Search Results (51)

Conyza sumatrensis is a plant of the Asteraceae family widespread in the tropical and subtropical regions of all continents. The plant is applied in folk medicine to treat malaria and helminth infections as well as other diseases. In Uganda, for example, the plant is traditionally used against ectoparasites and for wound healing. In this work, we describe a comprehensive analytical approach to investigate the metabolic profile of C. sumatrensis supported by database-assisted annotation and in silico techniques. The study aimed to analyze the metabolic profile of C. sumatrensis using multiple analytical techniques due to the complexity of the plant composition. Therefore, we employed a combination of thin-layer chromatography (TLC), high-performance liquid chromatography (HPLC), gas chromatography-mass spectrometry (GC-MS), and liquid chromatography-tandem mass spectrometry (LC-MS/MS). From the GC-MS experiments, more than 40 compounds could be annotated that had not been found in C. sumatrensis before. A number of these substances are known to possess relevant bioactivities, including antibacterial properties. Indeed, ethanolic extracts demonstrated antimicrobial activity against representative strains of both Gram-positive and Gram-negative bacteria, as shown by agar diffusion and microdilution assays. Using LC-MS/MS data, a feature-based molecular network was constructed to facilitate the comparison of two extraction solvents: water and ethanol. The majority of the features were detected in both of the extracts. However, some features were only detected using one of the extraction solvents. Our work provided valuable insights into the chemical profile of C. sumatrensis and lays the foundation for future research into its medicinal properties. Full article

The serendipitous discovery of antiparasitic drugs, such as quinine and artemisinin, of plant origin reveals that searching new chemical pharmacophores from medicinal plants is valuable. The present study sought to explore the antiplasmodial, antileishmanial, and antitrypanosomal activities of Lippia adoensis extracts. Crude extracts of L. adoensis leaves and twigs, which were obtained by extraction using 70% ethanol in water, were assayed for antiplasmodial activity against P. falciparum 3D7 and Dd2 through the SYBR green I-based fluorescence assay; and for antileishmanial, antitrypanosomal, and cytotoxic effects on Leishmania donovani, Trypanosoma brucei brucei, and Vero cells, respectively, using resazurin colorimetric assays. In vitro phytochemical analysis of L. adoensis extracts was performed using standard methods. Moreover, liquid chromatography–mass spectrometry (LC-MS) feature-based detection and molecular networking flow on Global Natural Product Social (GNPS) were also used for the phytochemical screening of L. adoensis extracts. Crude extracts from L. adoensis inhibited the growth of P. falciparum (3D7 and Dd2) (IC₅₀s; (3D7): 10.00 and 97.46 μg/mL; (Dd2): 29.48 and 26.96 μg/mL), L. donovani (IC₅₀s: 22.87–10.52 μg/mL), and T. brucei brucei (IC₅₀s: 2.30–55.06 μg/mL). The extracts were found to be non-cytotoxic to Vero cells, thus yielding median cytotoxic concentrations (CC₅₀s) above 100 μg/mL. In vitro phytochemical analysis of the crude extracts revealed the presence of alkaloids, terpenoids, phenolic compounds, and carbohydrates. The LC-MS tandem molecular networking flow predicted that the extracts contained valsafungin A and bacillamidin in the first cluster, and fatty acids, ketone, and aldehyde derivatives in the second cluster. Overall, the present study demonstrated the antiparasitic effects of L. adoensis extracts, thus justifying the use of this plant in the traditional treatment of fever and malaria conditions. Nevertheless, detailed metabolomic studies and antiparasitic mechanisms of action of the extracts are expected to unveil the potential antiparasitic hit compounds. Full article

The development of a sensitive and specific blood test for the early detection of breast cancer is crucial to improve screening and patient outcomes. Existing methods, such as mammography, have limitations, necessitating the exploration of alternative approaches, including circulating factors. Using 598 prospectively collected blood samples, a multivariate plasma-derived lipid biomarker signature was developed that can distinguish healthy control individuals from those with breast cancer. Liquid chromatography with high-resolution and tandem mass spectrometry (LC-MS/MS) was employed to identify lipids for both extracellular vesicle-derived and plasma-derived signatures. For each dataset, we identified a signature of 20 lipids using a robust, statistically rigorous feature selection algorithm based on random forest feature importance applied to cross-validated training samples. Using an ensemble of machine learning models, the plasma 20-lipid signature generated an area under the curve (AUC) of 0.95, sensitivity of 0.91, and specificity of 0.79. The results from this study indicate that lipids extracted from plasma can be used as target analytes in the development of assays to detect the presence of early-stage breast cancer. Full article

Background: Collectively, leishmaniasis and Chagas disease cause approximately 8 million cases and more than 40,000 deaths annually, mostly in tropical and subtropical regions. The current drugs used to treat these diseases have limitations and many undesirable side effects; hence, new drugs with better clinical profiles are needed. Fungal endophytes associated with plants are known to produce a wide array of bioactive secondary metabolites, including antiprotozoal compounds. In this study, we analyzed endophytic fungal isolates associated with Theobroma cacao and Coffea arabica crop plants, which yielded extracts with antitrypanosomatid activity. Methods: Crude extracts were subjected to bioassay-guided isolation by HPLC, followed by spectrometric and spectroscopic analyses via mass spectrometry (MS) and nuclear magnetic resonance (NMR), Results: Compounds 1–9 were isolated and displayed novel antitrypanosomal and antileishmanial activities ranging from 0.92 to 32 μM. Tandem liquid chromatography–mass spectrometry (LC–MS) analysis of the organic extracts from different strains via the feature-based Global Natural Products Social (GNPS) molecular networking platform allowed us to dereplicate a series of metabolites (10–23) in the extracts. Molecular docking simulations of the active compounds, using the 3-mercaptopyruvate sulfurtransferase protein from L. donovani (Ld3MST) and the cruzipain enzyme from T. cruzi as putative molecular targets, allowed us to suggest possible mechanisms for the action of these compounds. Conclusions: The isolation of these antiprotozoal compounds confirms that crop plants like coffee and cacao harbor populations of endophytes with biomedical potential that confer added value to these crops. Full article

Accurately predicting the remaining useful life (RUL) of lithium-ion batteries (LIBs) not only prevents battery system failure but also promotes the sustainable development of the energy storage industry and solves the pressing problems of industrial and energy crises. Because of the capacity regeneration phenomenon and random interference during the operation of lithium-ion batteries, the prediction precision and generalization performance of a single model can be poor. This article proposes a novel RUL prediction based on data pre-processing methods and the CNN-LSTM-ASAN framework. The model is based on a fusion technique for optimizing the tandem fusion of the Convolutional Neural Network (CNN) and the Long Short-Term Memory Network (LSTM). Firstly, the improved adaptive noise fully integrates empirical mode decomposition (ICEEMDAN) and the Pearson correlation coefficient (PCC), which are used to estimate the global deterioration tendency component and the local capacity restoration component, to reconstruct the dataset and eliminate the noise. Then, the Adaptive Sparse Attention Network (ASAN) is added in the model construction stage to improve the training efficiency of the model. The reconstructed degraded data are features extracted by the CNN-LSTM-ASAN model. Finally, the proposed method is validated against models such as DCLA, using the NASA public datasets, the CALCE public datasets, and the self-use datasets. And the results show that the root mean square error (RMSE) of the model is below 1.5%. Full article

The chemical characterization of natural products is often a complex task that demands powerful analytical techniques. Liquid chromatography with high-resolution tandem mass spectrometry (HRMS/MS) is often employed, yet it can face hard challenges when isomeric species are present, and reference standards are lacking. In such cases, the confidence level in compound identification can be significantly improved by the collection of orthogonal information on target analytes. In this work, 23 key compounds in Boswellia serrata extract (BSE), 12 of which correspond to boswellic acids (BAs) and 11 to triterpenoidic acid isomers, were identified by combining RPLC followed by serial UV and ESI(-)-FTMS and FTMS/MS detections with the evaluation of the reactivity towards C=C bond epoxidation with meta-chloroperoxybenzoic acid (m-CPBA), proposed as a fast chemical tool to gather information about C=C bond steric hindrance, a key structural feature of BAs and related compounds. The interpretation of UV spectra acquired after chromatographic separation corroborated the identification of the substitution patterns of enonic and dienic residues in ketoboswellic and dehydroboswellic acids. Moreover, MS/MS based on higher-energy collision-induced dissociation (HCD) unveiled new fragmentation pathways, providing important structural details on target analytes. The integrated approach developed during this study might pave the way for a deeper understanding of the BSE bioactive properties. Moreover, it can be considered an example of a more general strategy for the analysis of complex mixtures of natural compounds including also isomeric species. Full article

Human–object interaction (HOI) detection identifies a “set of interactions” in an image involving the recognition of interacting instances and the classification of interaction categories. The complexity and variety of image content make this task challenging. Recently, the Transformer has been applied in computer vision and received attention in the HOI detection task. Therefore, this paper proposes a novel Part Refinement Tandem Transformer (PRTT) for HOI detection. Unlike the previous Transformer-based HOI method, PRTT utilizes multiple decoders to split and process rich elements of HOI prediction and introduces a new part state feature extraction (PSFE) module to help improve the final interaction category classification. We adopt a novel prior feature integrated cross-attention (PFIC) to utilize the fine-grained partial state semantic and appearance feature output obtained by the PSFE module to guide queries. We validate our method on two public datasets, V-COCO and HICO-DET. Compared to state-of-the-art models, the performance of detecting human–object interaction is significantly improved by the PRTT. Full article

Leaf chlorophyll content (LCC) is an important indicator of plant nutritional status and can be a guide for plant disease diagnosis. In this study, we took apple leaves infected with mosaic disease as a research object and extracted two types of information on spectral and textural features from hyperspectral images, with a view to realizing non-destructive detection of LCC. First, the collected hyperspectral images were preprocessed and spectral reflectance was extracted in the region of interest. Subsequently, we used the successive projections algorithm (SPA) to select the optimal wavelengths (OWs) and extracted eight basic textural features using the gray-level co-occurrence matrix (GLCM). In addition, composite spectral and textural metrics, including vegetation indices (VIs), normalized difference texture indices (NDTIs), difference texture indices (DTIs), and ratio texture indices (RTIs) were calculated. Third, we applied the maximal information coefficient (MIC) algorithm to select significant VIs and basic textures, as well as the tandem method was used to fuse the spectral and textural features. Finally, we employ support vector regression (SVR), backpropagation neural network (BPNN), and K-nearest neighbors regression (KNNR) methods to explore the efficacy of single and combined feature models for estimating LCC. The results showed that the VIs model (R² = 0.8532, RMSE = 2.1444, RPD = 2.6179) and the NDTIs model (R² = 0.7927, RMSE = 2.7453, RPD = 2.2032) achieved the best results among the single feature models for spectra and texture, respectively. However, textural features generally exhibit inferior regression performance compared to spectral features and are unsuitable for standalone applications. Combining textural and spectral information can potentially improve the single feature models. Specifically, when combining NDTIs with VIs as input parameters, three machine learning models outperform the best single feature model. Ultimately, SVR achieves the highest performance among the LCC regression models (R² = 0.8665, RMSE = 1.8871, RPD = 2.7454). This study reveals that combining textural and spectral information improves the quantitative detection of LCC in apple leaves infected with mosaic disease, leading to higher estimation accuracy. Full article

Nigella sativa (NS) is a native herb consumed habitually in several countries worldwide, possessing manifold therapeutic properties. Among them, anti-inflammatory features have been reported, presumably relating to mechanisms involved in the nuclear factor kappa-B pathway, among others. Given the observed association between neuroimmune factors and mental illness, the primary aim of the present study was to examine the effects of chronic NS use on manic-like behavior in rats, as well as analyze levels of brain inflammatory mediators following NS intake. Using male and female rats, baseline tests were performed; thereafter, rats were fed either regular food (control) or NS-containing food (treatment) for four weeks. Following intervention, behavioral tests were induced (an open field test, sucrose consumption test, three-chamber sociality test, and amphetamine-induced hyperactivity test). Subsequently, brain samples were extracted, and inflammatory mediators were evaluated, including interleukin-6, leukotriene B4, prostaglandin E2, tumor necrosis factor-α, and nuclear phosphorylated-p65. Our findings show NS to result in a marked antimanic-like effect, in tandem with a positive modulation of select inflammatory mediators among male and female rats. The findings reinforce the proposed therapeutic advantages relating to NS ingestion. Full article

Tetrodotoxin (TTX), an extremely potent low-molecular-weight neurotoxin, is widespread among marine animals including ribbon worms (Nemertea). Previously, studies on the highly toxic palaeonemertean Cephalothrix cf. simula showed that toxin-positive structures are present all over its body and are mainly associated with glandular cells and epithelial tissues. The highest TTXs concentrations were detected in a total extract from the intestine of the anterior part of the body and also in a total extract from the proboscis. However, many questions as to the TTXs distribution in the organs of the anterior part of the worm’s body and the functions of the toxins in these organs are still unanswered. In the present report, we provide additional results of a detailed and comprehensive analysis of TTXs distribution in the nemertean’s proboscis, buccal cavity, and cephalic gland using an integrated approach including high-performance liquid chromatography–tandem mass spectrometry (HPLC–MS/MS), confocal laser scanning microscopy with anti-TTX antibodies, light and electron microscopies, and observations of feeding behavior. For the proboscis, we have found a TTXs profile different from that characteristic of other organs and tissues. We have also shown for the first time that the major amount of TTXs is localized in the anterior part of the proboscis that is mainly involved in hunting. TTX-containing glandular cells, which can be involved in the prey immobilization, have been found in the buccal cavities of the nemerteans. A significant contribution of the cephalic gland to the toxicity of this animal has been shown for the first time, and the role of the gland is hypothesized to be involved not only in protection against potential enemies but also in immobilizing prey. The data obtained have made it possible to extend the understanding of the role and features of the use of TTXs in the organs of the anterior part of nemertean’s body. Full article

A magnetic adsorbent based on a C-nanofiber (Fe₃O₄@C–NFs) nanocomposite was synthesized using a simple one-pot co-precipitation method. The characterized results showed that the obtained C–nanofiber–coated magnetic nanoparticles had many attractive features such as a large specific surface area and a highly interwoven and branched mesoporous structure, as well as distinguished magnetism. The nanocomposite was then used as an adsorbent in the magnetic solid phase extraction (MSPE) of four typical tetracyclines (oxytetracycline, tetracycline, chlortetracycline, and doxycycline) in aquatic products. The TCs in the extract were determined using ultra-high-performance liquid chromatography–tandem mass spectrometry (UHPLC-MS/MS). Experimental variables of MSPE, including the sorbent amount, pH condition, adsorption and desorption time, and desorption solvent, were investigated and optimized systematically. The method validation indicated that the developed method showed good linearity (R² > 0.995) in the range of 1.0–200 ng/mL. The average recoveries at the spiked levels ranged from 90.7% to 102.7% with intra-day and inter-day relative standard deviations (RSDs, n = 6) ranging from 3.72% to 8.17% and 4.20% to 9.69%, respectively. The limit of detection (LOD) and limit of quantification (LOQ) for the four kinds of TCs were 0.7 μg/kg and 2.0 μg/kg, respectively. Finally, MSPE based on C-nanofiber-coated magnetic nanoparticles was successfully applied to TC analysis in real aquatic products (grass carp, large yellow croaker, snakehead, mandarin fish, Penaeus vannamei, swimming crab, etc.). Compared with traditional extraction methods, the proposed method for TC analysis in aquatic products is more sensitive, effective, recyclable, and environmentally friendly. Full article

Early onset ataxia (EOA) and developmental coordination disorder (DCD) both affect cerebellar functioning in children, making the clinical distinction challenging. We here aim to derive meaningful features from quantitative SARA-gait data (i.e., the gait test of the scale for the assessment and rating of ataxia (SARA)) to classify EOA and DCD patients and typically developing (CTRL) children with better explainability than previous classification approaches. We collected data from 18 EOA, 14 DCD and 29 CTRL children, while executing both SARA gait tests. Inertial measurement units were used to acquire movement data, and a gait model was employed to derive meaningful features. We used a random forest classifier on 36 extracted features, leave-one-out-cross-validation and a synthetic oversampling technique to distinguish between the three groups. Classification accuracy, probabilities of classification and feature relevance were obtained. The mean classification accuracy was 62.9% for EOA, 85.5% for DCD and 94.5% for CTRL participants. Overall, the random forest algorithm correctly classified 82.0% of the participants, which was slightly better than clinical assessment (73.0%). The classification resulted in a mean precision of 0.78, mean recall of 0.70 and mean F1 score of 0.74. The most relevant features were related to the range of the hip flexion–extension angle for gait, and to movement variability for tandem gait. Our results suggest that classification, employing features representing different aspects of movement during gait and tandem gait, may provide an insightful tool for the differential diagnoses of EOA, DCD and typically developing children. Full article

The prevalence of gout and the adverse effects of current synthetic anti-gout drugs call for new natural and effective xanthine oxidase (XOD) inhibitors to target this disease. Based on our previous finding that an edible seaweed Pterocladiella capillacea extract inhibits XOD, XOD-inhibitory and anti-inflammatory activities were used to evaluate the anti-gout potential of different P. capillacea extract fractions. Through affinity ultrafiltration coupled with liquid chromatography tandem mass spectrometry (LC-MS/MS), feature-based molecular networking (FBMN), and database mining of multiple natural products, the extract’s bioactive components were traced and annotated. Through molecular docking and ADMET analysis, the possibility and drug-likeness of the annotated XOD inhibitors were predicted. The results showed that fractions F4, F6, F4-2, and F4-3 exhibited strong XOD inhibition activity, among which F4-3 reached an inhibition ratio of 77.96% ± 4.91% to XOD at a concentration of 0.14 mg/mL. In addition, the P. capillacea extract and fractions also displayed anti-inflammatory activity. Affinity ultrafiltration LC-MS/MS analysis and molecular networking showed that out of the 20 annotated compounds, 8 compounds have been previously directly or indirectly reported from seaweeds, and 4 compounds have been reported to exhibit anti-gout activity. Molecular docking and ADMET showed that six seaweed-derived compounds can dock with the XOD activity pocket and follow the Lipinski drug-like rule. These results support the value of further investigating P. capillacea as part of the development of anti-gout drugs or related functional foods. Full article

This study determined for the first time the structure of the peptides (i.e., peptidomics) in soy protein hydrolysates and elucidated their effects on an oil’s oxidative stability during frying cycles. The oil investigated was palm olein during 0, 4, 8, and 12 frying cycles of plantain banana chips. Proteins were extracted and hydrolyzed with two proteases. Trypsin hydrolysate (HTRY) exhibited higher anti-radical activity (DPPH, 70.2%) than the control (unhydrolyzed proteins, 33.49%) and pepsin hydrolysate (HPEP, 46.1%) at 200 µg/mL. HPEP however showed a 4.6-fold greater reduction of ferric ions (FRAP) while also possessing a higher peroxyl radical scavenging ability (716 ± 30 µM Trolox Eq/g) than HTRY (38.5 ± 35 µM Trolox Eq/g). During oil oxidative stability tests, HPEP improved the oxidative stability of the palm olein oil after 8 and 12 frying cycles, characterized by lower concentrations of hydroperoxides, and carbonyl and volatile compounds. HTRY however exerteda pro-oxidant activity. Structural data from SDS-PAGE and tandem mass spectrometry showed that the mechanism for the greater activity of the pepsin hydrolysate occurred due to unique structural features and a higher percentage of short-chain peptides. This was justified by a 25, 31, and 48% higher contents of tryptophan, histidine, and methionine, respectively (important amino acids with hydrogen atom transfer and electron-donating capacities) in the peptides identified in the pepsin hydrolysate. Full article

The vast pool of structurally and functionally distinct secondary metabolites (i.e., natural products (NPs)) is constantly being expanded, a process also driven by the rapid progress in the development of analytical techniques. Such NPs often show potent biological activities and are therefore prime candidates for drug development and medical applications. The ethyl acetate extract of the tuber of Citrullus naudinianus (C. naudinianus), an African melon with edible fruits and seeds, shows in vitro immunomodulatory activity presumably elicited by cucurbitacins that are known major constituents of this plant. Further potentially immunomodulatory cucurbitacins or cucurbitacin derivatives were assumed to be in the tuber. Given the typically high content of cucurbitacins with similar physicochemical features but often distinct bioactivities, an efficient and reliable separation process is a prerequisite for their detailed characterization and assessment in terms of bioactivity. We therefore developed a detection method to screen and differentiate cucurbitacins via high-performance liquid chromatography/quadrupole-time-of-flight tandem mass spectrometry (HPLC-QTOF-MS/MS). In order to confirm the identification, the fragmentation patterns of two cucurbitacins and one 23,24-dihydrocucurbitacin were also investigated. Six characteristic fragments were identified and three of them were employed for the identification of cucurbitacins and 23,24-dihydrocucurbitacins in the extract. As a result, in addition to eight previously reported cucurbitacins from this plant four distinct 23,24-dihydrocucurbitacins (B, D, E, and I) were putatively identified and newly found in the ethyl acetate extract of the tuber of C. naudinianus. The established methodology enables rapid and efficient LC-MS-based analysis and identification of cucurbitacins and 23,24-dihydrocucurbitacins in plant extracts. Full article