Schön Klinik Neustadt

The global challenge of microbial resistance to antimicrobial drugs has prompted the search for new agents from natural sources like medicinal plants. This study focuses on Moeroris amara, a traditional Indian medicinal plant, to assess its antibacterial potential against Staphylococcus aureus (ATCC 6538) and Escherichia coli (ATCC 25922), common causes of skin and urinary tract infections, respectively. Samples were collected from Aliaga, Nueva Ecija, and ethanol (95%) was used to extract the plant components. Proximate analysis determined the nutritional composition, while phytochemical screening assessed Total Phenolic Content (TPC) and Total Flavonoid Content (TFC). Disk diffusion method was used to assess the antibacterial activity. The analysis revealed Moeroris amara leaves contain 7.6% ash, 5.4% crude fat, 15.0% crude fiber, 13.6% moisture and 19.30% crude protein, suggesting potential health benefits. High levels of phenolic and flavonoid compounds were found, with TPC at 260.65 mg GAE/g and TFC at 193.65 mg RHE/g. The ethanolic leaf extracts exhibited significant inhibitory activity against both bacterial strains. In conclusion, M. amara shows promise as a natural source for antimicrobial agents due to its significant nutritional and antibacterial properties. Its high phenolic and flavonoid content contribute to antioxidant and antibacterial activities. Further research is recommended to explore its potential as functional foods and natural antimicrobial agents.

Since 2016, >21000 patients with prostate cancer (PC) used our personalized online decision aid in routine care in Germany. We analyzed the effects of this online decision aid for men with nonmetastatic PC in a randomized controlled trial. In the randomized controlled EvEnt-PCA trial, 116 centers performed 1:1 allocation of 1115 patients with nonmetastatic PC to use an online decision aid (intervention=I) or a printed brochure (control=C). The primary outcome was treatment decision after 14 mo. The secondary endpoints were knowledge, acceptance, decisional conflict, physician-patient communication, anxiety and depression, decision regret, and quality of life. With complete data from 1000 patients, there was no difference between the two groups in terms of primary or secondary endpoints. In the intervention group, consultation time was shorter (I: 61 min vs C: 70 min, p=0.02), and more patients provided a summary of their preparation (I: 76% vs C: 56%, p<0.001). Life expectancy (I: 53% vs C: 47%, p=0.03), radiotherapy (I: 90% vs C: 84%, p=0.005), rehabilitation (I: 69% vs C: 62%, p=0.02), and psycho-oncology (I: 21% vs C: 16%, p=0.07) were discussed more frequently in the intervention group. Additionally, more counseling sessions with general practitioners (I: 43% vs C: 35%, p=0.01) and radiotherapists (I: 24% vs C: 18%, p=0.03) were held in the intervention group. The use of the online decision aid was associated with structural improvements, for example, significantly shorter doctor-patient consultation time. Our findings suggest stronger guideline adherence in the counseling process and promotion of health literacy. Successful implementation of our online decision aid in routine care can provide an example for solving this key obstacle for shared decision-making.

Laboratory automation has transformed bioanalytical research, yet smaller research laboratories face challenges in adopting such technologies due to limited resources, time, and technical expertise, while already facing complex bioanalytical methods. To address these barriers, we developed a robotic-arm-based camera detection system featuring two software applications designed to simplify laboratory automation. Both applications use fiducial markers (Augmented Reality University of Cordoba (ArUco)), for object detection. The first application creates a 3D digital model of the robot’s environment using ArUco markers and a Python-based Open Computer Vision (OpenCV) simulated stereo vision setup, enabling automated computer-aided design (CAD) in FreeCAD. This facilitates safe and efficient robot arm navigation. The second application integrates a deep learning neural network for automated digital display recognition, achieving an in-house error rate of 1.69%, comparable to manual readings. By leveraging low-cost hardware and open-source software available on GitHub, the system is accessible to smaller research facilities, reducing programming complexity and enabling broader adoption of laboratory automation in bioanalytical workflows. This work demonstrates an affordable and effective solution for integrating robotic arms into scientific workflows, enhancing reproducibility and efficiency in bioanalytical research.