Piotr Formanowicz

High-density lipoprotein (HDL) is a multifunctional lipoprotein involved in lipid transport, immune regulation, and redox homeostasis. Chronic inflammation and oxidative stress alter HDL composition and function, promoting the formation of dysfunctional HDL particles, impairing cholesterol efflux, and contributing to cardiovascular disease progression. To investigate the regulatory mechanisms underlying these changes, we developed a Petri net-based model of HDL metabolism and extended it to represent inflammatory conditions and selected therapeutic interventions. As a literature-derived qualitative model, it provides a simplified systems-level representation of HDL metabolism and its interactions with inflammation and oxidative stress. Nevertheless, the model captures the essential processes governing these pathways. Using in silico knock-out analysis, we evaluated the impact of disabling individual processes on global network dynamics and identified key regulatory bottlenecks. Network control was highly uneven and concentrated within a limited set of transitions. Under inflammatory conditions, the dominant bottlenecks included reduced LCAT-mediated cholesterol esterification, adiponectin-dependent regulation, and oxidized HDL-associated activity, indicating that HDL dysfunction arises from a tightly interconnected immunometabolic network linking lipid metabolism, inflammation, and oxidative stress. Therapeutic interventions reorganized network control toward the inhibition of inflammation, the restoration of redox balance, and the reconstitution of HDL-mediated cholesterol export. Inhibition of inflammation and oxidation-reduction remained dominant regulatory nodes across pathological and therapeutic states, suggesting a core regulatory backbone of HDL metabolism. These findings demonstrate that inflammation and oxidative stress fundamentally reshape HDL network control and identify a limited set of regulatory bottlenecks that may represent promising targets for therapeutic intervention in cardiovascular and chronic inflammatory diseases. However, because the model represents a simplified qualitative abstraction of HDL biology, the identified bottlenecks should be interpreted as systems-level hypotheses requiring further experimental validation.

In many complex systems that can be modeled using Petri nets, time can be a very important factor which should be taken into account during creation and analysis of the model. Time data can describe the starting moments of some actions or their duration before their immediate effects start to influence some other areas of the modeled system. Places in a Petri net often portray static components of the system, but they can also describe states. Such a state can have time restrictions, for example, indicating how long it can influence other elements in the model. Time values describing some systems may be inconsistent or incomplete, which can cause problems during the creation of the model. In this paper, a new extension of time Petri nets is proposed which allows the creation of models with different types of time data that previously were able to be properly used in separate types of well-known time Petri nets. The new time Petri net introduced here solves this problem by integrating different aspects of already existing time Petri nets into one unified structure.

Operationen an Wortmengen als Mittel zur Bestimmung des Ähnlichkeitsgrades zwischen verwandten Sprachen (am Beispiel des Niedersorbischen und des Polnischen) Die slawischen Sprachen zeichnen sich im Vergleich mit anderen indoeuropäischen Sprachen durch einen hohen Ähnlichkeitsgrad aus, der z. B. im gemeinsamen Wortschatz ersichtlich wird. Die Ähnlichkeit zwischen zwei Vertretern der Gruppe ist unterschiedlich und hängt von den Wechselbeziehungen ab, die sich im Laufe der Jahrhunderte entwickelt haben. Anhand von literarischen Texten als Quellenmaterial werden in dieser Arbeit lexikalische Gemeinsamkeiten und Unterschiede des Niedersorbischen und Polnischen analysiert. Die Studie konzentriert sich auf identische oder ähnliche Wortnotierungen und nimmt spezifische Unterschiede der Vergleichssprachen auf, wobei einige wiederkehrende Unterschiede berücksichtigt werden. In der Analyse wird eine Methode des lexikalischen Vergleichs angewendet, die auf der Grundlage von Levenshtein-Distanzen eine Bewertung von Ähnlichkeitsgraden auf lexikalischer Ebene ermöglicht. Die Textgrundlage ist das Werk „Der kleine Prinz“, das aus dem Französischen in beide Vergleichssprachen übersetzt wurde. Dabei werden sowohl Fälle identischer Notierung als auch vorab definierte Notationsunterschiede in Betracht gezogen.

This study examines algorithmic support for punishment adjustment in judicial discretionary systems, based on the example of drunk-driving cases in Poland. Using an extensive case study, the research explores how algorithm-based sentencing models can address problems in judicial decision-making processes and mitigate inconsistencies arising from misapplied human discretion. The study’s objectives are the following: (1) identify factors influencing punishment severity in drunk-driving cases in Poland; (2) compare these findings to statutory rules governing punishment in Polish criminal proceedings to uncover inconsistencies; (3) evaluate the practical implications of discretion in this category of cases and propose remedial measures; (4) integrate algorithmic sentencing guidelines into the proposed solution; and (5) introduce a novel metric to quantify undesirable judicial decisions, enhancing automated judge-support systems in criminal proceedings.

Graphlet Degree Distribution Agreement (GDDA) is a comparison metric created for graphlets. Unfortunately, it has been reported and confirmed that it has an issue with the stability of result values in low-density graphs. Recently, graphlets have been modified for comparison of Petri net-based models of biological systems. It is crucial to find out if those problems reaper in the case of the modified graphlets, establish a scale of the instability impact on the GDDA value, and find out if it is a common problem for models of biological systems represented by Petri nets. Our results confirmed that the GDDA metric when used for the new graphlets is also unstable for part of the searched density spectrum. Because of that, some biological networks represented by Petri nets are vulnerable to this problem. While instability occurs in the Petri net environment, its characteristics have changed. The scale of an instability impact on the GDDA value is around half of its original size. The localization of vulnerable density areas is different from that in the case of undirected Protein-Protein Interaction (PPI) models. In contrast to the latter, the existence of a high stability area for very sparse models has been observed.

Background/Objectives: Chronic kidney disease (CKD), the most common cause of which is hypertension and diabetes, is a recognized risk factor for cardiovascular disease (CVD). This study investigated the association between selected serum biomarkers in the context of intima-media thickness (IMT) changes, a common predictor of subsequent cardiovascular (CV) events. Methods: A total of 251 individuals were enrolled in the study, divided into groups based on the severity of CKD, the presence of CVD, and healthy controls. For this purpose, the data from the following groups of participants were analyzed: (1) end-stage renal disease (ESRD) (n = 106), (2) pre-dialyzed (PRE) (n = 48), (3) patients at stages 1 and 2 of CKD (CKD1-2) (n = 37), (4) patients with CVD and no kidney disease (CARD) (n = 28), and (5) healthy controls (HV) (n = 31). To find markers associated with elevated IMT, the each group with CVD (ESRD, PRE and CARD) was separated into two subgroups with normal and elevated IMT and compared in the relation of the studied serum biomarkers. Results: The findings identified glucose as the only marker exclusively associated with CVD. Markers uniquely linked to CKD included urea, creatinine, eGFR, total protein, CEL, neopterin, total calcium, phosphates, iPTH, sodium, iron, ferritin, and AST. All other markers reflected a combined influence of both CKD and CVD. By comparing patients with normal and elevated IMT, distinct types of CKD–CVD interactions were observed, i.e., independent (additive effects of CKD and CVD) for MPO, ALP, MMP-9, and MMP-9/TIMP-1; combined (enhanced effect due to interactions) for AOPPs and TIMP-1; and conditional (CVD impact specific to CKD patients) for AGEs, 3-NT, magnesium, UIBC, TIBC, ALT, and TIMP-1/MMP-9. However, certain markers, i.e., CML, sRAGEs, carbamylated protein groups, protein carbamylation, hsCRP, TC, HDL-C, LDL-C, TG, IL-18, klotho, FGF-23, klotho/FGF-23 ratio, potassium, NT-proBNP, and AIP were associated with both CKD and CVD, though the exact nature of their interaction could not be determined using IMT as a distinguishing factor. Conclusions: The results showed that relations between IMT and the remaining studied factors were not trivial, and most of the analyzed parameters were altered in CKD patients, especially if compared to patients with CVD but without CKD. IMT cannot be used as a universal CVD marker.

Background: Cardiovascular diseases have long been studied to identify their causal factors and counteract them effectively. Atherosclerosis, an inflammatory process of the blood vessel wall, is a common cardiovascular disease. Among the many well-known risk factors, hypercholesterolemia is undoubtedly a significant condition for atherosclerotic plaque formation and is linked to atherosclerosis on many levels, i.e. cell interactions, cytokines levels, diet, and lifestyle. Current studies suggest that controlling balance between proinflammatory (M1) and anti-inflammatory (M2) types of macrophages may be used for patient condition improvement and necrotic core reduction. Methods: This study considered the effects of hypercholesterolemia on the population dynamics of macrophages (M0, M1, M2, foam cells) in atherosclerotic plaque. A mathematical model using a matrix approach to population dynamics was proposed and tested in various scenarios. In order to check model sensitivity and variability associated with error propagation, the uncertainty analysis was performed based on the Monte Carlo approach. Results: Simulations of macrophage population dynamics provided the assessment of necrotic core development and plaque instability. Excess lipid levels emerged as the most critical factor for necrotic core development. However, plaque growth can be significantly slowed if macrophages and foam cells can maintain proper lipid levels. This balance may be disrupted by proinflammatory lipids that eventually will increase plaque size, what is also reflected by M1/M2 dynamics. Conclusion: Hypercholesterolemia accelerates atherosclerosis development, leading to earlier cardiovascular incidents. In silico results suggest that reducing lipid intake and portion of proinflammatory lipids is crucial to slowing plaque development and reducing rupture risk, all of which requires preserving fragile M1/M2 balance. Targeting the inflammatory microenvironment and macrophage polarization represents a promising approach for atherosclerosis management.

Graph theory is used in many areas of chemical sciences, especially in molecular chemistry. It is particularly useful in the structural analysis of chemical compounds and in modeling chemical reactions. One of its applications concerns determining the structural formula of a chemical compound. This can be modeled as a variant of the well-known graph realization problem. In the classical version of the problem, a sequence of natural numbers is given, and the question is whether there exists a graph in which the vertices have degrees equal to the given numbers. In the variant considered in this paper, instead of a sequence of natural numbers, a sequence of sets of natural numbers is given, and the question is whether there exists a multigraph such that each of its vertices has a degree equal to a number from one of the sets. This variant of the graph realization problem matches the nature of the problem of determining the structural formula of a chemical compound better than other variants considered in the literature. We propose a polynomial time exact algorithm solving this variant of the problem.