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Graphical abstract, Highlights • New method for DSB repair focus (IRIF) detection and multiparameter analysis. • Trainable deep learning-based method. • Fully automated analysis of multichannel 3D datasets. • Trained and tested on realistic and challenging datasets. • Comparable to expert analysis and superior to available methods., DNA double-strand breaks (DSBs), marked by ionizing radiation-induced (repair) foci (IRIFs), are the most serious DNA lesions and are dangerous to human health. IRIF quantification based on confocal microscopy represents the most sensitive and gold-standard method in radiation biodosimetry and allows research on DSB induction and repair at the molecular and single-cell levels. In this study, we introduce DeepFoci – a deep learning-based fully automatic method for IRIF counting and morphometric analysis. DeepFoci is designed to work with 3D multichannel data (trained for 53BP1 and γH2AX) and uses U-Net for nucleus segmentation and IRIF detection, together with maximally stable extremal region-based IRIF segmentation. The proposed method was trained and tested on challenging datasets consisting of mixtures of nonirradiated and irradiated cells of different types and IRIF characteristics – permanent cell lines (NHDFs, U-87) and primary cell cultures prepared from tumors and adjacent normal tissues of head and neck cancer patients. The cells were dosed with 0.5–8 Gy γ-rays and fixed at multiple (0–24 h) postirradiation times. Under all circumstances, DeepFoci quantified the number of IRIFs with the highest accuracy among current advanced algorithms. Moreover, while the detection error of DeepFoci remained comparable to the variability between two experienced experts, the software maintained its sensitivity and fidelity across dramatically different IRIF counts per nucleus. In addition, information was extracted on IRIF 3D morphometric features and repair protein colocalization within IRIFs. This approach allowed multiparameter IRIF categorization of single- or multichannel data, thereby refining the analysis of DSB repair processes and classification of patient tumors, with the potential to identify specific cell subclones. The developed software improves IRIF quantification for various practical applications (radiotherapy monitoring, biodosimetry, etc.) and opens the door to advanced DSB focus analysis and, in turn, a better understanding of (radiation-induced) DNA damage and repair.

Background: Infections, mostly those associated with colonization of wound by different pathogenic microorganisms, are one of the most serious health complications during a medical treatment. Therefore, this study is focused on the isolation, characterization, and identification of microorganisms prevalent in superficial wounds of patients (n = 50) presenting with bacterial infection. Methods: After successful cultivation, bacteria were processed and analyzed. Initially the identification of the strains was performed through matrix-assisted laser desorption/ionization time-of-flight mass spectrometry based on comparison of protein profiles (2–30 kDa) with database. Subsequently, bacterial strains from infected wounds were identified by both matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and sequencing of 16S rRNA gene 108. Results: The most prevalent species was Staphylococcus aureus (70%), and out of those 11% turned out to be methicillin-resistant (mecA positive). Identified strains were compared with patients’ diagnoses using the method of artificial neuronal network to assess the association between severity of infection and wound microbiome species composition. Artificial neuronal network was subsequently used to predict patients’ prognosis (n = 9) with 85% success. Conclusions: In all of 50 patients tested bacterial infections were identified. Based on the proposed artificial neuronal network we were able to predict the severity of the infection and length of the treatment. Keywords: Bacterial strains, MALDI-TOF, Sequencing, Superficial wounds