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To evaluate the efficacy of Seisan kata as an aerobic power training mode, four male (28.5 +/- 4.2 y) Chito-Ryu karate black belt practitioners did kata continuously for 10 min. In separate sessions the kata (formal, organized movement sequences) were done at rates of 1 (PACE) and 2 (FAST) kata "cycles" per minute. Heartrate (HR) and VO2 were monitored continuously during the sessions. VO2 during the PACE and FAST sessions averaged 73 +/- 3 and 94 +/- 2% of leg cycling VO2peak, respectively. The corresponding HRs were 93 +/- 6 and 101 +/- 3% of HRmax (leg cycle test). PACE and FAST post-exercise blood lactates were 12 +/- 4 and 22 +/- 6%, respectively, of the maximal leg cycle test values. These data indicate that karate kata can be used as an effective and specific means for training aerobic power in karate practitioners.
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PMID:Oxygen uptake, heartrate and blood lactate responses to the Chito-Ryu Seisan kata in skilled karate practitioners. 836 35

We introduce a Feasible Artificial Intelligence with Simple Trajectories for Predicting Adverse Catastrophic Events (FAST-PACE) solution for preparing immediate intervention in emergency situations. FAST-PACE utilizes a concise set of collected features to construct an artificial intelligence model that predicts the onset of cardiac arrest or acute respiratory failure from 1 h to 6 h prior to its occurrence. Data from the trajectory of 29,181 patients in intensive care units of two hospitals includes periodic vital signs, a history of treatment, current health status, and recent surgery. It excludes the results of laboratory data to construct a feasible application in wards, out-hospital emergency care, emergency transport, or other clinical situations where instant medical decisions are required with restricted patient data. These results are superior to previous warning scores including the Modified Early Warning Score (MEWS) and the National Early Warning Score (NEWS). The primary outcome was the feasibility of an artificial intelligence (AI) model predicting adverse events 1 h to 6 h prior to occurrence without lab data; the area under the receiver operating characteristic curve of this model was 0.886 for cardiac arrest and 0.869 for respiratory failure 6 h before occurrence. The secondary outcome was the superior prediction performance to MEWS (net reclassification improvement of 0.507 for predicting cardiac arrest and 0.341 for predicting respiratory failure) and NEWS (net reclassification improvement of 0.412 for predicting cardiac arrest and 0.215 for predicting respiratory failure) 6 h before occurrence. This study suggests that AI consisting of simple vital signs and a brief interview could predict a cardiac arrest or acute respiratory failure 6 h earlier.
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PMID:Predicting Cardiac Arrest and Respiratory Failure Using Feasible Artificial Intelligence with Simple Trajectories of Patient Data. 3147 May 43