BACKGROUND: Independent peak torque (IPT) ratios may lead to misinterpretation of shoulder rotator imbalances. OBJECTIVE: To compare shoulder rotator IPT conventional ratio (CR) and dynamic control ratio (DCR) with ten-degree angle specific torque (AST) CR and DCR. METHODS: Twenty healthy adult males (24.65 ± 2.4 yrs) performed concentric (C) and eccentric (E) internal rotation (IR) and external rotation (ER) of the right shoulder on an isokinetic dynamometer at 60 • /s and 180 • /s through 150 • of ROM. RESULTS: IPT DCR was significantly different than AST DCR at several angles at both test speeds. IPT CR were not significantly different than any AST CR at either speed. The last 3 ten degree AST DCR were also compared. AST DCR was significantly different at two angles at 60 • /s and at three angles at 180 • /s. CONCLUSION: DCR analysis should use a ten degree AST interpretative approach in order to avoid erroneous interpretations of shoulder rotator strength imbalances. IPT ratio tests should only be used to calculate CR.
BackgroundApproximately 24% of physical therapists report regularly using yoga to strengthen major muscle groups. Although clinicians and athletes often use yoga as a form of strength training, little is known about the activation of specific muscle groups during yoga poses, including the gluteus maximus and medius.
Gaining insight into different cell behaviors is key to better understanding different pathologies. These behaviors may be explained in part through close observation of 3D cell morphology. Therefore, the objective of this research was to develop a machine learning (ML) framework that can predict 3D subcellular morphological variation of endothelial cells (ECs) to generate digital twins. ECs were cultured and their membrane, nucleus, and focal adhesion (FA) sites were stained and imaged with confocal microscopy. The multicellular confocal z stacks were segmented resulting in a total of 60 single-cell stacks. Fifty randomly picked cells were augmented 20-fold to train the ML framework, and the remaining 10 were used for an independent test of prediction accuracy. The ML framework was based on an open-source conditional generative adversarial network (cGAN), which was expanded to make 3D predictions using membrane only as input to predict nucleus and FA morphology. After training the framework, the results on the independent test showed an average prediction accuracy of ~87% for nucleus and ~70% for FA sites. The predictions were used to build a digital twin of each EC and compared to their respective ground truth, showing an average ~79% global accuracy and ~84% accuracy in FA-Nucleus distribution. The results presented show the effectiveness of the developed ML framework to generate digital twins of ECs using limited amount of data. These digital twins can be used to couple EC morphology with different behaviors. The ML framework can be potentially expanded to predict morphology of other subcellular structures as well as to study other types of cells.
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