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19 pages, 4791 KiB  
Article
Green Radish Polysaccharides Ameliorate Hyperlipidemia in High-Fat-Diet-Induced Mice via Short-Chain Fatty Acids Production and Gut Microbiota Regulation
by Xiong Geng, Weina Tian, Miaomiao Zhuang, Huayan Shang, Ziyi Gong and Jianrong Li
Foods 2024, 13(24), 4113; https://doi.org/10.3390/foods13244113 - 19 Dec 2024
Viewed by 617
Abstract
The objective of this study was to examine the hypolipidemic effect and potential mechanism of action of green radish polysaccharide (GRP) in hyperlipidemic mice. We found that in mice fed a high-fat diet, supplementing with GRP reduced body weight and liver index, significantly [...] Read more.
The objective of this study was to examine the hypolipidemic effect and potential mechanism of action of green radish polysaccharide (GRP) in hyperlipidemic mice. We found that in mice fed a high-fat diet, supplementing with GRP reduced body weight and liver index, significantly improved serum lipid levels and markers of liver damage, and mitigated oxidative stress and inflammation. Mechanistically, in these hyperlipidemic mice, the size of fat cells was reduced by GRP, and the abnormal accumulation of lipid droplets was reduced. We also found that GRP regulates the composition of the intestinal microbiota, including the ratio of Firmicutes to Mycobacteria F/B and the levels of Blautia spp., which have been shown to alleviate liver damage and treat hyperlipidemia. Metabolite pathway analysis using the Kyoto Encyclopedia of Genes and Genomes identified the glycolysis/glycolytic metabolism and propionate metabolism pathways as potential targets for GRP in the amelioration of hyperlipidemia. Full article
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<p>GRP reduced HFD-induced body weight gain; (<b>A</b>) representative pictures of mice; (<b>B</b>) hepatosomatic index of mice; (<b>C</b>) diagram of changes in mice body weight. * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Effects of GRP on serum biochemical indicators. The levels of TG, TC, HDL-C, AST, ALT, and LDL-C in the serum were evaluated (<b>A</b>–<b>F</b>). The results are expressed as means ± SD. Statistically significant results between all groups were expressed by lowercase letters. * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Effects of GRP on oxidative levels and inflammatory indicators. The levels of SOD, CAT, GSH-Px, TNF-α, IL-6, and LPS were evaluated (<b>A</b>–<b>F</b>). The results are expressed as means ± SD. Statistically significant results between all groups were expressed by lowercase letters. * <span class="html-italic">p</span> &lt; 0.05 and ns: no significance.</p>
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<p>GRP ameliorates defects in gut, liver, and adipose tissue cells’ structure induced by high-fat diet. (<b>A</b>) Representative pictures of hematoxylin and eosin (H&amp;E) staining for liver tissue. (<b>B</b>) Representative images of HE staining in the ileum of mice were shown. (<b>C</b>) Representative pictures of oil red O staining for liver fat. (<b>D</b>) Representative pictures of hematoxylin and eosin (H&amp;E) staining for adipose tissue. Pictures were shown as 20× zoom, scale: 100 μm. (<b>E</b>) Change in the colon villi length of the mice in the three groups. (<b>F</b>) Mean size of mice adipocytes. Statistically significant results between groups are indicated by small letters. * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Effect of GRP on intestinal microbial structure in HFD-fed mice. (<b>A</b>) Statistical differences between the groups of alpha diversity; (<b>B</b>) Venn diagram of the ASVs for LFD, HFD, and GRP; (<b>C</b>) β-diversity PCA and PCoA results.</p>
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<p>GRP changed the composition of gut microbiota in HFD-fed mice. (<b>A</b>) Average relative abundance at the phylum level in each group. (<b>B</b>) Average relative abundance at the family level in each group. (<b>C</b>) Average relative abundance at the genus level in each group. (<b>D</b>) LEfSe analysis was conducted to identify fecal microbial taxa that accounted for the greatest differences among all the groups.</p>
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<p>Contents of fecal short-chain fatty acids in three mice groups; statistically significant results between all groups were expressed by lowercase letters. * <span class="html-italic">p</span> &lt; 0.05.</p>
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24 pages, 725 KiB  
Review
Antibiofilm Effects of Novel Compounds in Otitis Media Treatment: Systematic Review
by Ana Jotic, Katarina Savic Vujovic, Andja Cirkovic, Dragana D. Božić, Snezana Brkic, Nikola Subotic, Bojana Bukurov, Aleksa Korugic and Ivana Cirkovic
Int. J. Mol. Sci. 2024, 25(23), 12841; https://doi.org/10.3390/ijms252312841 - 29 Nov 2024
Viewed by 1317
Abstract
Otitis media (OM) is a frequent disease with incidence rate of 5300 cases per 100,000 people. Recent studies showed that polymicrobial biofilm formation represents a significant pathogenic mechanism in recurrent and chronic forms of OM. Biofilm enables bacteria to resist antibiotics that would [...] Read more.
Otitis media (OM) is a frequent disease with incidence rate of 5300 cases per 100,000 people. Recent studies showed that polymicrobial biofilm formation represents a significant pathogenic mechanism in recurrent and chronic forms of OM. Biofilm enables bacteria to resist antibiotics that would typically be recommended in guidelines, contributing to the ineffectiveness of current antimicrobial strategies. Given the challenges of successfully treating bacterial biofilms, there is an growing interest in identifying novel and effective compounds to overcome antibacterial resistance. The objective of this review was to provide an overview of the novel compounds with antibiofilm effects on bacterial biofilm formed by clinical isolates of OM. The systematic review included studies that evaluated antibiofilm effect of novel natural or synthetic compounds on bacterial biofilm formed from clinical isolates obtained from patients with OM. The eligibility criteria were defined using the PICOS system: (P) Population: all human patients with bacterial OM; (I) Intervention: novel natural or synthetic compound with biofilm effect; (C) Control standard therapeutic antimicrobial agents or untreated biofilms, (O) Outcome: antibiofilm effect (biofilm inhibition, biofilm eradication), (S) Study design. The PRISMA protocol for systematic reviews and meta-analysis was followed. From 3564 potentially eligible studies, 1817 duplicates were removed, and 1705 were excluded according to defined exclusion criteria. A total of 41 studies with available full texts were retrieved by two independent authors. Fifteen articles were selected for inclusion in the systematic review which included 125 patients with OM. A total of 17 different novel compounds were examined, including N-acetyl-L-cysteine (NAC), tea tree oil, xylitol, eugenol, Aloe barbadensis, Zingiber officinale, Curcuma longa, Acacia arabica, antisense peptide nucleic acids, probiotics Streptococcus salivarius and Streptococcus oralis, Sodium 2-mercaptoethanesulfonate (MESNA), bioactive glass, green synthesized copper oxide nanoparticles, radish, silver nanoparticles and acetic acid. Staphylococcus aureus was the most commonly studied pathogen, followed by Pseudomonas aeruginosa and Haemophilus influenzae. Biofilm inhibition only by an examined compound was assessed in six studies; biofilm eradication in four studies, and both biofilm inhibition and biofilm eradication were examined in five studies. This systematic review indicates that some compounds like NAC, prebiotics, nanoparticles and MESNA that have significant effects on biofilm are safe and could be researched more extensively for further clinical use. However, a lack of data about reliable and efficient compounds used in therapy of different types of otitis media still remains in the literature. Full article
(This article belongs to the Special Issue Biofilm Antimicrobial Strategies: Outlook and Future Perspectives)
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<p>PRISMA flow diagram depicting the process followed for the selection of the studies.</p>
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18 pages, 4060 KiB  
Article
Green Radish Polysaccharide Prevents Alcoholic Liver Injury by Interfering with Intestinal Bacteria and Short-Chain Fatty Acids in Mice
by Xiong Geng, Miaomiao Zhuang, Weina Tian, Huayan Shang, Ziyi Gong, Yanfang Lv and Jianrong Li
Foods 2024, 13(23), 3733; https://doi.org/10.3390/foods13233733 - 22 Nov 2024
Viewed by 748
Abstract
This study aimed to ascertain the potential benefits of green radish polysaccharide (GRP) in treating alcoholic liver disease (ALD) in mice and explore its mechanism of action. Using biochemical analysis, high-throughput sequencing of gut microbiota, and gas chromatography–mass spectrometry to measure short-chain fatty [...] Read more.
This study aimed to ascertain the potential benefits of green radish polysaccharide (GRP) in treating alcoholic liver disease (ALD) in mice and explore its mechanism of action. Using biochemical analysis, high-throughput sequencing of gut microbiota, and gas chromatography–mass spectrometry to measure short-chain fatty acids (SCFAs) in feces, we found that GRP intervention significantly improved lipid metabolism and hepatic function in mice subjected to excessive alcohol intake. The GRP intervention reduced malondialdehyde levels by 66% and increased total superoxide dismutase levels by 22%, thereby mitigating alcohol-induced oxidative stress. Furthermore, GRP intervention in mice with alcohol consumption resulted in a reduction in tumor necrosis factor, interleukin 6, and lipopolysaccharide levels by 12%, 9%, and 25%, respectively, effectively attenuating alcoholic liver inflammation. 16S rRNA amplicon sequencing demonstrated that excessive alcohol consumption markedly altered the gut microbiota composition in mice. The GRP treatment resulted in a significant reduction in the number of beneficial bacteria (Lactobacillus and Lachnospiraceae_NK4A136_group) and an increase in the proportion of harmful bacteria (Muribaculaceae and Verrucomicrobiota). The metabolomic analyses of the SCFAs demonstrated an increase in the contents of SCFAs, acetic acid, propionic acid, and butyric acid, following GRP supplementation. Furthermore, the metabolic levels of cholinergic synapses and glycolysis/gluconeogenesis were found to be modulated. In conclusion, these findings suggest that GRP may attenuate alcohol-induced oxidative damage in the liver by modulating the gut microbiota and hepatic metabolic pathways. This may position GRP as a potential functional component for ALD prevention. Full article
(This article belongs to the Special Issue Advances on Functional Foods with Antioxidant Bioactivity)
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<p>GRP did not affect body weight in alcohol-induced mice but decreased alcohol-induced organ index: (<b>A</b>) representative weights; (<b>B</b>) organ index. The results are expressed as the mean ± SD (<span class="html-italic">n</span> = 6–8). * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Effects of GRP on serum biochemical indices and histopathology. (<b>A</b>–<b>C</b>) Representative images of hematoxylin and eosin (H&amp;E) staining of liver tissues. The images are shown at a 20× zoom level; the ratio was 100 μm. (<b>D</b>–<b>G</b>) The levels of TC, TG, ALT, and AST in serum were measured, and the results are expressed as the mean ± SD (n = 6–8). ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>The levels of T-SOD, GSH-Px, MDA, CAT, TNF-α, LPS, and IL-6 in the liver tissues of each group were measured (<b>A</b>–<b>G</b>), and the results are expressed as the mean ± SD (<span class="html-italic">n</span> = 6–8). * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>GRP altered gut microbial composition and diversity in alcohol-induced mice: (<b>A</b>) Shannon index; (<b>B</b>) Chao1 index; (<b>C</b>) Venn diagram; (<b>D</b>) PCA diagram.</p>
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<p>GRP altered the composition of the gut microbiota in alcohol-administered mice: (<b>A</b>) average relative abundance of each group at the gate level; (<b>B</b>) average relative abundance (<span class="html-italic">n</span> = 6) for each genus level.</p>
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<p>Fecal metabolomics LC-MS analysis. (<b>A</b>) The OPLS-DA rating scale shows the differences in metabolites among the groups. The horizontal coordinate indicates the inter-group change, and the vertical coordinate indicates the intra-group change. (<b>B</b>–<b>H</b>) The acetic acid, propionic acid, butyric acid, valeric acid, caproic acid, isovaleric acid, and isobutyric acid levels are expressed as the mean ± SD (<span class="html-italic">n</span> = 6). * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>GRP improves the predictive pathways associated with ALD’s effect. Green dots represent metabolic pathways, and other dots represent metabolite molecules. The size of the metabolic pathway point indicates the number of metabolite molecules connected to it, where the higher the number, the larger the point, and the size of the metabolite molecular point indicates the log<sub>2</sub>(FC) value through the gradient change. The colors in the figure represent correlations, with green representing negative correlations and red representing positive correlations. The red boxes are the two main metabolic pathways described below.</p>
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19 pages, 4442 KiB  
Article
Phytotoxicity Assessment of Agro-Industrial Waste and Its Biochar: Germination Bioassay in Four Horticultural Species
by Romina Zabaleta, Eliana Sánchez, Ana Laura Navas, Viviana Fernández, Anabel Fernandez, Daniela Zalazar-García, María Paula Fabani, Germán Mazza and Rosa Rodriguez
Agronomy 2024, 14(11), 2573; https://doi.org/10.3390/agronomy14112573 - 1 Nov 2024
Viewed by 791
Abstract
This study investigated the phytotoxicity of agro-industrial wastes (almond, walnut, pistachio and peanut shells, asparagus spears, and brewer’s spent grain) and their biochar through germination bioassays in several horticultural species: green pea, lettuce, radish, and arugula. Biowaste was pyrolyzed under controlled conditions to [...] Read more.
This study investigated the phytotoxicity of agro-industrial wastes (almond, walnut, pistachio and peanut shells, asparagus spears, and brewer’s spent grain) and their biochar through germination bioassays in several horticultural species: green pea, lettuce, radish, and arugula. Biowaste was pyrolyzed under controlled conditions to produce biochar, and both biowaste and biochar were characterized. Germination bioassay was conducted using seeds exposed to different dilutions of aqueous extract of biowaste and their biochar (0, 50, and 100%). Germination percentage, seed vigor, germination index, and root and aerial lengths were evaluated. The results showed that the phytotoxicity of the biowaste was significantly different to that of its biochar. The biochar obtained demonstrated changing effects on germination and seedling growth. In particular, biochar extracts from spent brewers grains, walnut shells, and pistachio shells showed 5–14% increases in seed vigor and root and aerial length. Furthermore, the response of different species to both agro-industrial waste and biochar revealed species-specific sensitivity. Seeds of lettuce and arugula species were more sensitive to aqueous extracts than radish and green peas. This knowledge not only elucidates the behavior of agro-industrial waste-based biochar in the early stage of plant development but also provides valuable insights regarding phytotoxicity, seed sensitivity, and the variables involved in germination. Full article
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<p>Logic diagram, a methodological scheme of the germination bioassay in horticultural species: evaluation of biowaste and biochar.</p>
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<p>Biowastes: (<b>a</b>) Alm, (<b>b</b>) Wal, (<b>c</b>) Pea, (<b>d</b>) Pis, (<b>e</b>) Asp, and (<b>f</b>) Bre.</p>
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<p>Biochars: (<b>a</b>) AlmB, (<b>b</b>) WalB, (<b>c</b>) PeaB, (<b>d</b>) PisB, (<b>e</b>) AspB, and (<b>f</b>) BreB.</p>
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<p>Photographs taken at the end of the Petri dish assay. Horticultural seeds emerging in control extract: (<b>a</b>) <span class="html-italic">Lactuca sativa</span>, (<b>b</b>) <span class="html-italic">Eruca sativa</span>, (<b>c</b>) <span class="html-italic">Raphanus sativus</span>, and (<b>d</b>) <span class="html-italic">Pisum sativum</span>.</p>
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<p>Relationships between biomass and biochar types and control and their influence of variables (GI, GP, SV, AL, and RL) with discriminatory potential through a multivariate statistical procedure, such as linear discriminant analysis (LDA).</p>
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<p>Relationships between types of seeds and their influence on variables with discriminatory potential through a multivariate statistical procedure, such as linear discriminant analysis (LDA). GI (germination index), GP (germination percentage), SV (seed vigor), AL (aerial length) and RL (root length).</p>
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14 pages, 2780 KiB  
Article
Effects of Light Spectra on Growth, Physiological Responses, and Antioxidant Capacity in Five Radish Varieties in an Indoor Vertical Farming System
by Panita Chutimanukul, Pakin Piew-ondee, Thanyaluk Dangsamer, Akira Thongtip, Supattana Janta, Praderm Wanichananan, Ornprapa Thepsilvisut, Hiroshi Ehara and Preuk Chutimanukul
Horticulturae 2024, 10(10), 1059; https://doi.org/10.3390/horticulturae10101059 - 3 Oct 2024
Cited by 2 | Viewed by 2026
Abstract
Radish (Raphanus sativus L.) is highly nutritious and contains antioxidants that help reduce the risk of diseases. Light is a crucial factor in their growth and the stimulation of secondary metabolite production. Therefore, this study aimed to investigate the effects of light [...] Read more.
Radish (Raphanus sativus L.) is highly nutritious and contains antioxidants that help reduce the risk of diseases. Light is a crucial factor in their growth and the stimulation of secondary metabolite production. Therefore, this study aimed to investigate the effects of light spectra on the development, physiological responses, and antioxidant capacity of radish varieties including cherry belle (CB), black Spanish (BS), hailstone white (HW), Malaga violet (MV), and sparkler white tip (SW) under a controlled environment. Various spectra of red (R), green (G), and blue (B) light were used. The study found that using a combination of red and blue light (3R:1B) resulted in the highest growth in root diameter, fresh weight, and dry weight across all five radish varieties, with values ranging from 1.83 to 4.63 cm, 13.58 to 89.33 g, and 1.20 to 4.64 g, respectively. In terms of physiological responses, the CB and BS varieties showed a higher photosynthetic rate after exposure to mixed red and blue light (1R:3B, 3R:1B). Additionally, adding green light to the red and blue light also enhanced the photosynthetic rate, with statistically significant differences ranging from 3.31 to 3.99 µmol m−2 s−1. The SW variety of radish exhibited an increase in phenolic compounds, flavonoids, and anthocyanins when exposed to light spectra of 1R:1G:1B, 1R:2G:1B, 1R:3G:1B, and 1R:3B. The highest levels of phenolic compounds were 4.67–5.14 mg GAE/g DW, flavonoids were 1.62–1.96 mg Rutin/g DW, and anthocyanins were 1.20–1.58 µg/g DW. However, the antioxidant capacity of five radish varieties under different light spectra did not show significant differences. Thus, the growth, photosynthesis, and antioxidant capacity depend on the optimal light spectrum for each radish variety. Full article
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<p>Five different light spectrum treatments were applied to the five radish cultivars.</p>
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<p>The phenotype of cherry belle (<b>A</b>), black Spanish (<b>B</b>), hailstone white (<b>C</b>), Malaga violet (<b>D</b>), and sparkler white tip (<b>E</b>) under five light treatments (1R:1G:1B, 1R:2G:1B, 1R:3G:1B, 1R:3B, and 3R:1B). R, G, and B represent the red, green, and blue light spectra, respectively, and the number before the letter stands for the ratio of each light spectrum.</p>
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<p>Biomass and plant growth of five radish varieties on different light spectrum treatments on root diameter (<b>A</b>), root length (<b>B</b>), root fresh weight (<b>C</b>), and root dry weight (<b>D</b>). R, G, and B represent the red, green, and blue light spectra, respectively, and the number before the letters stands for the ratio of each light spectrum. Values are means with standard deviations (<span class="html-italic">n</span> = 4). The letters above the bars indicate statistically significant differences by Duncan’s multiple range tests (<span class="html-italic">p</span> &lt; 0.05). The absence of letters indicates no significant difference.</p>
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<p>Physiological responses of five radish varieties on different light spectrum treatments on photosynthetic rate (<b>A</b>), transpiration rate (<b>B</b>), stomatal conductance (<b>C</b>), and intercellular CO<sub>2</sub> concentration (<b>D</b>). R, G, and B represent the red, green, and blue light spectra, respectively, and the number before the letters stands for the ratio of each light spectrum. Values are means with standard deviations (<span class="html-italic">n</span> = 4). The letters above the bars indicate statistically significant differences by Duncan’s multiple range tests (<span class="html-italic">p</span> &lt; 0.05). The absence of letters indicates no significant difference.</p>
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<p>The quantities of secondary metabolites and antioxidant capacity of five radish varieties on different light spectrum treatments on total phenolic (<b>A</b>), flavonoid (<b>B</b>), anthocyanin (<b>C</b>), and DPPH scavenging activity (<b>D</b>). R, G, and B represent the red, green, and blue light spectra, respectively, and the number before the letters stands for the ratio of each light spectrum. Values are means with standard deviations (<span class="html-italic">n</span> = 4). The letters above the bars indicate statistically significant differences by Duncan’s multiple range tests (<span class="html-italic">p</span> &lt; 0.05). The absence of letters indicates no significant difference.</p>
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22 pages, 3255 KiB  
Article
Supramolecular Solvent-Based Extraction of Microgreens: Taguchi Design Coupled-ANN Multi-Objective Optimization
by Anja Vučetić, Lato Pezo, Olja Šovljanski, Jelena Vulić, Vanja Travičić, Gordana Ćetković and Jasna Čanadanović-Brunet
Processes 2024, 12(7), 1451; https://doi.org/10.3390/pr12071451 - 11 Jul 2024
Viewed by 940
Abstract
Supramolecular solvent-based extraction (SUPRAS) stands out as a promising approach, particularly due to its environmentally friendly and efficient characteristics. This research explores the optimization of SUPRAS extraction for sango radish and kale microgreens, focusing on enhancing the extraction efficiency. The Taguchi experimental design [...] Read more.
Supramolecular solvent-based extraction (SUPRAS) stands out as a promising approach, particularly due to its environmentally friendly and efficient characteristics. This research explores the optimization of SUPRAS extraction for sango radish and kale microgreens, focusing on enhancing the extraction efficiency. The Taguchi experimental design and artificial neural network (ANN) modeling were utilized to systematically optimize extraction parameters (ethanol content, SUPRAS: equilibrium ratio, centrifugation rate, centrifugation time, and solid-liquid ratio). The extraction efficiency was evaluated by measuring the antioxidant activity (DPPH assay) and contents of chlorophylls, carotenoids, phenolics, and anthocyanidins. The obtained results demonstrated variability in phytochemical contents and antioxidant activities across microgreen samples, with the possibility of achieving high extraction yields using the prediction of optimized parameters. The optimal result for sango radish can be achieved at an ethanol content of 35.7%; SUPRAS: equilibrium ratio of 1 v/v, centrifugation rate of 4020 rpm, centrifugation time of 19.84 min, and solid-liquid ratio of 30.2 mg/mL. The following parameters are predicted for maximal extraction efficiency for kale: ethanol content of 35.64%; SUPRAS: equilibrium ratio of 1 v/v; centrifugation rate of 3927 rpm; centrifugation time of 19.83 min; and solid-liquid ratio of 30.4 mg/mL. Additionally, laboratory verification of predicted SUPRAS parameters showed very low divergency degrees for both microgreens (–3.09 to 2.36% for sango radish, and −2.57 to 3.58% for kale). This potential of SUPRAS extraction, coupled with statistical and computational optimization techniques, can enhance the recovery of valuable bioactive compounds from microgreens and contribute to green extraction applications. Full article
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<p>Sango radish (<b>left</b>) and kale (<b>right</b>) microgreens samples.</p>
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<p>The main effects plot for the S/N analysis of the SUPRAS extraction efficiency: (<b>a</b>) Sango Radish; (<b>b</b>) Kale microgreens.</p>
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<p>Normal probability plots: (<b>a</b>) Sango Radish; (<b>b</b>) Kale microgreens.</p>
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<p>Standard score (SS) analysis of the Taguchi experimental data using positive polarity and the same significance coefficient (0.2) for all tested parameters.</p>
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<p>Cluster analysis: (<b>a</b>) Sango Radish; (<b>b</b>) Kale microgreens.</p>
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<p>PCA analysis: (<b>a</b>) Sango Radish; (<b>b</b>) Kale microgreens.</p>
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<p>Relative influences of variables on SUPRAS extraction outputs. (EtOH—ethanol content; EqS—SUPRAS: Equilibrium ratio; CRPM—centrifugation rate; Ctime—centrifugation time; SLR—solid: liquid ratio): (<b>a</b>) Sango radish; (<b>b</b>) Kale.</p>
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21 pages, 7117 KiB  
Article
Green Manuring Enhances Soil Multifunctionality in Tobacco Field in Southwest China
by Yu Feng, Hua Chen, Libo Fu, Mei Yin, Zhiyuan Wang, Yongmei Li and Weidong Cao
Microorganisms 2024, 12(5), 949; https://doi.org/10.3390/microorganisms12050949 - 7 May 2024
Viewed by 1107
Abstract
The use of green manure can substantially increase the microbial diversity and multifunctionality of soil. Green manuring practices are becoming popular for tobacco production in China. However, the influence of different green manures in tobacco fields has not yet been clarified. Here, smooth [...] Read more.
The use of green manure can substantially increase the microbial diversity and multifunctionality of soil. Green manuring practices are becoming popular for tobacco production in China. However, the influence of different green manures in tobacco fields has not yet been clarified. Here, smooth vetch (SV), hairy vetch (HV), broad bean (BB), common vetch (CV), rapeseed (RS), and radish (RD) were selected as green manures to investigate their impact on soil multifunctionality and evaluate their effects on enhancing soil quality for tobacco cultivation in southwest China. The biomass of tobacco was highest in the SV treatment. Soil pH declined, and soil organic matter (SOM), total nitrogen (TN), and dissolved organic carbon (DOC) content in CV and BB and activity of extracellular enzymes in SV and CV treatments were higher than those in other treatments. Fungal diversity declined in SV and CV but did not affect soil multifunctionality, indicating that bacterial communities contributed more to soil multifunctionality than fungal communities. The abundance of Firmicutes, Rhizobiales, and Micrococcales in SV and CV treatments increased and was negatively correlated with soil pH but positively correlated with soil multifunctionality, suggesting that the decrease in soil pH contributed to increases in the abundance of functional bacteria. In the bacteria–fungi co-occurrence network, the relative abundance of key ecological modules negatively correlated with soil multifunctionality and was low in SV, CV, BB, and RS treatments, and this was associated with reductions in soil pH and increases in the content of SOM and nitrate nitrogen (NO3-N). Overall, we found that SV and CV are more beneficial for soil multifunctionality, and this was driven by the decrease in soil pH and the increase in SOM, TN, NO3-N, and C- and N-cycling functional bacteria. Full article
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<p>Tobacco biomass of different green manure treatments at 45, 65 and 90 days after tobacco transplanting. CK—winter fallow control; SV—returning smooth vetch; HV—returning hairy vetch; BB—returning broad bean; CV—returning common vetch; RS—returning rapeseed; RD—returning radish. Different lowercase letters on the top of boxes indicate significant differences among treatments at the same stage (<span class="html-italic">p</span> &lt; 0.05); 45, 65, and 90DAT: 45, 65, and 90 days after transplanting, respectively. Data were collected during 13 June, 3 July, and 28 July in 2022 at Houxiang village, China.</p>
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<p>Analysis of the fungal community diversity of rhizosphere soil under different green manure treatments at different growth stages of tobacco. (<b>a</b>) Shannon index; (<b>b</b>) ACE index. CK—winter fallow control; SV—returning smooth vetch; HV—returning hairy vetch; BB—returning broad bean; CV—returning common vetch; RS—returning rapeseed; RD—returning radish. *, ** and *** indicate the effect at 0.05, 0.01 and 0.001 significant levels. 45, 65, and 90DAT: 45, 65, and 90 days after transplanting, respectively. Data were collected during 13 June, 3 July, and 28 July in 2022 at Houxiang village, China.</p>
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<p>PCoA plots of the bacterial (<b>a</b>–<b>c</b>) and fungal (<b>d</b>–<b>f</b>) community in rhizosphere soil in the different treatments at growth stages of tobacco. CK—winter fallow control; SV—returning smooth vetch; HV—returning hairy vetch; BB—returning broad bean; CV—returning common vetch; RS—returning rapeseed; RD—returning radish. 45, 65, and 90DAT: 45, 65, and 90 days after transplanting, respectively. Data were collected during 13 June, 3 July, and 28 July in 2022 at Houxiang village, China.</p>
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<p>Composition of the rhizosphere soil fungal communities in different treatments across the three sampling dates. The left set of graphs shows phylum-level changes, and the right set of graphs shows genus-level changes. CK—winter fallow control; SV—returning smooth vetch; HV—returning hairy vetch; BB—returning broad bean; CV—returning common vetch; RS—returning rapeseed; RD—returning radish.</p>
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<p>Taxonomic cladogram showing the results of LEfSe analysis of bacterial (<b>a</b>) and fungal (<b>b</b>) communities.</p>
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<p>Development of soil multifunctionality as affected by six green manure options. CK—winter fallow control; SV—returning smooth vetch; HV—returning hairy vetch; BB—returning broad bean; CV—returning common vetch; RS—returning rapeseed; RD—returning radish. Different lowercase letters at the tops of boxes indicate significant differences among treatments at the same stage (<span class="html-italic">p</span> &lt; 0.05); 45, 65, and 90DAT: 45, 65, and 90 days after transplanting, respectively. Data were collected during 13 June, 3 July, and 28 July in 2022 at Houxiang village, China.</p>
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<p>(<b>a</b>) Weighted gene co-expression network analysis (WGCNA) of bacterial–fungal interactions based on all samples; (<b>b</b>) relative abundance of ecological modules in the different treatments. CK—winter fallow control; SV—returning smooth vetch; HV—returning hairy vetch; BB—returning broad bean; CV—returning common vetch; RS—returning rapeseed; RD—returning radish. Different lowercase letters at the tops of boxes indicate significant differences among treatments at the same stage (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Microbial community composition of the co-expression network modules at the (<b>a</b>) phylum and (<b>b</b>) genus levels.</p>
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<p>FAPROTAX (<b>a</b>) and FUNGuild (<b>b</b>) analysis of ecological modules, with black dots indicating the relative abundance of bacterial and fungal ASVs in each functional group (metabolic pathway).</p>
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<p>FAPROTAX (<b>a</b>) and FUNGuild (<b>b</b>) analysis of ecological modules, with black dots indicating the relative abundance of bacterial and fungal ASVs in each functional group (metabolic pathway).</p>
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<p>(<b>a</b>) Environmental factors affecting the abundance of key ecological modules identified through Mantel tests. (<b>b</b>) Linear regression analysis of the relative abundance of modules 2 and 3 with environmental factors. *, ** and *** indicate the effect at 0.05, 0.01 and 0.001 significant levels.</p>
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<p>(<b>a</b>) Environmental factors affecting the abundance of key ecological modules identified through Mantel tests. (<b>b</b>) Linear regression analysis of the relative abundance of modules 2 and 3 with environmental factors. *, ** and *** indicate the effect at 0.05, 0.01 and 0.001 significant levels.</p>
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<p>Regression analysis of soil multifunctionality and ecological modules.</p>
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17 pages, 2100 KiB  
Article
Algae Extracts in Horticulture: Characterization of Algae-Based Extracts and Impact on Turnip Germination and Radish Culture
by Daniel Santos, João Cotas, Leonel Pereira and Kiril Bahcevandziev
Sustainability 2024, 16(6), 2529; https://doi.org/10.3390/su16062529 - 19 Mar 2024
Cited by 1 | Viewed by 2194
Abstract
Algae are rich in nutrients and bioactive compounds, playing a crucial role as biostimulants for plants, enhancing growth and resilience. Four algae-based extracts were tested: the raw extract of red macroalgae Calliblepharis jubata (CJ), Ulasco (UA), Grasco (GR) and “AgriAlgae Foliar” (AA), the [...] Read more.
Algae are rich in nutrients and bioactive compounds, playing a crucial role as biostimulants for plants, enhancing growth and resilience. Four algae-based extracts were tested: the raw extract of red macroalgae Calliblepharis jubata (CJ), Ulasco (UA), Grasco (GR) and “AgriAlgae Foliar” (AA), the latter with microalgae. The extracts were evaluated for their physicochemical parameters (pH, electrical conductivity and solids), macro and microelements, phenolic compounds and antioxidants. Afterwards, seed germination trials were carried out with turnip seeds (Brassica rapa var. cymosa L.), and pot trials were carried out with Cherry Belle (Flora Lusitana, Cantanhede, Portugal) radish plants (Raphanus sativus L.), to verify the biostimulant potential of the extracts in horticulture. In the pot trials, all the treatments led to better yields and nutritional quality. The UA 0.12 extract influenced the heaviest roots (40.32 ± 11.89 g), on average, and the GR 0.10 extract in roots with the highest percentage of proteins (1.866 ± 0.004% dm), phenolic compounds (0.12121 mg eq. gallic acid/g fm) and antioxidants (0.0754 ± 0.0000 mg eq. ascorbic acid/g fm). The radishes treated with the AA 0.003 extract showed the greatest uniformity, the healthiest leaves, with the highest flavonoid content and the heaviest aerial part (19.52 ± 5.99 g). All the extract treatments resulted in a visible mitigation of abiotic stress and consequently better results, showing that these can be crucial for sustainable agriculture. Full article
(This article belongs to the Section Sustainable Agriculture)
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<p>Classification used for the average size of radish leaves in order (Big (B)-Medium (M)-Small (S)).</p>
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<p>Composition of UA, CJ, GR and AA alga extracts in phenolic compounds (CF) in blue (g gallic acid equivalents (GAE)/L) and antioxidants (AO) in orange (g ascorbic acid equivalents (AAE)/L) (average values; different letters indicate statistically significant differences by <span class="html-italic">t</span>-test (<span class="html-italic">p</span> &lt; 0.05)).</p>
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<p>Percentage (%) of turnip seed germination 7, 10 and 14 days after inoculation in blue, orange and yellow, respectively, influenced by the extract concentrations/treatment applied. UA, CJ, GR and AA represent the extract followed by the respective concentrations used (<span class="html-italic">v</span>/<span class="html-italic">v</span>) (mean values n = 3; different letters indicate significant differences by the Holm–Sidak multiple comparison method (<span class="html-italic">p</span> &lt; 0.05)).</p>
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<p>Radicle length (RL) in blue and epicotyl length (EL) in orange of turnip greens depending on the different extracts UA, CJ, GR and AA, followed by the respective concentrations (<span class="html-italic">v</span>/<span class="html-italic">v</span>) (average values n = 15; different letters indicate significant differences by the Holm–Sidak multiple comparison method (<span class="html-italic">p</span> &lt; 0.05)).</p>
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<p>Photographic record of radishes grouped by treatment (from left to right and top to bottom: UA, CJ, C, GR and AA).</p>
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25 pages, 10231 KiB  
Article
Comprehensive Evaluation of Multispectral Image Registration Strategies in Heterogenous Agriculture Environment
by Shubham Rana, Salvatore Gerbino, Mariano Crimaldi, Valerio Cirillo, Petronia Carillo, Fabrizio Sarghini and Albino Maggio
J. Imaging 2024, 10(3), 61; https://doi.org/10.3390/jimaging10030061 - 29 Feb 2024
Cited by 3 | Viewed by 2318
Abstract
This article is focused on the comprehensive evaluation of alleyways to scale-invariant feature transform (SIFT) and random sample consensus (RANSAC) based multispectral (MS) image registration. In this paper, the idea is to extensively evaluate three such SIFT- and RANSAC-based registration approaches over a [...] Read more.
This article is focused on the comprehensive evaluation of alleyways to scale-invariant feature transform (SIFT) and random sample consensus (RANSAC) based multispectral (MS) image registration. In this paper, the idea is to extensively evaluate three such SIFT- and RANSAC-based registration approaches over a heterogenous mix containing Triticum aestivum crop and Raphanus raphanistrum weed. The first method is based on the application of a homography matrix, derived during the registration of MS images on spatial coordinates of individual annotations to achieve spatial realignment. The second method is based on the registration of binary masks derived from the ground truth of individual spectral channels. The third method is based on the registration of only the masked pixels of interest across the respective spectral channels. It was found that the MS image registration technique based on the registration of binary masks derived from the manually segmented images exhibited the highest accuracy, followed by the technique involving registration of masked pixels, and lastly, registration based on the spatial realignment of annotations. Among automatically segmented images, the technique based on the registration of automatically predicted mask instances exhibited higher accuracy than the technique based on the registration of masked pixels. In the ground truth images, the annotations performed through the near-infrared channel were found to have a higher accuracy, followed by green, blue, and red spectral channels. Among the automatically segmented images, the accuracy of the blue channel was observed to exhibit a higher accuracy, followed by the green, near-infrared, and red channels. At the individual instance level, the registration based on binary masks depicted the highest accuracy in the green channel, followed by the method based on the registration of masked pixels in the red channel, and lastly, the method based on the spatial realignment of annotations in the green channel. The instance detection of wild radish with YOLOv8l-seg was observed at a [email protected] of 92.11% and a segmentation accuracy of 98% towards segmenting its binary mask instances. Full article
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<p>Overall methodology comprising of individual sub-techniques.</p>
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<p>(<b>a</b>–<b>e</b>) Blue, green, red, near-infrared, and RedEdge instances of <span class="html-italic">R. raphanistrum</span> weed; (<b>f</b>) RGB instance of heterogenous mix of <span class="html-italic">Triticum aestivum</span> crop and <span class="html-italic">R. raphanistrum</span> weed.</p>
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<p>Locational information of the bread wheat farm in Department of Agronomy, University of Napoli Federico II (study area marked in red polygon). Source: Imagery @2020 Airbus, Maxar Technologies, Google Earth.</p>
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<p>Methodology for homography estimation.</p>
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<p>Structure of JSON file.</p>
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<p>Methodology for MS image registration based on spatial realignment of pixel annotations.</p>
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<p>Methodology for MS image registration based on registration of binary masks.</p>
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<p>Methodology for MS image registration based on registration of masked pixels.</p>
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<p>Methodology for training annotated MS images towards instance segmentation.</p>
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<p>Metrics represented through 2D boxplots towards comparison of registration based on spatial realignment of annotations, binary masks, and masked pixels. (<b>a</b>) Mean Intersection over union for annotations, binary masks, and masked pixels; (<b>b</b>) normalized correlation coefficient for annotations, binary masks, and masked pixels.</p>
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<p>Metrics represented through 2D boxplots towards comparison of registration based on spatial realignment of annotations, binary masks, and masked pixels. (<b>a</b>) Mean Intersection over union for annotations, binary masks, and masked pixels; (<b>b</b>) normalized correlation coefficient for annotations, binary masks, and masked pixels.</p>
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<p>3D scatterplot for comparison of registration errors observed across registration methods based on spatial realignment of annotations, binary masks, and masked pixels.</p>
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<p>YOLOv8l-seg performance metrics.</p>
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<p>Confusion matrix of predicted wild radish labels.</p>
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<p>Wild radish instances predicted using YOLOv8l-seg.</p>
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<p>Few instances from the dataset. (<b>a</b>) Moving image (blue channel); (<b>b</b>) ground truth of moving image; (<b>c</b>) binary mask derived after spatial realignment of annotations; (<b>d</b>) registered binary mask; (<b>e</b>) pixels masked from ground truth; (<b>f</b>) binary mask derived from registered pixels; (<b>g</b>) reference image (RedEdge channel); (<b>h</b>) binary mask of reference image; (<b>i</b>) binary mask of one wild radish instance; (<b>j</b>) binary mask instances predicted with YOLOv8l-seg; (<b>k</b>) registered binary mask instances; (<b>l</b>) masked wild radish pixels (dilated); (<b>m</b>) masked wild radish pixels (registered).</p>
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<p>Few instances from the dataset. (<b>a</b>) Moving image (blue channel); (<b>b</b>) ground truth of moving image; (<b>c</b>) binary mask derived after spatial realignment of annotations; (<b>d</b>) registered binary mask; (<b>e</b>) pixels masked from ground truth; (<b>f</b>) binary mask derived from registered pixels; (<b>g</b>) reference image (RedEdge channel); (<b>h</b>) binary mask of reference image; (<b>i</b>) binary mask of one wild radish instance; (<b>j</b>) binary mask instances predicted with YOLOv8l-seg; (<b>k</b>) registered binary mask instances; (<b>l</b>) masked wild radish pixels (dilated); (<b>m</b>) masked wild radish pixels (registered).</p>
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30 pages, 3973 KiB  
Article
The Effects of Light Spectrum and Intensity, Seeding Density, and Fertilization on Biomass, Morphology, and Resource Use Efficiency in Three Species of Brassicaceae Microgreens
by Reed John Cowden, Bo Markussen, Bhim Bahadur Ghaley and Christian Bugge Henriksen
Plants 2024, 13(1), 124; https://doi.org/10.3390/plants13010124 - 1 Jan 2024
Cited by 6 | Viewed by 3269
Abstract
Light is a critical component of indoor plant cultivation, as different wavelengths can influence both the physiology and morphology of plants. Furthermore, fertilization and seeding density can also potentially interact with the light recipe to affect production outcomes. However, maximizing production is an [...] Read more.
Light is a critical component of indoor plant cultivation, as different wavelengths can influence both the physiology and morphology of plants. Furthermore, fertilization and seeding density can also potentially interact with the light recipe to affect production outcomes. However, maximizing production is an ongoing research topic, and it is often divested from resource use efficiencies. In this study, three species of microgreens—kohlrabi; mustard; and radish—were grown under five light recipes; with and without fertilizer; and at two seeding densities. We found that the different light recipes had significant effects on biomass accumulation. More specifically, we found that Far-Red light was significantly positively associated with biomass accumulation, as well as improvements in height, leaf area, and leaf weight. We also found a less strong but positive correlation with increasing amounts of Green light and biomass. Red light was negatively associated with biomass accumulation, and Blue light showed a concave downward response. We found that fertilizer improved biomass by a factor of 1.60 across species and that using a high seeding density was 37% more spatially productive. Overall, we found that it was primarily the main effects that explained microgreen production variation, and there were very few instances of significant interactions between light recipe, fertilization, and seeding density. To contextualize the cost of producing these microgreens, we also measured resource use efficiencies and found that the cheaper 24-volt LEDs at a high seeding density with fertilizer were the most efficient production environment for biomass. Therefore, this study has shown that, even with a short growing period of only four days, there was a significant influence of light recipe, fertilization, and seeding density that can change morphology, biomass accumulation, and resource input costs. Full article
(This article belongs to the Special Issue Horticultural Crops Cultivation and Physiology)
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<p>Biplot of principal component analysis (PCA) for FW (kg/m<sup>2</sup>), DW (kg/m<sup>2</sup>), FW Light EUE (g FW/kWh light), FW Total EUE (g FW/kWh total), FW Op CUE (g FW/dollar operating costs), FW WUE (g FW/L H<sub>2</sub>O), and FW SUE (g FW/m<sup>2</sup>/day) for Unfertilized (<b>left panel</b>) and Fertilized (<b>right panel</b>) treatments. Kohlrabi is shown in black, mustard in green, and radish in red.</p>
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<p>Facet graph detailing the kohlrabi (<b>top</b>), mustard (<b>middle</b>), and radish (<b>bottom</b>) FW and DW means (kg/m<sup>2</sup>) ± 95% confidence intervals at every combination of our experimental factors, seen as ‘Seeding Density: Fertilizer’. Coloured columns are associated with the Light Recipes: yellow (24VHELED), green (HBG), brown (HFR), red (HR), and purple (NG). Lowercase letters show the results of Tukey’s HSD within each panel, where common letters are not significantly different from one another at α = 0.05.</p>
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<p>Regression plots between light components (<b>top</b> to <b>bottom</b>: Far-Red, Red, Green, and Blue) on the <span class="html-italic">x</span>-axis and FW (g/cup) on the <span class="html-italic">y</span>-axis for kohlrabi (red-hued circles), mustard (blue-hued diamonds), and radish (black-hued squares). The lighter hues in each corresponding figure indicate the High SD, while the darker colors indicate the Standard SD. Equations and R<sup>2</sup> shown in each corresponding panel for the associated nearest SD.</p>
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<p>Regression plots between Far-Red light (µmols/m<sup>2</sup>/s) on the <span class="html-italic">x</span>-axis and height (cm; <b>top</b>), leaf Weight (g leaf FW/plant; <b>second from top</b>), leaf area (cm<sup>2</sup>/plant; <b>second from bottom</b>), and leaf area index (cm<sup>2</sup>/cm<sup>2</sup>; <b>bottom</b>) on the <span class="html-italic">y</span>-axis. Kohlrabi (red circles), mustard (blue diamond), and radish (black squares) values are shown as means ± SE, with corresponding R<sup>2</sup>. The <span class="html-italic">p</span>-values are from corresponding linear models.</p>
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<p>Regression plots between height (cm) on the <span class="html-italic">x</span>-axis and leaf FW (g leaf FW/plant), leaf DW (g leaf DW/plant), leaf area (cm<sup>2</sup>/plant), and total FW (kg/m<sup>2</sup>) on the <span class="html-italic">y</span>-axis. R<sup>2</sup> and equations are shown next to each associated regression line.</p>
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<p>Scatterplots and quadratic curve fitting between FW (kg/m<sup>2</sup>) on the <span class="html-italic">y</span>-axis and Photosynthetically Active Radiation Cumulative Light Integral (PAR CLI; mol/m<sup>2</sup>) on the <span class="html-italic">x</span>-axis. Species shown as kohlrabi (red circle), mustard (blue diamond), and radish (black square) The corresponding R<sup>2</sup> shown above each respective line.</p>
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<p>Regression plots showing the production of FW (<b>left panel</b>) and DW (<b>right panel</b>) biomass as a result of starting SD, in grams/cup. The red squares are treatments without Fertilizer, while the black circles received Fertilizer. Each point is the mean for that species at that treatment level. R<sup>2</sup> shown for each corresponding line.</p>
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<p>Regression plots showing the mean biomass of kohlrabi (circle), mustard (diamond), and radish (square) microgreens due to fertilization application at the levels of none (0), half duration (0.5), and full duration (1) for fresh weight (<b>left panel</b>) and dry weight (<b>right panel</b>) in units of kg/m<sup>2</sup>. Black lines show the 24VHELED, while the Red lines show the HR Light Recipe.</p>
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12 pages, 1159 KiB  
Article
Using Brassica Cover Crops as Living Mulch in a Vineyard, Changes over One Growing Season
by Corynne O’Farrell, Tom Forge and Miranda M. Hart
Int. J. Plant Biol. 2023, 14(4), 1105-1116; https://doi.org/10.3390/ijpb14040081 - 1 Dec 2023
Cited by 2 | Viewed by 1661
Abstract
Farmers hoping to manage cropping systems sustainably are turning to cover crops to help mitigate plant pathogens. Plants with biofumigant properties are used to control soil-borne pathogens in agricultural settings, especially in till systems, where the brassicas are incorporated into the soil as [...] Read more.
Farmers hoping to manage cropping systems sustainably are turning to cover crops to help mitigate plant pathogens. Plants with biofumigant properties are used to control soil-borne pathogens in agricultural settings, especially in till systems, where the brassicas are incorporated into the soil as green manure or seed meal. The effect of these crops is not well studied in no-till systems; thus, it is hard to know if they are as effective as green manure. Whether or not these cover crops can effect changes during a single growth season has not yet been studied. This study compared the response of the soil microbial community to four different brassica cover crops, two of which are commonly used in vineyards (Sinapis alba L. (white mustard) and Raphanus sativus (L.) Domin (tillage radish)) as well as two brassicas that are native or naturalized to the Okanagan (Capsella bursa-pastoris (L.) Medik. (Shepherd’s purse) and Boechera holboelli (Hornem.) Á. Löve and D. Löve (Holbøll’s rockcress)). Cover crops did not affect fungal species richness, but B. holboelli recover crops were associated with increased evenness among fungal taxa. Both C. bursa-pastoris and S. alba had lower levels of plant parasitic nematodes compared to non-brassica controls. These results were apparent only after a single growing season, which indicates growers could use this approach as needed, minimizing long-term exposure to biofumigants for beneficial soil microbes. Full article
(This article belongs to the Section Plant–Microorganisms Interactions)
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<p>Relative abundance of soil fungi (10 most abundant classes) grown with different cover crop treatments. C, undisturbed control; RC, rockcress; SP, Shepherd’s purse; TR, tillage radish; WM, white mustard. Values were obtained in QIIME2. PERMANOVA; F = 1.320; <span class="html-italic">p</span> = 0.237; R<sup>2</sup> = 0.227. Dispersion; <span class="html-italic">p</span> = 0.918.</p>
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<p>Pielou’s evenness index for soil fungal communities associated with different cover crop treatments. C, undisturbed control; RC, rockcress; SP, Shepherd’s purse; TR, tillage radish; WM, white mustard (<span class="html-italic">n</span> = 3 for control, <span class="html-italic">n</span> = 5 for all other treatments). Hollow red circles represent individual replicates, solid red dots and black lines represent mean and standard error. Group means sharing the same letter are not significantly different.</p>
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<p>Effect of cover crop treatment on parasitic (PPN) to free-living (FLN) nematode ratio. C, undisturbed control; RC, rockcress; SP, Shepherd’s purse; TR, tillage radish; WM, white mustard (<span class="html-italic">n</span> = 5). Data obtained using a mixed effect logistic regression model with plot number as a random factor. Hollow red circles represent individual replicates, solid red dots and black lines represent mean and standard error. ANOVA; <span class="html-italic">χ<sup>2</sup></span> = 13.87; <span class="html-italic">df</span> = 4; <span class="html-italic">p</span> = 0.008. Group means sharing the same letter are not significantly different.</p>
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17 pages, 4985 KiB  
Article
Effect of Waste Cooking Oil-Based Composite Materials on Radish Growth and Biochemical Responses
by Anita Staroń, Joanna Ciuruś and Magda Kijania-Kontak
Materials 2023, 16(23), 7350; https://doi.org/10.3390/ma16237350 - 25 Nov 2023
Cited by 1 | Viewed by 1497
Abstract
Waste cooking oil poses a serious threat to human health and the environment, both in households and in larger communities. One of the applications of waste cooking oil is composite materials called vegeblocks, which can be used for construction purposes. These composites are [...] Read more.
Waste cooking oil poses a serious threat to human health and the environment, both in households and in larger communities. One of the applications of waste cooking oil is composite materials called vegeblocks, which can be used for construction purposes. These composites are formed by the process of polymerisation, esterification and polyesterification. The resulting materials exhibit mechanical strength in line with the requirements for paving blocks. Composite materials that have been annealed for a minimum of 20 h at 200 °C or higher have the highest tensile strength (above 5 MPa). In contrast, composites with the highest flexural strength were obtained after processing at 210 °C for 16 h. The Saxa 2 variety showed the greatest inhibition of storage root growth (almost 43% compared to the control sample), as well as stimulation of root and leaf blade growth (by a maximum of 61.5% and 53.5%, respectively, compared to the control sample). The composite obtained from the maximum process parameters resulted in significant growth of both the root and the green part of both radish varieties by up to 35%. The study showed that the presence of vegeblocks in the plants causes stress conditions, resulting in increased peroxidase content compared to the control sample. The presence of the oil composite in the soil did not increase the amount of catalase in the radish, and even a reduction was observed compared to the control sample. Full article
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<p>(<b>a</b>) FT-IR spectrum and (<b>b</b>) TG/DTA spectra of vegeblock No. 10.</p>
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<p>(<b>A</b>) A photograph and (<b>B</b>) SEM micrograph of vegeblock No. 8.</p>
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<p>(<b>A</b>) SEM micrograph and (<b>B</b>) EDX map of vegeblock No. 8.</p>
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<p>Flexural and split tensile strength of vegeblocks No. 1 through No. 10.</p>
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<p>FTIR spectra of waste cooking oil and sulfuric acid mixtures mixed and stored at room temperature and cured at 200 °C: (<b>A</b>) sample A and A-200, (<b>B</b>) sample B and B-200.</p>
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<p>Results of GPC analysis of: (<b>A</b>) WCO sample, (<b>B</b>) WCO and sulfuric acid mixture, (<b>C</b>) mixtures cured at 200 °C.</p>
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<p>Inhibition of leaf and storage root growth in De dix-huit jours and Saxa 2 radish varieties when compared to the control radish samples.</p>
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<p>Carotenoids, peroxidase, total chlorophyll, and catalase content in De dix-huit jours and Saxa 2 radish varieties.</p>
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<p>Pareto charts of the effects for (<b>a</b>) flexural strength and (<b>b</b>) split tensile strength.</p>
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<p>Pareto charts of the effects for catalase, peroxidase, carotenoids, and total chlorophyll for De dix-huit jours variety.</p>
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<p>Pareto charts of the effects for catalase, peroxidase, carotenoids, and total chlorophyll for Saxa 2 radish variety.</p>
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34 pages, 4890 KiB  
Review
Quercetin as a Therapeutic Product: Evaluation of Its Pharmacological Action and Clinical Applications—A Review
by Mohd Aamir Mirza, Syed Mahmood, Ayah Rebhi Hilles, Abuzer Ali, Mohammed Zaafar Khan, Syed Amir Azam Zaidi, Zeenat Iqbal and Yi Ge
Pharmaceuticals 2023, 16(11), 1631; https://doi.org/10.3390/ph16111631 - 20 Nov 2023
Cited by 38 | Viewed by 9361
Abstract
Quercetin is the major polyphenolic flavonoid that belongs to the class called flavanols. It is found in many foods, such as green tea, cranberry, apple, onions, asparagus, radish leaves, buckwheat, blueberry, broccoli, and coriander. It occurs in many different forms, but the most [...] Read more.
Quercetin is the major polyphenolic flavonoid that belongs to the class called flavanols. It is found in many foods, such as green tea, cranberry, apple, onions, asparagus, radish leaves, buckwheat, blueberry, broccoli, and coriander. It occurs in many different forms, but the most abundant quercetin derivatives are glycosides and ethers, namely, Quercetin 3-O-glycoside, Quercetin 3-sulfate, Quercetin 3-glucuronide, and Quercetin 3′-metylether. Quercetin has antioxidant, anti-inflammatory, cardioprotective, antiviral, and antibacterial effects. It is found to be beneficial against cardiovascular diseases, cancer, diabetes, neuro-degenerative diseases, allergy asthma, peptic ulcers, osteoporosis, arthritis, and eye disorders. In pre-clinical and clinical investigations, its impacts on various signaling pathways and molecular targets have demonstrated favorable benefits for the activities mentioned above, and some global clinical trials have been conducted to validate its therapeutic profile. It is also utilized as a nutraceutical due to its pharmacological properties. Although quercetin has several pharmacological benefits, its clinical use is restricted due to its poor water solubility, substantial first-pass metabolism, and consequent low bioavailability. To circumvent this limited bioavailability, a quercetin-based nanoformulation has been considered in recent times as it manifests increased quercetin uptake by the epithelial system and enhances the delivery of quercetin to the target site. This review mainly focuses on pharmacological action, clinical trials, patents, marketed products, and approaches to improving the bioavailability of quercetin with the use of a nanoformulation. Full article
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<p>Molecular structure of quercetin.</p>
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<p>Quercetin and its derivates.</p>
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<p>A schematic diagram represents the pharmacological efficacy of quercetin.</p>
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<p>A schematic illustration of the Mechanism of action of quercetin in prostate cancer.</p>
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<p>Nanostructured lipid carriers (NLCs): a mixture of oil and solid dispersed in aqueous sol containing surfactant.</p>
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<p>Liposomes: biocompatible spherical vehicle with a main structure consisting of natural phospholipid (soybean phosphatidylcholine/egg/dipalmitoyl phosphatidyl chlorine (DPPC)).</p>
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<p>Systematic representation of multifunctional activities of Quercetin and its transition to novel lipid-based formulation for targeted delivery.</p>
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22 pages, 3623 KiB  
Review
Uranium and Fluoride Accumulation in Vegetable and Cereal Crops: A Review on Current Status and Crop-Wise Differences
by Saloni Sachdeva, Mike A. Powell, Girish Nandini, Hemant Kumar, Rakesh Kumar and Prafulla Kumar Sahoo
Sustainability 2023, 15(18), 13895; https://doi.org/10.3390/su151813895 - 19 Sep 2023
Cited by 2 | Viewed by 2004
Abstract
Uranium (U) and fluoride (F) contamination in agricultural products, especially vegetable and cereal crops, has raised serious concerns about food safety and human health on a global scale. To date, numerous studies have reported U and F contamination in vegetable [...] Read more.
Uranium (U) and fluoride (F) contamination in agricultural products, especially vegetable and cereal crops, has raised serious concerns about food safety and human health on a global scale. To date, numerous studies have reported U and F contamination in vegetable and cereal crops at local scales, but the available information is dispersed, and crop-wise differences are lacking. This paper reviews the current status of knowledge on this subject by compiling relevant published literatures between 1983 and 2023 using databases such as Scopus, PubMed, Medline, ScienceDirect, and Google Scholar. Based on the median values, F levels ranged from 0.5 to 177 mg/kg, with higher concentrations in non-leafy vegetables, such as Indian squash “Praecitrullus fistulosus” (177 mg/kg) and cucumber “Cucumis sativus” (96.25 mg/kg). For leafy vegetables, the maximum levels were recorded in bathua “Chenopodium album” (72.01 mg/kg) and mint “Mentha arvensis” (44.34 mg/kg), where more than 50% of the vegetable varieties had concentrations of >4 mg/kg. The concentration of U ranged from 0.01 to 17.28 mg/kg; tubers and peels of non-leafy vegetables, particularly radishes “Raphanus sativus” (1.15 mg/kg) and cucumber “Cucumis sativus” (0.42 mg/kg), contained higher levels. These crops have the potential to form organometallic complexes with U, resulting in more severe threats to human health. For cereal crops (based on median values), the maximum F level was found in bajra “Pennisetum glaucum” (15.18 mg/kg), followed by chana “Cicer arietinum” (7.8 mg/kg) and split green gram “Vigna mungo” (4.14 mg/kg), while the maximum accumulation of U was recorded for barley “Hordeum vulgare” (2.89 mg/kg), followed by split green gram “Vigna mungo” (0.45 mg/kg). There are significant differences in U and F concentrations in either crop type based on individual studies or countries. These differences can be explained mainly due to changes in geogenic and anthropogenic factors, thereby making policy decisions related to health and intake difficult at even small spatial scales. Methodologies for comprehensive regional—or larger—policy scales will require further research and should include strategies to restrict crop intake in specified “hot spots”. Full article
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<p>Uncovering the pathways of uranium (U) and fluoride (F<sup>−</sup>)from source to agricultural ecosystems. These contaminants can enter agricultural systems from both geogenic and anthropogenic processes and then they can enter the food chain in two main ways: firstly, by being present as particulate matter that humans and animals breathe in, and by foliar uptake by crops; secondly, specific forms of U and F<sup>−</sup> in water and soil are absorbed by crops through processes, namely, diffusion and xylem/symplastic transport systems.</p>
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<p>(<b>a</b>) Number of records for fluoride (F<sup>−</sup>) and uranium (U) in soil–plant system. (<b>b</b>) Altogether, the figures for irrigation water amount to 16%, agricultural soil shows 29%, and agricultural crops make up 55%. Note: VEG: vegetable; GRN: grain; IRR.WATER: Irrigation water.</p>
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<p>Occurrence of fluoride (F<sup>−</sup>) content in (<b>a</b>) soils and (<b>b</b>) vegetables across the countries, and (<b>c</b>) in cereal crops. Abbreviation: AVG—average; RSD—relative standard deviation; BJR—Bajra; CHN—Chana; BRY—Barley; WHT—Wheat; GGM—Green gram; RIC—Rice; BBE—Black eye bean; MAZ—Maize; MLL—Millet; LKT—Kulthi (<a href="#app1-sustainability-15-13895" class="html-app">Supplementary Table S2</a>).</p>
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<p>Occurrence of uranium (U) in agro-ecosystems with (<b>a</b>) the average amount of U found in vegetables, farming soil, and water used for irrigation; (<b>b</b>) the average U levels in different countries, along with their corresponding variability (represented as RSD% values). Abbreviation: AVG—average; RSD—relative standard deviation.</p>
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<p>The relationship between the average U content in the soil and cereal crops for (<b>a</b>) all reporting countries and (<b>b</b>–<b>d</b>) country-wise cereals. Abbreviation: AVG—average; RIC—Rice; MAZ—Maize; WHT—Wheat.</p>
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<p>Uranium (U) and fluoride (F<sup>−</sup>) uptake in plants: Impacts on yield and physiology. The figure depicts the process of U and F<sup>−</sup> uptake by plants, involving both active and passive transport systems. Upon entering the plant cells, these toxic elements exert detrimental effects, including heightened cell death, elevated levels of reactive oxygen species (ROS), decreased photosynthesis, and impaired nutrient uptake, as indicated by the blue and orange colors, respectively. These cumulative impacts ultimately lead to a significant reduction in the agricultural yield of cereals and vegetables.</p>
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20 pages, 10298 KiB  
Article
Green-Mediated Synthesis of NiCo2O4 Nanostructures Using Radish White Peel Extract for the Sensitive and Selective Enzyme-Free Detection of Uric Acid
by Abdul Ghaffar Solangi, Aneela Tahira, Baradi Waryani, Abdul Sattar Chang, Tajnees Pirzada, Ayman Nafady, Elmuez A. Dawi, Lama M. A. Saleem, Mohsen Padervand, Abd Al Karim Haj Ismail, Kangle Lv, Brigitte Vigolo and Zafar Hussain Ibupoto
Biosensors 2023, 13(8), 780; https://doi.org/10.3390/bios13080780 - 1 Aug 2023
Cited by 4 | Viewed by 2376
Abstract
The ability to measure uric acid (UA) non-enzymatically in human blood has been demonstrated through the use of a simple and efficient electrochemical method. A phytochemical extract from radish white peel extract improved the electrocatalytic performance of nickel–cobalt bimetallic oxide (NiCo2O [...] Read more.
The ability to measure uric acid (UA) non-enzymatically in human blood has been demonstrated through the use of a simple and efficient electrochemical method. A phytochemical extract from radish white peel extract improved the electrocatalytic performance of nickel–cobalt bimetallic oxide (NiCo2O4) during a hydrothermal process through abundant surface holes of oxides, an alteration of morphology, an excellent crystal quality, and increased Co(III) and Ni(II) chemical states. The surface structure, morphology, crystalline quality, and chemical composition were determined using a variety of analytical techniques, including powder X-ray diffraction (XRD), scanning electron microscopy (SEM), high-resolution transmission electron microscopy (HR-TEM), and X-ray photoelectron spectroscopy (XPS). The electrochemical characterization by CV revealed a linear range of UA from 0.1 mM to 8 mM, with a detection limit of 0.005 mM and a limit of quantification (LOQ) of 0.008 mM. A study of the sensitivity of NiCo2O4 nanostructures modified on the surface to UA detection with amperometry has revealed a linear range from 0.1 mM to 4 mM for detection. High stability, repeatability, and selectivity were associated with the enhanced electrochemical performance of non-enzymatic UA sensing. A significant contribution to the full outperforming sensing characterization can be attributed to the tailoring of surface properties of NiCo2O4 nanostructures. EIS analysis revealed a low charge-transfer resistance of 114,970 Ohms that offered NiCo2O4 nanostructures prepared with 5 mL of radish white peel extract, confirming an enhanced performance of the presented non-enzymatic UA sensor. As well as testing the practicality of the UA sensor, blood samples from human beings were also tested for UA. Due to its high sensitivity, stability, selectivity, repeatability, and simplicity, the developed non-enzymatic UA sensor is ideal for monitoring UA for a wide range of concentrations in biological matrixes. Full article
(This article belongs to the Special Issue Recent Advances in Nano-Biomaterial-Based Biosensors)
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<p>(<b>a</b>) Powder XRD diffraction patterns of pristine, 5 mL, and 10 mL of radish peel extract-assisted NiCo<sub>2</sub>O<sub>4</sub> nanostructures, (<b>b</b>–<b>f</b>) Corresponding SEM images of (<b>b</b>) pristine, (<b>c</b>,<b>d</b>) 5 mL, and (<b>e</b>,<b>f</b>) 10 mL of radish peel extract-assisted NiCo<sub>2</sub>O<sub>4</sub> nanostructures respectively.</p>
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<p>(<b>a</b>) TEM/HRTEM images of pristine NiCo<sub>2</sub>O<sub>4</sub> nanostructures and FFT conversion at right side with d-spacing value; (<b>b</b>–<b>d</b>) corresponding elemental mapping of (<b>e</b>) EDS spectra of pristine NiCo<sub>2</sub>O<sub>4</sub> nanostructures.</p>
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<p>(<b>a</b>) TEM/HRTEM images of NiCo<sub>2</sub>O<sub>4</sub> nanostructures prepared with 5 mL of radish white peel extract and FFT with d-spacing value on right-hand side; (<b>b</b>–<b>d</b>) elemental mapping of (<b>e</b>) EDS spectrum of NiCo<sub>2</sub>O<sub>4</sub> nanostructures prepared with 5 mL of radish white peel extract.</p>
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<p>XPS-resolved Co 2p, Ni 2p, O1s spectra for pristine (<b>a</b>–<b>c</b>) and (<b>d</b>–<b>f</b>) XPS-resolved Co 2p, Ni 2p, O1s of 5 mL of radish peel extract-assisted NiCo<sub>2</sub>O<sub>4</sub> nanostructures.</p>
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<p>(<b>a</b>) Cyclic voltammograms at a scan rate of 50 mV/s of MGCE with 5 mL and 10 mL of radish white peel extract-assisted NiCo<sub>2</sub>O<sub>4</sub> and pristine NiCo<sub>2</sub>O<sub>4</sub>-modified GCE in the presence of 0.5 mM of UA in 0.1 M PBS pH 7.0. (<b>b</b>) Cyclic voltammograms at 50 mV/s of bare GCE and modified with 5 mL of assisted NiCo<sub>2</sub>O<sub>4</sub> in electrolyte and equal in the presence of 0.5 mM UA in 0.1 M PBS pH 7.0.</p>
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<p>(<b>a</b>) Cyclic voltammograms at a scan rate of 50 mV/s of MGCE with 5 mL of radish white peel extract-assisted NiCo<sub>2</sub>O<sub>4</sub>-modified GCE in the presence of 0.5 mM of UA in 0.1 M PBS pH 7.0. (<b>b</b>) Linear plot of peak current against square root of scan rate.</p>
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<p>(<b>a</b>) Cyclic voltammograms at a scan rate of 50 mV/s of MGCE with 5 mL of radish white peel extract-assisted NiCo<sub>2</sub>O<sub>4</sub>-modified GCE in the presence of different pH values of 0.5 mM of UA in 0.1 M PBS. (<b>b</b>) Linear plot of peak current versus different pH values of 0.5 mM of UA in 0.1 M PBS.</p>
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<p>(<b>a</b>) Cyclic voltammograms at a scan rate of 50 mV/s of MGCE with 5 mL of radish white peel extract-assisted NiCo<sub>2</sub>O<sub>4</sub> in the presence of various concentrations of UA in 0.1 M PBS pH 7.0. (<b>b</b>) Linear plot of peak current versus successive increase in UA concentrations.</p>
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<p>(<b>a</b>) Linear sweep voltammetry at a scan rate of 10 mV/s of MGCE with 5 mL of radish white peel extract-assisted NiCo<sub>2</sub>O<sub>4</sub> in the presence of various concentrations of UA in 0.1 M PBS pH 7.0. (<b>b</b>) Linear plot of peak current versus successive increase in UA concentrations.</p>
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<p>(<b>a</b>) Chronoamperometric response curves measured at an applied potential of 0.3 V of MGCE with 5 mL of radish white peel extract-assisted NiCo<sub>2</sub>O<sub>4</sub> in the presence of various concentrations of UA in 0.1 M PBS pH 7.0. (<b>b</b>) Linear plot of peak current versus successive increase in UA concentrations.</p>
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<p>(<b>a</b>) Cyclic voltammograms at a scan rate of 50 mV/s of MGCE with 5 mL of radish white peel extract-assisted NiCo<sub>2</sub>O<sub>4</sub> in the presence of 0.5 mM UA and other competing interfering agents in 0.1 M PBS pH 7.0; (<b>b</b>) bar graph of peak current with addition of interfering species for the illustration of the variation of the peak current.</p>
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<p>(<b>a</b>) Cyclic voltammograms at a scan rate of 50 mV/s of MGCE with 5 mL of radish white peel extract-assisted NiCo<sub>2</sub>O<sub>4</sub> in the presence of 0.5 mM UA in 0.1 M PBS pH 7.0; (<b>b</b>) bar graph of peak current for the description of change in the peak current with increasing number of CV cycles. Linear plot of peak current versus successive increase in UA concentrations.</p>
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<p>(<b>a</b>) Chronoamperometric response of MGCE with 5 mL of radish white peel extract-assisted NiCo<sub>2</sub>O<sub>4</sub> in 0.5 mM prepared in 0.1 M PBS pH 7.0 for the demonstration of stability of modified electrode; (<b>b</b>) EIS Nyquist plots collected for the MGCE with pristine, 5 mL, and 10 mL of radish white peel extract-assisted NiCo<sub>2</sub>O<sub>4</sub> in 0.5 mM UA using frequency range of 100 kHz to 1 Hz, amplitude of 10 mV, and biasing potential of 0.6 V.</p>
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<p>(<b>a</b>) Cyclic voltammograms at a scan rate of 50 mV/s of MGCE with 5 mL of radish white peel extract assisted NiCo<sub>2</sub>O<sub>4</sub> for the quantitation of UA form diluted human blood real samples in 0.1M PBS pH 7.0 and successive addition method; (<b>b</b>) Cyclic voltammograms at a scan rate of 50 mV/s of MGCE with 5 mL of radish white peel extract assisted NiCo<sub>2</sub>O<sub>4</sub> for the quantitation of UA form diluted human urine real samples in 0.1M PBS pH 7.0 and successive addition method.</p>
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<p>The various phytochemicals present in the radish (<span class="html-italic">Raphanus sativus</span>) peel extract.</p>
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<p>Stepwise synthesis of NiCo<sub>2</sub>O<sub>4</sub> nanostructures using radish white peel extract during hydrothermal method, UA detection, and real blood sample analysis.</p>
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