Dr Damian Mikulski awarded for the Best Doctoral Thesis by StatSoft and the Polish Statistical Society

We are delighted to announce that Dr Damian Mikulski has been awarded a prize in the 27th edition of the competition for the best doctoral dissertation using statistical and data analysis methods implemented in the Statistica software suite. The competition is jointly organized by StatSoft Poland and the Polish Statistical Association.

This distinction is particularly meaningful to us, as it highlights the high scientific quality of research carried out at the Department of Biostatistics and Translational Medicine and demonstrates the value of combining advanced statistical methodology with real-world clinical research.

Dr Mikulski received the award for his doctoral thesis entitled “The role of circulating miRNAs in predicting treatment outcomes, complications, and toxicity in autologous hematopoietic stem cell transplantation,” which he defended in May 2025 at the Faculty of Medicine, Medical University of Lodz. The thesis was supervised by Professor Wojciech Fendler, Head of the Department of Biostatistics and Translational Medicine.

The dissertation was based on a series of four original peer-reviewed publications (combined Impact Factor: 20.7; 410 points according to the Polish Ministry of Education and Science). The studies investigated circulating miRNAs in blood serum as potential biomarkers for predicting treatment outcomes and the risk of selected complications – such as bacteremia, other infectious complications, and hepatotoxicity – in patients with hematological diseases, including multiple myeloma and lymphomas, undergoing autologous stem cell transplantation. Beyond standard statistical techniques, such as logistic regression and Cox proportional hazards models, the analyses also incorporated more advanced data-driven approaches, including classification models based on machine learning methods such as neural networks.

Announcement of the competition results: here

ublications included in the doctoral dissertation:
https://link.springer.com/article/10.1186/s40364-024-00585-x
https://pmc.ncbi.nlm.nih.gov/articles/PMC10565214/
https://pmc.ncbi.nlm.nih.gov/articles/PMC11050045/
https://icjournal.org/search.php?where=aview&id=10.3947/ic.2024.0021&code=0086IC&vmode=FULL

Figure legend:
MiRNA-seq analysis results

Samples were drawn at four timepoints: (T1) before conditioning chemotherapy, (T2) on the day of AHSCT (day 0), day + 7 (T3), and + 14 day after AHSCT (T4). (A) heatmap of miRNAs differently expressed across study timepoint assessed by repeated measures ANOVA. The serum miRNA profiles tend to cluster by the study time points- two clusters- “early” (T1 and T2) and “late” (T3 and T4) are visible. One minus Pearson correlation distance metric and complete linkage method were used. (B-H) Plots for seven miRNAs differentially expressed across AHSCT procedure in miRNA-seq stage of the study. Asterisks denote the significance level (paired t-test with Bonferroni correction): *- p ≤ 0.05; **- p ≤ 0.01. (I-J) Volcano plots showing differentially expressed miRNAs in patients with platelet delayed engraftment (DE) (I) and neutrophil DE (J). Red dots represent upregulated miRNAs; blue dots represent downregulated miRNAs; grey dots represent miRNAs with no significant difference. (K) Volcano plot showing differentially expressed miRNAs in patients who developed bacteremia. Only miRNAs in T1 and T2 (before the event occurrence) were included in the analysis to establish potential predictors for further classifier development.

Dr Damian Mikulski awarded for the Best Doctoral Thesis by StatSoft and the Polish Statistical Society