Current technologies have become a source of omnipresent electromagnetic pollution from generated electromagnetic fields and resulting electromagnetic radiation. In many cases this pollution is much stronger than any natural sources of electromagnetic fields or radiation. The harm caused by this pollution is still open to question since there is no clear and definitive evidence of its negative influence on humans. This is despite the fact that extremely low frequency electromagnetic fields were classified as potentially carcinogenic. For these reasons, in recent decades a significant growth can be observed in scientific research in order to understand the influence of electromagnetic radiation on living organisms. However, for this type of research the appropriate selection of relevant model organisms is of great importance. It should be noted here that the great majority of scientific research papers published in this field concerned various tests performed on mammals, practically neglecting lower organisms. In that context the objective of this paper is to systematise our knowledge in this area, in which the influence of electromagnetic radiation on lower organisms was investigated, including bacteria,E. coliandB. subtilis, nematode,Caenorhabditis elegans, land snail,Helix pomatia, common fruit fly,Drosophila melanogaster, and clawed frog,Xenopus laevis.

The objective of this study is to assess the impact of low-frequency electromagnetic fields (LF EMFs) generated by power infrastructure on the nearby environment. Measurements of electric (E) and magnetic (H) field intensities were conducted around high-voltage power lines, transformer stations and facilities related to them. Numerical simulations were also performed to model the distribution of the field values around real buildings in close proximity to power delivery systems. Given the ongoing scientific debate regarding the effects of EMFs on living organisms, the current analysis was based on the existing standards—particularly ICNIRP 2010 guidelines, which set the maximum allowable E and magnetic induction (B) values at 5 kV/m and 200 μT, respectively. Stricter national regulations were also examined, such as Poland’s 1 kV/m E limit in residential areas and Belgium’s 10 μT limit for B. The results showed that while most cases complied with ICNIRP 2010 standards, certain stricter local regulations were exceeded. Specifically, 9 of 14 cases exceeded Poland’s E limits, and 8 failed to meet Belgium’s B requirements. Only in one place—a warehouse near 110 kV power lines (in a critical case)—the ICNIRP limit B was exceeded. These findings underscore the variability in regulatory standards and highlight the need for localized assessments of EMF exposure.

This review summarises potential applications of artificial intelligence (AI) in histopathological diagnostics of leukemias reported in the available literature. It compares existing AI models focused on analysing image-based cellular morphology, trained on datasets of either blood smears or bone marrow slides. Key findings indicate a rising trend in research output and literature on that topic, with models achieving high accuracy rates — up to over 95% in leukemia detection and subtype classification. The implications suggest that AI can significantly enhance diagnostic precision, reduce subjectivity, and streamline workflows in hematopathology. Possible limitations and difficulties of introducing AI to routine diagnostics are also elaborated on. Overall, integrating AI into leukemia diagnostics holds promise for improving early detection, supporting clinical decision-making, and advancing treatment in haematological malignancies.

The article proposes an efficient line detection method using a 2D convolution filter. The proposed method was compared with the Hough transform, the most popular method of straight lines detection. The developed method is suitable for local detection of straight lines with a slope from -45˚ to 45˚. Also, it can be used for curve detection which shape is approximated with the short straight sections. The new method is characterized by a constant computational cost regardless of the number of set pixels. The convolution is performed using the logical conjunction and sum operations. Moreover, design of the developed filter and the method of filtration allows for parallelization. Due to constant computation cost, the new method is suitable for implementation in the hardware structure of real-time image processing systems.

In this paper, a new method for analysing gastroesophageal reflux disease (GERD) is shown. This novel method uses wavelet transform (WT) and wavelet-based fractal analysis (WBFA) on esophageal pH-metry measurements. The esophageal pH-metry is an important diagnostic tool supporting the physician’s work in diagnosing some forms of reflux diseases. Interpreting the results of 24-h pH-metry monitoring is time-consuming, and the conclusions of such an analysis can sometimes be too subjective. There is no strict procedure or reference values to follow when the impedance measurements are assessed. Therefore, an attempt was made to develop a point of reference for the assessment process, helping to distinguish healthy patients from GERD patients. In this approach, wavelet transform (WT) and wavelet-based fractal analysis (WBFA) were used to aid the diagnostic process. With this approach, it was possible to develop two efficient computer methods to classify healthy and sick patients based on the pH measurement data alone. The WT method provided a sensitivity value of 93.33%, with 75% specificity. The results of the fractal analysis confirmed that the tested signals have features that enable their automatic classification and assignment to a group of sick or healthy people. The article will be interesting for those studying the application of wavelet and fractal analysis in biomedical waveforms. The authors included in the work a description of the implementation of the fractal and wavelet analysis, the descriptions of the results of the analyses, and the conclusions drawn from them. The work will also be of interest to those who study the methods of using machine learning and artificial intelligence in computer-aided, automatic medical diagnostics.