Unknown Compounds Research Articles

Highly efficient purification of long-chain alkenones using silver dimercaptotriazine flash chromatography

Long-chain alkenones (LCAs) are important tools for paleotemperature reconstructions in lacustrine and marine environments. However, some neutral lipid compounds that co-elute with LCAs often occur in natural sediment samples, which seriously interferes with the identification and quantification of the LCAs. In this study, we report a new flash column chromatography based on silver(I)-dimercaptotriazine (Ag-DMT) functionalized silica material (≡Si-DMT-Ag) for purifying and isolating the individual LCAs from complex lake and marine sediment samples. Compared to silver(I)-mercaptopropyl (Ag-MP) functionalized (≡Si-(CH2)3-S-Ag) and silver nitrate (SiO2 + AgNO3) impregnated silica materials used by previous studies, the Ag-DMT stationary phase displays the best efficiency in removing co-eluting compounds (including wax ester, hopanoid, hopanoic acid, sterane, alkanoic acid, and some unknown compounds) from the LCAs in our study lake and marine sediment samples. The Ag-DMT material also shows its high retention capacity for the LCAs, probably due to a strong interaction of more positively charged silver sites on Ag-DMT with double bonds. Employing the Ag-DMT chromatography, we develop an optimal solvent elution scheme to efficiently isolate the individual LCAs with the same chain length but different numbers of unsaturation for their isotopic analysis in the future. Our study provides a highly effective method for eliminating co-eluting compounds and isolating individual LCAs for paleoclimate studies.

Adaptive resonance demodulation semantic-induced zero-shot compound fault diagnosis for railway bearings

For the challenges of diverse compound faults and low identification accuracy of railway bearings, a new zero-shot diagnosis model based on adaptive resonance demodulation semantic is proposed for the compound fault diagnosis of railway bearings. The model adopts adaptive resonance demodulation to identify the optimal resonance frequency band rich in fault information in bearing signals, and constructs the single and compound fault semantics of samples without separating the compound fault signals, thus improving the interpretability of semantic features. Moreover, spatial Euclidean distance is used to measure the similarity of features and semantics in the mapping space, which enables the identification of unknown compound faults by single faults. Verification through railway bearing data shows that this model effectively improves the compound fault identification accuracy under zero samples and is better than the comparison models. The research results can provide theoretical reference for the research and application of railway bearing fault diagnosis.

Profiling of bioactive compounds identified in functional gummies by metabolomics analysis using High-Resolution Accurate Mass-Spectrometry

The consumers demand for healthier and fortified products received much attention these days. The main aim of this study was to prepare functional gummies that contain beneficial bioactive compounds such as flavonoids, terpenoids, polyphenols, and phenolics. The gummies were prepared using varying concentrations of jamun pulp and pear pomace powder, which are both known for their health benefits. Optimization of the prepared gummies was done on the basis of their antioxidant profile. The bioactive compounds found in the gummies were characterized by High-Resolution Accurate Mass-Spectrometry. These bioactive compounds were divided into different categories, such as phenolic acids, flavonoids, as well as terpenoids. Analytical characterization was employed to identify both known and unknown bioactive compounds and metabolites in the optimized product. The results showed that the optimized gummies (FG3) exhibited desirable sensory, textural, and functional properties. FG3 contains a substantial amount of dietary fibre and bioactive compounds, including flavonoids, terpenoids, phenolics, triterpenes, which exhibits health benefits such as anti-cancerous, anti-inflammatory etc., Overall, the developed gummies provide a promising solution to address health-related issues.

Development of a Non-Target Screening and Quantitative Analysis Strategy Based on UPLC-Q-TOF/MS and UPLC-QQQ/MS to Improve the Quality Control of Wuling Capsule.

Herbal medicine has been widely valued because of its remarkable efficacy and minimal side effects. The quantitative analysis of herbal medicines is essential to ensure their safety and efficacy. The simultaneous detection of multiple quality markers (Q-markers) has emerged as an important approach and trend in herbal medicine quality control. In recent years, non-targeted screening has become an effective strategy for the discovery and identification of unknown compounds. This study developed a non-targeted screening and quantitative analysis strategy to discover, identify and quantify the multiple components that truly represent the efficacy of Wuling capsule. Within this strategy, 18 types of flavonoids were tentatively discovered and identified from Wuling capsule by analyzing mass cleavage pathways, the precise molecular weights of compounds, and comparing the data with a database. Ten types of flavonoids were determined after the comparison of the standards. Additionally, following the evaluation of the regression equation, linear range, limit of detection (LOD), limit of quantitation (LOQ), precision, repeatability, and recovery of the proposed quantitative method, six flavonoids were quantified. This method successfully screened, identified, and quantified the potential active components in Wuling capsule, providing insights for improving the quality control standards in other herbal medicines.

Phytochemical analysis of water and ethanol liquid extracts prepared using freshly harvested leaves of Mitragyna speciosa (Korth.)

Mitragyna speciosa, also known as kratom, has been reported to have a broad range of pharmacological properties. Freshly harvested leaves and their water extracts are consumed in Southeast Asia while preparations made from dried leaf material are consumed in Western countries. Our study evaluated the phytochemical composition of freshly harvested kratom leaves using LCMS/MS analysis of water and ethanol liquid extracts. Mitragynine and its congeners, including 7-hydroxymitragynine, speciocilliatine, speciogynine, paynantheine, as well as bioactive phenolics including chlorogenic acid, o-coumaric acid, quercitrin, and rutin were identified. However, 7-hydroxymitragynine was detected solely in the water–liquid extract. Currently, unknown compounds were also present in the chromatograms and mass spectra. The study results support that 7-hydroxymitragynine is a post-harvest oxidative derivative or metabolite of mitragynine. Further rigorous and comprehensive evaluations of the phytochemical composition of freshly harvested kratom leaves utilising advanced spectrometric methods are needed to establish the full spectrum of phytochemicals within the plant.

A multi-technique based analytical platform for characterization and identification of a newly emerged steroid 6β-chlorotestosterone and its in-vivo epimeric metabolites

Doping athletes tend to use new and non-approved drugs in an attempt to circumvent the dope test. Recently, a package of unknown drug was obtained from black market claiming to contain steroid-like compound, which could not be readily identified due to the lack of commercially available reference standards. In order to characterize and identify the chemical structure of the unknown compound, ultra performance liquid chromatography high resolution mass spectrometry (UPLC-HRMS), high performance liquid chromatography tandem quadrupole mass spectrometry (HPLC-MS/MS), gas chromatography tandem quadrupole mass spectrometry (GC–MS/MS) and nuclear magnetic resonance (NMR) instruments were applied in this work. Based on the combined data, the unknown compound was identified as 6β-chlorotestosterone. The aim of this study was to find urinary metabolic biomarkers of 6β-chlorotestosterone for clinical research and doping control analysis. Ten in-vivo metabolites including four phase I metabolites and six phase II metabolites were characterized and potentially identified. The detailed structure of the phase I metabolite was successfully obtained by GC–MS/MS analysis of trimethylsilylation (TMS) released upon dissolution, overcoming the absence of useful fragment ions to demonstrate the structure of sulfate metabolites. Two strategies including metabolic mechanisms and retention time variation of steroids were introduced to determine and distinguish 3α/β- and 5α/β- epimeric metabolites. A comprehensive evaluation was conducted on time of appearance and detection of each metabolite. The sulfate metabolite 6β-chloro-5β-androstan-17-one-3β-O-sulfate (S1) could be detected up to 12 days after oral administration, which was considered to be the long term biomarker for 6β-chlorotestosterone abuse in clinical research and doping control analysis.

Prediction of mass spectrometry ionization efficiency based on COSMO-RS and machine learning algorithms.

Non-targeted analysis of high-resolution mass spectrometry (MS) can identify thousands of compounds, which also gives a huge challenge to their quantification. The aim of this study is to investigate the impact of mass spectrometry ionization efficiency on various compounds in food at different solvent ratios and to develop a predictive model for mass spectrometry ionization efficiency to enable non-targeted quantitative prediction of unknown compounds. This study covered 70 compounds in 14 different mobile phase ratio environments in positive ion mode to analyze the rules of the matrix effect. With the organic phase ratio from low to high, most compounds changed by 1.0 log units in log IE. The addition of formic acid enhanced the signal but also promoted the matrix effect, which often occurred in compounds with strong ionization capacity. It was speculated that the matrix effect was mainly in the form of competitive charge and charged droplet' gasification sites during MS detection. Subsequently, we present a log IE prediction method built using the COSMO-RS software and the artificial neural network (ANN) algorithm to address this difficulty and overcome the shortcomings of previous models, which always ignore the matrix effect. This model was developed following the principles of QSAR modeling recommended by the Organization for Economic Cooperation and Development (OECD). Furthermore, we validated this approach by predicting the log IE of 70 compounds, including those not involved in the log IE model development. The results presented demonstrate that the method we put forward has an excellent prediction accuracy for log IE (R2pred = 0.880), which means that it has the potential to predict the log IE of new compounds without authentic standards.

Seasonal toxicity of urban road dust in runoff process-studies in Poland

Urban road dust (URD) is one of the most important non-point sources of pollution in agglomerations. The aim of this study was to assess the seasonal toxic effects of URD runoff in two regions of Poland. The concentrations of elements in URD and leachate were studied. The impact of pollutants in URD runoff on water organisms was evaluated using Daphtoxkit F and Rotoxkit F (LC50). The acute toxicity tests for crustaceans and rotifers were selected as the response of these taxa reflects the impact on zooplankton, a key component of aquatic ecosystem and the basis of most food webs. The concentrations of elements were found to vary depending on the site, although URD samples collected in Katowice agglomeration (Upper Silesia) had higher values of elements (Mn, Cu, Zn, As) compared to Wrocław (Lower Silesia). The concentrations of Mn, Zn, As, Cr, and Mg in water-soluble fraction of URD were higher in summer and winter in the Upper Silesia region due to rainwater runoff resulting from traffic, industries, post-industrial waste, and the presence of old heating systems. When comparing the content of elements in the water-soluble fraction between seasons, Zn, As, Cr, and Al concentrations were slightly higher in winter. The highest mortality of Daphnia magna and Brachiouns calyciflorus was observed in URD from both agglomerations in winter. However, the mortality is likely due to the concentration of elements or/and the coexistence of an unknown compound or a synergistic effect of the studied elements. This study highlights the alarming seasonal sources of elements in URD runoff, which will directly enter the food chain and affect the entire ecosystem, and human health.

Integrating classic AI and agriculture: A novel model for predicting insecticide-likeness to enhance efficiency in insecticide development

The integration of artificial intelligence (AI) into smart agriculture boosts production and management efficiency, facilitating sustainable agricultural development. In intensive agricultural management, adopting eco-friendly and effective pesticides is crucial to promote green agricultural practices. However, exploring new insecticides species is a difficult and time-consuming task that involves significant risks. Enhancing compound druggability in the lead discovery phase could considerably shorten the discovery cycle, accelerating insecticides research and development. The Insecticide Activity Prediction (IAPred) model, a novel classic artificial intelligence-based method for evaluating the potential insecticidal activity of unknown functional compounds, is introduced in this study. The IAPred model utilized 27 insecticide-likeness features from PaDEL descriptors and employed an ensemble of Support Vector Machine (SVM) and Random Forest (RF) algorithms using the hard-vote mechanism, achieving an accuracy rate of 86 %. Notably, the IAPred model outperforms current models by accurately predicting the efficacy of novel insecticides such as nicofluprole, overcoming the limitations inherent in existing insecticide structures. Our research presents a practical approach for discovering and optimizing novel insecticide lead compounds quickly and efficiently.

Evaluation of Density Functional Theory-Generated Data for Infrared Spectroscopy of Novel Psychoactive Substances Using Unsupervised Learning

Novel psychoactive substances (NPSs) are compounds plotted to modify the chemical structures of prohibited substances, offering alternatives for consumption and evading legislation. The prompt emergence of these substances presents challenges in health concerns and forensic assessment because of the lack of analytical standards. A viable alternative for establishing these standards involves leveraging in silico methods to acquire spectroscopic data. This study assesses the efficacy of utilizing infrared spectroscopy (IRS) data derived from density functional theory (DFT) for analyzing NPSs. Various functionals were employed to generate infrared spectra for five distinct NPS categories including the following: amphetamines, benzodiazepines, synthetic cannabinoids, cathinones, and fentanyls. PRISMA software was conceived to rationalize data management. Unsupervised learning techniques, including Hierarchical Cluster Analysis (HCA), Principal Component Analysis (PCA), and t-distributed stochastic neighbor embedding (t-SNE), were utilized to refine the assessment process. Our findings reveal no significant disparities among the different functionals used to generate infrared spectra data. Additionally, the application of unsupervised learning demonstrated adequate segregation of NPSs within their respective groups. In conclusion, integrating theoretical data and dimension reduction techniques proves to be a powerful strategy for evaluating the spectroscopic characteristics of NPSs. This underscores the potential of this combined methodology as a diagnostic tool for distinguishing IR spectra across various NPS groups, facilitating the evaluation of newly unknown compounds.

Examining dietary supplements containing Euterpe oleracea Mart. (açaí) for potential botanical-drug interactions involving CYP3A4 inhibition

BackgroundBotanical dietary supplements (BDS) are consumed more by cancer patients than otherwise healthier patients to alleviate side effects of chemotherapy drugs or increase quality of life. The enzyme responsible for the metabolism of most anticancer drugs is CYP3A4, an enzyme predominantly located in the liver. HypothesisOur research team examined the potential risk between CYP3A4-interactive cancer drugs and Euterpe oleracea Mart. (açaí) berry and found that the compounds that passively and non-passively diffused from methanol açaí extract displayed significant inhibition of liver CYP3A4 – suggesting the potential for interactions between açaí and drugs metabolized by CYP3A4 if used synchronously. This study aims to identify the compounds responsible for the CYP3A4 inhibition using the newly automated Bioactivity – Global Natural Product Social Molecular Networking (GNPS) tool. MethodsDietary supplement capsules from two manufacturers (F1 and F2) containing aqueous extracts of açaí fruits were extracted separately by methanol (ME) and acidic methanol (AC) and standardized to cyanidin-3-O-glucoside (CG) concentration at a human-relevant dose. After solvent removal, a parallel artificial membrane permeability assay (PAMPA) was utilized to filter intestinal passive diffusion of the four extract constituents so that compounds could be tested for inhibition of hepatic CYP3A4. Passively and non-passively diffused constituents of extracts from PAMPA assays were injected into the liquid chromatography-mass spectrometry (LC–MS) and characterized for visualization and comparison of chemical entities. To elucidate the non-passively diffused compounds responsible for significant CYP3A4 inhibition, the Bioactivity – GNPS tool was used to hypothesize which compounds within the formulation extract were potentially active to accelerate their identification. ResultsThere was no significant inhibition of CYP3A4 by passively diffused constituents for any extract. There was, however, significant inhibition by non-passively diffused constituents of both F1ME and F1AC extracts with 50 % CYP3A4 inhibition at the donor compartment concentrations 5.764 and 15.58 ng/ml CG, respectively. Six compounds were predicted to be inhibitors of CYP3A4 from methanol F1 extract, including betaine and 5 unknown compounds. ConclusionThis indicates that BDS containing açaí may not inhibit CYP3A4 through passively diffused compounds. Compounds from açaí BDS that inhibit CYP3A4 may be absorbed through other mechanisms, such as transporters.

Identification and genomic insights into a strain of Bacillus velezensis with phytopathogen-inhibiting and plant growth-promoting properties

The use of biological agents offers a sustainable alternative to chemical control in managing plant diseases. In this study, Bacillus velezensis IFST-221 was isolated from the rhizosphere of a healthy maize plant amidst a population showing severe disease symptoms. The investigation demonstrated a broad-spectrum antagonistic activity of IFST-221 against eight species of pathogenic ascomycetes and oomycetes, suggesting its potential utility in combating plant diseases like maize ear rot and cotton Verticillium wilt. Additionally, our study unveiled that IFST-221 has demonstrated significant plant growth-promoting properties, particularly in maize, cotton, tomato, and broccoli seedlings. This growth promotion was linked to its ability to produce indole-3-acetic acid, nitrogen fixation, phosphate and potassium solubilization, and biofilm formation in laboratory conditions. A complete genome sequencing of IFST-221 yielded a genome size of 3.858 M bp and a GC content of 46.71%. The genome analysis identified 3659 protein-coding genes, among which were nine secondary metabolite clusters with known antimicrobial properties. Additionally, three unknown compounds with potentially novel properties were also predicted from the genomic data. Genome mining also identified several key genes associated with plant growth regulation, colonization, and biofilm formation. These findings provide a compelling case for the application of B. velezensis IFST-221 in agricultural practices. The isolate’s combined capabilities of plant growth promotion and antagonistic activity against common plant pathogens suggest its promise as an integrated biological agent in disease management and plant productivity enhancement.

New Phases in the Sc−Mn−Si and Sc−Mn−Al−Si Systems Through Molten Indium Flux Synthesis

New Phases in the Sc−Mn−Si and Sc−Mn−Al−Si Systems Through Molten Indium Flux Synthesis

Design, synthesis and characterization of novel 1,5- and 2,5-coumarin-4-yl-methyl regioisomers of 5-pyrazol-3-yl-tetrazoles as promising anticancer and antifungal agents

A novel series of compounds was synthesized using 3-arylsydnone 1a-b. Initially, 3-arylsydnone was converted into 1-aryl-1H-pyrazole-3-carbonitriles 2a-bvia [3+2] cycloaddition with acrylonitrile by ring transformation which once again underwent [2+3] cycloaddition with sodium azide to afford 5-(1-aryl-1H-pyrazol-3-yl)-1H-tetrazoles 3a-b.These were subjected to N-alkylation with 4-bromomethyl coumarins 4c-e to afford regioisomers viz., 2,5- and 1,5-disubstituted tetrazole derivatives 5f-k and 6f-k. Single crystal X-ray analysis of compound 6f was carried out. All the synthesized compounds were characterized using various techniques including 1H NMR, 13C NMR, FT-IR, elemental analysis, and mass spectroscopy.These previously unknown compounds were screened for their anticancer activity against HCT-116 colon cancer and non-cancerous L929 cell line at a single high dose (10−5 M) concentration assay. Among the tested compounds, 6g and 5f have exhibited excellent activity with very low IC50 valuei.e.,27.85 µg/mLand 37.86 µg/mL respectively, comparable with standard Cisplatin. In the non-cancerous L929 cell line, the tested compound 6f was proven to be less toxic, and all the tested compounds exhibited toxicity at higher concentrations comparable with standard Cisplatin. Further, the in vitro antifungal activity against Candida albicans for these compounds 5f-k and 6f-k revealed potential to moderate activities compared to the standard. Moreover, docking analysis was performed to know the exact binding mode of the newly synthesized compounds with N-myristoyltransferase (PDB ID: 1IYL) from Candida albicanswhich was selected as the antifungal targets. The drug-likeness of the compound was assessed using Molinspiration, while the toxicity profile was evaluated using Protox.

Electron ionization mass spectrometry feature peak relationships combined with deep classification model to assist similarity algorithm for fast and accurate identification of compounds.

Gas chromatography-mass spectrometry (GC-MS) combines chromatography and MS, providing full play to the advantages of high separation efficiency of GC, strong qualitative ability of MS, and high sensitivity of detector. In GC-MS data processing, determining the experimental compounds is one of the most important analytical steps, which is usually realized by one-to-one similarity calculations between the experimental mass spectrum and the standard mass spectrum library. Although the accuracy of the algorithm has been improved in recent years, it is still difficult to distinguish structurally similar mass spectra, especially isomers. At the same time, the library capacity is very large and increasing every year, and the algorithm needs to perform large numbers of calculations with irrelevant compounds in the library to recognize unknown compounds, which leads to a significant reduction in efficiency. This work proposed to exclude a large number of irrelevant mass spectra by presearching, perform preliminary similarity calculations using similarity algorithms, and finally improve the accuracy of similarity calculations using deep classification models. The replica library of NIST17 is used as the query data, and the master library is used as the reference database. Compared with the traditional recognition algorithm, the preprocessing algorithm has reduced the time by 4.2h, and by adding the deep learning models 1 and 2 as the final determination, the recognition accuracy has been improved by 1.9% and 6.5%, respectively, based on the original algorithm. This method improves the recognition efficiency compared to conventional algorithms and at the same time has better recognition accuracy for structurally similar mass spectra and isomers.

Ensemble multiclassification model for predicting developmental toxicity in zebrafish

In recent years, with the rapid development of society, organic compounds have been released into aquatic environments in various forms, posing a significant threat to the survival of aquatic organisms. The assessment of developmental toxicity is an important part of environmental safety risk systems, helping to identify the potential impacts of organic compounds on the embryonic development of aquatic organisms and enabling early detection and warning of potential ecological risks. Additionally, binary classification models cannot accurately classify organic compounds. Therefore, it is crucial to construct a multiclassification model for predicting the developmental toxicity of organic compounds. In this study, binary and multiclassification models were developed based on the ToxCast™ Phase I chemical library and literature data. The random forest, support vector machine, extreme gradient boosting, adaptive gradient boosting, and C5.0 decision tree algorithms, as well as 8 types of molecular fingerprint were used to establish a multiclassification base model for predicting developmental toxicity through 5-fold cross-validation and external validation. Ultimately, a multiclassification ensemble model was derived through a voting method. The performance of the binary ensemble model, as measured by the balanced accuracy, was 0.918, while that of the multiclassification model was 0.819. The developmental toxicity voting ensemble model (DT-VEM) achieved accuracies of 0.804, 0.834, and 0.855. Furthermore, by utilizing the XGBoost machine learning algorithm to construct separate models for molecular descriptors and substructure molecular fingerprints, we identified several substructures and physical properties related to developmental toxicity. Our research contributes to a more detailed classification of developmental toxicity, providing a new and valuable tool for predicting the developmental toxicity effects of unknown compounds. This supplement addresses the limitations of previous tools, as it offers an enhanced ability to predict potential developmental toxicity in novel compounds.

Ecophysiological responses of Ostreopsis towards temperature: A case study of benthic HAB facing ocean warming

Reports of the benthic dinoflagellate Ostreopsis spp. have been increasing in the last decades, especially in temperate areas. In a context of global warming, evidences of the effects of increasing sea temperatures on its physiology and its distribution are still lacking and need to be investigated. In this study, the influence of temperature on growth, ecophysiology and toxicity was assessed for several strains of O. cf. siamensis from the Bay of Biscay (NE Atlantic) and O. cf. ovata from NW Mediterranean Sea. Cultures were acclimated to temperatures ranging from 14.5 °C to 32 °C in order to study the whole range of each strain-specific thermal niche. Acclimation was successful for temperatures ranging from 14.5 °C to 25 °C for O. cf. siamensis and from 19 °C to 32 °C for O. cf. ovata, with the highest growth rates measured at 22 °C (0.54–1.06 d−1) and 28 °C (0.52–0.75 d−1), respectively. The analysis of cellular content of pigments and lipids revealed some aspects of thermal acclimation processes in Ostreopsis cells. Specific capacities of O. cf. siamensis to cope with stress of cold temperatures were linked with the activation of a xanthophyll cycle based on diadinoxanthin. Lipids (neutral reserve lipids and polar ones) also revealed species-specific variations, with increases in cellular content noted under extreme temperature conditions. Variations in toxicity were assessed through the Artemia franciscana bioassay. For both species, a decrease in toxicity was observed when temperature dropped under the optimal temperature for growth. No PLTX-like compounds were detected in O. cf. siamensis strains. Thus, the main part of the lethal effect observed on A. franciscana was dependent on currently unknown compounds. From a multiclonal approach, this work allowed for defining specificities in the thermal niche and acclimation strategies of O. cf. siamensis and O. cf. ovata towards temperature. Potential impacts of climate change on the toxic risk associated with Ostreopsis blooms in both NW Mediterranean Sea and NE Atlantic coast is further discussed, taking into account variations in the geographic distribution, growth abilities and toxicity of each species.

Review of an up-to-date, emphatic, and concise perspective on e-cigarettes

Electronic cigarettes entered the market in 2007 as an alternative to conventional tobacco products, and their use has increased significantly worldwide. Due to the emergence of various harmful effects and attracting attention, their sale was banned in our country and many other countries. However, according to the research, the use of e-cigarettes is increasing rapidly in the world and in Türkiye, and the rate of use at young ages is quite high especially. Its supply can be easily made through online sales on many websites. Despite this growing popularity, little is known about the potential effects of e-cigarettes on human health. This is particularly important in the presence of flavoring compounds, solvents, additives, and other ingredients that are added intentionally or unintentionally and whose long-term effects are uncertain. This is because every day a new product with new ingredients, flavors, and properties is introduced to the market. Previous studies have focused on measuring known toxicants, particularly those found in traditional cigarettes, while fewer have investigated unknown compounds and transformation products formed during the e-cigarette process in these diverse and ever-evolving products. It seems that the e-cigarette market is growing and developing rapidly in our country, as in the whole world, and product sales continue to increase despite all kinds of bans. However, when we look at the literature, the research conducted is insufficient, as mentioned above, and there are points that need to be further emphasized.

Detecting Pathogenic Phytophthora Species Using Volatile Organic Compounds.

There are several highly damaging Phytophthora species pathogenic to forest trees, many of which have been spread beyond their native range by the international trade of live plants and infested materials. Such introductions can be reduced through the development of better tools capable of the early, rapid, and high-throughput detection of contaminated plants. This study utilized a volatilomics approach (solid-phase microextraction coupled to gas chromatography-mass spectrometry) to differentiate between several Phytophthora species in culture and discriminate between healthy and Phytophthora-inoculated European beech and pedunculate oak trees. We tentatively identified 14 compounds that could differentiate eight Phytophthora species from each other in vitro. All of the Phytophthora species examined, except Phytophthora cambivora, uniquely produced at least one compound not observed in the other species; however, most detected compounds were shared between multiple species. Phytophthora polonica had the most unique compounds and was the least similar of all the species examined. The inoculated seedlings had qualitatively different volatile profiles and could be distinguished from the healthy controls by the presence of isokaurene, anisole, and a mix of three unknown compounds. This study supports the notion that volatiles are suitable for screening plant material, detecting tree pathogens, and differentiating between healthy and diseased material.

[Triazolamine][Acetate] as a novel basic ionic liquid catalyze green synthesis of new (methylsulfonyl)benzo[h]chromen-2-amine derivatives

A new functionalized stable ionic liquid as [Triazolamine][Acetate] was prepared for the first times. The novel catalyst was well characterized by various techniques and confirmed molecular structure. The ionic liquid as a novel catalyst was carried out for the green synthesis of new (methylsulfonyl)benzo[h]chromen-2-amine derivatives via three components reaction of aldehydes, alpha-naphthol, and (methylsulfonyl)acetonitrile. A simple and an efficient procedure for the synthesis of new un-known compounds using a green and novel reusable ionic liquid as catalyst under thermal conditions in ethanol as solvent was reported. The easy preparation of ionic liquids without any toxic solvent, short reaction times, and simple work-up with excellent yields are advantages of the present research. [Triazolamine][Acetate] as new green catalyst can be reused several times without significant loss of its catalytic activity.