Trends in Nanoparticles for Leishmania Treatment: A Bibliometric and Network Analysis

Leishmaniasis is a neglected tropical illness with a wide variety of clinical signs ranging from visceral to cutaneous symptoms, resulting in millions of new cases and thousands of fatalities reported annually. This article provides a bibliometric analysis of the main authors' contributions, institutions, and nations in terms of productivity, citations, and bibliographic linkages to the application of nanoparticles (NPs) for the treatment of leishmania. The study is based on a sample of 524 Scopus documents from 1991 to 2022. Utilising the Bibliometrix R-Tool version 4.0 and VOSviewer software, version 1.6.17 the analysis was developed. We identified crucial subjects associated with the application of NPs in the field of antileishmanial development (NPs and drug formulation for leishmaniasis treatment, animal models, and experiments). We selected research topics that were out of date and oversaturated. Simultaneously, we proposed developing subjects based on multiple analyses of the corpus of published scientific literature (title, abstract, and keywords). Finally, the technique used contributed to the development of a broader and more specific "big picture" of nanomedicine research in antileishmanial studies for future projects.

Machine learning in antibacterial discovery and development: A bibliometric and network analysis of research hotspots and trends

Machine learning (ML) methods are used in cheminformatics processes to predict the activity of an unknown drug and thus discover new potential antibacterial drugs. This article conducts a bibliometric study to analyse the contributions of leading authors, universities/organisations and countries in terms of productivity, citations and bibliographic linkage. A sample of 1596 Scopus documents for the period 2006-2022 is the basis of the study. In order to develop the analysis, bibliometrix R-Tool and VOSviewer software were used. We determined essential topics related to the application of ML in the field of antibacterial development (Computer model in antibacterial drug design, and Learning algorithms and systems for forecasting). We identified obsolete and saturated areas of research. At the same time, we proposed emerging topics according to the various analyses carried out on the corpus of published scientific literature (Title, abstract and keywords). Finally, the applied methodology contributed to building a broader and more specific "big picture" of ML research in antibacterial studies for the focus of future projects.

Prediction of acute toxicity of pesticides for Americamysis bahia using linear and nonlinear QSTR modelling approaches

Globally, pesticides are toxic substances with wide applications. However, the widespread use of pesticides has received increasing attention from regulatory agencies due to their various acute and chronic effects on multiple organisms. In this study, Quantitative Structure-Toxicity Relationship (QSTR) models were established using Multiple Linear Regression (MLR) and five Machine Learning (ML) algorithms to predict pesticide toxicity in Americamysis bahia. The most influential descriptors included in the MLR model are RBF, JGI2, nCbH, nRCOOR, nRSR, nPO4 and 'Cl-090', with positive contributions to the dependent variable (negative decimal logarithm of median lethal concentration at 96-h). The Random Forest (RF) regression model was superior amongst the five ML models. We observed higher values of R² (0.812) and lower values of RMSE (0.595) and MAE (0.462) in the cross-validation training set and external validation set. Similarly, this study had a high level of fitness and was internally robust and externally predictive compared to models presented in similar studies. The results suggest that the developed QSTR models are suitable for reliably predicting the aquatic toxicity of structurally diverse pesticides and can be used for screening, prioritising new pesticides, filling data gaps and overcoming the limitations of in vivo and in vitro tests.

Machine Learning Study of Metabolic Networks vs ChEMBL Data of Antibacterial Compounds

Antibacterial drugs (AD) change the metabolic status of bacteria, contributing to bacterial death. However, antibiotic resistance and the emergence of multidrug-resistant bacteria increase interest in understanding metabolic network (MN) mutations and the interaction of AD vs MN. In this study, we employed the IFPTML = Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) algorithm on a huge dataset from the ChEMBL database, which contains >155,000 AD assays vs >40 MNs of multiple bacteria species. We built a linear discriminant analysis (LDA) and 17 ML models centered on the linear index and based on atoms to predict antibacterial compounds. The IFPTML-LDA model presented the following results for the training subset: specificity (Sp) = 76% out of 70,000 cases, sensitivity (Sn) = 70%, and Accuracy (Acc) = 73%. The same model also presented the following results for the validation subsets: Sp = 76%, Sn = 70%, and Acc = 73.1%. Among the IFPTML nonlinear models, the k nearest neighbors (KNN) showed the best results with Sn = 99.2%, Sp = 95.5%, Acc = 97.4%, and Area Under Receiver Operating Characteristic (AUROC) = 0.998 in training sets. In the validation series, the Random Forest had the best results: Sn = 93.96% and Sp = 87.02% (AUROC = 0.945). The IFPTML linear and nonlinear models regarding the ADs vs MNs have good statistical parameters, and they could contribute toward finding new metabolic mutations in antibiotic resistance and reducing time/costs in antibacterial drug research.

Anaerobic digestate management, environmental impacts, and techno-economic challenges

Digestate is a nutrient-rich by-product from organic waste anaerobic digestion but can contribute to nutrient pollution without comprehensive management strategies. Some nutrient pollution impacts include harmful algal blooms, hypoxia, and eutrophication. This contribution explores current productive uses of digestate by analyzing its feedstocks, processing technologies, economics, product quality, impurities, incentive policies, and regulations. The analyzed studies found that feedstock, processing technology, and process operating conditions highly influence the digestate product characteristics. Also, incentive policies and regulations for managing organic waste by anaerobic digestion and producing digestate as a valuable product promote economic benefits. However, there are not many governmental and industry-led quality assurance certification systems for supporting commercializing digestate products. The sustainable and safe use of digestate in different applications needs further development of technologies and processes. Also, incentives for digestate use, quality regulation, and social awareness are essential to promote digestate product commercialization as part of the organic waste circular economy paradigm. Therefore, future studies about circular business models and standardized international regulations for digestate products are needed.

Towards machine learning discovery of dual antibacterial drug-nanoparticle systems

Artificial Intelligence/Machine Learning (AI/ML) algorithms may speed up the design of DADNP systems formed by Antibacterial Drugs (AD) and Nanoparticles (NP). In this work, we used IFPTML = Information Fusion (IF) + Perturbation-Theory (PT) + Machine Learning (ML) algorithm for the first time to study of a large dataset of putative DADNP systems composed by >165 000 ChEMBL AD assays and 300 NP assays vs. multiple bacteria species. We trained alternative models with Linear Discriminant Analysis (LDA), Artificial Neural Networks (ANN), Bayesian Networks (BNN), K-Nearest Neighbour (KNN) and other algorithms. IFPTML-LDA model was simpler with values of Sp ≈ 90% and Sn ≈ 74% in both training (>124 K cases) and validation (>41 K cases) series. IFPTML-ANN and KNN models are notably more complicated even when they are more balanced Sn ≈ Sp ≈ 88.5%-99.0% and AUROC ≈ 0.94-0.99 in both series. We also carried out a simulation (>1900 calculations) of the expected behavior for putative DADNPs in 72 different biological assays. The putative DADNPs studied are formed by 27 different drugs with multiple classes of NP and types of coats. In addition, we tested the validity of our additive model with 80 DADNP complexes experimentally synthetized and biologically tested (reported in >45 papers). All these DADNPs show values of MIC < 50 μg mL^-1 (cutoff used) better that MIC of AD and NP alone (synergistic or additive effect). The assays involve DADNP complexes with 10 types of NP, 6 coating materials, NP size range 5-100 nm vs. 15 different antibiotics, and 12 bacteria species. The IFPTML-LDA model classified correctly 100% (80 out of 80) DADNP complexes as biologically active. IFPMTL additive strategy may become a useful tool to assist the design of DADNP systems for antibacterial therapy taking into consideration only information about AD and NP components by separate.

Predicting Metabolic Reaction Networks with Perturbation-Theory Machine Learning (PTML) Models

BACKGROUND

Checking the connectivity (structure) of complex Metabolic Reaction Networks (MRNs) models proposed for new microorganisms with promising properties is an important goal for chemical biology.

OBJECTIVE

In principle, we can perform a hand-on checking (Manual Curation). However, this is a challenging task due to the high number of combinations of pairs of nodes (possible metabolic reactions).

RESULTS

The CPTML linear model obtained using the LDA algorithm is able to discriminate nodes (metabolites) with the correct assignation of reactions from incorrect nodes with values of accuracy, specificity, and sensitivity in the range of 85-100% in both training and external validation data series.

METHODS

In this work, we used Combinatorial Perturbation Theory and Machine Learning techniques to seek a CPTML model for MRNs &gt;40 organisms compiled by Barabasis' group. First, we quantified the local structure of a very large set of nodes in each MRN using a new class of node index called Markov linear indices fk. Next, we calculated CPT operators for 150000 combinations of query and reference nodes of MRNs. Last, we used these CPT operators as inputs of different ML algorithms.

CONCLUSION

Meanwhile, PTML models based on Bayesian network, J48-Decision Tree and Random Forest algorithms were identified as the three best non-linear models with accuracy greater than 97.5%. The present work opens the door to the study of MRNs of multiple organisms using PTML models.

Producción más limpia como estrategia ambiental preventiva en el proceso de elaboración de pasta de cacao. Un caso en la Amazonia Ecuatoriana

El cacao, es uno de los cultivos de mayor importancia en Ecuador, destinado a la exportación para ser utilizado en la elaboración de chocolate en otros países. El objetivo de este trabajo fue analizar el desempeño ambiental y las potencialidades de aplicación de mecanismos de producciones más limpias (PML) en la Asociación de productores de cacao fino de aroma “Tsatsayaku”. Las herramientas de PML utilizadas fueron la revisión ambiental inicial, ecomapas, y análisis del proceso. Los resultados muestran que la generación de mucilago y cascarilla son los principales problemas ambientales del proceso. Se propusieron y evaluaron 2 opciones de mejora para controlar y disminuir la contaminación del proceso. El análisis muestra una relación B/C de 1.38 y el Valor Actual Neto es de $ 36,001.37 con una Tasa Interna de Retorno de 249.29%. Finalmente, la aplicación de prácticas y tecnologías enmarcadas en sistemas de PML puede optimizar la utilización de materias primas y reducir los contaminantes sólidos y líquidos generados.

Ethnopharmacology, phytochemistry and pharmacology of the genus Hedyosmum (Chlorantaceae): A review

ETHNOPHARMACOLOGICAL RELEVANCE

The genus Hedyosmum (family: Chloranthaceae) represents an interesting source of natural active compounds, and the 45 species of this genus are widespread in Central and South America and to a lesser extent Southeast Asia (southern China and western Malaysia). Several species are traditionally used in folk medicine. However, the data made available in recent years have not been organized and compared.

AIM OF THIS REVIEW

The present study is a critical assessment of the state-of-the-art concerning the traditional uses, the phytochemistry and the pharmacology of species belonging to the genus Hedyosmum to suggest further research strategies and to facilitate the exploitation of the therapeutic potential of Hedyosmum species for the treatment of human disorders.

MATERIALS AND METHODS

The present review consists of a systematic overview of scientific literature concerning the genus Hedyosmum published between 1965 and 2018. Moreover, an older text, dated from 1843, concerning the traditional uses of H. bonplandianum Kunth has also been considered. Several databases (Francis & Taylor, Google Scholar, PubMed, SciELO, SciFinder, Springer, Wiley, and The Plant List Database) have been used to perform this work.

RESULTS

Sixteen species of the genus Hedyosmum have been mentioned as traditional remedies, and a large number of ethnomedicinal uses, including for the treatment of pain, depression, migraine, stomach-ache and ovary diseases, have been reported. Five species have been used as flavouring agents, tea substitutes or foods. Sesterterpenes, sesquiterpene lactones, monoterpenes, hydroxycinnamic acid derivatives, flavonoids, and neolignans have been reported as the most important compounds in these species. Studies concerning their biological activities have shown that members of the Hedyosmum genus possesses promising biological properties, such as analgesic, antinociceptive, antidepressant, anxiolytic, sedative, and hypnotic effects. Preliminary studies concerning the antibacterial, antioxidant, antiplasmodial, and antifungal activities of these plants as well as their cytotoxic activities against different tumour cell lines have been reported. Some active compounds from the Hedyosmum genus have been used as starting points for the innovative and bioinspired development of synthetic molecules. A critical assessment of these papers has been performed, and some conceptual and methodological problems have been identified regarding the materials and methods and the experimental design used in these studies, including a lack of ethnopharmacological research.

CONCLUSIONS

The present review partially confirms the basis for some of the traditional uses of Hedyosmum species (mainly H. brasiliense) through preclinical studies that demonstrated their antinociceptive and neuroprotective effects. Due to promising preliminary results, further studies should be conducted on 13-hydroxy-8,9-dehydroshizukanolide and podoandin. Moreover, several essential oils (EOs) from this genus have been preliminarily investigated, and the cytotoxic and antibacterial activities of H. brasiliense and H. sprucei EOs certainly deserve further investigation. From the promising findings of the present analysis, we can affirm that this genus deserves further research from ethnopharmacological and toxicological perspectives.

Beyond model interpretability using LDA and decision trees for α-amylase and α-glucosidase inhibitor classification studies

In this report are used two data sets involving the main antidiabetic enzyme targets α-amylase and α-glucosidase. The prediction of α-amylase and α-glucosidase inhibitory activity as antidiabetic is carried out using LDA and classification trees (CT). A large data set of 640 compounds for α-amylase and 1546 compounds in the case of α-glucosidase are selected to develop the tree model. In the case of CT-J48 have the better classification model performances for both targets with values above 80%-90% for the training and prediction sets, correspondingly. The best model shows an accuracy higher than 95% for training set; the model was also validated using 10-fold cross-validation procedure and through a test set achieving accuracy values of 85.32% and 86.80%, correspondingly. Additionally, the obtained model is compared with other approaches previously published in the international literature showing better results. Finally, we can say that the present results provided a double-target approach for increasing the estimation of antidiabetic chemicals identification aimed by double-way workflow in virtual screening pipelines.

Quantitative structure-activity relationship analysis and virtual screening studies for identifying HDAC2 inhibitors from known HDAC bioactive chemical libraries

Histone deacetylases (HDAC) are emerging as promising targets in cancer, neuronal diseases and immune disorders. Computational modelling approaches have been widely applied for the virtual screening and rational design of novel HDAC inhibitors. In this study, different machine learning (ML) techniques were applied for the development of models that accurately discriminate HDAC2 inhibitors form non-inhibitors. The obtained models showed encouraging results, with the global accuracy in the external set ranging from 0.83 to 0.90. Various aspects related to the comparison of modelling techniques, applicability domain and descriptor interpretations were discussed. Finally, consensus predictions of these models were used for screening HDAC2 inhibitors from four chemical libraries whose bioactivities against HDAC1, HDAC3, HDAC6 and HDAC8 have been known. According to the results of virtual screening assays, structures of some hits with pair-isoform-selective activity (between HDAC2 and other HDACs) were revealed. This study illustrates the power of ML-based QSAR approaches for the screening and discovery of potent, isoform-selective HDACIs.