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opencc-by-4.0Dec 2017View details →

Figs 2A-F in Antiproliferative and genotoxic potential from extracts and fractions of Richardia brasiliensis Gomes (Rubiaceae) by the Allium cepa L. test system

Figs 2A-F. Meristem cells of Allium cepa root strain submitted to treatments with extracts and fractions of Richardia brasiliensis. A. Cell binucleada; B. cell with micronucleus (long black arrow); C. Disorganized prophase (short black arrow); D. Break chromosome (short arrow unfilled) in metaphase; E. Anaphase bridge; F. Break chromosome in telophase. Bars = 10 µm.

Figure 2 in Aquatic pollutants are associated with reproductive alterations and genotoxicity in estuarine fish (Sciades herzbergii - Bloch, 1794) from the Amazon Equatorial Coast

Figure 2. Differential Pulse Voltamograms using ECV/CoPc at different concentrations of Acenaphthene and Naphthalene respectively (1) real sample; (2) 0.0383 mg/L and 0.0319 mg/L (0.249 µmol L-1 and 0.249 µmol L-1); (3) 0.0766 mg/L and 0.0637 mg/L (0.497 µmol L-1 and 0.497 µmol L-1); (4) 0.0152 mg/L and 0.0126 mg/L (0.99 µmol L-1 and 0.99 µmol L-1) of buffer solution BR 0.2 mol L-1, pH 2, v = 0.025 V s-1.

Figure 4 in Aquatic pollutants are associated with reproductive alterations and genotoxicity in estuarine fish (Sciades herzbergii - Bloch, 1794) from the Amazon Equatorial Coast

Figure 4. Biplot of Principal Component Analysis (PCA) applied to the association between variables, Gonadossomatic Index and genotoxic biomarkers on the Ovaries and Gills in S. herzbergii at São Jose Bay. GSI = gonadossomatic index; ATR = atresic ovarian follicles; MMC = ovarian melanomacrophagus; RET = oocyte cytoplasmatic retraction; MN = micronucleus; GHL = gill hyperplasia; GLF = gill lamellar fusion; GC = gill lamelar congestion; GED = gill epithelium displacement.

Figure 2 in Antiproliferative, genotoxic and mutagenic potential of synthetic chocolate food flavoring

Figure 2. Normal cells and cells with chromosomal and nuclear alterations observed in root meristem cells of A. cepa exposed to different concentrations of chocolate flavoring. (A) interphase with two active regions; (B) normal prophase; (C) normal metaphase; (D) normal anaphase; (E) normal telophase; (F) micronucleus (arrow); (G) nuclear nuds, and (H) (arrow) - chromosomal break.

Figure 1 in Antiproliferative, genotoxic and mutagenic potential of synthetic chocolate food flavoring

Figure 1. Cell cycle phase indices obtained for the different concentrations of chocolate flavoring at exposure times 0 hour (Co), 24h and 48 hours. Co: Control; NC: Negative Control.

opencc-by-4.0Jan 2019View details →

Overview of available toxicity data for calystegines - results of the in silico genotoxicity predictions

Results of the in silico genotoxicity predictions complementing the EFSA scientific report on calystegines: https://doi.org/10.2903/j.efsa.2019.5574

Figure 5 in Determination of oxidative, genotoxic, and histopathologic effects of metal pollution on the fish fauna inhabiting Karasu River, Turkey

Figure 5. Pathological damages in A. mossulensis A) separation from the granular layer (black arrow), hyperemia (orange arrows) and infiltration (red arrow) in the cerebellum, B) separation in pia mater (black arrow), congestion and damage in vein (orange arrow), degeneration in periventricular layer (green arrow) and axonopathy (circle) C) dilatation (orange arrow) and vacuolization (yellow arrows) in the cortex H & E.

Figure 4 in Determination of oxidative, genotoxic, and histopathologic effects of metal pollution on the fish fauna inhabiting Karasu River, Turkey

Figure 4. Pathological damages in brain of C. capoeta A) vascular dilatation (orange arrow), degeneration (red arrow) and dilatation (black arrow), B) vacuolization (yellow arrows) and infiltration (blue arrow) in the Purkinje layer, C) vascular dilatation with congestion (orange arrow) H & E.

Figure 1 in Determination of oxidative, genotoxic, and histopathologic effects of metal pollution on the fish fauna inhabiting Karasu River, Turkey

Figure 1. Sampling stations; Station 1: Dumlu, Station 2: Ilıca, Station 3: Aşkale I, Station 4: Aşkale II.

Figure 2 in Determination of oxidative, genotoxic, and histopathologic effects of metal pollution on the fish fauna inhabiting Karasu River, Turkey

Figure 2. The erythrocytic nuclear abnormalities determined in the three fish species A) micronucleus (arrow) in C. capoeta, B) notched nucleus (arrow) in C. capoeta, C) binucleated nucleus (arrow) in A. mossulensis, D) kidney shaped nucleus (black arrows) and bud nucleus (red arrow) in A. mossulensis, E) lobed nucleus (arrow) in S. cephalus, F) Bud nucleus (arrow) in S. cephalus Giemsa.

opencc-by-4.0Apr 2023View details →

Figure 2 in Genotoxic effects of oxyclozanide on hemocytes of Galleria mellonella (Lepidoptera: Pyralidae) larvae

Figure 2. Tail length (μm) in hemocytes of seventh instar larvae of G. mellonella. Bars represent the means (± SE) of four replicates. Means followed by different letters are significantly different from each other, p <0.05 (LSD Test).

opencc-by-4.0Apr 2023View details →

Figure 4 in Genotoxic effects of oxyclozanide on hemocytes of Galleria mellonella (Lepidoptera: Pyralidae) larvae

Figure 4. Tail moment in hemocytes of seventh instar larvae of G. mellonella. Bars represent the means (± SE) of four replicates. Means followed by different letters are significantly different from each other, p <0.05 (LSD Test).

opencc-by-4.0Apr 2023View details →

Figure 3 in Genotoxic effects of oxyclozanide on hemocytes of Galleria mellonella (Lepidoptera: Pyralidae) larvae

Figure 3. Tail DNA% in hemocytes of seventh instar larvae of G. mellonella. Bars represent the means (± SE) of four replicates. Means followed by different letters are significantly different from each other, p <0.05 (LSD Test).

opencc-by-4.0Feb 2023View details →

In vitro genotoxicity testing using γH2AX biomarker, Microscopy Dataset

The Dataset was used and is supplementary to the paper "In vitro genotoxicity testing using γH2AX biomarker, microscopy and automatic image analysis in ImageJ - a pilot study with valinomycin". It contains both RAW single-channel images and numerical results obtained with BioImage Analysis and evaluation available through the GitHub repository: <a href="https://github.com/martinschatz-cz/genotoxicity-bia">https://github.com/martinschatz-cz/genotoxicity-bia</a>. Naming Convention for images ChannelName_YYYYMMD_Well_PossitionInWell_AcqRun.tiff Naming Convention for results Well_AllResults_YYYY-MM-DD_Results.csv The csv are ‘,’ separated, and automatically named by analysis script.   Folder Structure <ul> <li> Images (1069 files, as Images.zip) <ul> <li> 4H - all images for all wells </li> <li> 24H - all images for all wells </li> </ul> </li> <li> Results (36 files) <ul> <li> Results_4H (as Results_4h.zip) </li> <li> Results_24H (as Results_24h.zip) </li> </ul> </li> </ul>   Measurement Settings <ul> <li> Manufacturer and model of microscope: Olympus IX83 P2ZF </li> <li> Objective lens magnification, NA: 10x Olympus IX3 Nosepiece, LensNA=0.3 </li> <li> Excitation filters (mounted in the light source) <ul> <li> Violet:  395/25nm           LED module 1, DAPI </li> <li> Green: 555/28nm         LED module 5, Cy3 </li> </ul> </li> <li> Quad band filter set for DAPI/FITC/Cy3/Cy5 </li> <li> Quad band polychroic mirror (mounted in the filter turret): <ul> <li> BP 411-454nm, </li> <li> BP 495-536nm, </li> <li> BP 577-617nm </li> <li> BP 655-810nm </li> </ul> </li> <li> Emission filters (mounted in the fast emission filter wheel, infront the camera): <ul> <li> DAPI:               BP 421-445nm </li> <li> Cy3:                 BP 581-619nm </li> </ul> </li> <li> Illumination light source: Lumencor Lumencor Spectra X Lamp </li> <li> Pixel size: 650nm x 650nm </li> <li> Camera manufacturer and model: Hamamatsu ORCA-Flash4.0 </li> <li> Software program(s) and version: OLYMPUS cellSens Dimension 3.2 (Build 23706) </li> <li> Image acquisition settings  <ul> <li> expposure 500 ms </li> <li> gain: 0 </li> <li> binning: 4 x 4 </li> </ul> </li> <li> Experiment manager: ZDC + autofocus, two channels: DAPI and Cy3 </li> </ul>   Image Processing and Analysis The data analysis workflow consists of several stages, each of which was executed by a specific script. Firstly, the raw data were manually cleaned and automatically sorted and organized using sort_wells.ijm in FIJI. Secondly, image analysis was performed using Process_WFolder_macro_v1.ijm in FIJI, which processed the image data and extracted the relevant features. Finally, the results were further processed using SF_dataVis_and_statistics_mean_XYh.ipynb in Python (Jupyter Notebook), which generated the final output in the form of a CSV file.   In this repository, you can access the resulting CSV file, which contains the final results of our analysis. Additionally, we have provided the scripts used to process the data, which are available on our GitHub repository (LINK). You will find instruction how to create local Jupyter Hub for Python scripts. These scripts are accompanied by a short manual that provides an overview of the data analysis workflow and helps users navigate through the code. By making our scripts available, we hope to facilitate transparency and reproducibility of our research. If you encounter any issues, please report them through the GitHub repository: <a href="https://github.com/martinschatz-cz/genotoxicity-bia">https://github.com/martinschatz-cz/genotoxicity-bia</a>. We believe that our work can be useful for other researchers and analysts who are interested in studying similar datasets. We invite you to explore the contents of this repository and use the data and scripts provided here to further your research.   Cell lines and culture conditions Human cervical adenocarcinoma (HeLa) and a Chinese hamster ovary (CHO-K1) cell lines were obtained from American Type Culture Collection (ATCC). The HeLa cells were grown MEM supplemented with 10 % FBS and NEAA. CHO-K1 cells were cultivated with DMEM supplemented with L-proline (final concentration 35 mg/l). The cell incubation took place in a humidified atmosphere of 5% CO2 at 37 °C.   Direct measurement of DNA DSBs The cells were seeded in concentration 0,5 × 105 cells/ml into the 96-well plate (VWR, 10062-900). The cells were rinsed by phosphate buffered saline (PBS;) after 24h incubation, and medium with reduced FBS content (5 %) was added. Valinomycin was dissolved in DMSO and added to cells in two final concentrations (30 and 15 𝞵M). After 4h/24h incubation the visualization was done using and following protocol of HCS DNA Damage Kit. The cells were fixed by 4% paraformaldehyde solution for 15 min at room temperature. The cells were rinsed once by PBS and the permeabilization was performed using Triton® X-100 () solution by incubation for 15 min at room temperature. The wells were rinsed with PBS once and the plate was blocked by 1% BSA blocking solution. After 1 hour incubation at room temperature the blocking solution was removed and 100 𝞵l of pH2AX mouse monoclonal antibody solution (1:1000 in BSA) was pipetted into each well incubated for 1 hour at room temperature. After three times rinsing by PBS the 100 𝞵l of Alexa Fluor® 555 goat anti-mouse IgG (H+L; 1:2000) and Hoechst 33342 (1:6000) solution was incubated for 1 hour at room temperature protected from light. After the incubation the wells were rinsed three times by PBS. The plate was stored with 100 𝞵l in the refrigerator (4 °C) until the image analysis was performed.

opencc-by-4.0Jun 2023View details →

Nanomaterial genotoxicity evaluation using the high-throughput p53-binding protein 1 (53BP1) assay - data from the article published in PlosOne

Data that have been used in the manuscript "Nanomaterial genotoxicity evaluation using the high-throughput p53-binding protein 1 (53BP1) assay" by M. Fontaine et al., published in PlosOne in 2023.

opencc-by-nd-4.0Jul 2017View details →

Database specific for the pesticide active substance and their metabolites, comprising the main genotoxicity endpoints

In 2014, EFSA has commissioned the compilation of a database specific for the pesticide residues including active substances and their metabolites, which comprises different genotoxicity endpoints, i.e. point mutations, structural and numerical chromosome aberrations, and DNA damage. Data collection on individual genotoxicity studies has been retrieved from regulatory toxicological reports (Draft or Renewal Assessment Reports, i.e. DARs or RARs, respectively) as provided by the Rapporteur Member State (RMS) during the pesticide peer review process at European Level. The final EFSA conclusion on the overall genotoxic potential of active substance or metabolites taking into account all available information is not included in the database.   The database contains identity and genotoxicity information on more than 290 active substances and some of their metabolites. The database represents a practical tool to complement in-silico tools i.e. QSAR (Quantitative structure–activity relationship models), grouping and read across for prediction of the genotoxicity hazard of the pesticides residues, and it supposes to enlarge the chemical domains for their application. Format: xls; contact: data.collection@efsa.europa.eu, pesticides.ppr@efsa.europa.eu  DISCLAIMER Without prejudice to the legal notice applicable to EFSA's website available here, the following legal notice applies to the Pesticide genotoxicity database and any documents, data or information contained therein. Users are advised to read this legal notice carefully before accessing, using or reading any document, data or information made available in this context, or making any other use of the Database. The Pesticide genotoxicity database is a compilation of chemical and genotoxicity information on active substances and some of their metabolites. The database contains the results of individual studies as initially assessed by the Rapporteur member state (RMS) and reported in the respective Draft assessment reports (DARs) or Renewal Assessment Reports (RARs). The final EFSA Conclusions on the respective active substances are available to the public on the EFSA Journal. The database includes the data that was available at the moment of compilation of the database (December 2016) and will be updated on a regular basis by including or deleting of information as a result of renewal procedure of active substances (Regulation (EU) No 1107/2009).  EFSA makes no representations or warranties about the accuracy or suitability of any document, information, data provided in the Database. In case of discrepancy between the data provided in the original scientific output (DARs/RARs) and that in this database, preference shall be given to the former. This database does not disclose any commercially sensitive or otherwise confidential information. Unless otherwise stated, the owners of the data compiled in this database are the applicants under Regulation (EU) No 1107/2009, and by acceiding the Database you acknowledge that agreement for reuse of these data should be sought from them. The information provided in the Database and related materials are not intended to constitute advice of any kind or the rendering of consulting, or other professional services of any kind. Acceding the Database does not establish any contractual relationship with EFSA. Users are advised to consult with an attorney, food consultant or other professional to determine what may be best for your individual needs. By acceding the Database, you also acknowledge that the documents, data or information made available by EFSA may contain inaccuracies or errors. The content of the information provided is for your information and use only. It may be subject to change at any time and without prior notice by EFSA.

openMay 2024View details →

Evaluation of in vitro genotoxicity tests

In  human health risk assessment of chemicals and pharmaceuticals, assessment of genetic toxicity for regulatory purposes usually starts with a standard battery of in vitro genotoxicity tests. This battery is needed to cover the different genetic toxicity endpoints. The tests included in the battery (partly) differ in biology, due to which resulting data may seem contradictory, thereby complicating accurate interpretation of the findings. This could be overcome through application of mathematical modelling. One of the advantages of mathematical modelling is that strengths and weaknesses of each test are taken into account. Furthermore, the generated predictions are objective and inform on the associated uncertainties. This approach was explored by the working group “Predictivity of In Vitro Genotoxicity Testing”, formed in the context of the 8th International Workshop on Genotoxicity Testing (IWGT). We applied mathematical modelling to a database with (partly) publicly available in vitro and in vivo data for genotoxicity. The results indicate that a mammalian in vitro clastogenicity test and a mammalian cell gene mutation test together provide the strongest weight-of-evidence for predicting genotoxic potential. Remarkably, the Ames bacterial mutation test appeared to contribute very little to these predictions when used in combination with in vitro genetic toxicity tests using cells of mammalian origin. Further research comparing additional in vitro genotoxicity data with in vivo data will provide additional insights  whether it is indeed time to reconsider the composition of the standard in vitro genotoxicity battery.

Figure 1 in Aquatic pollutants are associated with reproductive alterations and genotoxicity in estuarine fish (Sciades herzbergii - Bloch, 1794) from the Amazon Equatorial Coast

Figure 1. Sampling site: São José Bay, in the Island of Maranhão, Brazil.

Figure 3 in Determination of oxidative, genotoxic, and histopathologic effects of metal pollution on the fish fauna inhabiting Karasu River, Turkey

Figure 3. Pyramidal neurons (circles) in normal fish brain section (A. mossulensis) H & E.