Binary classification of chalcone derivatives with LDA or KNN based on their antileishmanial activity and molecular descriptors selected using the Successive Projections Algorithm feature-selection technique

Feasibility study on exhaled-breath analysis by untargeted Selected-Ion Flow-Tube Mass Spectrometry in children with cystic fibrosis, asthma, and healthy controls: Comparison of data pretreatment and classification techniques

Selected-Ion Flow-Tube Mass Spectrometry (SIFT-MS) has been applied in a clinical context as diagnostic tool for breath samples using target biomarkers. Exhaled breath sampling is non-invasive and therefore much more patient friendly compared to bronchoscopy, which is the golden standard for evaluating airway inflammation. In the actual pilot study, 55 exhaled breath samples of children with asthma, cystic-fibrosis and healthy individuals were included. Rather than focusing on the analysis of target biomarkers or on the identification of biomarkers, different data analysis strategies, including a variety of pretreatment, classification and discrimination techniques, are evaluated regarding their capacity to distinguish the three classes based on subtle differences in their full scan SIFT-MS spectra. Proper data-analysis strategies are required because these full scan spectra contain much external, i.e. unwanted, variation. Each SIFT-MS analysis generates three spectra resulting from ion-molecule reactions of analyte molecules with H₃O⁺, NO⁺ and O₂⁺. Models were built with Linear Discriminant Analysis, Quadratic Discriminant Analysis, Soft Independent Modelling by Class Analogy, Partial Least Squares - Discriminant Analysis, K-Nearest Neighbours, and Classification and Regression Trees. Perfect models, concerning overall sensitivity and specificity (100% for both) were found using Direct Orthogonal Signal Correction (DOSC) pretreatment. Given the uncertainty related to the classification models associated with DOSC pretreatments (i.e. good classification found also for random classes), other models are built applying other preprocessing approaches. A Partial Least Squares - Discriminant Analysis model with a combined pre-processing method considering single value imputation results in 100% sensitivity and specificity for calibration, but was less good predictive. Pareto scaling prior to Quadratic Discriminant Analysis resulted in 41/55 correctly classified samples for calibration and 34/55 for cross-validation. In future, the uncertainty with DOSC and the applicability of the promising preprocessing methods and models must be further studied applying a larger representative data set with a more extensive number of samples for each class. Nevertheless, this pilot study showed already some potential for the untargeted SIFT-MS application as a rapid pattern-recognition technique, useful in the diagnosis of clinical breath samples.

4-Hydroxyhalcone effects on cisplatin-induced genotoxicity model

BACKGROUND

The genotoxicity of cisplatin (CP) as a platinum-based antineoplastic agent due to its oxidative stress induction was well known. In this research, we examined 4-hydroxychalcone (4-HCH) as a natural food that presents flavonoid effects on reactive oxygen species (ROS) production and CP-induced in vivo genotoxicity.

METHOD AND MATERIALS

Cytotoxicity of CP and 4-HCH was measured on human embryonic kidney 293 cells with MTT assay. Then, intracellular ROS content at IC50 concentration of CP was measured with 2',7'-dichlorofluorescein diacetate (DCFDA) dye. Finally, 4-HCH was administered intraperitoneally at 10 and 40 mg/kg/BW doses as a pre and post-treatment schedule in a mice model of CP genotoxicity (7 mg/kg). Acridine-orange-stained bone marrow cells were quantified for micronucleus presence examination.

RESULTS

The calculated IC50 of CP and 4-HCH were reported around 19.4 and 133.6 μM, respectively, on HEK293 cells. Also, it was observed that 4-HCH at 0.2, 2 and 10 μM concentrations did not show obvious cytotoxicity. The fluorimetry confirmed that pre-treatment with 10 μM and co-treatment with 2 μM of 4-HCH could attenuate the CP-induced ROS production (P < 0.05 and P < 0.01, respectively). Also, the lowest micronucleated cells were seen in 10 mg/kg 4-HCH-treated group after CP exposure (39 ± 7.9, P < 0.0001).

DISCUSSION

Our results demonstrated the antigenotoxic action of 4-HCH in CP-treated mice bone marrow cells for the first time in both concentrations of 10 and 40 mg/kg especially in the form of co-treatment. Further studies required clinical application of this compound in a combination of CP to attenuate the normal cells' genotoxicity side effects.

Role of Melanin Chemiexcitation in Melanoma Progression and Drug Resistance

Melanoma is the deadliest type of skin cancer. Human melanomas often show hyperactivity of nitric oxide synthase (NOS) and NADPH oxidase (NOX), which, respectively, generate nitric oxide (NO ^· ) and superoxide (O₂ ^·- ). The NO ^· and O₂ ^- react instantly with each other to generate peroxynitrite (ONOO^-) which is the driver of melanin chemiexcitation. Melanoma precursors, the melanocytes, are specialized skin cells that synthesize melanin, a potent shield against sunlight's ultraviolet (UV) radiation. However, melanin chemiexcitation paradoxically demonstrates the melanomagenic properties of melanin. In a loop, the NOS activity regulates melanin synthesis, and melanin is utilized by the chemiexcitation pathway to generate carcinogenic melanin-carbonyls in an excited triplet state. These carbonyl compounds induce UV-specific DNA damage without UV. Additionally, the carbonyl compounds are highly reactive and can make melanomagenic adducts with proteins, DNA and other biomolecules. Here we review the role of the melanin chemiexcitation pathway in melanoma initiation, progression, and drug resistance. We conclude by hypothesizing a non-classical, positive loop in melanoma where melanin chemiexcitation generates carcinogenic reactive carbonyl species (RCS) and DNA damage in normal melanocytes. In parallel, NOS and NOX regulate melanin synthesis generating raw material for chemiexcitation, and the resulting RCS and reactive nitrogen species (RNS) regulate cellular proteome and transcriptome in favor of melanoma progression, metastasis, and resistance against targeted therapies.

FRCD: A comprehensive food risk component database with molecular scaffold, chemical diversity, toxicity, and biodegradability analysis

The presence of natural toxins, pesticide residues, and illegal additives in food products has been associated with a range of potential health hazards. However, no systematic database exists that comprehensively includes and integrates all research information on these compounds, and valuable information remains scattered across numerous databases and extensive literature reports. Thus, using natural language processing technology, we curated 12,018 food risk components from 152,737 literature reports, 12 authoritative databases, and numerous related regulatory documents. Data on molecular structures, physicochemical properties, chemical taxonomy, absorption, distribution, metabolism, excretion, toxicity properties, and physiological targets within the human body were integrated to afford the comprehensive food risk component database (FRCD, http://www.rxnfinder.org/frcd/). We also analyzed the molecular scaffold and chemical diversity, in addition to evaluating the toxicity and biodegradability of the food risk components. The FRCD could be considered a highly promising tool for future food safety studies.

Selection of characteristic wavelengths using SPA for laser induced fluorescence spectroscopy of mine water inrush

In the process of prevention and control of water inrush disaster, it is of great significance to identify the type of water inrush source for coal mine safety production accurately and quickly. The application of laser induced fluorescence (LIF) technology to identify the water inrush in coal mine broke the shortage of the traditional hydrochemical method, which could realize the accurate and rapid identification of water inrush types. Firstly, in order to avoid the influence of random variations of spectral data, four kinds of common pretreatment methods were analyzed and compared, and the moving average smoothing method was chosen to preprocess the original fluorescence spectral data. Then, for the purpose of selecting the appropriate sample division method to improve the predictive performance of the model, four common sample division methods were compared, and the sample set partitioning based on joint x-y distance (SPXY) method was used to divide the samples into training set and test set. Further, the 10 characteristic wavelengths were selected by successive projections algorithm (SPA) to reduce the amount of data. Finally, the selected data was taken as input, the sigmoid function was selected as the activation function of extreme learning machine (ELM), and the number of hidden layer neurons was set to 34, which realized the construction of water source identification model. The prediction accuracy of ELM model for the training set and test set were 99.0% and 94.0%, respectively. In addition, the water samples collected at different time were mixed in the same way to form the independent verification set, and the prediction accuracy of the ELM water source identification model for independent verification set was 91.5%. The results shown that it was feasible to select the characteristic wavelengths of fluorescence spectra by using the SPA. The data of 10 characteristic wavelengths could fully represent the effective information of whole band spectrum. And it also provided a theoretical basis for the development of a special online identification instrument for mine water inrush.

Non-destructive prediction of protein content in wheat using NIRS

A steady and accurate model used for quality detection depends on precise data and appropriate analytical methods. In this study, the authors applied partial least square regression (PLSR) to construct a model based on the spectral data measured to predict the protein content in wheat, and proposed a new method, global search method, to select PLSR components. In order to select representative and universal samples for modeling, Monte Carlo cross validation (MCCV) was proposed as a tool to detect outliers, and identified 4 outlier samples. Additionally, improved simulated annealing (ISA) combined with PLSR was employed to select most effective variables from spectral data, the data's dimensionality reduced from 100 to 57, and the standard error of prediction (SEP) decreased from 0.0716 to 0.0565 for prediction set, as well as the correlation coefficients (R²) between the predicted and actual protein content of wheat increased from 0.9989 to 0.9994. In order to reduce the dimensionality of the data further, successive projections algorithm (SPA) was then used, the combination of these two methods was called ISA-SPA. The results indicated that calibration model built using ISA-SPA on 14 effective variables achieved the optimal performance for prediction of protein content in wheat comparing with other developed PLSR models (ISA or SPA) by comprehensively considering the accuracy, robustness, and complexity of models. The coefficient of determination increased to 0.9986 and the SEP decreased to 0.0528, respectively.

Absence of genotoxic effects of the chalcone (E)-1-(2-hydroxyphenyl)-3-(4-methylphenyl)-prop-2-en-1-one) and its potential chemoprevention against DNA damage using in vitro and in vivo assays

The chalcone (E)-1-(2-hydroxyphenyl)-3-(4-methylphenyl)-prop-2-en-1-one), or 2HMC, displays antileishmanial, antimalarial, and antioxidant activities. The aim of this study was to investigate the cytotoxic, genotoxic, mutagenic, and protective effects of 2HMC using the Ames mutagenicity test, the mouse bone marrow micronucleus test, and the comet assay in mice. In the assessment using the Ames test, 2HMC did not increase the number of His⁺ revertants in Salmonella typhimurium strains, demonstrating lack of mutagenicity. 2HMC showed no significant increase in micronucleated polychromatic erythrocyte frequency (MNPCE) in the micronucleus test, or in DNA strand breaks using the comet assay, evidencing absence of genotoxicity. Regarding cytotoxicity, 2HMC exhibited moderate cytotoxicity in mouse bone marrow cells by micronucleus test. 2HMC showed antimutagenic action in co-administration with the positive controls, sodium azide (SA) and 4-nitroquinoline-1-oxide (4NQO), in the Ames test. Co-administered and mainly pre-administered with cyclophosphamide (CPA), 2HMC caused a decrease in the frequency of MNPCE using the micronucleus test and in DNA strand breaks using the comet assay. Thus, 2HMC exhibited antimutagenic and antigenotoxic effects, displaying a DNA-protective effect against CPA, SA, and 4NQO carcinogens. In conclusion, 2HMC presented antimutagenic, antigenotoxic and moderate cytotoxic effects; therefore it is a promising molecule for cancer prevention.

Immunosuppressive Effects of Natural α,β-Unsaturated Carbonyl-Based Compounds, and Their Analogs and Derivatives, on Immune Cells: A Review

The immune system is complex and pervasive as it functions to prevent or limit infections in the human body. In a healthy organism, the immune system and the redox balance of immune cells maintain homeostasis within the body. The failure to maintain the balance may lead to impaired immune response and either over activity or abnormally low activity of the immune cells resulting in autoimmune or immune deficiency diseases. Compounds containing α,β-unsaturated carbonyl-based moieties are often reactive. The reactivity of these groups is responsible for their diverse pharmacological activities, and the most important and widely studied include the natural compounds curcumin, chalcone, and zerumbone. Numerous studies have revealed the mainly immunosuppressive and anti-inflammatory activities of the aforesaid compounds. This review highlights the specific immunosuppressive effects of these natural α,β-unsaturated carbonyl-based compounds, and their analogs and derivatives on different types of immune cells of the innate (granulocytes, monocytes, macrophages, and dendritic cells) and adaptive (T cells, B cells, and natural killer cells) immune systems. The inhibitory effects of these compounds have been comprehensively studied on neutrophils, monocytes and macrophages but their effects on T cells, B cells, natural killer cells, and dendritic cells have not been well investigated. It is of paramount importance to continue generating experimental data on the mechanisms of action of α,β-unsaturated carbonyl-based compounds on immune cells to provide useful information for ensuing research to discover new immunomodulating agents.

An Introduction to the Basic Concepts in QSAR-Aided Drug Design

The need for the development of new drugs to combat existing and newly identified conditions is unavoidable. One of the important tools used in the advanced drug development pipeline is computer-aided drug design. Traditionally, to find a drug many ligands were synthesized and evaluated for their effectiveness using suitable bioassays and if all other drug-likeness features were met, the candidate(s) would possibly reach the market. Although this approach is still in use in advanced format, computational methods are an indispensable component of modern drug development projects. One of the methods used from very early days of rationalizing the drug design approaches is Quantitative Structure-Activity Relationship (QSAR). This chapter overviews QSAR modeling steps by introducing molecular descriptors, mathematical model development for relating biological activities to molecular structures, and model validation. At the end, several successful cases where QSAR studies were used extensively are presented.

An Ensemble Successive Project Algorithm for Liquor Detection Using Near Infrared Sensor

Spectral analysis technique based on near infrared (NIR) sensor is a powerful tool for complex information processing and high precision recognition, and it has been widely applied to quality analysis and online inspection of agricultural products. This paper proposes a new method to address the instability of small sample sizes in the successive projections algorithm (SPA) as well as the lack of association between selected variables and the analyte. The proposed method is an evaluated bootstrap ensemble SPA method (EBSPA) based on a variable evaluation index (EI) for variable selection, and is applied to the quantitative prediction of alcohol concentrations in liquor using NIR sensor. In the experiment, the proposed EBSPA with three kinds of modeling methods are established to test their performance. In addition, the proposed EBSPA combined with partial least square is compared with other state-of-the-art variable selection methods. The results show that the proposed method can solve the defects of SPA and it has the best generalization performance and stability. Furthermore, the physical meaning of the selected variables from the near infrared sensor data is clear, which can effectively reduce the variables and improve their prediction accuracy.

An Introduction to the Basic Concepts in QSAR-Aided Drug Design

The need for the development of new drugs to combat existing and newly identified conditions is unavoidable. One of the important tools used in the advanced drug development pipeline is computer-aided drug design. Traditionally, to find a drug many ligands were synthesized and evaluated for their effectiveness using suitable bioassays and if all other drug-likeness features were met, the candidate(s) would possibly reach the market. Although this approach is still in use in advanced format, computational methods are an indispensable component of modern drug development projects. One of the methods used from very early days of rationalizing the drug design approaches is Quantitative Structure-Activity Relationship (QSAR). This chapter overviews QSAR modeling steps by introducing molecular descriptors, mathematical model development for relating biological activities to molecular structures, and model validation. At the end, several successful cases where QSAR studies were used extensively are presented.

Pharmacological evaluation and docking studies of α,β-unsaturated carbonyl based synthetic compounds as inhibitors of secretory phospholipase A₂, cyclooxygenases, lipoxygenase and proinflammatory cytokines

Arachidonic acid and its metabolites have generated high level of interest among researchers due to their vital role in inflammation. The inhibition of enzymes involved in arachidonic acid metabolism has been considered as synergistic anti-inflammatory effect. A series of novel α,β-unsaturated carbonyl based compounds were synthesized and evaluated for their inhibitory activity on secretory phospholipase A₂ (sPLA₂), cyclooxygenases (COX), soybean lipoxygenase (LOX) in addition to proinflammatory cytokines comprising IL-6 and TNF-α. Six α,β-unsaturated carbonyl based compounds (2, 3, 4, 12, 13 and 14) exhibited strong inhibition of sPLA₂ activity, with IC₅₀ values in the range of 2.19-8.76 μM. Nine compounds 1-4 and 10-14 displayed inhibition of COX-1 with IC₅₀ values ranging from 0.37 to 1.77 μM (lower than that of reference compound), whereas compounds 2, 10, 13 and 14 strongly inhibited the COX-2. The compounds 10-14 exhibited strong inhibitory activity against LOX enzyme. All compounds were evaluated for the inhibitory activities against LPS-induced TNF-α and IL-6 release in the macrophages. On the basis of screening results, five active compounds 3, 4, 12, 13 and 14 were found strong inhibitors of TNF-α and IL-6 release in a dose-dependent manner. Molecular docking experiments were performed to clarify the molecular aspects of the observed COX and LOX inhibitory activities of the investigated compounds. Present findings increases the possibility that these α,β-unsaturated carbonyl based compounds might serve as beneficial starting point for the design and development of improved anti-inflammatory agents.