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Chemical Actions

Chemical Actions: A broad term referring to the various effects and interactions of chemical substances, including their physiological, biological, and therapeutic actions.
This encompasses the study of how chemicals influence or elicit responses in living organisms, as well the development and application of chemical compounds for medicinal and industrial purposes.
Chemical Actions involves indepht analysis of chemical structure, reactivity, and mechanism of action to understand and harness the power of chemicals for enhancing human health and advanceing scientific discovery.

Most cited protocols related to «Chemical Actions»

1
Constructing a Structure-Based Chemical Ontology from Existing Nomenclature
Each node or category name in ClassyFire’s chemical ontology or ChemOnt, was created by extracting common or existing chemical classification category terms from the scientific literature and available chemical databases. We used existing terms to avoid “reinventing the wheel”. By making use of commonly recognized or widely used terms that already exist in the chemical literature, we believed that the taxonomy (and the corresponding ontology) should be more readily adopted and understood. This dictionary creation process was iterative and required the manual review of a large number of specialized chemical databases, textbooks and chemical repositories. Because the same compounds can often be classified into multiple categories, an analysis of the specificity of each categorical term was performed. Those terms that were determined to be clearly generic (e.g. organic acid, organoheterocyclic compound) or described large numbers of known compounds were assigned to SuperClasses. Terms that were highly specific (e.g. alpha-imino acid or derivatives, yohimbine alkaloids) or which described smaller numbers of compounds that clearly fell within a larger SuperClass were assigned to Classes or SubClasses. This assignment also depended on their relationship to higher-level categories. In some cases multiple, equivalent terms were used to describe the same compounds or categories (imidazolines vs. dihydroimidazoles). To resolve these disputes, the frequency with which the competing terms were used was objectively measured (using Google page statistics or literature count statistics). Those having the highest frequency would generally take precedence. However, attention was also paid to the scientific community and expert panels. When available, the IUPAC term was used to name a specific category. Otherwise, if the experts clearly recommended a set of (less frequently used) terms, these would take precedence over terms initially chosen by our initial “popularity” selection criteria. Examples include the terms “Imidazolines” (229,000 Google hits) and “Dihydroimidazoles” (4590 Google hits). The other popular terms were then added as synonyms. A total of 9012 English synonyms were added to the ChemOnt terminology data set.
In a number of cases, new SuperClass and Class terms were created for chemical categories not explicitly defined in the literature. Of these, the resulting “novel” categories were typically constructed from the IUPAC nomenclature for organic and inorganic compounds. Because our chemical dictionary was built from extant or common terms, it contains many community-specific categories commonly used in the (bio-)chemical nomenclature (e.g. primary amines, steroids, nucleosides). Moreover, due to the diverse nature of active and biologically interesting compounds, many chemical categories linked to specific chemical activities or based on biomimetic skeletons (e.g. alpha-sulfonopeptides, piperidinylpiperidines) were added. For instance, several compounds from the category of imidazo[1,2-a]pyrimidines (CHEMONTID:0004377) have been shown to display GABA(A) antagonist activity, and a potential to treat anxiety disorders [35 (link)].
After all the dictionary terms were identified and compiled (4825 terms to date), each term was formally defined using a precise, yet easily understood text description that included the structural features corresponding to that chemical category (Fig. 3). These formal definitions and the corresponding category mappings formed the basis of the structural classification algorithm and the classification rules described below. Once defined, the terms in this Chemical Classification Dictionary were progressively added to the taxonomic structure to form the structure-based hierarchy underlying ClassyFire’s chemical classification scheme. With the combination of the taxonomic structure and the Chemical Classification Dictionary, ChemOnt can be formally viewed as an ontology (albeit purely a structural ontology).

The chemical taxonomy. The taxonomy is illustrated with the OBO-Edit software, showing definitions synonyms, references, and extended information

Djoumbou Feunang Y., Eisner R., Knox C., Chepelev L., Hastings J., Owen G., Fahy E., Steinbeck C., Subramanian S., Bolton E., Greiner R, & Wishart D.S. (2016). ClassyFire: automated chemical classification with a comprehensive, computable taxonomy. Journal of Cheminformatics, 8, 61.
Publication 2016
Acids Alkaloids Amines Anxiety Disorders Attention Chemical Actions derivatives GABA Antagonists Generic Drugs Imidazolines Imino Acids Inorganic Chemicals Nucleosides Pyrimidines Skeleton Steroids Yohimbine
2
Ensemble Machine Learning for Toxicity Prediction
All the newly added prediction models on the ProTox-II platform are based on machine learning algorithms. A Random Forest (RF) algorithm (26 ) is used to construct the classification and prediction models for hepatotoxicity, cytotoxicity, mutagenicity, and carcinogenicity. The RF-based models are constructed using 500 decision trees and GINI index criterion. The advantage of using RF-based classifier is that it tends to avoid overfitting.
For the construction of theTox21 based toxicological pathway prediction, an ensemble approach is used including RF and Support Vector Machine (SVM) classifiers. The radial basis function (RBF) is used as kernel function for the SVM algorithm. Immunotoxicity prediction model is based on Bernoulli–Naive Bayes algorithm, as explained in the published work (24 (link)).
Here, two different fingerprints are used: MACCS molecular fingerprints-166 bits and Morgan circular fingerprints-2048 bits (http:/www.rdkit.org/). These two fingerprints have shown an optimal performance for prediction of chemical activity (11 ,24 (link)).
Additionally, a selective oversampling of minority class is introduced in the construction of the models. For each of the prediction end-points, the active (positive) and inactive (negative) data are fragmented using RECAP (27 (link)) and ROTBONDS (28 (link)) fragmentation methods. The propensity score (PS) (12 ) for each of the uniquely occurring fragments in both the sets is computed. Only those molecules having the highest propensity scores for fragments conserved for the active class are oversampled and added into model construction. The same ratio of active and inactive compounds was maintained for all the folds of cross-validation, using the fragment-based similarities between the compounds.
The prediction models are based on python programming language. Machine learning packages like scikit-learn (http:/scikit-learn.org) and cheminformatics package RDKit (http:/www.rdkit.org/) are used for the model implementation. All data are standardized using KNIME (29 ). A template script (Sample API script http://tox.charite.de/protox_II/simple_api.py) has been provided under the description ‘using the API’ on the FAQ section of the ProTox-II webserver.
Banerjee P., Eckert A.O., Schrey A.K, & Preissner R. (2018). ProTox-II: a webserver for the prediction of toxicity of chemicals. Nucleic Acids Research, 46(Web Server issue), W257-W263.
Publication 2018
Carcinogens Chemical Actions Cytotoxin MACC combination Minority Groups Mutagens Oxidase, Protoporphyrinogen Python
3
Rate-Equation Approach for Mechanistic Modelling
The rate-equation approach is a well-established methodology employing the mass-action law of chemical reactions for translating biochemical interactions into a mathematical model. Such a model is mechanistic, i.e. each component x and parameter of the network model has its counterpart within the biological process. Thus, it allows to infer knowledge about the underlying network and drives biological discoveries, e.g. in [17 (link)–19 (link)]. Furthermore, in [20 (link)], an overview about unravelling dynamical features from biological systems by ODE modelling is presented. The time evolution x(t) of the concentrations of biochemical compounds is computed by solving the corresponding ODE system
x˙(t)=f(x(t),θx),
with parameters θx, e.g. reaction rates or Hill coefficients. Initial concentrations x0 are either fixed using prior assumptions or estimated from the data. The internal states x(t) are mapped to observations y(t) via an observation function g, i.e.
y(t)=g(x(t,θx,x0),θy)+ϵ(t),
where independent additive Gaussian errors ϵ ∼ N(0, σ2) are assumed. In addition to the dependency on kinetic rates and initial concentrations, the observation function g may introduce observational parameters θy, which are subsumed in θ = {θx, x0, θy}. To quantify the discrepancy between model response and measurements, the scaled negative likelihood-function is calculated via
-2log(L):=L(θ)=∑i=1nyi-g(x(ti,θx,x0),θy)σi2+const.,
for n measured data-points yi with standard deviation σi. If the measurement noise is not normally distributed, transformations resulting in Gaussian errors can be applied in most cases. Often, a log-transformation is sufficient [21 (link)]. Eq 5 can be amended by penalisation terms representing prior knowledge. These can be e.g. quadratic terms for normally distributed parameters, with mean and standard deviation taken from literature, or for derived model components such as the ratio between protein concentrations in different cell compartments. To estimate model parameters, we apply maximum likelihood [22 ] by minimising the scaled negative log-likelihood
L(θ^)=minθL(θ).
leading to an optimised parameter vector θ^ . To ensure positive values and improve numerical performance, all entries of θ are optimised on a logarithmic scale throughout the manuscript. Finding the global optimum can be challenging due to the existence of multiple local minima. Therefore, we performed a deterministic multi-start optimisation strategy with widely dispersed initial guesses [23 (link)].
In general, no analytical solution of the ODE system (Eq (3)) exists or is available, hence numerical solvers are utilised to approximate the dynamics. Here, we used CVODES from the SUNDIALS suite [24 (link)] for ODE integration. The minimisation in Eq (6) is performed numerically using the trust-region-based large-scale non-linear optimisation algorithm lsqnonlin [25 (link)] as implemented in MATLAB. Gradient-based parameter estimation strategies depend on the sensitivities of the model function, i.e. inner derivatives of the likelihood. In order to ensure numerical accuracy, we computed and supplied forward sensitivities [26 ] by extending the ODE system with the appropriate sensitivity equations. All model analyses, optimisation and visualisation of model responses for the manuscript were performed using the freely available Data2Dynamics modelling environment [27 (link)] for MATLAB (http://www.data2dynamics.org/). Additionally, implementations of the toy models and analyses using the freely available dMod/cOde packages for R (https://github.com/dkaschek/) can be found in the Supplementary Section 1.
Maiwald T., Hass H., Steiert B., Vanlier J., Engesser R., Raue A., Kipkeew F., Bock H.H., Kaschek D., Kreutz C, & Timmer J. (2016). Driving the Model to Its Limit: Profile Likelihood Based Model Reduction. PLoS ONE, 11(9), e0162366.
Publication 2016
Biological Evolution Biological Processes Biopharmaceuticals Cells Chemical Actions Cloning Vectors derivatives Hypersensitivity Kinetics Proteins
4
Comprehensive Estrogen Receptor Assay Dataset

Provided training set. The data that were suggested to be used by the participants as a training set to develop and optimize their models was derived from ToxCast™ and Tox21 programs (Dix et al. 2007 (link); Huang et al. 2014 (link); Judson et al. 2010 (link)). Concentration-response data from a collection of 18 in vitro HTS assays exploring multiple sites in the mammalian ER pathway were generated for 1,812 chemicals (Judson et al. 2015 (link); U.S. EPA 2014c ). This chemical library included 45 reference ER agonists and antagonists (including negatives), as well as a wide array of commercial chemicals with known estrogen-like activity (Judson et al. 2015 (link)). A mathematical model was developed to integrate the in vitro data and calculate an area under the curve (AUC) score, ranging from 0 to 1, which is roughly proportional to the consensus AC50 value across the active assays (Judson et al. 2015 (link)). A given chemical was considered active if its agonist or antagonist score was higher than 0.01. In order to reduce the number of potential false positives this threshold can be increased to 0.1.
Prediction set. We identified > 50,000 chemicals [at the level of Chemical Abstracts Service Registry Number (CASRN)] for use in this project as a virtual screening library to be prioritized for further testing and regulatory purposes. This set was intended to include a large fraction of all man-made chemicals to which humans may be exposed. These chemicals were collected from different sources with significant overlap and cover a variety of classes, including consumer products, food additives, and human and veterinary drugs. The following list includes the sources used in this project:
This virtual chemical library has undergone stringent chemical structure processing and normalization for use in the QSAR modeling study (see “Chemical Structure Curation”) and made available for download on ToxCast™ Data web site under CERAPP data (https://www3.epa.gov/research/COMPTOX/CERAPP_files.html, PredictionSet.zip) (U.S. EPA 2016 ), is intended to be employed for a large number of other QSAR modeling projects, not just those focused on endocrine-related targets.
Experimental evaluation set. A large volume of estrogen-related experimental data has accumulated in the literature over the past two decades. The information on the estrogenic activity of chemicals was mined and curated to serve as a validation set for predictions of the different models. For this purpose, in vitro experimental data were collected from different overlapping sources, including the U.S. EPA’s HTS assays, online databases, and other data sets used by participants to train models:
The full data set consisted of > 60,000 entries, including binding, agonist, and antagonist information for ~ 15,000 unique chemical structures. For the purpose of this project, this data set was cleaned and made more consistent by removing in vivo data, cytotoxicity information, and all ambiguous entries (missing values, undefined/nonstandard end points, and unclear units). Only 7,547 chemical structures from the experimental evaluation set that overlapped with the CERAPP prediction set, for a total of 44,641 entries, were kept and made available for download on the U.S. EPA ToxCast™ Data web site (https://www3.epa.gov/research/COMPTOX/CERAPP_files.html, EvaluationSet.zip) (U.S. EPA 2016 ). The non-CERAPP chemicals were excluded from the evaluation set (see “Chemical Structure Curation” section). Then, all data entries were categorized into three assay classes: (a) binding, (b) reporter gene/transactivation, or (c) cell proliferation. The training set end point to model is the ER model AUC that parallels the corresponding individual assay AC50 values, and therefore all units for activities in the experimental data set were converted to μM to have approximately equivalent concentration–response values for the evaluation set. Chemicals with cell proliferation assays were considered as actives if they exceeded an arbitrary threshold of 125% proliferation. For entries where testing concentrations were reported in the assay name field, those values were converted to μM and considered as the AC50 value if the compound was reported as active. All inactive compounds were arbitrarily assigned an AC50 value of 1 M.
Mansouri K., Abdelaziz A., Rybacka A., Roncaglioni A., Tropsha A., Varnek A., Zakharov A., Worth A., Richard A.M., Grulke C.M., Trisciuzzi D., Fourches D., Horvath D., Benfenati E., Muratov E., Wedebye E.B., Grisoni F., Mangiatordi G.F., Incisivo G.M., Hong H., Ng H.W., Tetko I.V., Balabin I., Kancherla J., Shen J., Burton J., Nicklaus M., Cassotti M., Nikolov N.G., Nicolotti O., Andersson P.L., Zang Q., Politi R., Beger R.D., Todeschini R., Huang R., Farag S., Rosenberg S.A., Slavov S., Hu X, & Judson R.S. (2016). CERAPP: Collaborative Estrogen Receptor Activity Prediction Project. Environmental Health Perspectives, 124(7), 1023-1033.
Publication 2016
agonists antagonists Biological Assay Cell Proliferation Chemical Actions Cytotoxin Estrogens Food Additives Genes, Reporter Homo sapiens Mammals System, Endocrine Trans-Activation, Genetic Veterinary Drugs
5
Automated Screening of Developmental Toxicity
Our analysis was designed to account for the unusual distributional properties of developmentally normal, spontaneous movement responses, and avoid bias toward one side of a bidirectional behavioral response where either direction could represent aberrant health effects of a chemical. Post-filtering (described in the previous section), the remaining experimental period (21–48 s) was subdivided according to light pulses: background (B) = 9 s prior to first pulse (21–29 s); pulse1 = first pulse of light; latency1 = 1 s immediately following first pulse; excitatory (E) = 8 s between light pulse (32–39 s); pulse2 = second light pulse; latency2 = 1 s immediately following second pulse; refractory (R) = 7 s after second pulse (42–48 s).
The statistical analysis of activity considered only the background (B), excitatory (E), and refractory (R) intervals. The overall pattern of activity within each B, E, or R interval was compared to that interval's negative control (0 μM) activity using a combination of percent change (−50 % change from control for hypoactivity; 75 % change from control for hyperactivity) and a Kolmogorov–Smirnov test (Bonferroni-corrected p value threshold = 0.05/5 concentrations = 0.01). The percent change thresholds for hypo- and hyperactivity were parameterized so that the distributions of negative control responses were equivalent across activity-associated chemicals (i.e., “hits”). The Kolmogorov–Smirnov test compared the empirical cumulative distribution function (eCDF) between the chemical-treated samples and the negative control. For each chemical concentration set of n = 32 embryo wells, the treated wells were compared to vehicle (negative control) wells across two replicate plates for each chemical. This permits scalability of the procedure to custom batches of chemicals in either new experiments or retests (see “Quality control, identifying outliers and quantifying expected responses”).
The 5 dpf developmental morphology data, as described in Truong et al. (2014) (link), were used to estimate the predictive value of this early, 24 hpf response. We modified the calculation of statistical significance from that paper in order to account for changing proportions from censored wells. Fisher's exact test was used to define a morphology lowest effect level (LEL) if the incidence of the 5 dpf morphological endpoints were significantly different between the control fish and a concentration of chemical. Fish indicated as MO24 or 5 dpf mortality (MORT) were not included in the count data used in the test. The relative risk (RR) associating movement at 24 hpf (early effects) and the morphological endpoints measured at 5 dpf (later effects) was estimated from the number of chemicals positive for both early and late effects (true positive = TP); positive for early effects but negative for later effects (false positive = FP); negative for early effects but positive for later effects (false negative = FN); and negative for both early and late effects (true negative = TN), as:
(TPTP+FP)∕(FNFN+TN).
Reif D.M., Truong L., Mandrell D., Marvel S., Zhang G, & Tanguay R.L. (2015). High-throughput characterization of chemical-associated embryonic behavioral changes predicts teratogenic outcomes. Archives of Toxicology, 90, 1459-1470.
Publication 2015
Chemical Actions DNA Replication Embryo Fishes Light Movement Movement Disorders Pulse Rate Pulses TpTp

Most recents protocols related to «Chemical Actions»

1
PARG-Mediated DePARylation Inhibition
Not available on PMC !

Example 6

Recombinant full length PARG protein is generated from Sf9 insect cells. Recombinant PAR is purified from a biochemical assay using PARP1. PARG is incubated with PAR in the presence of DMSO (Negative control, NC) or small chemical compounds for 20 minutes at room temperature. Positive control (PC) only contains PAR. Samples (1 μl) were spotted onto a nitrocellulose membrane. Then, the membrane was baked for 30 minutes at 60° C. and blocked with TBST buffer (0.15 M NaCl, 0.01 M Tris-HCl at pH 7.4, 0.1% Tween 20) supplemented with 5% milk for 30 minutes at room temperature. After washing with TBST, the membrane was incubated with monoclonal anti-PAR antibody (Trevigen, Inc.) for overnight at 4° C. Following standard western blot method, the signals were visualized by chemiluminescent detection. With the chemical inhibition of the dePARylation activity of PARG, we are able to detect the dot signals of PAR.

US11858879B2. PARG inhibitors and method of use thereof (2024-01-02). City of Hope [US]. Inventors: Xiaochun Yu [US], Shih-Hsun Chen [US], Yate-Ching Yuan [US], Hongzhi Li [US], David Horne [US].
Patent 2024
Antibodies, Anti-Idiotypic Biological Assay Buffers Chemical Actions compound 20 Digestion Insecta JTB protein, human Milk, Cow's Monoclonal Antibodies Nitrocellulose PARP1 protein, human Psychological Inhibition Sf9 Cells Sodium Chloride Sulfoxide, Dimethyl Tissue, Membrane Tromethamine Tween 20 Western Blotting
2
Gas Sampling and Analysis Protocol
All the gas samples were collected using a TQC-1500 gas sampler and Fluorinated ethylene propylene (FEP) gas sampling bag. The FEP sampling bag with airtight valve has the characteristics of solvent corrosion resistance, heat resistance, aging resistance and radiation resistance, and is ideal for storing various gaseous samples with strong volatility, corrosion and high chemical activity. To eliminate air pollution as much as possible, we run the gas sampler without connecting the sampling bag and sampling for 1 min to empty the residual gas in the sampler; then we connect the pre-vacuum sampling bag to the sampler and start sampling. After sampling, the valve of the sample bag was closed and sealed by aluminum foil, then the sampler was closed. The gas samples were transported from Kenya to China by ocean shipping and analyzed a month after sampling. Gas samples were analyzed at the Key Laboratory of Petroleum Resources Research, Chinese Academy of Science, Lanzhou, China. The abundances of CO2, N2, O2, and Ar were determined using a PrismaPlus mass spectrometer and gas chromatograph (GC9560). The 3He/4He and 4He/20Ne ratios were determined using a Noblesse mass spectrometer (Noblesse).
Jia Z., Wu H., Peng J., Lu Q., Huang W., Liu C., Wang F., Liu Y, & He M. (2023). The deep origin of ground fissures in the Kenya Rift Valley. Scientific Reports, 13, 3672.
Publication 2023
Air Pollution Aluminum Chemical Actions Chinese Corrosion fluorinated ethylene propylene Gas Chromatography Petroleum Radiation Solvents Vacuum Volatility
3
Measuring Luciferase Activity in HELN RARβ Cells
The HELN retinoic acid receptor (RAR) β cell line was already described (24 (link)). Briefly, we firstly generated the HELN cell line by transfecting HeLa cells with the p-ERE-βGlob-Luc-SVNeo plasmid. Secondly, we transfected HELN cells with pRARβ(ERαDBD)-puro plasmid. pERE-βGlob-Luc-SVNeo contains a luciferase gene driven by an estrogen receptor binding site in front of the β-globin promoter and a neomycin phosphotransferase gene under the control of the SV40 promoter. In pRARβ(ERαDBD), the encoded chimeric RARβ protein contains the DBD of the estrogen receptor α. Puromycin N-acetyl transferase selection marker expression confers resistance to puromycin.
To measure the activity of the chemicals, HELN RARβ cells were seeded at a density of 40 000 cells per well in 96-well white opaque tissue culture plates (Dutscher, Brumath, France). Compounds were added 8 hours later alone in presence of the pan RAR-agonist TTNPB 100 nM or the pan RXR-agonist CD3254 100 nM, and cells were incubated for 16 hours. At the end of incubation, the culture medium was replaced by a medium containing 0.3 mM luciferin. Luciferase activity was measured for 2 seconds in intact living cells using a microBeta Wallac luminometer (Perkin Elmer, Villebon sur Yvette, France). Tests were performed in quadruplicate in at least 3 independent experiments and data were expressed as mean ± SEM. Results are expressed in percentage of the maximal activity obtained in presence of TTNP 100 nM. Dose–response curves were fitted using the sigmoid dose–response function of a graphics and statistics software package (Graph-Pad Prism, version 4, 2003, Graph-Pad Software Inc., San Diego, CA, USA).
Ren X.M., Chang R.C., Huang Y., Amorim Amato A., Carivenc C., Grimaldi M., Kuo Y., Balaguer P., Bourguet W, & Blumberg B. (2023). 2,4-Di-tert-butylphenol Induces Adipogenesis in Human Mesenchymal Stem Cells by Activating Retinoid X Receptors. Endocrinology, 164(4), bqad021.
Publication 2023
4-(2-(5,6,7,8-tetrahydro-5,5,8,8-tetramethyl-2-naphthalenyl)-1-propenyl)benzoic acid beta-Globins CD 3254 Cell Lines Cells Chemical Actions Chimera Estrogen Receptor alpha Estrogen Receptors Genes Genes, vif HeLa Cells Kanamycin Kinase Luciferases Luciferins Neoplasm Metastasis Plasmids prisma Proteins Puromycin puromycin N-acetyltransferase Retinoic Acid Receptor retinoic acid receptor beta Sigmoid Colon Simian virus 40 Tissues
4
Zeolite Reactivity in Concrete Pore Solutions
The investigation of the chemical activity of zeolite in contact with different solutions containing Ca2+, Na+, K+ and SO23− ions involved the monitoring of phase transitions. The testing was based on the assumptions made for the pozzolanic activity tests [24 (link)]. For this, the zeolite powder was placed in alkaline solutions with compositions simulating the pore solution in concrete. Six samples with compositions as detailed in Table 3 were prepared.
The samples were placed in closed plastic containers. Material for XRD characterization was collected after 1, 3, 7, 14, 21, 28 and 180 days. Particular attention was paid to the changes that occurred in the peaks of Ca(OH)2 and clinoptilolite. After testing, the test material was discarded.
Following the X-ray examination, the samples collected after 7 and 180 days were also examined by thermal analysis (DTA-TG) to determine changes in the content of water bound in hydration products and Ca(OH)2 [32 ].
The microstructures of the 21-day samples were examined by scanning electron microscopy.
, & Czapik P. (2023). The Impact of Ions Contained in Concrete Pore Solutions on Natural Zeolites. Materials, 16(4), 1416.
Publication 2023
Attention Chemical Actions clinoptilolite Ions Phase Transition Powder Radiography Scanning Electron Microscopy Zeolites
5
Comprehensive Soil Heavy Metal Risk Evaluation
There are numerous methods to evaluate soil heavy metal risk, the most robust and widely used being the index evaluation method [15 ]. This method is based on the total concentrations of heavy metals, and it can visually reflect the correlation between measured and background concentrations of heavy metals to evaluate their risk in the soil. According to their evaluation criteria, index evaluation methods can be divided into single pollution [16 (link)], Nemeiro [17 (link)], geological accumulation [18 (link)], and potential ecological risk index methods [19 (link)]. Ecological risk evaluation, which is based on the total amount of soil heavy metals, often does not effectively represent the chemical activity and bioavailability of heavy metals. In contrast, evaluation based on the morphology of heavy metals can more realistically predict the ecological risk of soil heavy metals and provide a more scientific basis for pollution prevention and control [20 (link)]. Therefore, morphology-based evaluation methods, including the commonly used risk assessment code (RAC) method [21 (link)] and ratio of secondary phase to primary phase (RSP) method [22 (link)], have become more popular. To select the method that can most objectively and comprehensively reflect the pollution level and ecological risk of soil heavy metals, different evaluation methods should be compared, and the environmental effects and behavioral characteristics of heavy metals should be comprehensively evaluated.
Li Q., Han Z., Tian Y., Xiao H, & Yang M. (2023). Risk Assessment of Heavy Metal in Farmlands and Crops Near Pb–Zn Mine Tailing Ponds in Niujiaotang, China. Toxics, 11(2), 106.
Publication 2023
Chemical Actions Health Risk Assessment Metals, Heavy

Top products related to «Chemical Actions»

1
Cox activity assay kit by Cayman Chemical
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The COX Activity Assay Kit is a laboratory tool designed to measure the activity of the cyclooxygenase (COX) enzyme. This kit provides a quantitative assessment of COX activity, which is an important enzyme involved in the production of prostaglandins and other eicosanoids.
2
2 2 diphenyl 1 picrylhydrazyl (dpph) by Merck Group
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DPPH is a chemical compound used as a free radical scavenger in various analytical techniques. It is commonly used to assess the antioxidant activity of substances. The core function of DPPH is to serve as a stable free radical that can be reduced, resulting in a color change that can be measured spectrophotometrically.
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GraphPad Prism 5 is a data analysis and graphing software. It provides tools for data organization, statistical analysis, and visual representation of results.
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Apigenin is a naturally occurring plant flavonoid compound. It is a light yellow crystalline solid that is widely used as a laboratory reagent in biochemical research.
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Trolox is a water-soluble vitamin E analog that functions as an antioxidant. It is commonly used in research applications as a reference standard for measuring antioxidant capacity.
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Acetonitrile by Merck Group
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Acetonitrile is a colorless, volatile, flammable liquid. It is a commonly used solvent in various analytical and chemical applications, including liquid chromatography, gas chromatography, and other laboratory procedures. Acetonitrile is known for its high polarity and ability to dissolve a wide range of organic compounds.
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Formic acid by Merck Group
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Formic acid is a colorless, pungent-smelling liquid chemical compound. It is the simplest carboxylic acid, with the chemical formula HCOOH. Formic acid is widely used in various industrial and laboratory applications.
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P-hydroxybenzoic acid is a chemical compound that serves as a versatile intermediate for the synthesis of various pharmaceutical and industrial products. It is a white or colorless crystalline solid that has a melting point around 214°C. P-hydroxybenzoic acid is commonly used as a starting material in the production of certain drugs, personal care products, and other chemical derivatives.
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Syringic acid by Merck Group
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Syringic acid is a phenolic compound that can be used as a chemical reagent in laboratory research and analysis. It serves as a standard reference material for analytical techniques such as chromatography and spectroscopy. The specific core function of syringic acid is to act as a calibration and measurement standard for the quantification of similar phenolic compounds in various samples.
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Epicatechin is a natural compound found in various plants and is commonly used in laboratory settings. It serves as a standard reference material for analytical and research purposes. Epicatechin exhibits antioxidant properties and is often employed in the evaluation of antioxidant activity and the development of analytical methods.

More about "Chemical Actions"

Chemical Actions encompasses the diverse effects and interactions of chemical substances, including their physiological, biological, and therapeutic impacts.
This broad term involves the in-depth analysis of chemical structure, reactivity, and mechanism of action to understand and harness the power of chemicals for enhancing human health and advancing scientific discovery.
Key subtopics within Chemical Actions include pharmacology, toxicology, drug development, and green chemistry.
Synonyms and related terms include chemical biology, chemical ecology, and chemical engineering.
Commonly used abbreviations in this field include COX (cyclooxygenase), DPPH (2,2-diphenyl-1-picrylhydrazyl), and ABTS (2,2'-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid)).
Analytical techniques like GraphPad Prism 5 and advanced instrumentation using solvents like acetonitrile and formic acid enable the measurement of parameters like antioxidant activity (Trolox equivalent), enzyme inhibition (COX Activity Assay Kit), and compound identification (P-hydroxybenzoic acid, Syringic acid, Epicatechin).
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