GCGACNN: A Graph Neural Network and Random Forest for Predicting Microbe-Drug Associations.

The interaction between microbes and drugs encompasses the sourcing of pharmaceutical compounds, microbial drug degradation, the development of drug resistance genes, and the impact of microbial communities on host drug metabolism and immune modulation. These interactions significantly impact drug efficacy and the evolution of drug resistance. In this study, we propose a novel predictive model, termed GCGACNN. We first collected microbe, disease, and drug association data from multiple databases and the relevant literature to construct three association matrices and generate similarity feature matrices using Gaussian similarity functions. These association and similarity feature matrices were then input into a multi-layer Graph Neural Network for feature extraction, followed by a two-dimensional Convolutional Neural Network for feature fusion, ultimately establishing an effective predictive framework. Experimental results demonstrate that GCGACNN outperforms existing methods in predictive performance.

The effects of a care map for total knee replacement patients.

AIMS: The aim of this study was to establish and evaluate the effectiveness of a care map for total knee replacement patients. BACKGROUND: Bureau of National Health Insurance in Taiwan is about to launch a diagnosis-related group. This major reform has seriously affected the running of medical institutions, which are facing unprecedented management pressure. DESIGN: A quasi-experimental control group design was carried out. METHODS: Eighty-three patients were recruited, with 39 experimental group patients received nursing care based on a care map, while 44 patients who were in control group received routine nursing care. An interdisciplinary team designed the care map, which included items required for patient care from outpatient to postdischarge. RESULTS: (1) The mean age of patients was 72·73 (SD 8·42) years. Mean length of stay was 4·92 (SD 0·77) days for the experimental group and 7·09 (SD = 1·09) for the control group. Difference between groups was significant (t = -10·285, p < 0·001). The medical cost for the experimental group was less than that for the control group (t = -6·03, p < 0·001). (2) The self-care efficacy score before discharge for the experimental group was higher than that for the control group (t = 5·90, p < 0·001). (3) Significant improvements were observed in activities of daily living for both groups with the passage of time after discharge (F = 229·034, p < 0·001), and the experimental group was better than the control group (F = 40·895, p < 0·001). The instrumental activities of daily living abilities of both groups were also significant improvements with the passage of time after discharge (F = 46·568, p < 0·001), and the experimental group was better than the control group (F = 32·163, p < 0·001). CONCLUSIONS: A care map for total knee replacement patient can shorten length of stay, save medical cost and improve patient's functional recovery. RELEVANCE TO CLINICAL PRACTICE: Results of this study can be used as a basis for practical implementation of care map in total knee replacement patients.