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opencc-by-4.0Mar 2024View details →

IrCytoToxDB: a dataset of iridium(III) complexes cytotoxicities against various cell lines

<h1>If you use this dataset, please cite our paper: <a href="https://doi.org/10.1038/s41597-024-03735-w">https://doi.org/10.1038/s41597-024-03735-w</a></h1> IrCytoToxDB contains 4546 experimentally measured cytotoxicity values of 1295 unique iridium(III) complexes against 177 different cell lines reported in the 389 literature papers from 2008 to 2025. The 15 columns of this dataset are explained as follows: <ol> <li>L1 — SMILES representation of the L1 ligand attached to the iridium ion</li> <li>L2 — SMILES representation of the L2 ligand attached to the iridium ion</li> <li>L3 — SMILES representation of the L3 ligand attached to the iridium ion</li> <li>L4 — SMILES representation of the L4 ligand attached to the iridium ion</li> <li>Counterion — SMILES representation of the counterion (if the complex molecule is charged)</li> <li>Abbreviation_in_the_article — the original abbreviation depicting the complex in the article</li> <li>IC50Dark(M*10^-6) — value of IC50 originally presented in the article</li> <li>IC50Dark_standard_error(M*10^-6) — standard error of IC50 originally presented in the article</li> <li>IC50Light(M*10^-6) — value of IC50 under irradiation originally presented in the article</li> <li>IC50Light_standard_error(M*10^-6) — standard error of IC50 under irradiation originally presented in the article</li> <li>Excitation_Wavelength(nm) — excitation wavelength related to IC50Light values</li> <li>Irradiation_Time(minutes) — irradiation time related to IC50Light values</li> <li>Irradiation_Power(W*m^-2) — power of light source related to IC50Light values</li> <li>Cell_line — cell line (HeLa, A549, etc.)</li> <li>Time(h) — time of exposure of the complexes to the cell line</li> <li>DOI — doi of a data source for given values</li> <li>Year — year of a data source for given values </li> <li>Comments — additional comments regarding the data</li> </ol> Additional remarks: <ul> <li>The array of iridium(III) complexes could be formally mainly in two parts – bis-cyclometalated Ir(III) complexes and half-sandwich Ir(III) complexes. The former usually contain two cyclometalated ligands and one or two ancillary (or third cyclometalated) ligand; for these L1 and L2 correspond to the cyclometalated ligands and L3 (or L3 and L4) corresponds to the ancillary ligand. The latter usually contain one cyclopentadiene-(Cp-)-based ligand, one bidentate ligand and one monodentate ligand; for these L1 corresponds to the Cp--based ligand, L2 corresponds to the bidentate ligand and L3 corresponds to the monodentate ligand.</li> <li>Some ligands make formally covalent bonds with the Ir(III) ion. For these a negatively charged bond-forming atom is drawn in the SMILES of corresponding ligand.</li> </ul>

opencc-by-4.0Nov 2021View details →

RDF version of the data from Hagar I. Labouta et al. Meta-Analysis of Nanoparticle Cytotoxicity via Data-Mining the Literature. NanoImpact (2019)

This is an RDFied version of the dataset published by Hagar I. Labouta et al. Meta-Analysis of Nanoparticle Cytotoxicity via Data-Mining the Literature. NanoImpact (2019). The original dataset publication DOI: <a href="https://doi.org/10.1021/acsnano.8b07562">https://doi.org/10.1021/acsnano.8b07562</a> The Original publication authors: Hagar I. Labouta, Nasimeh Asgarian, Kristina Rinker, and David T. Cramb

opencc-by-4.0Nov 2021View details →

RDF version of the data from Anastasios G. Papadiamantis et al. Predicting Cytotoxicity of Metal Oxide Nanoparticles Using Isalos Analytics Platform (2020)

This is an RDFied version of the dataset published in Papadiamantis, A.G. et al. Predicting Cytotoxicity of Metal Oxide Nanoparticles Using Isalos Analytics Platform. Nanomaterials 2020, 10, 2017. The original dataset publication DOI: <a href="https://doi.org/10.3390/nano10102017">https://doi.org/10.3390/nano10102017</a> The Original publication authors: Papadiamantis, A.G.; Jänes, J.; Voyiatzis, E.; Sikk, L.; Burk, J.; Burk, P.; Tsoumanis, A.; Ha, M.K.; Yoon, T.H.; Valsami-Jones, E.; Lynch, I.; Melagraki, G.; Tämm, K.; Afantitis, A.

opencc-by-4.0Dec 2021View details →

Complement activation induces excessive T cell cytotoxicity in severe COVID-19: Analysis of single cell data cohort 1 (Berlin).

This repository contains the R Markdown files with the analysis of CyTOF and scRNA-seq data corresponding to cohort 1 (Berlin) analysed in Georg et al. 2021 "Complement activation induces excessive T cell cytotoxicity in severe COVID-19". Additionally, here we include the necessary CyTOF data to reproduce this analysis. CyTOF data: <ul> <li>The debarcoded fcs files (before batch-correction) can be found in <a href="https://flowrepository.org/id/FR-FCM-Z4P5">https://flowrepository.org/id/FR-FCM-Z4P5</a>. \</li> <li>Here you can find the necessary data to reproduce the analysis (cytof_analysis.Rmd, cytof_analysis.html): <ul> <li>data_norm_all.csv: single-cell protein expression data (after batch-normalization and in linear scale).</li> <li>data_Tcells_annotated.csv: single-cell protein expression of gated T cells with cluster annotation.</li> <li>phenograph_CD4_k30.csv, phenograph_CD8_k30.csv, phenograph_TCRgd_k30.csv: output from Louvain Clustering computed with PhenoGraph (<a href="https://github.com/jacoblevine/PhenoGraph">https://github.com/jacoblevine/PhenoGraph</a>) per T cell compartment.</li> <li>clusterannotation.csv: annotation for each cluster and metacluster</li> </ul> </li> </ul> scRNA-seq data: <ul> <li>The raw data can be found in <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE175450">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE175450</a></li> <li>Other files to reproduce the analysis (scRNAseq_analysis_1preprocessing.Rmd, scRNAseq_analysis_2clustering.Rmd, scRNAseq_analysis_3convalescent.Rmd): <ul> <li><a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_Sawitzki_RECAST_09_2021.xlsx">scRNAseq_Sawitzki_RECAST_09_2021.xlsx</a>: Single-cell metadata.</li> <li>scRNAseq_samples.tsv: Samples metadata.</li> <li><a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_genelist_annotation.xlsx">scRNAseq_genelist_annotation.xlsx</a>: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE175450">G</a>ene list for the annotation of T cells (Also in Mendeley, see Data and Code Availability).</li> <li><a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_GO_RESPONSE_TO_TYPE_I_INTERFERON.txt">scRNAseq_GO_RESPONSE_TO_TYPE_I_INTERFERON.txt</a>, <a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_GO_DEFENSE_RESPONSE_TO_VIRUS.txt">scRNAseq_GO_DEFENSE_RESPONSE_TO_VIRUS.txt</a>, , <a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_GO_T_CELL_MEDIATED_CYTOTOXICITY.txt">scRNAseq_GO_T_CELL_MEDIATED_CYTOTOXICITY.txt</a>: Gene lists for the signatures “Response to Type I Interferon” , “Defense Response to virus” and “Cytotoxicity” used for GSEA. (Also in Table S2).</li> <li><a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_traj18_trav10.txt">scRNAseq_traj18_trav10.txt</a>,<a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_trbv25.txt">scRNAseq_trbv25.txt</a>: sequences to determine the proportion of TRAV10-TRAJ18-TRBV25 pairing T cell clones across all T cell clusters.</li> </ul> </li> </ul>

opencc-by-4.0Aug 2023View details →

Influence of protein corona on cytotoxicity of metal oxide nanoparticles against human keratinocyte cell line (HaCaT)

The model identified, among the factors determining the cytotoxic properties of metal oxide nanoparticles against HaCaT cell lines, a number of variables related to the processes occurring on the surface of nanoparticles in a biological medium, including the ability to form protein corona. The selected descriptors describe both the electronic structure of the metal oxides that are the components of the nanoparticles, i.e. the ionization potential (IP_ActivM_SM_#1, IP_ActivM_SM_#2) and the initial nanoforms, i.e. the particle size (Primary size) and the percentage content of the metal oxide which is the main component of the nanoparticle (Purity_#1) ; characterize nanoparticles in the medium, i.e. the isoelectric point (PZZP_#2), stability (Stability), potential for dissolution (Dissolution), generation of reactive oxygen species (ROS production) and protein adsorption (Protein adsorption). The listed descriptors reflect the features that are discussed in the literature as potentially related to the toxic effect of nanoparticles.

opencc-by-4.0Aug 2023View details →

Predictive nano-QSAR modeling of the cytotoxicity using epithelial cells obtained from Chinese hamster ovary (CHO-K1 cell line) for hybrid TiO2-based nanomaterials

Results obtained from developed model indicated that the cytotoxicity of hybrid TiO2-based nanomaterials is related to additive electronegativity (χmix) of studied nanomaterials that are indirectly related to the electron generation and ROS formation. ROS production is the most common toxicity cause as discussed in the literature in the case of nanoparticles. The high efficiency of surface modified TiO2-based semiconductors can be attributed to the involvement of TiO2 band gap (Eg) excitation and absence of noble metals at the TiO2 surface. It can be expected that noble metals (i.e. Pd/Pt) may trap holes (h+), at the same time photo-generated electrons can be then transferred from the valence band to the conduction band of TiO2 and to its surface where redox processes were initiated. Thus, observed reduction of the electron–hole pair recombination influences the reactive oxygen species (ROS) formation and the photocatalytic redox process initiation. Since the electronegativity was positively correlated with the cytotoxicity it can be expected that some ions are released from the TiO2 surface easier than others.

opencc-by-4.0Oct 2023View details →

Hepatitis D infection induces IFN-β-mediated NK cell activation and TRAIL-dependent cytotoxicity

The dataset contains bulk RNA-seq read count matrices of NK cells from healthy donors after 48 h of co-culture with hepatitis D virus infected and non-infected HepG2-hNTCP cells. The samples are described in the table RNA-seq_samples.xls that is included with the count matrices. Further details are given in the publication associated with this dataset (Groth et al., Front Immunol, 2023, https://doi.org/10.3389/fimmu.2023.1287367).

Fig. 3 in Tannic acid-modified silver nanoparticles enhance the anti-ACanthamoeba activity of three multipurpose contact lens solutions without increasing their cytotoxicity

Fig. 3 Anti-Acanthamoeba activity of AgTANPs conjugated with ReNu MultiPlus contact lens solution after 6 h of incubation in relation to cytotoxicity

Fig. 5 a–d Acanthamoeba trophozoites after 6 h in Tannic acid-modified silver nanoparticles enhance the anti-ACanthamoeba activity of three multipurpose contact lens solutions without increasing their cytotoxicity

Fig. 5 a–d Acanthamoeba trophozoites after 6 h of incubation. a Control culture in PYG medium. b Incubation with AgTANPs. c Incubation with SCA. d Incubation with AgTANPs conjugated with SCA.The arrow shows a rounded form. All images (× 40) represent the population of treated amoebae and were taken under a live cell imaging microscope (EVOS FLoid Cell Imaging Station). For abbreviations, see Figs. 1 and 2

Fig. 2 in Tannic acid-modified silver nanoparticles enhance the anti-ACanthamoeba activity of three multipurpose contact lens solutions without increasing their cytotoxicity

Fig. 2 Anti-Acanthamoeba activity of AgTANPs conjugated with Solo Care Aqua (SCA) contact lens solution after 6 h of incubation in relation to cytotoxicity

Fig. 4 in Tannic acid-modified silver nanoparticles enhance the anti-ACanthamoeba activity of three multipurpose contact lens solutions without increasing their cytotoxicity

Fig. 4 Anti-Acanthamoeba activity of AgTANPs conjugated with Opti-Free contact lens solution after 6 h of incubation in relation to cytotoxicity

Fig. 1 in Tannic acid-modified silver nanoparticles enhance the anti-ACanthamoeba activity of three multipurpose contact lens solutions without increasing their cytotoxicity

Fig. 1 High-resolution scanning transmission electron microscopy image of the distribution and diameters of the tannic acid-modified silver nanoparticles (AgTANPs)

opencc-zeroMay 2024View details →

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Supplementary data for: Comparison of transcriptomic profiles between HFPO-DA and prototypical PPARa, PPARg, and cytotoxic agents in mouse, rat, and pooled human hepatocytes

Like many per- or polyfluorinated alkyl substances (PFAS), toxicity studies with HFPO-DA (ammonium,2,3,3,3-tetrafluoro-2-(heptafluoropropoxy)-propanoate), a short-chain PFAS used in the manufacture of some types of fluorinated polymers, indicate that the liver is the primary target of toxicity in rodents following oral exposure. Although the current weight of evidence supports the PPARa mode of action (MOA) for liver effects in HFPO-DA-exposed mice, alternate MOAs have also been hypothesized including PPARg or cytotoxicity. To further evaluate the MOA for HFPO-DA in rodent liver, transcriptomic analyses were conducted on samples from primary mouse, rat and pooled human hepatocytes treated for 12, 24 or 72 hours with various concentrations of HFPO-DA, or agonists of PPARa (GW7647), PPARg (rosiglitazone), or cytotoxic agents (i.e., acetaminophen or d-galactosamine). Concordance analyses of enriched pathways across chemicals within each species demonstrated greatest concordance between HFPO-DA and PPARa agonist GW7647-treated hepatocytes compared to the other chemicals evaluated. These findings were supported by benchmark concentration modeling and predicted upstream regulator results. In addition, transcriptomic analyses across species demonstrated a greater transcriptomic response in rodent hepatocytes treated with HFPO-DA or agonists of PPARa or PPARg, indicating rodent hepatocytes are more sensitive to HFPO-DA or PPARa/g agonist treatment. These results are consistent with previously published transcriptomic analyses and further support that liver effects in HFPO-DA-exposed rodents are mediated through rodent-specific PPARa signaling mechanisms as part of the MOA for PPARa activator-induced rodent hepatocarcinogenesis. Thus, effects observed in mouse liver are not appropriate endpoints for toxicity value development for HFPO-DA in human health risk assessment.

Figure 8 in Molecular docking studies and evaluation of the antiretroviral activity and cytotoxicity of the species Lafoensia pacari Saint-Hilaire

Figure 8. Representation of complex isosurface (A, C, and E) and 2D interactions within the enzyme active site (B, D, and F): AG/5TIQ (A, B); AE/5TIQ (C, D); PU/5TIQ (E, F).

Figure 7 in Molecular docking studies and evaluation of the antiretroviral activity and cytotoxicity of the species Lafoensia pacari Saint-Hilaire

Figure 7. Representation of complex ribbons (A, C, and E) and 2D interactions within the enzyme active site (B, D, and F): AG/5TIQ (A, B); AE/5TIQ (C, D); PU/5TIQ (E, F).

Figure 4 in Molecular docking studies and evaluation of the antiretroviral activity and cytotoxicity of the species Lafoensia pacari Saint-Hilaire

Figure 4. Spectra of 1H-NMR (A) and J-resolved 1H-1H (B) expanded on the region of aromatic signals of the acetanolic subfraction - washing water - acids (88.82%) of L. pacari obtained in methanol-d4.

Figure 3 in Molecular docking studies and evaluation of the antiretroviral activity and cytotoxicity of the species Lafoensia pacari Saint-Hilaire

Figure 3. (A) Scores of PCA (71.5% PC1 and 21.3% PC2) of 6 subfractions obtained from the ethanolic extract of L. pacari classified by (RT%) activity of each subfraction. (B) Loadings from PC1 (71.5%) with NMR signals discriminating between subfractions.

Figure 5. Spectrum region HSQC 1H-13C in Molecular docking studies and evaluation of the antiretroviral activity and cytotoxicity of the species Lafoensia pacari Saint-Hilaire

Figure 5. Spectrum region HSQC 1H-13C (A) and HMBC 1H-13C (B) with region of aromatics compounds of the acid wash water acetate subfraction (88.83%) of L. pacari obtained in methanol-d4.

Figure 2 in Molecular docking studies and evaluation of the antiretroviral activity and cytotoxicity of the species Lafoensia pacari Saint-Hilaire

Figure 2. Scores of PCA of obtained subfractions from the ethanolic extract of L.pacari classified by the (RT%) activity of each subfraction.

Figure 3 in Cytotoxicity of iodine-131 radiopharmaceutical in tumor and non-tumor human cells and radioprotection by integral juices of Vitis labrusca L.

Figure 3. Mean absorbance and standard deviation of MRC-5 cells treated with conventional and organic grape juices, exposed or not to UV-C irradiation (10 and 20 µL/mL), treated alone or in cytoprotective tests with I-131 (1.85 MBq/mL). CO: Control; 1x104 cells per well, incubated for 24 and 48 hours, n = 3, Tukey test.*Statistically significant result compared to Control;# Statistically significant result compared to treatment with I-131; & Statistically significant result compared to treatment with the same juice concentration, without I-131.