Protocol based management of common sports injuries by integrated approach of Sandhi Marmabhighata - An open labeled clinical trial

Effective management of Colles fracture using Murivenna and Abha Guggulu - A case report

Colles fracture is the commonest fracture encountered in orthopedic practice that demands prompt therapeutic intervention, and adequate follow-up to ensure complete healing. Various types of fractures, methods of reduction, and healing have been explained in the classical Ayurveda texts. These techniques are scientific and time-tested. This paper aims to report the successful management of Colles fracture case using Ayurveda and modern techniques with the use of Murivenna (an oil-based herbal formulation mentioned in the contemporary texts of Ayurveda), half-cast POP, and aluminum splinted bandage along with the internal medicine AbhaGuggulu. A 75-year-old moderately built woman diagnosed with Colles fracture was treated with a closed manipulative reduction technique followed by a below-elbow half-cast POP and an aluminum splint. Murivenna was poured anteriorly to the fractured site and Abha Guggulu was administered internally. Re-bandaging was done on the 7th day and 21st day. The bandage was removed on the 35th day. The patient's condition improved considerably with a good range of wrist movements and then she was advised to commence rehabilitation. This integrative method, adhering to Ayurvedic principles and modern techniques is unique, patient-friendly, and without adverse events.

Machine Leaning-Based Optimization Algorithm for Myocardial Injury under High-Intensity Exercise in Track and Field Athletes

In order to train at high-intensity, athletics can again cause varying degrees of myocardial damage. Evaluating the balance between exercise myocardial injury and exercise intensity should actively prevent myocardial injury caused by high-intensity athletic training. In this paper, an intelligent optimization algorithm is used to investigate the degree of myocardial injury. The basic idea is to define the measured data and the output of the numerical model as an objective function of the structural parameters, to obtain the structural parameters by finding ways to continuously optimize the objective function to be close to the observed values, and to identify the injury based on the changes in these parameters before and after myocardial injury. The objective function can be defined in various ways, and the myocardial injury optimization algorithm can be chosen. In order to obtain the best computational results, numerical simulations of damage identification are performed using the objective function and three machine learning-based optimization algorithms. The computational results show that the combination of the objective function and the machine learning algorithms provides good accuracy and computational speed in identifying myocardial injury.