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Lately, the herb's essential oils have received the intense attention of researchers and pharmaceutical agencies. The chemical composition, antioxidant, analgesic activity, anti-hyperpyrexia, and hepatorenal protective effects of the Kleinia anteuphorbium (L.) DC. Essential oil (KAEO) were evaluated. The KAEO was yanked by steam distillation and subjected to a GC/MS system. The antioxidant powers of KAEO were evaluated by DPPH, NO, and FRAP tests. Besides the anti-hyperpyrexia, the analgesic effects were assessed. Moreover, the hepatorenal curative effects were evaluated in a carbon tetrachloride-rat model. Sixty-eight active components were identified by GC/MS. α-pinene was the most dominant monoterpene in KAEO. KAEO showed anti-hyperpyrexia, anti-captive, and great antioxidant powers through DPPH, NO, and FRAP. The oral LD50 of KAEO was 187.5 mg/kg in rats. Furthermore, KAEO treatment succeeded in reliving the adverse effects of CCL4 in rats as a standard toxic model through reliving the hepatic pathological and oxidative stress and restoring the hepatic and renal functions. Overall, the obtained results demonstrated that KAEO might be used as a potential natural medicine to relieve a variety of modern symptoms and hepatorenal disorders associated with oxidative stress.

Utilizing synthetic insecticides to control mosquito populations has many adverse side effects as they can cause environmental pollution and insecticide resistance. Therefore, the search for eco-friendly and effective mosquito control agents has led to the exploration of plant extracts with larvicidal efficacy. In this study, we aimed to explore the larvicidal activity of four Egyptian plant extracts: Rosmarinus officinalis, Melissa officinalis, Cichorium intybus, and Beta vulgaris var. cicla against Culex pipiens and to identify the chemical constituents present in the active extracts. The methanolic extracts of the four plants were prepared and tested against the third instar larvae of C. pipiens using a standard method. The lethal concentrations (LC50 and LC90) were calculated using probit analysis. The active extracts were subjected to ultra-performance liquid chromatography-mass spectroscopy (UPLC-MS/MS) analysis and the data were processed using the global natural products social molecular networking (GNPS) platform to generate molecular networks and identify the compounds based on spectral similarity and database matching. The methanolic extracts of R. officinalis L. and M. officinalis L. had the highest larvicidal activity, where LC50 values were 9.795 and 26.505 μg/mL, respectively. Their exposure caused a high mortality rate and disrupted the biochemical and physiological parameters (total carbohydrates, total lipids, total protein, acetylcholinesterase (AChE), and glutathione S-transferase (GST) in the body of C. pipiens when compared with Cichorium intybus (Chicory) and Beta vulgaris var. cicla (Chard). The UPLC-MS/MS analysis with the aid of the GNPS platform, revealed the presence of 23 and 41 metabolites from R. officinalis L. and M. officinalis L., respectively. The identified metabolites may act as larvicidal agents by interfering with the physiological or biochemical processes of the mosquito larvae. Overall, the findings suggest that the methanolic extracts of R. officinalis and M. officinalis are potential sources of natural larvicides for mosquito control.

Low-light images captured at night often suffer from improper exposure, color distortion, and noise, which degrades the image quality and have a negative influence on subsequent applications. Many existing deep learning-based methods enhance low-light images through spatial domain, which may sacrifice the original image information. In this paper, we put forward a deep learning network for enhancing low-light images based on wavelet transform. We utilize the wavelet transform to divide the image into various frequency scales and then analyze the frequency characteristics of different low-light images in the wavelet domain. The proposed network comprises a low-frequency restoration subnet and high-frequency reconstruction subnet that uses an optimal coefficient of wavelet decomposition to construct a frequency pyramid. Furthermore, we utilized different attention mechanisms to extract frequency information from different images, gradually restoring the brightness information and details of low-light images. Additionally, we utilized a self-constructed multi-scale exposure low-light image dataset for training. Numerous experiments on publicly accessible datasets and our established dataset show that the proposed approach quantitatively and qualitatively surpasses state-of-the-art approaches, particularly for real and complex low-light scenarios. Furthermore, our method produces better visual effects than others and performs well in real-time and real-word downstream vision tasks.

Background/Objectives: Marrubium vulgare L. (M. vulgare), the white horehound, is well known for treating inflammation-related diseases. Methods: In this context, we investigated the efficacy of M. vulgare ingredients in treating Alzheimer’s disease using various in vitro and in silico antioxidant, anti-inflammatory, anti-cholinesterase, and anti-tyrosinase mechanisms. Results: In our results, sixty-one components were tentatively identified using gas and liquid chromatography (GC-MS and LC-MSn) and categorized as hydrocarbons, fatty acids, and polyphenolics. The extract inhibited linoleic oxidation with an IC50 value of 114.72 µg/mL, captured iron (Fe2+) ions with an IC50 value of 164.19 µg/mL, and displayed reducing power. In addition, the extract showed radical-scavenging ability towards DPPH•, NO•, ABTS•+, and H2O2 assays compared to L-ascorbic acid and butylated hydroxytoluene. The DPPH• was scavenged by 77.62% at 100 µg/mL, and NO•, ABTS•+, and H2O2 were scavenged with IC50 values of 531.66, 117.51, and 143.10 µg/mL, respectively. M. vulgare also exhibited discriminating anti-inflammatory potency against cyclooxygenase (COX-2) with IC50 values of 619.15 µg/mL compared to celecoxib (p > 0.05). Notably, three Alzheimer’s biomarkers, acetylcholinesterase (AChE), butyrylcholinesterase (BChE), and tyrosinase were significantly inhibited. The molecular docking study supposed that the phenylethanoid glycosides of samioside and forsythoside B inhibited AChE and tyrosinase enzymes with low binding affinities of −9.969 and −8.804 kcal/mol, respectively. Marruboside was a proper inhibitor of COX and BChE enzymes with a binding score of −10.218 and −10.306 kcal/mol, respectively. Conclusions: M. vulgare extract showed significant inhibitory actions, which suggest that it could have a promising potential as an anti-Alzheimer agent.

Image generative models for ceramic tile design lack style diversity and controllability of high-quality generated styles. It is difficult to find a series of ceramic tiles with the same texture but distinct styles, that makes it a challenging for users to select from a limited number of tiles with a single style. Although, Generative Adversarial Networks (GANs) can slightly increase the style diversity of tile images, the style controllability remains very weak. Additionally, concatenating generated tile image blocks to obtain a larger texture region can easily result in seams at the boundaries that decrease image quality. In this paper, we propose a style transfer method for ceramic tiles texture generation that combines a classifier-guided StyleGAN with AdaIN-GAN to overcome the above limitations. Firstly, we introduce a new conditional classifier-guided module into the StyleGAN. With the guidance of the input condition vector, the output image is made to have the tile style characteristics that match the vector. At the same time, the fusion of the condition vectors realizes the style gradient effect of the tile image to expand the style diversity. Secondly, we use the AdaIN-GAN to color the original texture in tile style. The style images generated by StyleGAN are then used as a dataset for model training to enhance the generalization ability of the model and achieve a style transfer effect with fixed texture features but significantly diverse styles. Finally, a linear weighted image stitching method is adopted, which uses an adaptive kernel linear weighted matrix to cover and splice arbitrary seams with image blocks, thereby successfully eliminating seams and enhancing image continuity. When this method is applied to high-resolution tile image generation, the method still maintains higher continuity and clearer image quality. Extensive experiments and human evaluation confirm the superior performance of the proposed method compared with other SOTA methods. The experimental results also verify that the new tile images generated by the proposed algorithm have diverse styles and meet the design requirements for tile style diversity.

Remote sensing images obtained by a variety of sensors have been widely used in different Earth observation tasks. However, owing to budget and sensor technology constraints, a single sensor cannot simultaneously provide observational images with both high spatial and temporal resolution. This brings difficulties to remote sensing research which requires high spatial and temporal resolution data. To solve the above constraints, the spatiotemporal fusion (STF) method was proposed and has received widespread attention. The main challenge for remote sensing STF is to reconstruct both phenological and land cover type changes. To overcome this challenge, many STF methods have been proposed based on different principles and strategies. Since there are STF methods have been proposed recently, there is a need for new review to reflect the current research status. Therefore, in this review, we summarize the existing studies, discusses their basic principle and limitations, collect some recent applications, and provide a comprehensive overview of current advances. Furthermore, to facilitate and promote the research in this community, we also collect publicly available resources and introduce the most used quantitative metrics. Finally, we take a conversation about some open problems and challenges that require attention in the future.

Forest remote sensing change detection provides an important technical support for forest management decisions and analysis of forest disturbance factors. However, lack of data in specialized fields leads to the detection accuracy-improved difficulty. In this study, a forest remote sensing change detection model in the context of few-shot learning is proposed. The proposed model achieves end-to-end change detection algorithm for forest scenes from two perspectives of data augmentation and updated few-shot algorithm. First, forest fragments images are jointly generated by using feature extraction network and generative adversarial network. Then, forest fragments are blended into the original change detection dataset by Poisson blending method to achieve effective augmentation. Furthermore, the end-to-end change detection network is also updated using a few-shot learning. In addition, Metalearning module is added to the slow feature analysis algorithm based on the multiattention mechanism to realize the detection effect improvements. The proposed model improves the F1 score from 86% to 91% on the two datasets. Moreover, it increases the F1 score by 6.52% on average.

Egypt, one of the “cradles of civilization”, boasts a history spanning thousands of years, and a richness of archaeological and geosites, unmatched by many other countries in the world. Abu Roash is located within Greater Cairo, 9 km north of the Great Pyramids of Giza. In the present work worked out to establish a Geopark in the studied area, the main question of the study is can the area be a Geopark with local community support? A detailed field work was done in the study and enables the authors to put a detailed map for the future Geopark could be established in the area, The study also attempted, by means of a questionnaire, to assess the knowledge and attitudes of different stakeholders with regards to the geoheritage and the establishment of a geopark. The data compiled were intended to help formulate a strategy towards the design and promotion of what would be the first geopark in metropolitan Cairo. We believe that this geopark would offer geoeducational opportunities to the scientific community and to the general public at large, and would help expand geotourism in Egypt. Geotourism in Abu Roash can easily be coupled with tours of ancient Egyptian sites in the same area. This would provide significant economic benefits to residents of Abu Roash City.

This study shed light for the first time on the in vivo diabetic wound healing potential activity of natural marine soft coral polymeric nanoparticle in situ gel using an excision wound model. A Nephthea sp. methanol–methylene chloride extract loaded with pectin nanoparticles (LPNs) was created. For the preparation of in situ gel, ion-gelation techniques, the entrapment efficiency, the particle size, the polydispersity index, the zeta potential, the in-vitro drug release, and a transmission electron microscope were used and the best formula was selected. Using (UPLC-Q/TOF-MS), 27 secondary metabolites responsible for extract biological activity were identified. Isolation and identification of arachidic acid, oleic acid, nervonic acid, and bis-(2-ethylhexyl)-phthalate (DEHP) of Nephthea sp. was firstly reported here using NMR and mass spectral analyses. Moreover, LPN in situ gel has the best effects on regulating the proinflammatory cytokines (NF-κB, TNF-α, IL-6, and IL-1β) that were detected on days 7 and 15. The results were confirmed with an in vitro enzymatic inhibitory effect of the extract against glycogen synthase kinase (GSK-3) and matrix metalloproteinase-1 (MMP-1), with IC50 values of 0.178 ± 0.009 and 0.258 ± 0.011 µg/mL, respectively. The molecular docking study showed a free binding energy of −9.6 kcal/mol for chabrolosteroid E, with the highest binding affinity for the enzyme (GSK-3), while isogosterone B had −7.8 kcal/mol for the enzyme (MMP-1). A pharmacokinetics study for chabrolohydroxybenzoquinone F and isogosterone B was performed, and it predicted the mode of action of wound healing activity.

Exposure to food contaminants continues to be a substantial source of human health risks all over the world, particularly in developing countries. Carbendazim (CBZ) is a chemical fungicide used to control the spread of various fungi and other pathogens in the agriculture and veterinary sectors. The hazardous effects of CBZ on human health occur due to the accumulation of its residues in agricultural food products. In this study, the possible hepatoprotective effects of Adiantum capillus-veneris L. (ACVL) extract were evaluated in CBZ-treated rats. A GC-MS analysis revealed that ACVL extract contained several bioactive hydrocarbon components and fatty acids, and that the components exerted hepatic protection by mitigating oxidative stress via upregulating antioxidant agents and neutralizing nitrogen and oxygen free radicals. Moreover, ACVL extracts relieved hepatic inflammation via decreasing NO, NF-κB, and pro-inflammatory cytokines (TNF-a, IL-6) in the liver of CBZ-treated rats, both at protein and mRNA levels. In addition, the protective effect of ACVL has appeared in the histopathological figures and function markers in the livers of CBZ-treated rats. According to the present results, ACVL extract can protect the hepatic tissue and restore its functions to a control level in CBZ-treated rats; this effect may be attributed to its antioxidant and anti-inflammatory activities.

With high performances of image capturing tools, image information can be easily obtained by screenshots that make image copyright protection a challenging task. The existing screen-shooting watermarking algorithms suffer from a huge running time, in addition to their low robustness against different screenshot attacks, such as different distances and capturing angles of the screenshots. In this paper, a fast and robust high-capacity flexible watermarking algorithm for screenshot images is proposed. Firstly, Oriented FAST and Rotated BRIEF (ORB) feature points are extracted from the input image. Secondly, the feature points are then sorted in a descending order according to their response values. Then, the first five non-overlapping feature points are selected for the embedding by using Hamming window-based filtering method. Furthermore, we exploit the multi-resolution property of Discrete Wavelet Transform (DWT) and energy compaction property of Singular Value Decomposition (SVD) to embed the watermark. Therefore, the classical DWT combined with Singular Value Decomposition (SVD) are adopted to improve the robustness and capacity of the proposed watermarking algorithm. At the extraction side, the sum of the response values for the three RGB channels of the color-ripped image is calculated to improve the feature point localization accuracy. Experimental results show that the proposed screen-shooting watermarking algorithm improves running speed while ensuring the robustness. Furthermore, it has less time complexity and high robustness compared with the state-of-the-art watermarking algorithms against different screenshot attacks.

Gut microbial β-glucuronidase (GUS) is a potential therapeutic target to reduce gastrointestinal toxicity caused by irinotecan. In this study, the inhibitory effects of 17 natural cinnamic acid derivatives on Escherichia coli GUS (EcGUS) were characterised. Seven compounds, including caffeic acid ethyl ester (CAEE), had a stronger inhibitory effect (IC50 = 3.2–22.2 µM) on EcGUS than the positive control, D-glucaric acid-1,4-lactone. Inhibition kinetic analysis revealed that CAEE acted as a competitive inhibitor. The results of molecular docking analysis suggested that CAEE bound to the active site of EcGUS through interactions with Asp163, Tyr468, and Glu504. In addition, structure–activity relationship analysis revealed that the presence of a hydrogen atom at R1 and bulky groups at R9 in cinnamic acid derivatives was essential for EcGUS inhibition. These data are useful to design more potent cinnamic acid-type inhibitors of EcGUS.

The liver is a crucial organ among body organs due to its wide functions, in particular, detoxification and metabolism. Exposure to detrimental chemicals or viral infections may provoke liver dysfunction and ultimately induce liver tissue damage. Finding natural substances for liver disease treatment to overcome the conventional treatments’ side effects has attracted the attention of researchers worldwide. Our current work was conducted to investigate the hepato-therapeutic activities of essential oil (EO) isolated from Tagetes patula flowers. EO was extracted using the hydro-distillation (HD) technique and its chemical composition was identified by GC/MS. Then, the hepatic treatment potential of extracted EO was evaluated in vivo against CCL4 in rats. HD of T. patula flowers yielded highly chemical constituents of EO along with significant antioxidant potential. A coherent molecular network was fashioned via the Global Natural Products Social Molecular Networking (GNPS) to visualize the essential components and revealed that the sesquiterpene (E)-β-caryophyllene was the most predominant volatile constituent which accounted for 24.1%. The treatment of CCL4 led to significant induced oxidative stress markers malonaldehyde, total protein, and non-protein sulfhydryl, as well as elevated serum aminotransferase, gamma-glutamyl transferase, alkaline phosphatase, and bilirubin. In addition, it disrupted the level of lipid profile. The post-treatment using T. patula EO succeeded in relieving all toxic effects of CCl4 and recuperating the histopathological signs induced by CCL4. Silymarin was used as a standard hepatoprotective agent. The obtained results demonstrated that the extracted EO exerted high protective activities against the toxicity of CCL4. Moreover, the T. patula flowers EO can be used as a natural remedy to relieve many contemporary liver diseases related to oxidative stress.

The semantic segmentation of high-resolution remote sensing images (HRRSIs) is a basic task for remote sensing image processing and has a wide range of applications. However, the abundant texture information and wide imaging range of HRRSIs lead to the complex distribution of ground objects and unclear boundaries, which bring huge challenges to the segmentation of HRRSIs. To solve this problem, in this paper we propose an improved squeeze and excitation residual network (SERNet), which integrates several squeeze and excitation residual modules (SERMs) and a refine attention module (RAM). The SERM can recalibrate feature responses adaptively by modeling the long-range dependencies in the channel and spatial dimensions, which enables effective information to be transmitted between the shallow and deep layers. The RAM pays attention to global features that are beneficial to segmentation results. Furthermore, the ISPRS datasets were processed to focus on the segmentation of vegetation categories and introduce Digital Surface Model (DSM) images to learn and integrate features to improve the segmentation accuracy of surface vegetation, which has certain prospects in the field of forestry applications. We conduct a set of comparative experiments on ISPRS Vaihingen and Potsdam datasets. The results verify the superior performance of the proposed SERNet.

Purpose: Strictosidine-β-D-glucosidase (SGD) is considered as a key enzyme in the production of bisindole alkaloids in Catharanthus roseus. The present study illustrated the production of a short sequence of this enzyme in Escherichia coli without codon optimization. Methods: Strictosidine-β-D-glucosidase (sgd) gene short sequence (1434 bp), which lacks the conserved sequence KGFFVWS and the localization peptide sequence at the C-terminal, was amplified from cDNA of C. roseus leaves, cloned and expressed in Escherichia coli. The activity of the produced protein in cell free lysate was tested using total alkaloid extract of C. roseus leaves. Results: HPLC and LC-MS analysis of the assay mixture revealed the disappearance of the strictosidine peak. Conclusion: SGD short sequence can be produced in Escherichia coli in active form without codon optimization.

Background: The plant kingdom is considered one of the most common sources for structural and biological diversity. In particular, the wild category acquires our attention to investigate the phytochemical and the biological evaluations. Methods: Dobera glabra was exposed to phytochemical examination using HPLC-ESI-MS analysis. Furthermore, the anti-inflammatory activity was evaluated using carrageenan-induced rat paw edema model, whereas both the central and peripheral analgesic activities were tested via hot plate test in rats and acetic acid-induced writhing in mice, respectively. Results: Twenty phenolic compounds of D. glabra aqueous leaves extract were emphasized by liquid chromatography coupled with mass spectrometry. Moreover, D. glabra exhibited both anti-inflammatory and peripheral analgesic activities. Furthermore, D. glabra significantly decreased the immune expression of MMP-9, TNF-α and TGF-β1 in the hind paw of rats. Conclusion: D. glabra possess peripheral anti-nociceptive and anti-inflammatory effects in rats mediated through its anti-oxidant and anti-inflammatory activities. The activity of D. glabra leaves extract might be attributed to the presence of hydroxy and keto structures.

With the wide increase in global forestry resources trade, the demand for wood is increasing day by day, especially rare wood. Finding a computer-based method that can identify wood species has strong practical value and very important significance for regulating the wood trade market and protecting the interests of all parties, which is one of the important problems to be solved by the wood industry. This article firstly studies the establishment of wood microscopic images dataset through a combination of traditional image amplification technology and Mix-up technology expansion strategy. Then with the traditional Faster Region-based Convolutional Neural Networks (Faster RCNN) model, the receptive field enhancement Spatial Pyramid Pooling (SPP) module and the multi-scale feature fusion of Feature Pyramid Networks (FPN) module are introduced to construct a microscopic image identification model based on the migration learning fusion model and analyzes the three factors (Mix-up, Enhanced SPP and FPN modules) affecting the wood microscopic image detection model. The experimental results show that the proposed approach can identify 10 kinds of wood microscopic images, and the accuracy rate has increased from 77.8% to 83.8%, which provides convenient conditions for further in-depth study of the microscopic characteristics of wood cells and is of great significance to the field of wood science.

Infections associated with the emergence of multidrug resistance and mosquito-borne diseases have resulted in serious crises associated with high mortality and left behind a huge socioeconomic burden. The chemical investigation of Lavandulacoronopifolia aerial parts extract using HPLC–MS/MS led to the tentative identification of 46 compounds belonging to phenolic acids, flavonoids and their glycosides, and biflavonoids. The extract displayed larvicidal activity against Culex pipiens larvae (LC50 = 29.08 µg/mL at 72 h). It significantly inhibited cytochrome P-450 monooxygenase (CYP450), acetylcholinesterase (AChE), and carboxylesterase (CarE) enzymes with the comparable pattern to the control group, which could explain the mode of larvae toxification. The extract also inhibited the biofilm formation of Pseudomonas aeruginosa by 17–38% at different Minimum Inhibitory Concentrations (MICs) (0.5–0.125 mg/mL) while the activity was doubled when combined with ciprofloxacin (ratio = 1:1 v:v). In conclusion, the wild plant, L.coronopifolia, can be considered a promising natural source against resistant bacteria and infectious carriers.

In this study, the aerial parts of Moricandia sinaica were evaluated for their in vivo analgesic, anti-inflammatory and antipyretic activities. The analgesic activities were examined using acetic acid-induced writhing, the hot plate test and the tail flick method. The anti-inflammatory and the antipyretic activities were evaluated using carrageenan-induced paw edema in rats and brewer’s yeast-induced pyrexia in mice, respectively. The aqueous fraction of the methanol extract (MS-3) showed to be the most bioactive among the other investigated fractions. At the dose of 500 mg/kg, the fraction (MS-3) showed a significant percentage inhibition of the carrageenan-induced edema by 52.4% (p < 0.05). In addition, MS-3 exhibited a significant inhibition of acetic acid-induced writhes by 44.4% and 61.5% (p < 0.001) at 250-mg/kg and 500-mg/kg doses, respectively. At 120 min post-treatment, the rat groups treated with MS-3 displayed statistically significant reduction in rectal temperature (p < 0.001) by 1.7 °C and 2.2 °C at 250- and 500-mg/kg doses, respectively. The phytochemical composition of the fraction (MS-3) was characterized by high-performance liquid chromatography-mass spectrometry (HPLC-PDA-MS/MS). Molecular docking studies demonstrated that the polyphenols identified in MS-3 revealed good binding energy upon docking to some target proteins involved in pain response and inflammation, such as the cannabinoid receptors CB1 and CB2, the fatty acid amide hydrolase (FAAH) and the cyclooxygenase COX-1 and COX-2 enzymes. Based on the findings from the present work, it could be concluded that the aerial parts extract of M. sinaica exerts potential analgesic, anti-inflammatory and antipyretic effects in rats.

Marine microorganisms have drawn great attention as novel bioactive natural product sources, particularly in the drug discovery area. Using different strategies, marine microbes have the ability to produce a wide variety of molecules. One of these strategies is the co-culturing of marine microbes; if two or more microorganisms are aseptically cultured together in a solid or liquid medium in a certain environment, their competition or synergetic relationship can activate the silent biosynthetic genes to produce cryptic natural products which do not exist in monocultures of the partner microbes. In recent years, the co-cultivation strategy of marine microbes has made more novel natural products with various biological activities. This review focuses on the significant and excellent examples covering sources, types, structures and bioactivities of secondary metabolites based on co-cultures of marine-derived microorganisms from 2009 to 2019. A detailed discussion on future prospects and current challenges in the field of co-culture is also provided on behalf of the authors’ own views of development tendencies.

Lichens have recently received great attention due to their pharmacological potentials. The antigenotoxic potential of C. vitellina extract (25 and 50 µg/mL) was assessed in normal human peripheral blood lymphocytes (HPBL) against Mitomycin C (MMC) co-treatments. Flow cytometric analyses of cell cycle distribution, as well as apoptosis (Annexin V/PI), revealed that the extract had significantly (p ≤ 0.05) ameliorated the MMC toxicity by reducing the apoptotic cells and normalized the cell cycle phases. C. vitellina exhibited antigenotoxicity by ameliorating the diminished mitotic index and DNA single-strand breaks caused by MMC. Herein, the hydromethanolic extract (80%) of Candelariella vitellina (Japan) lichen, exhibited very low cytotoxicity towards normal human peripheral lymphocytes (HPBL) with IC50 >1000 µg/mL. In order to explore the antihelminthic effect, Echinococcus granulosus protoscoleces were used in vitro. Eosin staining revealed significant (p ≤ 0.05) dose and time-dependent scolicidal effects of the extract confirmed by degenerative alterations as observed by electron scan microscopy. Furthermore, primary and secondary metabolites were investigated using GC-MS and qualitative HPLC, revealing the presence of sugars, alcohols, different phenolic acids and light flavonoids. Significant antioxidant capacities were also demonstrated by DPPH radical-scavenging assay. In conclusion, the promising antigenotoxic, antihelminthic and antioxidant potentials of C. vitellina extract encourage further studies to evaluate its possible therapeutic potency.

A hitherto unknown polyoxygenated flavonol robinobioside (gossypetin-3-O-β-d-robinobioside) was isolated from the leaves of Caesalpinia gilliesii along with thirteen known phenolic secondary metabolites. The isolated compounds were characterized using spectroscopic analysis, including 1D and 2D NMR and mass spectrometry (MS) analyses. The extract reduced the level of liver damage in CCl4-induced liver injury in rats. A decrease of the liver biomarkers—aspartate aminotransferase (AST) and alanine aminotransferase (ALT) and an increase of total antioxidant capacity (TAC) levels—were observed similar to the liver protecting drug silymarin. In addition, the extract showed promising activity against carrageenan-induced paw edema in rats and protected their stomachs against ethanol-induced gastric ulcers in a concentration dependent fashion. The observed activities could be attributed to the high content of antioxidant polyphenols. Our results suggest that the C. gilliesii has the capacity to scavenge free radicals and can protect against oxidative stress, and liver and stomach injury.