Kyoto Tachibana University (京都橘大学, Kyōto Tachibana Daigaku) is a private university in Yamashina, Kyoto, Japan.The university was founded in 1967 (55 years ago) (1967) as a private university for women with the motto "to encourage and foster independent women". In 2005, the university became co-educational. In March 2017, the university had approximately 4,000 students..
Chronic low back pain (CLBP) significantly impairs quality of life and increases mortality among older adults, making its effective management essential for healthy aging. This quasi-randomized trial compared Pain Neuroscience Education emphasizing physical activity (PNE-PA) with traditional biomechanical treatments (BM) in older women with CLBP over 12 weeks. Community-dwelling women aged 65–90 years with CLBP were assigned to either group based on recruitment timing, using a double-blinded design. PNE-PA aimed to reduce pain-related fear by educating patients about pain neuroscience and encouraging physical activity. Outcomes included physical function, pain intensity, self-reported disability and psychological factors. Analyses used intention-to-treat and bootstrap resampling methods. Between-group differences were assessed using mean differences, 95% confidence intervals (CI), and Cohen’s d. The PNE-PA group (n = 24) showed significantly greater improvements than the BM group (n = 18) in Chair Stand Test (95% CI: 1.53 to 7.76, d = 0.88), step count (95% CI: 475.97 to 2550.42, d = 0.87), pain catastrophizing (95% CI: -10.64 to -1.95, d = -0.87), and fear-avoidance beliefs (95% CI: -7.40 to -0.14, d = -0.65). These findings suggest that Pain Neuroscience Education emphasizing physical activity was associated with better physical and psychological outcomes among older women with chronic low back pain.
With rapid advances in image-generative AI, interest has grown in applying these technologies across diverse domains. While current models excel at producing high-quality final images, they rarely model the intermediate stages of the drawing process. This study proposes a drawing support system that leverages generative AI to sequentially generate and modify images during the drawing process. To train the system, we constructed a custom dataset of time-series drawing images, which are typically unavailable in existing AI models. We developed an encoder–decoder model based on convolutional neural networks to predict the next frame from a current input image. To address error accumulation during recursive generation, we introduced a noise-augmented training method, incorporating noisy images into the dataset. Experimental results show that standard training suffers from image degradation over time, while the noise-augmented approach significantly improves stability and quality throughout the sequence. Averaged across all generated frames, the noise-augmented training achieved a PSNR of 20.48, SSIM of 0.81, and LPIPS of 0.13. As a benchmark, we compared this approach to PredRNN, a representative temporal model, which achieved a PSNR of 24.05, SSIM of 0.88, and LPIPS of 0.08. These results demonstrate the effectiveness of noise-augmented learning while also highlighting potential performance gains using temporal architectures. PredRNN also required more computation, with 6.41 M parameters and a 0.188 s inference time per sequence, compared to 5.20 M and 0.005 s for our model. Furthermore, in a sample-level analysis of 10 final images, the proposed model outperformed PredRNN in three cases across all metrics, suggesting its robustness in certain sequences despite architectural simplicity. Future directions include using more advanced models such as Variational Autoencoders and diffusion models, and enhancing consistency in long-term sequences. Our work confirms the feasibility of generating interactive drawing sequences using AI and sets the foundation for more robust and creative drawing support tools.
Several Aeromonas species, including Aeromonas hydrophila, have been known to produce CphA, a chromosomal metallo β-lactamase (MBL). We aimed to evaluate the clinical usefulness of modified carbapenem inactivation method (mCIM) in conjunction with EDTA (eCIM) or sodium mercaptoacetate (SMA-mCIM) for MBL-producing Aeromonas species. Fifty-six clinical isolates of Aeromonas species stored at the University of the Ryukyus Hospital were used in this study. The antimicrobial susceptibility testing was performed using a VITEK®2 AST-N404 card (bioMerieux, Marcy-l'Etoile, France) and the results were interpreted according to the CLSI guideline M45 3rd edition. The carbapenemase genes, namely blaGES, blaIMP, blaKPC, blaNDM, blaOXA-48-like, blaVIM, and blaCphA, were detected by PCR. The production of carbapenemase was evaluated using the mCIM and the differentiation of MBL was eCIM and SMA-mCIM. The resistance rates to imipenem and meropenem were 21.4 % (12/56) and 32.1 % (18/56), respectively. The mCIM showed a sensitivity of 100 % (33/33 blaCphA positive isolates) and a specificity of 95.7 % (22/23 blaCphA negative isolates) for the tested isolates. Among the 33 mCIM-positive isolates, 29 (87.9 %) for eCIM, and 6 (18.2 %) for SMA-mCIM were judged as MBL positive, respectively. It is suggested that the mCIM in conjunction with EDTA is useful for detecting MBL production in Aeromonas species harboring blaCphA.
AIM:Many Japanese nurses have moved to Australia, yet their experiences and perceived reasons for staying in Australia are understudied. This study identified why Japanese nurses who moved internationally to Australia continued to stay and work in Australia. METHODS:Semi-structured interviews were conducted with 15 Japanese nurses who hold Japanese nursing licenses and are registered as nurses in Australia. The transcripts were analyzed using thematic analysis. RESULTS:Five themes emerged: (1) capability to take leave and holidays without feeling guilty, (2) set shifts creates a better work-life balance, (3) incentives give motivation to work hard, (4) safe and protective workplace for nurses, and (5) positive environment for pursuing a career. CONCLUSIONS:Japanese nurses were satisfied with the Australian work environment where their profession is well-protected and highly respected, with more chances of career advancements. It suggests the need for a better working environment for nurses in Japan.
Background/Objectives: Psychological safety is essential for effective learning in high-fidelity simulation (HFS); however, no validated Japanese instrument currently exists to measure psychological safety among nursing students. This study aimed to translate the Psychological Safety in High-Fidelity Simulation (PS-HFS) scale into Japanese (PS-HFS-J) and evaluate its psychometric properties. Methods: Following COSMIN guidelines, the PS-HFS was translated through forward and back translation, reviewed by an expert panel, and tested for face validity via pilot testing. The scale’s reliability and validity were subsequently examined in 263 undergraduate nursing students using confirmatory factor analysis (CFA), Cronbach’s alpha, and intraclass correlation coefficients (ICC). Results: CFA confirmed a good fit of the original four-factor model (CFI = 0.990, TLI = 0.988, RMSEA = 0.026). The scale demonstrated excellent internal consistency (Cronbach’s α = 0.906 overall) and strong test-retest reliability (ICC range: 0.859–0.914). Content validity indices were also high (I-CVI = 0.80–1.00, S-CVI/Ave = 0.94). Conclusions: The PS-HFS-J is a reliable, valid, and culturally adapted instrument for assessing psychological safety in Japanese nursing education. It can support educational research, curriculum development, and faculty training, contributing to safer and more effective simulation-based education. Future studies should examine its applicability across diverse educational levels and clinical contexts.
