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Objective To investigate the learning curve threshold for robot-assisted mitral valve replacement, and to provide guidance for clinical practice. Methods A retrospective analysis was performed for the clinical data of 37 patients who underwent robot-assisted mitral valve replacement in Department of Cardiovascular Surgery in our hospital from December 2014 to December 2017, and the patients were ordered based on date of surgery. The cumulative sum (CUSUM) was calculated for time of operation, duration of cardiopulmonary bypass (CPB), and aortic cross-clamp time, and the CUSUM learning curve was modeled by curve fitting. The patients were divided into two groups based on the highest CUSUM of time of operation, and the two groups were compared in terms of general information, time of operation, duration of CPB, aortic cross-clamp time, total drainage volume (the sum of retrosternal drainage volume and thoracic drainage volume) during the 3 days before surgery, length of hospital stay, use of drugs and blood products in the perioperative period, and whether secondary thoracotomy was performed. Results The fitting curve reached the top at the 9th case on the time of operation, and the highest numbers of CPB and aortic cross-clamp time were 12 and 11, respectively. The patients were divided into two groups at the 9th case, and there were significant differences between the two groups in total drainage volume and the amount of red blood cell and plasma used after surgery on days 2 and 3 (Z=2.21-2.55,P

Gait analysis systems are critical for assessing motor function in rehabilitation and elderly care. This study aimed to develop and optimize an abnormal gait classification algorithm considering joint impairments using inertial measurement units (IMUs) and walkway systems. Ten healthy male participants simulated normal walking, walking with knee impairment, and walking with ankle impairment under three conditions: without joint braces, with a knee brace, and with an ankle brace. Based on these simulated gaits, we developed classification models: distinguishing abnormal gait due to joint impairments, identifying specific joint disorders, and a combined model for both tasks. Recursive Feature Elimination with Cross-Validation (RFECV) was used for feature extraction, and models were fine-tuned using support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGB). The IMU-based system achieved over 91% accuracy in classifying the three types of gait. In contrast, the walkway system achieved less than 77% accuracy in classifying the three types of gait, primarily due to high misclassification rates between knee and ankle joint impairments. The IMU-based system shows promise for accurate gait assessment in patients with joint impairments, suggesting future research for clinical application improvements in rehabilitation and patient management. [ABSTRACT FROM AUTHOR]

Phospholipase C is an essential isozyme involved in the phosphoinositide signaling pathway, which maintains cellular homeostasis. Gain-of-function and loss-of-function mutations in phospholipase C impact enzymatic activity and are therefore associated with several disorders. Alternative splicing variants of phospholipase C can interfere with complex signaling networks associated with oncogenic transformation and other diseases, including brain disorders. Cells and tissues with various mutations in phospholipase C contribute different phosphoinositide signaling pathways and disease progression; however, identifying cryptic mutations in phospholipase C remains challenging. Herein, we review both the mechanisms underlying phospholipase C regulation of the phosphoinositide signaling pathway and the genetic variation of phospholipase C in several brain disorders. In addition, we discuss the present challenges associated with the potential of deep-learning-based analysis for the identification of phospholipase C mutations in brain disorders. [ABSTRACT FROM AUTHOR]