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Objective To explore the clinical effectiveness of minimally invasive arthroscopy-assisted acupotomy debridement in the treatment of rheumatoid arthritis of the knee joint. Methods Based on inclusion and exclusion criteria, a total of 138 patients with rheumatoid arthritis of the knee were selected and divided into two groups: 69 patients in the control group received conventional drug treatment, 69 patients in the observation group received minimally invasive arthroscopy-assisted acupotomy debridement combined conventional drug treatment. Primary outcomes of intervention were Lysholm knee scale and Visual Analogue Scale (VAS) on pain. Results Lysholm score of the control group was lower than that of observation group after treatment (56. 49±12. 24 vs. 84. 90±12. 71, P

Copyright of Journal of Modern Medicine & Health is the property of Journal of Modern Medicine & Health and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Recent research shows that features extracted from pre-trained models trained on large unlabeled speech samples have excelled in SV tasks. However, the existing models cannot effectively optimize and aggregate frame-level features by using the topological structure characteristics between frame-level features, and the high network complexity is not conducive to real-time performance. At the same time, the existing models cannot make full use of complementarity between multiple input features to further improve the performance of the model. To this end, on the one hand, this paper introduces graph neural networks to optimize frame-level features by using the topological structure between frame-level features. On the other hand, this paper construct a multi-feature fusion mechanism based on multiple losses to make full use of the complementarity between different features to further improves the performance of the model. Experimental results on VoxCeleb show that the proposed model GACNPF achieves lower error rates and time complexity compared to existing models. More importantly, the model has good flexibility. It can fuse any kind of features. This paper can apply it to other classification tasks based on pre-trained feature extraction. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Clinical Nursing in Practice is the property of Journal of Clinical Nursing in Practice (Editorial Board, Shanghai Jiao Tong University Press) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Modern Medicine & Health is the property of Journal of Modern Medicine & Health and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of NTU Management Review is the property of NTU Management Review and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)