The bans on visiting nursing homes during the COVID-19 pandemic, while intended to protect residents, also have the risk of increasing the loneliness and social isolation that already existed among the older generations before the pandemic. To combat loneliness and social isolation in nursing homes, this trial presents a study during which social networks of nursing home residents and elderly hospital patients were maintained through virtual encounters and robots, respectively. The observational trial included volunteers who were either residents of nursing homes or patients in a geriatric hospital. Each volunteer was asked to fill in a questionnaire containing three questions to measure loneliness. The questionnaire also documented whether video telephony via the robot, an alternative contact option (for example, a phone call), or no contact with relatives had taken place. The aim was to work out the general acceptance and the benefits of virtual encounters using robots for different roles (users, relatives, nursing staff, facilities). Seventy volunteers with three possible interventions (non-contact, virtual encounters by means of a robot, and any other contact) took part in this trial. The frequency of use of the robot increased steadily over the course of the study, and it was regularly used in all facilities during the weeks of visitor bans (n = 134 times). In the hospital, loneliness decreased significantly among patients for whom the robot was used to provide contact (F(1,25) = 7.783, p = 0.01). In the nursing homes, no demonstrable effect could be achieved in this way, although the subject feedback from the users was consistently positive.

The number of people suffering from chronic wounds is increasing due to demographic changes and the global epidemics of obesity and diabetes. Innovative imaging techniques within the field of chronic wound diagnostics are required to improve wound care by predicting and detecting wound infections to accelerate the application of treatments. For this reason, the infection probability index (IPI) is introduced as a novel infection marker based on thermal wound imaging. To improve usability, the IPI was implemented to automate scoring. Visual and thermal image pairs of 60 wounds were acquired to test the implemented algorithms on clinical data. The proposed process consists of (1) determining various parameters of the IPI based on medical hypotheses, (2) acquiring data, (3) extracting camera distortions using camera calibration, and (4) preprocessing and (5) automating segmentation of the wound to calculate (6) the IPI. Wound segmentation is reviewed by user input, whereas the segmented area can be refined manually. Furthermore, in addition to proof of concept, IPIs’ correlation with C-reactive protein (CRP) levels as a clinical infection marker was evaluated. Based on average CRP levels, the patients were clustered into two groups, on the basis of the separation value of an averaged CRP level of 100. We calculated the IPIs of the 60 wound images based on automated wound segmentation. Average runtime was less than a minute. In the group with lower average CRP, a correlation between IPI and CRP was evident.

Even though animal trials are a controversial topic, they provide knowledge about diseases and the course of infections in a medical context. To refine the detection of abnormalities that can cause pain and stress to the animal as early as possible, new processes must be developed. Due to its noninvasive nature, thermal imaging is increasingly used for severity assessment in animal-based research. Within a multimodal approach, thermal images combined with anatomical information could be used to simulate the inner temperature profile, thereby allowing the detection of deep-seated infections. This paper presents the generation of anatomical thermal 3D models, forming the underlying multimodal model in this simulation. These models combine anatomical 3D information based on computed tomography (CT) data with a registered thermal shell measured with infrared thermography. The process of generating these models consists of data acquisition (both thermal images and CT), camera calibration, image processing methods, and structure from motion (SfM), among others. Anatomical thermal 3D models were successfully generated using three anesthetized mice. Due to the image processing improvement, the process was also realized for areas with few features, which increases the transferability of the process. The result of this multimodal registration in 3D space can be viewed and analyzed within a visualization tool. Individual CT slices can be analyzed axially, sagittally, and coronally with the corresponding superficial skin temperature distribution. This is an important and successfully implemented milestone on the way to simulating the internal temperature profile. Using this temperature profile, deep-seated infections and inflammation can be detected in order to reduce animal suffering.

Expiratory flow limitation (EFL) is an often unrecognized clinical condition with a multitude of negative implications. A mathematical EFL model is proposed to detect flow limitations automatically. The EFL model is a switching one-compartment lung mechanics model with a volume-dependent airway resistance to simulate the dynamic behavior during expiration. The EFL detection is based on a breath-by-breath model parameter identification and validated on clinical data of mechanically ventilated patients. In the severe flow limitation group 93.9 % ± 5 % and in the no limitation group 10.2 % ± 13.7 % of the breaths are detected as EFL. Based on the high detection rate of EFL, these results support the usefulness of the EFL detection. It is a first step toward an automated detection of EFL in clinical applications and may help to reduce underdiagnosis of EFL.

Patient-ventilator interaction critically influences the safety and effectiveness of mechanical ventilation, particularly under support modes such as pressure support ventilation (PSV). This study introduces a mathematical model of the respiratory center that integrates chemical feedback, mechanical feedback, and reflex mechanisms to simulate spontaneous breathing and its interaction with a mechanical ventilator. The model incorporates O₂ and CO₂ chemoreflexes, mechanical feedback, and neural control circuits. Simulation studies evaluated the model's performance under hypercapnia, hypoxia, varying PSV levels, and different types of patient-ventilator asynchrony, comparing outcomes to published literature data. The model accurately reproduced respiratory rate and tidal volume responses with mean absolute percentage errors below 12%, and generated asynchrony frequencies within a 5% acceptance margin. Characteristic ventilatory patterns and emergent asynchronies appeared naturally, without explicit programming of events, demonstrating the physiological plausibility of the model. This respiratory center model provides a robust tool for investigating respiratory control, testing ventilator strategies, and supporting the development of intelligent ventilation approaches, including in silico clinical trials. Its ability to reproduce both normal and pathological ventilatory patterns underscores its potential for translational and clinical research applications.