluorescence signal as the output. This dual-cycle biosensor shows a good linear relationship in the detection of miR-21 and miR-141 over the final concentration range of 250 fM to 50 nM, presenting an excellent limit of detection (2.95 amol). This system was used to detect miR-21 and miR-141 in MCF-7 and 22RV1 cells, as well as in 1% human serum. This system can be used to evaluate the expression levels of miRNAs in different biological matrices for the clinical diagnosis and prognosis of different cancers. Full article

Telomeres play a key role in maintaining chromosome stability and cellular aging. They consist of repetitive DNA sequences that protect chromosome ends and regulate cell division. Telomerase is a reverse transcriptase enzyme counteracts the natural shortening of telomeres during cell division by extending them. Its activity is pivotal in stem cells and cancer cells but absent in most normal somatic cells. Recent advances in biosensor technologies have facilitated the in situ detection of telomerase activity, which is essential for understanding its role in aging and cancer. Techniques such as fluorescence, electrochemistry, and DNA nanotechnology are now being employed to monitor telomerase activity in living cells, providing real-time insights into cellular processes. DNA-based biosensors, especially those incorporating molecular beacons, DNA walkers, and logic gates, have shown promise for enhancing sensitivity and specificity in telomerase imaging. These approaches also facilitate the simultaneous analysis of related cellular pathways, offering potential applications in early cancer detection and precision therapies. This review explores recent developments in intracellular telomerase imaging, highlighting innovative approaches such as DNA-functionalized nanoparticles and multi-channel logic systems, which offer non-invasive, real-time detection of telomerase activity in complex cellular environments. Full article

Breast cancer ranks as the second most prevalent cancer globally and is the most frequently diagnosed cancer among women; therefore, early, automated, and precise detection is essential. Most AI-based techniques for breast cancer detection are complex and have high computational costs. Hence, to overcome this challenge, we have presented the innovative LightweightUNet hybrid deep learning (DL) classifier for the accurate classification of breast cancer. The proposed model boasts a low computational cost due to its smaller number of layers in its architecture, and its adaptive nature stems from its use of depth-wise separable convolution. We have employed a multimodal approach to validate the model’s performance, using 13,000 images from two distinct modalities: mammogram imaging (MGI) and ultrasound imaging (USI). We collected the multimodal imaging datasets from seven different sources, including the benchmark datasets DDSM, MIAS, INbreast, BrEaST, BUSI, Thammasat, and HMSS. Since the datasets are from various sources, we have resized them to the uniform size of 256 × 256 pixels and normalized them using the Box-Cox transformation technique. Since the USI dataset is smaller, we have applied the StyleGAN3 model to generate 10,000 synthetic ultrasound images. In this work, we have performed two separate experiments: the first on a real dataset without augmentation and the second on a real + GAN-augmented dataset using our proposed method. During the experiments, we used a 5-fold cross-validation method, and our proposed model obtained good results on the real dataset (87.16% precision, 86.87% recall, 86.84% F1-score, and 86.87% accuracy) without adding any extra data. Similarly, the second experiment provides better performance on the real + GAN-augmented dataset (96.36% precision, 96.35% recall, 96.35% F1-score, and 96.35% accuracy). This multimodal approach, which utilizes LightweightUNet, enhances the performance by 9.20% in precision, 9.48% in recall, 9.51% in F1-score, and a 9.48% increase in accuracy on the combined dataset. The LightweightUNet model we proposed works very well thanks to a creative network design, adding fake images to the data, and a multimodal training method. These results show that the model has a lot of potential for use in clinical settings. Full article

In the domain of food science, apple grading holds significant research value and application potential. Currently, apple grading predominantly relies on manual methods, which present challenges such as low production efficiency and high subjectivity. This study marks the first integration of advanced computer vision, image processing, and machine learning technologies to design an innovative automated apple grading system. The system aims to reduce human interference and enhance grading efficiency and accuracy. A lightweight detection algorithm, FDNet-p, was developed to capture stem features, and a strategy for auxiliary positioning was designed for image acquisition. An improved DPC-AWKNN segmentation algorithm is proposed for segmenting the apple body. Image processing techniques are employed to extract apple features, such as color, shape, and diameter, culminating in the development of an intelligent apple grading model using the GBDT algorithm. Experimental results demonstrate that, in stem detection tasks, the lightweight FDNet-p model exhibits superior performance compared to various detection models, achieving an [email protected] of 96.6%, with a GFLOPs of 3.4 and a model size of just 2.5 MB. In apple grading experiments, the GBDT grading model achieved the best comprehensive performance among classification models, with weighted Jacard Score, Precision, Recall, and F1 Score values of 0.9506, 0.9196, 0.9683, and 0.9513, respectively. The proposed stem detection and apple body classification models provide innovative solutions for detection and classification tasks in automated fruit grading, offering a comprehensive and replicable research framework for standardizing image processing and feature extraction for apples and similar spherical fruit bodies. Full article

Strawberry crown and root rot diseases are caused by soil-borne pathogens including Macrophomina phaseolina (Mp) and Verticillium dahliae (Vd). The symptoms caused by these pathogens are very similar and difficult to distinguish, and traditional culture-based detection methods are laborious, time-consuming, and slow in providing results. In this work, we developed a duplex PCR-NALFIA assay using two pairs of species-specific primers labeled at the 5′ end with different molecules for the simultaneous identification of Mp and Vd. For the NALFIA assay, a lateral flow device (LFD) for the detection of two analytes was used. The method was developed by single and duplex PCR (Mp, Vd, Mp + Vd) using increasingly complex biological systems: (i) DNA from pure cultures of the pathogens; (ii) DNA from artificially inoculated cut melon stems; and (iii) DNA from artificially inoculated strawberry plants cv. Aromas. The duplex PCR protocol was effective in detecting the two pathogens within melon tissues and provided good results with strawberry crown tissues only when the DNA samples were purified by removing the PCR inhibitors. The amplicons were used for both agarose gel electrophoresis (AGE) and NALFIA assays and demonstrated the greater sensitivity of the NALFIA assay (10 pg) for simultaneous detection of the two pathogens. Full article

Background/Objectives: Respiratory viruses, including Influenza, RSV, and COVID-19, cause various respiratory infections. Distinguishing these viruses relies on diagnostic methods such as PCR testing. Challenges stem from overlapping symptoms and the emergence of new strains. Advanced diagnostics are crucial for accurate detection and effective management. This study leveraged nasopharyngeal metabolome data to predict respiratory virus scenarios including control vs. RSV, control vs. Influenza A, control vs. COVID-19, control vs. all respiratory viruses, and COVID-19 vs. Influenza A/RSV. Method: We proposed a stacking-based ensemble technique, integrating the top three best-performing ML models from the initial results to enhance prediction accuracy by leveraging the strengths of multiple base learners. Key techniques such as feature ranking, standard scaling, and SMOTE were used to address class imbalances, thus enhancing model robustness. SHAP analysis identified crucial metabolites influencing positive predictions, thereby providing valuable insights into diagnostic markers. Results: Our approach not only outperformed existing methods but also revealed top dominant features for predicting COVID-19, including Lysophosphatidylcholine acyl C18:2, Kynurenine, Phenylalanine, Valine, Tyrosine, and Aspartic Acid (Asp). Conclusions: This study demonstrates the effectiveness of leveraging nasopharyngeal metabolome data and stacking-based ensemble techniques for predicting respiratory virus scenarios. The proposed approach enhances prediction accuracy, provides insights into key diagnostic markers, and offers a robust framework for managing respiratory infections. Full article

Lucky bamboo (Dracaena sanderiana hort. ex. Mast. = Dracaena braunii) is a popular decorative plant in China. In March 2022, a severe outbreak of anthracnose disease occurred on the stems of lucky bamboo plants in a nursery garden in Nanjing, Jiangsu Province, China. Thirty-two fungal isolates were obtained from the infected stem tissues and were morphologically identified as Colletotrichum species. A multilocus phylogenetic analysis based on the internal transcribed spacer (ITS) region, the actin (ACT) gene, and the glyceraldehyde-3-phosphate dehydrogenase (GAPDH) gene indicated the isolate FGZ-1 as Colletotrichum gloeosporioides (Penz.) Penz. and Sacc. The pathogenicity of isolate FGZ-1 was verified by inoculating mycelial plugs on stem segments and spraying spores on the whole one-year-old lucky bamboo plants. Koch’s postulates were fulfilled via the re-isolation of C. gloeosporioides from the diseased tissues. To the best of our knowledge, this is the first report of C. gloeosporioides causing anthracnose on lucky bamboo in China. The detection of C. gloeosporioides on lucky bamboo in China expands the range of Colletotrichum species that are associated with anthracnose in this popular ornamental plant. This study lays a solid foundation for future investigations into the pathogenic mechanisms of anthracnose on D. sanderiana and control strategies for this disease, such as biocontrol agents and the construction of resistant cultivars. Full article

Traumatic brain injury (TBI) is one of the major causes of severe neurological disorders and long-term dysfunction in the nervous system. Besides inducing neurodegeneration, TBI alters stem cell activity and neurogenesis within primary neurogenic niches. However, the fate of dividing cells in other brain regions remains unclear despite offering potential targets for therapeutic intervention. Here, we investigated cell division and differentiation in non-neurogenic brain regions during the acute and delayed phases of TBI-induced neurodegeneration. We subjected mice to lateral fluid percussion injury (LFPI) to model TBI and analyzed them 1 or 7 weeks later. To assess cellular proliferation and differentiation, we administered 5-ethinyl-2′-deoxyuridine (EdU) and determined the number and identity of dividing cells 2 h later using markers of neuronal precursors and astro-, micro-, and oligodendroglia. Our results demonstrated a significant proliferative response in several brain regions at one week post-injury that notably diminished by seven weeks, except in the optic tract. In addition to active astro- and microgliosis, we detected oligodendrogenesis in the striatum and optic tract. Furthermore, we observed trauma-induced neurogenesis in the striatum. These findings suggest that subcortical structures, particularly the striatum and optic tract, may possess a potential for self-repair through neuronal regeneration and axon remyelination. Full article

Technological developments have allowed helicopter airborne laser scanning (HALS) to produce high-density point clouds below the forest canopy. We present a tree stem classification method that combines linear shape detection and model-based clustering, using four discrete methods to estimate stem diameter. Stem horizontal size was estimated every 25 cm below the living crown, and a cubic spline was used to estimate where there were gaps. Individual stem diameter at breast height (DBH) was estimated for 77% of field-measured trees. The root mean square error (RMSE) of DBH estimates was 7–12 cm using stem circle fitting. Adapting the approach to use an existing stem taper model reduced the RMSE of estimates (<1 cm). In contrast, estimates that were produced from a previously existing DBH estimation method (PREV) could be achieved for 100% of stems (DBH RMSE 6 cm), but only after location-specific error was corrected. The stem classification method required comparatively little development of statistical models to provide estimates, which ultimately had a similar level of accuracy (RMSE < 1 cm) to PREV. HALS datasets can measure broad-scale forest plantations and reduce field efforts and should be considered an important tool for aiding in inventory creation and decision-making within forest management. Full article

Accurate individual tree crown delineation (ITCD), which can be used to estimate various forest parameters such as biomass, stem density, and carbon storage, stands as an essential component of precision forestry. Currently, raster data such as the canopy height model derived from airborne light detection and ranging (LiDAR) data have been widely used in large-scale ITCD. However, the accuracy of current existing algorithms is limited due to the influence of understory vegetation and variations in tree crown geometry (e.g., the delineated crown boundaries consistently extend beyond their actual boundaries). In this study, we achieved more accurate crown delineation results based on an expansion process. First, the initial crown boundaries were extracted through watershed segmentation. Then, a “from the inside out” expansion process was guided by a novel gradient feature to obtain accurate crown delineation results across different forest conditions. Results show that our method produced much better performance (~75% matched on average) than other commonly used methods across all test forest plots. The erroneous situation of “match but over-grow” is significantly reduced, regardless of forest conditions. Compared to other methods, our method demonstrates a notable increase in the precisely matched rate across different plot types, with an average increase of 25% in broadleaf plots, 18% in coniferous plots, 23% in mixed plots, 15% in high-density plots, and 32% in medium-density plots, without increasing over- and under- segmentation errors. Our method demonstrates potential applicability across various forest conditions, facilitating future large-scale ITCD tasks and precision forestry applications. Full article

The Pet-Sang-Kard mixed herbal remedy (PSKMHR) is a traditional Thai medicinal formulation used as an herbal supplement for the treatment of hemorrhoids. This remedy consists of four specific herbal ingredients in the following proportions: 50 parts Cissus quadrangularis L. stems, 15 parts Eclipta prostrata L. aerial parts, 10 parts Rheum sp. rhizome, and 10 parts Boesenbergia rotunda (L.) Mansf. rhizome. This study presents the development, validation, and application of a high-performance liquid chromatography with photodiode array detection (HPLC-PDA) method designed for the simultaneous quantification of 13 key bioactive compounds, including rhaponticin, rhapontigenin, quercitrin, wedelolactone, aloe-emodin, rhein, emodin, chrysophanol, physcion, alpinetin, pinocembrin, pinostrobin, and panduratin A, present in the 70% ethanolic extract of PSKMHR. Method validation was conducted in accordance with Association of Official Analytical Collaboration (AOAC) international guidelines, evaluating parameters such as the specificity, linearity, accuracy, precision, and limit of detection. The results demonstrated exceptional linearity (R > 0.9999), high precision (% RSD < 2), and recovery rates within acceptable limits (98–102%) for all analytes. This developed method was successfully applied to quantify the 13 target compounds in the crude extracts of PSKMHR formulated from 10 market raw material samples, providing a robust analytical framework for quality control of this herbal remedy. Full article

Introduction: Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) stems from disrupted lipid metabolism in the liver, often linked to obesity, type 2 diabetes, and dyslipidemia. In Mexico, where obesity affects 36.9% of adults, MASLD prevalence has risen, especially with metabolic syndrome affecting 56.31% by 2018. MASLD can progress to Metabolic Dysfunction-Associated Steatohepatitis (MASH), affecting 5.27% globally, leading to severe complications like cirrhosis and hepatocellular carcinoma. Background: Visceral fat distribution varies by gender, impacting MASLD development due to hormonal influences. Insulin resistance plays a central role in MASLD pathogenesis, exacerbated by high-fat diets and specific fatty acids, leading to hepatic steatosis. Lipotoxicity from saturated fatty acids further damages hepatocytes, triggering inflammation and fibrosis progression in MASH. Diagnosing MASLD traditionally involves invasive liver biopsy, but non-invasive methods like ultrasound and transient elastography are preferred due to their safety and availability. These methods detect liver steatosis and fibrosis with reasonable accuracy, offering alternatives to biopsy despite varying sensitivity and specificity. Conclusions: MASLD as a metabolic disorder underscores its impact on public health, necessitating improved awareness and early management strategies to mitigate its progression to severe liver diseases. Full article

Biosurfactants are amphiphilic molecules capable of reducing the surface tension of water and forming emulsions between immiscible liquids. These versatile molecules find applications in different industrial sectors, standing out in environmental applications, such as the bioremediation agents of contaminated environments. Bioremediation is an emerging sustainable method of controlling the degradation of waste. The present study carried out a bibliometric analysis, reviewing all research published in the SCOPUS database up to 2023, focused on producing biosurfactants from sunflowers with applications in this sustainable method of waste degradation. Using sunflowers to produce biosurfactants proved an ecological, sustainable, and economical alternative to conventional substrates. The results showed that only the seed husks, the oil derived from the seed, and the sunflower stems were used to produce biosurfactants, emphasizing oil as the most used raw material, probably due to its rich linoleic acid content. The preliminary selection detected only 12 articles that addressed the subject under analysis. According to these studies, the tested biosurfactants showed high potential for application in sustainable environmental bioremediation processes and were able to decontaminate soil, water, and liquid effluents. The bibliometric analysis was performed with the VOSviewer software to evaluate the quality of the publications and, above all, to show a more comprehensive scenario of the subject based on the following bibliometric indicators: the most productive journals, publications by country, the most cited articles, the most recurrent keywords, and most productive institutions. These insights will undoubtedly help scientists to develop new and sustainable practices of waste degradation and contribute to bioremediation research using biosurfactants from sunflowers. By showcasing the environmental benefits and practicality of sunflower-derived biosurfactants, this study contributes to the broader discourse on sustainable bioremediation, fostering innovative and eco-friendly waste management solutions. Full article