Kim DY, et al. Brain Neurorehabil. 2023 Jul;16(2):e18. https://doi.org/10.12786/bn.2023.16.e18

To investigate and demonstrate persistent increase of peak cough flow after mechanical in-exsufflator application, in patients with neuromuscular diseases and pneumonia. A mechanical in-exsufflator was applied with patients in an upright or semi-upright sitting position (pressure setting, +40 and -40 cmH2O; in-exsufflation times, 2-3 and 1-2 seconds, respectively). Patients underwent five cycles, with 20-30 second intervals to prevent hyperventilation. Peak cough flow without and with assistive maneuvers, was evaluated before, and 15 and 45 minutes after mechanical in-exsufflator application. Peak cough flow was 92.6 L/min at baseline, and 100.4 and 100.7 L/min at 15 and 45 minutes after mechanical in-exsufflator application, respectively. Assisted peak cough flow at baseline, 15 minutes, and 45 minutes after mechanical in-exsufflator application was 170.7, 179.3, and 184.1 L/min, respectively. While peak cough flow and assisted peak cough flow increased significantly at 15 minutes after mechanical in-exsufflator application compared with baseline (p=0.030 and p=0.016), no statistical difference was observed between 15 and 45 minutes. Increased peak cough flow after mechanical in-exsufflator application persists for at least 45 minutes.

The aim of this study was to evaluate the characteristics of gait patterns in Charcot-Marie-Tooth disease type 1A (CMT1A) patients according to disease severity. Twenty-two CMT1A patients were enrolled and classified into two groups, according to the disease severity. The healthy control group consisted of 22 subjects with no gait impairment. Full barefoot three-dimensional gait analysis with temporospatial, kinematic, and kinetic data was performed among the mild and moderate CMT1A group and the control group. Minimal hip abduction, maximal hip extension generation, peak knee flexion moment at stance, ankle dorsiflexion at initial contact, maximal ankle plantarflexion at push-off and maximal ankle rotation moment at stance in the CMT1A group showed a significant difference compared to the control group (p < 0.05). In the moderate group, there were greater maximal hip flexion angles in swing, and smaller dorsiflexion angles at initial contact compared to the control group and mild group. CMT patients had typical gait characteristics and their gait patterns were different according to severity. The analysis of gait patterns in patients with CMT1A will help to understand gait function and provide important information for the treatment of patients with CMT in the future.

Yoo WK, et al. Brain Neurorehabil. 2025 Jan;18(1):e2. https://doi.org/10.12786/bn.2025.18.e2

The purpose of this study was to report the clinical validation of the posture analyzing and virtual reconstruction device (PAViR) system, focusing on the accuracy of sagittal spinal parameters, compared with the EOS imaging system. Seventy patients diagnosed with segmental and somatic dysfunction were recruited between February 2020 and November 2020. Each patient was examined using the EOS imaging system and PAViR; the sagittal parameters of human body posture [forward head posture (FHP), T1 tilt angle (T1t), knee flexion angle (KF), lumbar lordosis angle (LL), and pelvic tilt angle (PT)] were analyzed to verify the correlation between the results of the two devices. The median differences in the results of the two devices showed significant differences in FHP (T4-frontal head and T4-auditory canal), T1t, and PT. In the correlation analysis, the values of FHP (C7-auditory canal, T4-frontal head, and T4-auditory canal), T1t, and PT showed a moderate correlation between the two devices (r = 0.741, 0.795, 0.761, 0.621, and 0.692, respectively) (p < 0.001). The KF and LL was fairly correlated (r = 0.514 and 0.536, respectively) (p = 0.004, both). This study presents the potential of a novel skeletal imaging system without radiation exposure, based on a 3D red-green-blue-depth camera (PAViR), as a next-generation diagnostic tool by estimating more accurate parameters through continuous multi-data-based upgrades with artificial intelligence technology.