Corticobasal syndrome (CBS) is characterized by asymmetric dystonia, and myoclonus accompanied by higher cortical features including apraxia, alien limb phenomena, cortical sensory loss. Here, we report treatment course of 3 CBS patients. Asymmetric dystonia was seen in the first and second cases, a cortical sensory loss was seen in the third case and left lower limb apraxia was common in all cases. In the first and second cases, we performed an alcohol block on the obturator nerve and injected botulinum toxin into the lower leg to reduce dystonia. In the third case, patient was treated with a robotic assisted gait training, whole body therapeutic pool and gait training with laser pointer visual cueing. After appropriate treatment for patients, all 3 cases showed improvement in gait.
Apraxias ; Botulinum Toxins ; Cues ; Dystonia ; Emigrants and Immigrants ; Extremities ; Gait Apraxia ; Gait* ; Humans ; Leg ; Lower Extremity ; Myoclonus ; Nerve Block ; Neurological Rehabilitation ; Obturator Nerve ; Rehabilitation*

OBJECTIVE: To report the characteristics of myofascial trigger points (MTrPs) in the infraspinatus muscle and evaluate the therapeutic effect of trigger-point injections. METHODS: Medical records of 297 patients (221 women; age, 53.9±11.3 years) with MTrPs in the infraspinatus muscle were reviewed retrospectively. Because there were 83 patients with MTrPs in both infraspinatus muscles, the characteristics of total 380 infraspinatus muscles with MTrPs (214 one side, 83 both sides) were investigated. Specific characteristics collected included chief complaint area, referred pain pattern, the number of local twitch responses, and distribution of MTrPs in the muscle. For statistical analysis, the paired t-test was used to compare a visual analogue scale (VAS) before and 2 weeks after the first injection. RESULTS: The most common chief complaint area of MTrPs in the infraspinatus muscle was the scapular area. The most common pattern of referred pain was the anterolateral aspect of the arm (above the elbow). Active MTrPs were multiple rather than single in the infraspinatus muscle. MTrPs were frequently in the center of the muscle. Trigger-point injection of the infraspinatus muscle significantly decreased the pain intensity. Mean VAS score decreased significantly after the first injection compared to the baseline (7.11 vs. 3.74; p<0.001). CONCLUSION: Characteristics of MTrPs and the therapeutic effects of trigger-point injections of the infraspinatus muscle were assessed. These findings could provide clinicians with useful information in diagnosing and treating myofascial pain syndrome of the infraspinatus muscle.
Arm ; Female ; Humans ; Medical Records ; Muscles ; Myofascial Pain Syndromes* ; Pain, Referred ; Retrospective Studies ; Therapeutic Uses ; Trigger Points

Background: Conventional modality requires several days observation by Holter monitor to differentiate atrial fibril‑ lation (AF) between Paroxysmal atrial fibrillation (PAF) and Non-paroxysmal atrial fibrillation (Non-PAF). Rapid and practical differentiating approach is needed. Objective: To develop a machine learning model that observes 10-s of standard 12-lead electrocardiograph (ECG) for real-time classification of AF between PAF versus Non-PAF. Methods: In this multicenter, retrospective cohort study, the model training and cross-validation was performed on a dataset consisting of 741 patients enrolled from Severance Hospital, South Korea. For cross-institutional validation, the trained model was applied to an independent data set of 600 patients enrolled from Ewha University Hospital, South Korea. Lasso regression was applied to develop the model. Results: In the primary analysis, the Area Under the Receiver Operating Characteristic Curve (AUC) on the test set for the model that predicted AF subtype only using ECG was 0.72 (95% CI 0.65–0.80). In the secondary analysis, AUC only using baseline characteristics was 0.53 (95% CI 0.45–0.61), while the model that employed both baseline characteris‑ tics and ECG parameters was 0.72 (95% CI 0.65–0.80). Moreover, the model that incorporated baseline characteristics, ECG, and Echocardiographic parameters achieved an AUC of 0.76 (95% CI 0.678–0.855) on the test set. Conclusions Our machine learning model using ECG has potential for automatic differentiation of AF between PAF versus Non-PAF achieving high accuracy. The inclusion of  Echocardiographic parameters further increases model per‑ formance. Further studies are needed to clarify the next steps towards clinical translation of the proposed algorithm.