## 3 Results  
In the control group, the honeybees remained uninfected, whereas in the experimental group, the infestation level of the honeybees was 15.17 \(\times\) 10\({}^{6}\) (\(\pm\)2.16) _N. ceranue_ copy number/bee. In the samples isolated form infected honeybees, _N. ceranue_ spores were visible as characteristic oval shapes (Supplementary Materials Figure S1). _Nosema ceranue_ infection increases from the anterior to the posterior epithelium [13], which is why only the middle parts of the midgut were isolated for the analysis. The viability of cells can be observed using propidium iodide (PI) and Syto 9 stains. PI is a red fluorescent nucleic acid stain and enters only the damaged cells because it is a membrane-impermeant dye that is generally excluded from viable cells. It binds to double-stranded DNA by intercalating between base pairs. PI is excited at 488 and can be used in combination with other fluorochromes excited at 488 nm, such as the SYTO 9 stain, which has an excitation maximum at 483 nm and a fluorescence emission maximum at 503 nm. SYTO 9 stain is an excellent green fluorescent nuclear and chromosome counterstain that is permeant to both prokaryotic and eukaryotic cell membranes [22; 23]. After staining, the intestines of healthy honeybees contained more green (live) cells than samples isolated from infected honeybees. When cells appear green fluorescent, this indicates that the membrane is intact and the cell is alive. Red fluorescence indicates the presence of damaged cells, which were observed in a higher amount in _N. ceranue_-infected honeybees. The PI red fluorescent nucleic acid stain only enters the damaged cells, causing a reduction in the SYTO 9 stain because of its higher affinity to nucleic acid. The intestines isolated from _N. ceranae_-infected honeybees on the 21 st day contained dead cells concentrated in red/dead spots (Figure 1). The formation of such red/dead spots can lead to necrotic changes and even to an interruption of the intestinal continuity. All obtained images were the subject of computer image analysis, which is a method of processing and analyzing selected images stored in a digital system. In image analysis, three basic components can be distinguished: (1) image processing, (2) measurements and (3) the interpretation (analysis) of the results. Image analysis programs have many procedures (algorithms) for transforming images. The main four groups of transformations are as follows: geometric transformations (translations, rotations, reflections, distortions), point transformations (points are modified regardless of their proximity), filters (points are modified depending on their surroundings) and morphological transformations (selected points whose surroundings are modified corresponding to the previously modified pattern). Transformations allow the performance of complex operations related to particle analysis. They consist of removing unnecessary details, carrying out measurements on a generalized image, or enhancing certain image elements that are poorly visible. The most important transformations are as follows: erosion, dilation, and opening and closing. Therefore, even images that are blurred for the observer, as in Figure 1B,D, allow quantitative results (the number of live/dead cells) to be obtained after morphological transformations and the use of image analysis. After the analysis of the obtained images, the number of living cells for healthy and infected bees was obtained. The number of live and dead cells in infected and healthy bees met the requirements of normal distribution for the Kolmogorov-Smirnov test with the Lifofex correction (Figure 2). Based on the results of the significance test and the value of the obtained probability \(p<0.01\) (\(n=800\)), it can be concluded that the difference in the number of live and dead intestinal cells of healthy and infected bees is statistically significant. Figure 1: Example images of intestines isolated from healthy (**A,B**)—magnification 100\(\times\), and \(N\).