propafenone hydrochloride (propafenone, SR / propafenone SR / Rythmol SR)

Low-spatial-frequency perception enhancing dynamic convolutional network for system matrix recovery in magnetic particle imaging.

Dong Zhen Z, An Yu Y, Li Guanghui G, Guo Lishuang L et al.

Magnetic particle imaging (MPI) is an emerging molecular imaging modality known for its high sensitivity and long-term tracking. System matrix (SM)-based image reconstruction is one of the most important reconstruction methods for MPI. However, MPI reconstruction based on the measured SM is limited by the requirement of voxel-stepwise precise calibration of system function within the field of view (FOV), which is time-consuming and noise-prone. Additionally, any changes in system properties require recalibrating the SM. This hinders the clinical translation and practical application of MPI. To address these limitations, we propose a low-spatial-frequency perception enhancing dynamic convolutional network (LPEDCN) to recover SMs for accelerating calibration. Specifically, we observe and reveal a positive correlation between the percentage of low-spatial-frequency energy and the signal-to-noise ratio (SNR) in the spatial Fourier transform (SFT) domain of real-world SM rows. Based on this prior, we design a low-spatial-frequency perception enhancement (LPE) joint loss to suppress high-frequency noise during model training. We also introduce a context-modulated dynamic convolution (CMDC) to capture long-range contextual features. We evaluate LPEDCN on simulated (training set: 60 SMs split into 107880 SM-row slices; validation set: 20 SMs split into 37520 slices; test set: 20 SMs split into 32660 slices; with no overlap between sets), public real-world OpenMPI (training set: 1 SM split into 101861 SM-row slices; validation set: 1 SM split into 62863 slices; test set: 1 SM split into 85347 slices; with no overlap between sets), and in-house (test set: 2 SMs split into 225 and 272 slices, respectively) datasets. SM recovery accuracy is evaluated by normalized root-mean-square error (nRMSE), and the reconstruction quality is evaluated by peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). We assess LPEDCN by comparing it with seven state-of-the-art (SOTA) methods. Statistical significance is evaluated using two-tailed t-tests with p < 0.05 after False Discovery Rate (FDR) correction, and practical significance is assessed using Cohen's d for effect size. LPEDCN not only accelerates SM calibration in MPI, but also exhibits higher accuracy in SM recovery and better quality in image reconstruction compared to SOTA methods. For the core SM recovery metric nRMSE, LPEDCN significantly outperforms SOTA methods (p < 0.05, FDR corrected) for 2×, 4×, and 8× super-resolution (SR), respectively, with effect size values of 0.23-1.02 on the simulated dataset and 0.06-45.63 on the public real-world OpenMPI dataset. For the image reconstruction metrics PSNR and SSIM, LPEDCN is significantly superior to SOTA methods (p < 0.05, FDR corrected) for 2×, 4×, and 8× SR, respectively, with effect size values of 0.45-8.68 on the simulated dataset, and 0.10-4.65 on the OpenMPI dataset. These quantitative results consistently verify that LPEDCN yields practically meaningful performance improvements. Additionally, LPEDCN greatly reduces the SM calibration time and achieves excellent high-resolution reconstruction images. LPEDCN holds practical promise for improving MPI calibration efficiency.

Deep learning-based super-resolution reconstruction for improved image quality in shoulder MRI.

Yuan Wanying W, Luo Weiling W, Zhao Ying Y, Jiang Peilong P et al.

To compare the image quality of shoulder Magnetic Resonance Imaging (MRI) using three imaging acquisition or reconstruction approaches-Parallel Imaging (PI), Compressed Sensing (CS), and Deep Learning-Based Super-Resolution Reconstruction (SR)-and to evaluate reader-assessed image quality and preference. A retrospective study included 60 patients (27 males, 33 females; mean age 53.67 ± 12.11 years). Shoulder MRI was performed with PI, CS and SR (reconstructed from CS data) across six sequences. Quantitative and qualitative assessments were conducted by experienced observers. Three radiologists selected clinically preferred sequences after a 2-week washout. Consistency and inter-group comparisons were statistically analyzed. The quantitative and qualitative assessment between observers showed good consistency. The SR group showed significantly higher quantitative measurements across all six sequences of structures [e.g. SNR: Supraspinatus tendon SR 14.03 (10.60, 20.26), CS 11.87 (8.39, 15.96), PI 8.75 (5.18, 13.51), P < 0.001] than PI and CS. SR images generally showed lower edge rise distance (ERD) values, significant overall differences among PI, CS, and SR (P < 0.01), Qualitative assessment revealed that SR overall differences among PI, CS and SR, with statistically significant differences (P < 0.001). Furthermore, three reader preference results indicated a preference for SR images that all readers preferred SR images (86 %, 92 %, and 93 %). Compared to PI and CS, SR significantly improves image quality in shoulder MRI without increasing acquisition time. SR enhances the visualization of fine anatomical structures, providing improved image quality and supporting its potential application in clinical shoulder MRI.

Correction to: In vitro comparative quality evaluation of different brands of metformin hydrochloride tablets available in Mekelle City, Tigray Regional State, Ethiopia.

Welegebrial Brhane Gebrehiwot BG, Gezae Zenawi Z, Gebrekidan Samrawit Kidanemariam SK, Gebrerufael Gebru Gebremeskel GG et al.

Comparative outcomes of KangDuo and da Vinci robot-assisted ureteral reimplantation: a retrospective cohort study.

Bao Han H, Yang Qian Q, Wang Xiang X, Zhang Yu Y et al.

Ureteral reimplantation is central to distal ureteral reconstruction, but evidence comparing emerging robotic platforms with the established da Vinci system remains limited. We compared the perioperative safety and mid-term outcomes of KangDuo Surgical Robot-01 (KD-SR-01)-assisted and da Vinci-assisted ureteral reimplantation. We retrospectively analyzed 153 patients who underwent robot-assisted ureteral reimplantation between January 2024 and December 2025. Platform allocation was nonrandom and reflected patient choice after standardized counseling. The primary outcome was mid-term surgical failure, defined as persistent symptoms or radiographic obstruction requiring continued urinary drainage or further intervention; secondary outcomes were operative time, estimated blood loss, postoperative hospital stay, postoperative renal function, and complications. The primary analysis was multivariable regression adjusted for baseline differences in case complexity, with Firth penalized models used for the rare binary outcomes. Propensity score matching was retained as a sensitivity analysis. Before adjustment, the da Vinci group had longer strictures [5.0 (3.0-10.0) vs. 4.0 (3.0-5.0) cm; P=0.01] and more previous failed reconstructions (31.4% vs. 13.7%; P=0.03). All procedures were successfully completed without conversion. KD-SR-01 was associated with a longer operative time [adjusted difference, 13.91 minutes; 95% confidence interval (CI): 1.94-25.88; P=0.02]. No significant differences were observed in estimated blood loss, postoperative hospital stay, postoperative serum creatinine, or postoperative estimated glomerular filtration rate. Complications occurred in 3/51 (5.9%) KD-SR-01 and 7/102 (6.9%) da Vinci cases (adjusted odds ratio, 0.91; 95% CI: 0.20-3.49); surgical failure occurred in 1/51 (2.0%) and 2/102 (2.0%), respectively (adjusted odds ratio, 1.20; 95% CI: 0.09-10.93). Results were consistent in the propensity score-matched sensitivity analysis. KD-SR-01-assisted ureteral reimplantation is safe and feasible, with perioperative safety and mid-term clinical outcomes comparable to those of da Vinci-assisted surgery. Although the longer operative time with KD-SR-01 may reflect the surgeon's early learning curve and a more methodical operative workflow, these data suggest that the platform may be a feasible alternative in selected reconstructive cases.