Schizophrenia (SZ) stands as a severe psychiatric disorder. This study applied diffusion tensor imaging (DTI) data in conjunction with graph neural networks to distinguish SZ patients from normal controls (NCs) and showcases the superior performance of a graph neural network integrating combined fractional anisotropy and fiber number brain network features, achieving an accuracy of 73.79% in distinguishing SZ patients from NCs. Beyond mere discrimination, our study delved deeper into the advantages of utilizing white matter brain network features for identifying SZ patients through interpretable model analysis and gene expression analysis. These analyses uncovered intricate interrelationships between brain imaging markers and genetic biomarkers, providing novel insights into the neuropathological basis of SZ. In summary, our findings underscore the potential of graph neural networks applied to multimodal DTI data for enhancing SZ detection through an integrated analysis of neuroimaging and genetic features.
Humans ; Schizophrenia/pathology* ; Diffusion Tensor Imaging/methods* ; Male ; Female ; Adult ; Brain/metabolism* ; Young Adult ; Middle Aged ; White Matter/pathology* ; Gene Expression ; Nerve Net/diagnostic imaging* ; Graph Neural Networks

Bacteria-mediated anti-tumor therapy has received widespread attention due to its natural tumor-targeting ability and specific immune-activation characteristics. It has made significant progress in breaking the limitations of monotherapy and effectively eradicating tumors, especially when combined with traditional therapy, such as radiotherapy. According to their different biological characteristics, bacteria and their derivatives can not only improve the sensitivity of tumor radiotherapy but also protect normal tissues. Moreover, genetically engineered bacteria and bacteria-based biomaterials have further expanded the scope of their applications in radiotherapy. In this review, we have summarized relevant researches on the application of bacteria and its derivatives in radiotherapy in recent years, expounding that the bacteria, bacterial derivatives and bacteria-based biomaterials can not only directly enhance radiotherapy but also improve the anti-tumor effect by improving the tumor microenvironment (TME) and immune effects. Furthermore, some probiotics can also protect normal tissues and organs such as intestines from radiation via anti-inflammatory, anti-oxidation and apoptosis inhibition. In conclusion, the prospect of bacteria in radiotherapy will be very extensive, but its biological safety and mechanism need to be further evaluated and studied.

Objective:To construct a Traditional Chinese Medicine (TCM) nursing training system for nurses with medium-grade professional title in TCM hospitals based on Kirkpatrick model, so as to provide a reference for the training of nurses with medium-grade professional title in TCM hospitals.Methods:Based on the Kirkpatrick model, combined with the results of literature review, questionnaire survey and semi-structured interviews, the first draft of the training system was formulated and the expert consultation questionnaire was prepared. A total of 21 experts from Zhejiang Province were selected for 2 rounds of expert correspondence.Results:The questionnaire recovery rate of the two rounds of expert correspondence was 100%. The expert authority coefficients were 0.89 and 0.91, and the Kendall's W were 0.239 and 0.176 ( P<0.001) . The final training system included 3 first-level indicators, 11 second-level indicators, 37 third-level indicators and 145 fourth-level indicators. Conclusions:The construction method of the TCM nursing training system for nurses with medium-grade professional title is scientific and the content is reasonable and comprehensive, which can provide a scientific reference for the training of nurses with medium-grade professional title in TCM hospitals.