Publications

Publications

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