In vitro evaluation of the amoebicidal activity of rosemary (Rosmarinus officinalis L.) and cloves (Syzygium aromaticum L. Merr. & Perry) essential oils against Acanthamoeba polyphaga trophozoites

Several species of the genus Acanthamoeba cause human diseases. Treatment of infections involves various problems, emphasising the need to develop alternative antiprotozoal agents. We studied the anti-amoebic activity of Essential Oils (EOs), derived from rosemary (Rosmarinus officinalis L.) and cloves (Syzygium aromaticum L. Merr. & Perry), against Acanthamoeba polyphaga strain. The amoebicidal activity of cloves and rosemary EOs was preliminary demonstrated by the morphology change (modifications in the cell shape, the presence of precipitates in the cytoplasm, autophagic vesicles, membrane blends) of the treated trophozoites. The cell-counts, carried out after staining trophozoites with a Trypan blue solution, revealed that both EOs were active in a dose-dependent manner and in relation to the exposure time. This activity was evident after few hours, with encouraging results obtained in particular with cloves EO, able to act at the lower concentrations and after 1 h, probably for its high eugenol content (65.30%).     

Essential Oils Extracted from Organic Propolis Residues: An Exploratory Analysis of Their Antibacterial and Antioxidant Properties and Volatile Profile

The industrial processing of crude propolis generates residues. Essential oils (EOs) from propolis residues could be a potential source of natural bioactive compounds to replace antibiotics and synthetic antioxidants in pig production. In this study, we determined the antibacterial/antioxidant activity of EOs from crude organic propolis (EOP) and from propolis residues, moist residue (EOMR), and dried residue (EODR), and further elucidated their chemical composition. The EOs were extracted by hydrodistillation, and their volatile profile was tentatively identified by GC-MS. All EOs had an antibacterial effect on Escherichia coli and Lactobacillus plantarum as they caused disturbances on the growth kinetics of both bacteria. However, EODR had more selective antibacterial activity, as it caused a higher reduction in the maximal culture density (D) of E. coli (86.7%) than L. plantarum (46.9%). EODR exhibited mild antioxidant activity, whereas EOMR showed the highest antioxidant activity (ABTS = 0.90 μmol TE/mg, FRAP = 463.97 μmol Fe²⁺/mg) and phenolic content (58.41 mg GAE/g). Each EO had a different chemical composition, but α-pinene and β-pinene were the major compounds detected in the samples. Interestingly, specific minor compounds were detected in a higher relative amount in EOMR and EODR as compared to EOP. Therefore, these minor compounds are most likely responsible for the biological properties of EODR and EOMR. Collectively, our findings suggest that the EOs from propolis residues could be resourcefully used as natural antibacterial/antioxidant additives in pig production.     

Chemical Profile,In Vitro Biological Activity and Comparison of Essential Oils from Fresh and Dried Flowers of Lavandula angustifolia L.

The chemical composition of essential oils (EOs) from dried and fresh flowers of Lavandula angustifolia L. (lavender), named LA 2019 and LA 2020, respectively, grown in central Italy was analyzed and compared by GC and GC-MS. For both samples, 61 compounds were identified, corresponding to 97.9% and 98.1% of the total essential oils. Explorative data analysis, performed to compare the statistical composition of the samples, resulted in a high level of global similarity (around 93%). The compositions of both samples were characterized by 10 major compounds, with a predominance of Linalool (35.3–36.0%), Borneol (15.6–19.4%) and 1,8-Cineole (11.0–9.0%). The in vitro antibacterial activity assay by disk diffusion tests against Bacillus subtilis PY79 and Escherichia coli DH5α showed inhibition of growth in both indicator strains. In addition, plate counts revealed a bactericidal effect on E. coli, which was particularly noticeable when using oil from the fresh lavender flowers at the highest concentrations. An in vitro antifungal assay showed that the EOs inhibited the growth of Sclerotium rolfsii, a phytopathogenic fungus that causes post-harvest diseases in many fruits and vegetables. The antioxidant activity was also assessed using the ABTS free radical scavenging assay, which showed a different antioxidant activity in both EOs. In addition, the potential application of EOs as a green method to control biodeterioration phenomena on an artistic wood painting (XIX century) was evaluated.     

Comparative Chemical Profiles of the Essential Oils from Different Varieties of Psidium guajava L.

Guava (Psidium guajava) leaves are commonly used in the treatment of diseases. They are considered a waste product resulting from guava cultivation. The leaves are very rich in essential oils (EOs) and volatiles. This work represents the detailed comparative chemical profiles of EOs derived from the leaves of six guava varieties cultivated in Egypt, including Red Malaysian (RM), El-Qanater (EQ), White Indian (WI), Early (E), El-Sabahya El-Gedida (ESEG), and Red Indian (RI), cultivated on the same farm in Egypt. The EOs from the leaves of guava varieties were extracted by hydro-distillation and analyzed with GC-MS. The EOs were categorized in a holistic manner using chemometric tools. The hydro-distillation of the samples yielded 0.11–0.48% of the EO (v/w). The GC-MS analysis of the extracted EOs showed the presence of 38 identified compounds from the six varieties. The sesquiterpene compounds were recorded as main compounds of E, EQ, ESEG, RI, and WI varieties, while the RM variety attained the highest content of monoterpenes (56.87%). The sesquiterpenes, β-caryophyllene (11.21–43.20%), and globulol (76.17–26.42%) were detected as the major compounds of all studied guava varieties, while trans-nerolidol (0.53–10.14) was reported as a plentiful compound in all of the varieties except for the RM variety. A high concentration of D-limonene was detected in the EOs of the RM (33.96%), WI (27.04%), and ESEG (9.10%) varieties. These major compounds were consistent with those reported for other genotypes from different countries. Overall, the EOs’ composition and the chemometric analysis revealed substantial variations among the studied varieties that might be ascribed to genetic variability, considering the stability of the cultivation and climate conditions. Therefore, this chemical polymorphism of the studied varieties supports that these varieties could be considered as genotypes of P. guajava. It is worth mentioning here that the EOs, derived from leaves considered to be agricultural waste, of the studied varieties showed that they are rich in biologically active compounds, particularly β-caryophyllene, trans-nerolidol, globulol, and D-limonene. These could be considered as added value for pharmacological and industrial applications. Further study is recommended to confirm the chemical variations of the studied varieties at a molecular level, as well as their possible medicinal and industrial uses.     

Application of Artificial Neural Networks for Analysis of 13C NMR Spectra of Fucoidans

A new approach for structure determination of native and O-desulfated fucoidans by the analysis of their ¹³C NMR spectra by artificial neural networks (ANNs) is described. Two ANN models were studied: the simple three-layer feed-forward network, which employs supervised learning, and the adaptive resonance theory (ART) network with unsupervised learning. Training sets for the networks were constructed using chemical shifts of synthetic oligofucosides. The results obtained demonstrate that both models worked better in the case of desulfated fucoidans, while the ART-type networks gave better results in sulfated (native) fucoidan structure elucidation.     

Composition of the essential oil of Stachys benthamiana Boiss. from the south of Iran

The chemical composition of the essential oil (EO) obtained from the aerial parts of Stachys benthamiana Boiss. was analysed by using GC and GC/MS. Thirty-three components were identified in the oil. β-Bisabolene (19.2%), humulene epoxide II (10.7%), epi-α-bisabolol (7.2%), (E)-γ-bisabolene (6.9%), n-decanal (6.8%) and caryophyllene oxide (6.6%), were the main compounds in the EOs. This is the first report on the different chemical compositions of S. benthamiana EOs from the south of Iran.     

Comparative study to predict toxic modes of action of phenols from molecular structures

Quantitative structure–activity relationship models for the prediction of mode of toxic action (MOA) of 221 phenols to the ciliated protozoan Tetrahymena pyriformis using atom-based quadratic indices are reported. The phenols represent a variety of MOAs including polar narcotics, weak acid respiratory uncouplers, pro-electrophiles and soft electrophiles. Linear discriminant analysis (LDA), and four machine learning techniques (ML), namely k-nearest neighbours (k-NN), support vector machine (SVM), classification trees (CTs) and artificial neural networks (ANNs), have been used to develop several models with higher accuracies and predictive capabilities for distinguishing between four MOAs. Most of them showed global accuracy of over 90%, and false alarm rate values were below 2.9% for the training set. Cross-validation, complementary subsets and external test set were performed, with good behaviour in all cases. Our models compare favourably with other previously published models, and in general the models obtained with ML techniques show better results than those developed with linear techniques. We developed unsupervised and supervised consensus, and these results were better than our ML models, the results of rule-based approach and other ensemble models previously published. This investigation highlights the merits of ML-based techniques as an alternative to other more traditional methods for modelling MOA.     

Chemical composition variations,allelopathic, and antioxidant activities of Symphyotrichum squamatum (Spreng.) Nesom essential oils growing in heterogeneous habitats

The present study aimed to analyze the chemical composition of Symphyotrichum squamatum EOs growing in two different habitats to explore the ecological implication on the EOs production and evaluate their antioxidant and allelopathic potentialities. The EOs from the aerial parts collected from coastal Mediterranean belt and inland abandoned habitats in the Nile Delta of Egypt, were extracted and analyzed using gas chromatography-mass spectrometry. Sixty compounds were characterized as overall constituents of EOs from both samples. Sesquiterpenes were the main component and represented by 69.77% and 88.68% from coastal and inland sample, respectively. The coastal sample attained a relatively high content of monoterpenes compared to the inland sample. Major compounds from the EOs of the coastal habitat sample, were humulene epoxide, (-)-spathulenol, (-)-caryophyllene oxide, germacrene D, and α-humulene representing 59.72%. However, β-pinene, germacrene D, α-humulene, α-muurolene, humulene epoxide, (-)-caryophyllene oxide, and β-cadinene were the major compounds of EOs of the inland habitat sample, representing 63.70%. The correlation analysis revealed more correlation between the Egyptian inland S. squamatum and the Japanese ecospecies. However, the Egyptian coastal S. squamatum and Turkish ecospecies were more correlated to each other. The present data suggested that chemotypes of S. squamatum maintain their typical pattern despite ecological or climatic differences. The EOs of S. squamatum showed moderate antioxidant activity, wherein coastal and inland EOs have an IC₅₀ value of 382.53 and 559.63 μL L⁻¹, respectively. Also, the EOs from both habitats showed moderate allelopathic activity against the noxious weed Bidens pilosa. However, the activity of the coastal sample was more than inland one and could be attributed to the content of the major compounds, especially the oxygenated terpenes.     

Virtual screening and library enumeration of new hydroxycinnamates based antioxidant compounds: A complete framework

Designing of molecules for drugs is important topic from many decades. The search of new drugs is very hard, and it is expensive process. Computer assisted framework can provide the fastest way to design and screen drug-like compounds. In present work, a multidimensional approach is introduced for the designing and screening of antioxidant compounds. Antioxidants play a crucial role in ensuring that the body's oxidizing and reducing species are kept in the proper balance, minimizing oxidative stress. Machine learning models are used to predict antioxidant activity. Three hydroxycinnamates are selected as standard antioxidants. Similar compounds are searched from ChEMBL database using chemical structural similarity method. The libraries of new compounds are generated using evolutionary method. New compounds are also designed using automatic decomposition and construction building blocks. The antioxidant activity of all designed and searched compounds is predicted using machine learning models. The chemical space of searched and generated compounds is envisioned using t-distributed stochastic neighbor embedding (t-SNE) method. Best compounds are shortlisted, and their synthetic accessibility is predicted to further facilitate the experimental chemists. The chemical similarity between standard and selected compounds is also studied using fingerprints and heatmap.     

Composition,Anti-MRSA Activity and Toxicity of Essential Oils from Cymbopogon Species

Many of the essential oils obtained from medicinal plants possess proven antimicrobial activity and are suitable for medicinal purposes and applications in the food industry. The aim of the present work was the chemical analysis of 19 essential oils (EOs) from seven different Cymbopogon species (C. nardus, C. citratus, C winterianus, C. flexuosus, C. schoenanthus, C. martinii, C. giganteus). Five different chemotypes were established by GC/MS and TLC assay. The EOs, as well as some reference compounds, i.e., citronellol, geraniol and citral (neral + geranial), were also tested for their antimicrobial and antibiofilm activity against methicillin-resistant Staphylococcus aureus (MRSA) by the microdilution method and direct bioautography. The toxicity of EOs was evaluated by Danio rerio ‘Zebrafish’ model assay. All examined EOs showed moderate to high activity against MRSA, with the highest activity noted for C. flexuosus—lemongrass essential oil, both in microdilution and direct autobiography method. Significant difference in the toxicity of the examined EOs was also detected.     

基于机器学习方法的H1N1神经氨酸苷酶抑制剂的分类预测

流感是一种主要的呼吸道传染病, 在普通人群中有着较高的发病率, 而对于一些年老和高危病人还有较高的死亡率. 研究显示抑制神经氨酸苷酶(NA)可以阻断病毒RNA复制, 因此NA是有效治疗H1N1型流感病毒的重要药物靶标. 通过计算机方法进行虚拟筛选和预测NA抑制剂已经变得越来越重要. 针对酶活性位点进行基于结构的合理药物设计, 开发H1N1 病毒神经氨酸苷酶抑制剂, 已成为药物研究的热点之一. 本文通过多种机器学习方法(支持向量机(SVM)、k-最近相邻法(k-NN)和C4.5决策树(C4.5DT))对已知的神经氨酸苷酶抑制剂(NAIs)与非神经氨酸苷酶抑制剂(non-NAIs)建立分类预测模型. 其中227个结构多样性化合物(72个NAIs与155个non-NAIs)被用于测试分类预测系统, 并用递归变量消除法选择与神经氨酸苷酶抑制剂分类相关的性质描述符以提高预测精度. 本研究对独立验证集的总预测精度为75.9%-92.6%, NA 抑制剂的预测精度为64.3%-78.6%, 非H1N1抑制剂的预测精度为77.5%-97.5%. SVM法给出最好的总预测精度(92.6%). 本研究表明支持向量机等机器学习方法可以有效预测未知数据集中潜在的NA抑制剂, 并有助于发现与其相关的分子描述符.     

Improving machine learning performance on small chemical reaction data with unsupervised contrastive pretraining

Machine learning (ML) methods have great potential to transform chemical discovery by accelerating the exploration of chemical space and drawing scientific insights from data. However, modern chemical reaction ML models, such as those based on graph neural networks (GNNs), must be trained on a large amount of labelled data in order to avoid overfitting the data and thus possessing low accuracy and transferability. In this work, we propose a strategy to leverage unlabelled data to learn accurate ML models for small labelled chemical reaction data. We focus on an old and prominent problem—classifying reactions into distinct families—and build a GNN model for this task. We first pretrain the model on unlabelled reaction data using unsupervised contrastive learning and then fine-tune it on a small number of labelled reactions. The contrastive pretraining learns by making the representations of two augmented versions of a reaction similar to each other but distinct from other reactions. We propose chemically consistent reaction augmentation methods that protect the reaction center and find they are the key for the model to extract relevant information from unlabelled data to aid the reaction classification task. The transfer learned model outperforms a supervised model trained from scratch by a large margin. Further, it consistently performs better than models based on traditional rule-driven reaction fingerprints, which have long been the default choice for small datasets, as well as those based on reaction fingerprints derived from masked language modelling. In addition to reaction classification, the effectiveness of the strategy is tested on regression datasets; the learned GNN-based reaction fingerprints can also be used to navigate the chemical reaction space, which we demonstrate by querying for similar reactions. The strategy can be readily applied to other predictive reaction problems to uncover the power of unlabelled data for learning better models with a limited supply of labels.

Contrastive pretraining of chemical reactions by matching augmented reaction representations to improve machine learning performance on small reaction datasets.     

Phytochemical Profile and Herbicidal (Phytotoxic), Antioxidants Potential of Essential Oils from Calycolpus goetheanus (Myrtaceae) Specimens,and in Silico Study

The essential oil (EO) of Calycolpus goetheanus (Myrtaceae) specimens (A, B, and C) were obtained through hydrodistillation. The analysis of the chemical composition of the EOs was by gas chromatography coupled with mass spectrometry CG-MS, and gas chromatography coupled with a flame ionization detector CG-FID. The phytotoxic activity of those EOs was evaluated against two weed species from common pasture areas in the Amazon region: Mimosa pudica L. and Senna obtusifolia (L.) The antioxidant capacity of the EOs was determined by (DPPH^•) and (ABTS^•+). Using molecular docking, we evaluated the interaction mode of the major EO compounds with the molecular binding protein 4-hydroxyphenylpyruvate dioxygenase (HPPD). The EO of specimen A was characterized by β-eudesmol (22.83%), (E)-caryophyllene (14.61%), and γ-eudesmol (13.87%), while compounds 1,8-cineole (8.64%), (E)-caryophyllene (5.86%), δ-cadinene (5.78%), and palustrol (4.97%) characterize the chemical profile of specimen B’s EOs, and specimen C had α-cadinol (9.03%), δ-cadinene (8.01%), and (E)-caryophyllene (6.74%) as the majority. The phytotoxic potential of the EOs was observed in the receptor species M. pudica with percentages of inhibition of 30%, and 33.33% for specimens B and C, respectively. The EOs’ antioxidant in DPPH^• was 0.79 ± 0.08 and 0.83 ± 0.02 mM for specimens A and B, respectively. In the TEAC, was 0.07 ± 0.02 mM for specimen A and 0.12 ± 0.06 mM for specimen B. In the results of the in silico study, we observed that the van der Waals and hydrophobic interactions of the alkyl and pi-alkyl types were the main interactions responsible for the formation of the receptor–ligand complex.     

Antibacterial Activity of Thymus vulgaris L. Essential Oil Vapours and Their GC/MS Analysis Using Solid-Phase Microextraction and Syringe Headspace Sampling Techniques

While the inhalation of Thymus vulgaris L. essential oil (EO) is commonly approved for the treatment of mild respiratory infections, there is still a lack of data regarding the antimicrobial activity and chemical composition of its vapours. The antibacterial activity of the three T. vulgaris EOs against respiratory pathogens, including Haemophilus influenzae, Staphylococcus aureus, and Streptococcus pyogenes, was assessed in both liquid and vapour phases using the broth microdilution volatilisation (BMV) method. With the aim of optimising a protocol for the characterisation of EO vapours, their chemical profiles were determined using two headspace sampling techniques coupled with GC/MS: solid-phase microextraction (HS-SPME) and syringe headspace sampling technique (HS-GTS). All EO sample vapours exhibited antibacterial activity with minimum inhibitory concentrations (MIC) ranging from 512 to 1024 μg/mL. According to the sampling technique used, results showed a different distribution of volatile compounds. Notably, thymol was found in lower amounts in the headspace—peak percentage areas below 5.27% (HS-SPME) and 0.60% (HS-GTS)—than in EOs (max. 48.65%), suggesting that its antimicrobial effect is higher in vapour. Furthermore, both headspace sampling techniques were proved to be complementary for the analysis of EO vapours, whereas HS-SPME yielded more accurate qualitative results and HS-GTS proved a better technique for quantitative analysis.