Open Access

The introduction of fundamental hygiene protocols within the healthcare sector during the nineteenth century led to a significant reduction in mortality rates. Contemporary advancements, such as alcohol‑based sanitizers, have further enhanced hand hygiene practices. However, these measures are often overlooked in nursing facilities, resulting in low staff compliance rates and increased cross‑infection rates. Novel approaches, such as cold plasma hand disinfection, present promising alternatives due to their minimal skin damage and economic benefits. This study aims to compare the disinfectant efficacy of cold plasma aerosol under practical application conditions with an alcoholic hand disinfectant listed by the Association for Applied Hygiene. The microbial count on participants’ hands was measured, with particular attention paid to the spontaneous occurrence of fecal indicators and the presence of potentially infectious bacteria. A t‑test for independent samples was conducted to determine whether there was a significant difference between the two cohorts regarding the research question. Statistical analysis revealed that the mean log colony‑forming unit (CFU) values were significantly lower in the test cohort using only the cold plasma method for hand disinfection compared to the cohort using conventional alcohol‑based hand disinfection. Moreover, it was demonstrated that, unlike alcohol‑based hand disinfection, cold plasma application ensures the effective elimination of Staphylococcus aureus. The findings indicate that staff utilizing plasma disinfection have an average bacterial count that is 0.65 log units lower than those who regularly use alcohol‑based hand disinfection. In addition to the efficacy of cold plasma disinfection, its superiority over alcohol‑based hand disinfection was also established. Beyond offering economic and logistical advantages, cold plasma disinfection provides additional health benefits as it does not induce skin damage, unlike alcohol‑based hand disinfection.

Introduction: The Apple Watch valuably records event-based electrocardiograms (iECG) in children, as shown in recent studies by Paech et al. In contrast to adults, though, the automatic heart rhythm classification of the Apple Watch did not provide satisfactory results in children. Therefore, ECG analysis is limited to interpretation by a pediatric cardiologist. To surmount this difficulty, an artificial intelligence (AI) based algorithm for the automatic interpretation of pediatric Apple Watch iECGs was developed in this study. Methods: A first AI-based algorithm was designed and trained based on prerecorded and manually classified i.e., labeled iECGs. Afterward the algorithm was evaluated in a prospectively recruited cohort of children at the Leipzig Heart Center. iECG evaluation by the algorithm was compared to the 12-lead-ECG evaluation by a pediatric cardiologist (gold standard). The outcomes were then used to calculate the sensitivity and specificity of the Apple Software and the self-developed AI. Results: The main features of the newly developed AI algorithm and the rapid development cycle are presented. Forty-eight pediatric patients were enrolled in this study. The AI reached a specificity of 96.7% and a sensitivity of 66.7% for classifying a normal sinus rhythm. Conclusion: The current study presents a first AI-based algorithm for the automatic heart rhythm classification of pediatric iECGs, and therefore provides the basis for further development of the AI-based iECG analysis in children as soon as more training data are available. More training in the AI algorithm is inevitable to enable the AI-based iECG analysis to work as a medical tool in complex patients.