Research articles Book chapters Books

Furxhi I., Kalapus M., Costa A. Puzyn T. Artificial augmented dataset for the enhancement of nano-QSARs models. A methodology based on topological projections, Nanotoxicology, 17:6-7, 529-544 (2023) IF: 5,2, link

Fedorowicz, J.,  D. Cruz, C.,  Morawska, M.,  Ciura, K., Gilbert-Girard, S., Mazur, L.,   Mäkkylä, H.,  Ilina, P., Savijoki, K., Fallarero, A., Tammela, P.,  Sączewski, J.,. (2023).  Antibacterial and antibiofilm activity of permanently ionized quaternary ammonium fluoroquinolones, European Journal of Medicinal Chemistry, IF: 6,7, link

Ciura, K., Fryca, I.,  Gromelski, M.,.(2023). Prediction of the retention factor in cetyltrimethylammonium bromide modified micellar electrokinetic chromatography using a machine learning approach Author links open overlay panel, Microchemical journal, IF: 4,8, link

Mikolajczyk A., Zhdan U., Antoniotti S., Smolinski A., Jagiełło K., Skurski P., Harb M., Puzyn T., Polanski J. (2023). Retrosynthesis from transforms to predictive sustainable chemistry and nanotechnology: a brief tutorial review, Green Chemistry,25, 2971-2991, IF[2023] = 9.8, MNiSW = 200, link

Sengottiyan S., Mikolajczyk A., Jagiełło K., Swirog M., Puzyn T. (2023). Core, Coating, or Corona? The Importance of Considering Protein Coronas in nano-QSPR Modeling of Zeta Potential, ACS Nano, 17, 3, 1989–1997, IF[2023] = 18,027, MNiSW = 200,  link

Sengottiyan S., Mikolajczyk A., Puzyn T. (2023). How Does the Study MD of pH-Dependent Exposure of Nanoparticles Affect Cellular Uptake of Anticancer Drugs?, Int. J. Mol. Sci. 24(4), 3479, IF[2023] = 5,6, MNiSW = 140, link

Mancardi G., Mikolajczyk A., Annapoorani V. K., Bahl A., Blekos K., Burk J., Çetin Y. A. , Chairetakis K., Dutta S., Escorihuela L., Jagiello K., Singhal A., van der Pol R., Bañares M. A., Buchete N.-V., Calatayud M., Dumit V. I., Gardini D., Jeliazkova N., Haase A., Marcoulaki E., Martorell B., Puzyn T., Sevink GJ A. , Simeone F. C., Tämm K., Chiavazzo E. (2023). A computational view on nanomaterial intrinsic and extrinsic features for nanosafety and sustainability, Materials Today, corrected proof, IF[2023] = 24,2, MNiSW = 200, link

Kozlowska, L., Jagiello, K., Ciura, K.,  Sosnowska, A.,  Zwiech, R.,  Zbrog, Z., Wasowicz, W., Gromadzinska, J., (2023). The Effects of Two Kinds of Dietary Interventions on Serum Metabolic Profiles in Haemodialysis Patients, Biomolecules  13(5), 854; IF: 6,064 link

G. del Giudice, A. Serra, L. A. Saarimäki, K. Kotsis, I. Rouse, S. A. Colibaba, K. Jagiello, A. Mikolajczyk, M. Fratello, A. G. Papadiamantis, N. Sanabria, M. E. Annala, J. Morikka, P. A. S. Kinaret, E. Voyiatzis, G. Melagraki, A. Afantitis, K. Tämm, T. Puzyn, M. Gulumian, V. Lobaskin, I. Lynch, A. Federico & D. Greco An ancestral molecular response to nanomaterial particulates, Nature Nanotechnology, IF: 40,523, link

Sosnowska A., Bulawska N., Kowalska D., Puzyn, T., (2023) Towards higher scientific validity and regulatory acceptance of predictive models for PFAS, Green Chemistry, 25, 1261-1275, IF:9,8, link

Kowalska D., Sosnowska A., Bulawska N., Stepnik M., Besselink H., Behnisch P., Puzyn., (2023) How the Structure of Per- and Polyfluoroalkyl Substances (PFAS) Influences Their Binding Potency to the Peroxisome Proliferator-Activated and Thyroid Hormone Receptors—An In Silico Screening Study, Molecules, IF: 4,927, link

Hovorun M. D.., Voiteshenko S. I., Gorb L., (2023). Manifestations of intramolecular H-bonds of CH… O and OH… C type in quercetin molecule: Analysis of IR spectra by mean of density functional theory, Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, Volume 287, Part, IF: 4,83, link

Mikolajczyk, A., Falkowski, D. (2022).One Step before Synthesis: Structure–Property–Condition Relationship Models to Sustainable Design of Efficient TiO2-Based Multicomponent Nanomaterials." International Journal of Molecular Sciences 23.21 (2022): 13196. APA, IF: 5,6, link

Swirog, M., Mikolajczyk, A., Jagiello, K., Jänes, J., Tämm, K., & Puzyn, T. (2022). Predicting electrophoretic mobility of TiO2, ZnO, and CeO2 nanoparticles in natural waters: The importance of environment descriptors in nanoinformatics models. Science of The Total Environment, 840, 156572. IF: 9,8, link

Wyrzykowska, E., Mikolajczyk, A., Lynch, I. et al. (2022) Representing and describing nanomaterials in predictive nanoinformatics. Nat. Nanotechnol. 17, 924–932, IF: 40,523, link

Parnicka, P., Lisowski, W., Klimczuk, T., Mikolajczyk, A., & Zaleska-Medynska, A. (2022). A novel (Ti/Ce) UiO-X MOFs@ TiO2 heterojunction for enhanced photocatalytic performance: Boosting via Ce4+/Ce3+ and Ti4+/Ti3+ redox mediators. Applied Catalysis B: Environmental, 310, 121349, IF: 22,1, link

Miodyńska, M., Mikolajczyk, A., Mazierski, P., Klimczuk, T., Lisowski, W., Trykowski, G., & Zaleska-Medynska, A. (2022). Lead-free bismuth-based perovskites coupled with g–C3N4: A machine learning based novel approach for visible light induced degradation of pollutants. Applied Surface Science, 588, 152921., IF: 6,7, link

Sengottiyan, S., Malakar, K., Kathiravan, A., Velusamy, M., Mikolajczyk, A., & Puzyn, T. (2022). Integrated Approach to Interaction Studies of Pyrene Derivatives with Bovine Serum Albumin: Insights from Theory and Experiment. The Journal of Physical Chemistry B., IF: 2,9, link

El Yamani, N., Mariussen, E.,  Gromelski, M.,  Wyrzykowska, E., Grabarek, D., Puzyn, T.,  Tanasescu, S., Dusinska, M., Rundén-Pran E., (2022): Hazard identification of nanomaterials: In silico unraveling of descriptors for cytotoxicity and genotoxicity, NanoToday, IF: 20,72, link 

Pastewska, M., Żołnowska, B., Kovačević, S., Kapica, H., Gromelski, M., Stoliński, F., ... & Ciura, K. (2022). Modeling of Anticancer Sulfonamide Derivatives Lipophilicity by Chemometric and Quantitative Structure-Retention Relationships Approaches. Molecules, 27(13), 3965. IF: 4,927, MNiSW =140, link

Kozłowska, L.; Santonen, T.; Duca, R.C.; Godderis, L.; Jagiello, K.; Janasik, B.; Van Nieuwenhuyse, A.; Poels, K.; Puzyn, T.; Scheepers, P.T.J.; Sijko, M.; Silva, M.J.; Sosnowska, A.; Viegas, S.; Verdonck, J.; Wąsowicz, W.; on behalf of HBM4EU Chromates Study Team; on behalf of Statistical Team. (2022) HBM4EU Chromates Study: Urinary Metabolomics Study of Workers Exposed to Hexavalent Chromium, Metabolites, IF: 4,823, MNiSW =100, link

Stoliński, F., Rybińska-Fryca, A., Gromelski, M., Mikolajczyk, A., & Puzyn, T. (2022). NanoMixHamster: a web-based tool for predicting cytotoxicity of TiO2-based multicomponent nanomaterials toward Chinese hamster ovary (CHO-K1) cells. Nanotoxicology, 16(3), 276-289. IF: 6,612, MNiSW =140, link

Stoliński, F., Gromelski, M.,  Jagiello, K.,  Rybińska-Fryca, A.,  Williams, A.,  Halappanavar, S., Vogel, U.,  Puzyn, T., (2022): AOP173 Key Event associated Pathway Predictor – Online application for the prediction of benchmark dose lower bound (BMDLs) of a transcriptomic pathway involved in MWCNTs-induced lung fibrosis, Nanotoxicology, IF: 6,612, MNiSW =140, link

Jagiello, K., Judzińska, B, Sosnowska, A., Lynch, I., Halappanavar, S., Puzyn, T. (2022): Using AOP-Wiki to support the ecotoxicological risk assessment of nanomaterials: first steps in the development of novel Adverse Outcome Pathways, Environmental Science: Nano, IF: 8,131, MNiSW =140, link

Jagiello, K.; Ciura, K (2022): In Vitro to in vivo extrapolation to support developing Next Generation Risk Assessment (NGRA) strategy for nanomaterials, Nanoscale,IF: 7,79, MNiSW =140  link

Murugadoss S. Vinkovic Vrcek I., Pem B., Jagiello K., Judzinska B., Sosnowska A., Martens A., Willighagen E.L., Puzyn., Dusinska M., Cimpan M.R., Fessard V., Hoet P.H., (2021) A strategy towards the generation of testable adverse outcome pathways for nanomaterials, ALTEX - Alternatives to animal experimentation. IF: 5,787, MNiSW =100 link 

Sizochenko N., Mikolajczyk A., Syzochenko M., Puzyn T., Leszczynski J. Zeta potentials (ζ) of metal oxide nanoparticles: A meta-analysis of experimental data and a predictive neural networks modeling, NanoImpact, Volume 22, 2021, 100317, IF[2023] = 4,9, MNiSW = 100, link

Ciura K., Ptaszyńska N., Kapica H., Pastewska M., Łęgowska A., Rolka K., Kamysz W., Sawicki W., Greber E., (2020) Can Immobilized Artificial Membrane Chromatography Support the Characterization of Antimicrobial Peptide Origin Derivatives? Antibiotics ,10, 1237 IF [2019] = 4.639, MNiSW = 70, link

Ciura K., Kovaćević S., Pastewska M., Kapica H., Kornela M., Sawicki W. (2020) Prediction of the chromatographic hydrophobicity index with immobilized artificial membrane chromatography using simple molecular descriptors and artificial neural networks Journal of Chromatography A 1660, ID: 462666 IF [2019] = 4.759, MNiSW = 100,  link

Kinaret P.A.S, Serra A., Federico A., Kohonen P., Nymark P., Liampa I.,Ha M.K., Choi J-S., Jagiello K.,Sanabria N., Melagraki G., Cattelani L., Fratello M., Sarimveis H., Afantitis A.,Yoon T-H., Gulumian M., Grafström R., Puzyn T., Greco D. (2020): Transcriptomics in Toxicogenomics, Part I: Experimental Design, Technologies, Publicly Available Data, Regulatory Aspects. Nanomaterials, 10(4), 750. IF: 4.034, link 

Federico, A.; Serra, A.; Ha, M.K.; Kohonen, P.; Choi, J.-S.; Liampa, I.; Nymark, P.; Sanabria, N.; Cattelani, L.; Fratello, M.; Kinaret, P.A.S.; Jagiello, K.; Puzyn, T.; Melagraki, G.; Gulumian, M.; Afantitis, A.; Sarimveis, H.; Yoon, T.-H.; Grafström, R.; Greco, D., (2020): Transcriptomics in Toxicogenomics, Part II: Preprocessing and Differential Expression Analysis for High  Quality Data, Nanomaterials,  10(5),  903, link.

Serra, A.; Fratello, M.; Cattelani, L.; Liampa, I.; Melagraki, G.; Kohonen, P.; Nymark, P.; Federico, A.; Kinaret, P.A.S.; Jagiello, K.; Ha, M.K.; Choi, J.-S.; Sanabria, N.; Gulumian, M.; Puzyn, T.; Yoon, T.-H.; Sarimveis, H.; Grafström, R.; Afantitis, A.; Greco, D., (2020): Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment. Nanomaterials, 10(4), 708, link

Jagiello K., Halappanavar S., Rybinska-Fryca A., Williams A., Vogel U., Puzyn T. (2020): Transcriptomic-based and AOP-informed structure-activity relationships to predict pulmonary pathology induced by multiwalled caron nanotubes, Small link

Isigonis P., Afantitis A., Antunes D., Bartonova A., Beitollahi A., Bohmer N., Bouman E., Chaudhry Q., Cimpan M.R., Cimpan E., Doak S., Dupin D., Fedrigo D., Fessard V., Gromelski M., Gutleb A.C., Halappanavar S., Hoet P., Jeliazkova N., Jomini S., Lindner S., Linkov I., Longhin E.M., Lynch I., Malsch I., Marcomini A., de la Fuente J.M., Melagraki G., Murphy F., Neaves M., Packroff R., Pfuhler S., Puzyn T., Rahman Q., Semenzin E., Serchi T., Steinbach Ch., Trump B., Vinković Vrček I., Warheit D., Wiesner M.R., Willighagen E., Dusinska M.. (2020): RiskGONE and NanoSolveIT Project: Risk governance of emerging technologies demonstrated in terms of its applicability to nanomaterials. Nanomaterials, link

Rybińska-Fryca A., Mikolajczyk A., Puzyn T. (2020): Structure–activity prediction networks (SAPNets): a step beyond Nano-QSAR for effective implementation of the safe-by-design concept. Nanoscale, link

Wyrzykowska E., Jagiełło K., Rasulev B., Puzyn T. (2020): Descriptors in nano-QSAR/QSPR modeling. In: Gajewicz A., Puzyn T.: Computational nanotoxicology: challenges and perspectives, 245–302, Jenny Stanford Publishing, Singapore. ISBN 9789814800648

Gajewicz A., Puzyn T.: (Eds.) Computational nanotoxicology: challenges and perspectives. 1st Edition, ISBN 9789814800648. Singapore: Jenny Stanford Publishing, 2020