Application of near-infrared spectroscopy to assess the effect of the cupping size on the spatial hemodynamic response from the area inside and outside the cup of the biceps.

Cupping therapy is a popular intervention for improving muscle recovery after exercise although clinical evidence is weak. Previous studies demonstrated that cupping therapy may improve microcirculation of the soft tissue to accelerate tissue healing. However, it is unclear whether the cupping size could affect the spatial hemodynamic response of the treated muscle. The objective of this study was to use 8-channel near-infrared spectroscopy to assess this clinical question by assessing the effect of 3 cupping sizes (35, 40, and 45 mm in inner diameter of the circular cup) under -300 mmHg for 5 min on the muscle hemodynamic response from the area inside and outside the cup, including oxyhemoglobin and deoxy-hemoglobin in 18 healthy adults. Two-way factorial design was used to assess the interaction between the cupping size (35, 40, and 45 mm) and the location (inside and outside the cup) and the main effects of the cupping size and the location. The two-way repeated measures ANOVA demonstrated an interaction between the cupping size and the location in deoxy-hemoglobin (P = 0.039) but no interaction in oxyhemoglobin (P = 0.100), and a main effect of the cup size (P = 0.001) and location (P = 0.023) factors in oxyhemoglobin. For the cupping size factor, the 45-mm cup resulted in a significant increase in oxyhemoglobin (5.738±0.760 µM) compared to the 40-mm (2.095±0.312 µM, P<0.001) and 35-mm (3.134±0.515 µM, P<0.01) cup. Our findings demonstrate that the cupping size and location factors affect the muscle hemodynamic response, and the use of multi-channel near-infrared spectroscopy may help understand benefits of cupping therapy on managing musculoskeletal impairment.

Using texture analysis of ultrasound images to assess the effect of cupping therapy on muscle quality of the triceps.

The objectives were to investigate whether cupping therapy improves muscle quality and the interaction between duration and negative pressure of cupping therapy affects muscle quality. A 2×2 factorial design with repeated measures was used to examine the efficacy of cupping therapy on improving muscle quality. The independent factors were cupping pressures at -225 and -300 mmHg and cupping durations at 5 and 10 min, and the dependent factor was texture of B-mode ultrasound image of the triceps. Four cupping protocols were applied to 12 participants at 4 different days. Texture analysis including contrast, correlation, energy, and homogeneity was applied to assess muscle quality from 480 ultrasound images. The two-way repeated measures ANOVA showed that there was an interaction between the pressure and duration factors on the superficial layer of the triceps in contrast (F = 5.434, P = 0.004) and correlation (F = 6.274, P = 0.029). In contrast texture, the superficial layer of the triceps showed a significant increase in three protocols: -225 mmHg for 5 min (1.0434 ± 0.130), -300 mmHg for 5 min (1.0339 ±0.1407), and -300 mmHg for 10 min (1.0563 ±0.1432) except -225 mmHg for 10 min (0.9704 ±0.0985). In correlation texture, the superficial layer of the triceps showed a significant decrease in all protocols: -225 mmHg for 5 min (0.9556 ± 0.07), -225 mmHg for 10 min (0.9831 ± 0.0708), -300 mmHg for 5 min (0.9976 ± 0.055), and -300 mmHg for 10 min (0.9406 ± 0.0809). The results indicate that the interaction between the pressure and duration factors of cupping therapy significantly increases contrast texture and significantly decreases correlation texture of the superficial layer of the triceps after cupping therapy. Cupping therapy decreases homogeneity among soft tissues of the treated muscle.

Lumbar Disc Herniation Automatic Detection in Magnetic Resonance Imaging Based on Deep Learning.

Background: Lumbar disc herniation (LDH) is among the most common causes of lower back pain and sciatica. The causes of LDH have not been fully elucidated but most likely involve a complex combination of mechanical and biological processes. Magnetic resonance imaging (MRI) is a tool most frequently used for LDH because it can show abnormal soft tissue areas around the spine. Deep learning models may be trained to recognize images with high speed and accuracy to diagnose LDH. Although the deep learning model requires huge numbers of image datasets to train and establish the best model, this study processed enhanced medical image features for training the small-scale deep learning dataset. Methods: We propose automatic detection to assist the initial LDH exam for lower back pain. The subjects were between 20 and 65 years old with at least 6 months of work experience. The deep learning method employed the YOLOv3 model to train and detect small object changes such as LDH on MRI. The dataset images were processed and combined with labeling and annotation from the radiologist's diagnosis record. Results: Our method proves the possibility of using deep learning with a small-scale dataset with limited medical images. The highest mean average precision (mAP) was 92.4% at 550 images with data augmentation (550-aug), and the YOLOv3 LDH training was 100% with the best average precision at 550-aug among all datasets. This study used data augmentation to prevent under- or overfitting in an object detection model that was trained with the small-scale dataset. Conclusions: The data augmentation technique plays a crucial role in YOLOv3 training and detection results. This method displays a high possibility for rapid initial tests and auto-detection for a limited clinical dataset.

Effects of Muscle Fatigue and Recovery on Complexity of Surface Electromyography of Biceps Brachii.

This study aimed to investigate the degree of regularity of surface electromyography (sEMG) signals during muscle fatigue during dynamic contractions and muscle recovery after cupping therapy. To the best of our knowledge, this is the first study assessing both muscle fatigue and muscle recovery using a nonlinear method. Twelve healthy participants were recruited to perform biceps curls at 75% of the 10 repetitions maximum under four conditions: immediately and 24 h after cupping therapy (-300 mmHg pressure), as well as after sham control (no negative pressure). Cupping therapy or sham control was assigned to each participant according to a pre-determined counter-balanced order and applied to the participant's biceps brachii for 5 min. The degree of regularity of the sEMG signal during the first, second, and last 10 repetitions (Reps) of biceps curls was quantified using a modified sample entropy (Ems) algorithm. When exercise was performed immediately or 24 h after sham control, Ems of the sEMG signal showed a significant decrease from the first to second 10 Reps; when exercise was performed immediately after cupping therapy, Ems also showed a significant decrease from the first to second 10 Reps but its relative change was significantly smaller compared to the condition of exercise immediately after sham control. When exercise was performed 24 h after cupping therapy, Ems did not show a significant decrease, while its relative change was significantly smaller compared to the condition of exercise 24 h after sham control. These results indicated that the degree of regularity of sEMG signals quantified by Ems is capable of assessing muscle fatigue and the effect of cupping therapy. Moreover, this measure seems to be more sensitive to muscle fatigue and could yield more consistent results compared to the traditional linear measures.

Prediction of Lumbar Disc Bulging and Protrusion by Anthropometric Factors and Disc Morphology.

The relationship between reduced disc height and disc bulging and/or protrusion has been controversial. The purposes of this study were to examine the relationship between disc morphology and disc bulging and protrusion and to establish a model for predicting disc bulging and protrusion. This is a retrospective study. A total of 452 MRI scans from a spine study were analysed, 210 (46.5%) were men. Logistic regression analysis was applied to identify the association between anthropometric factors, disc morphology factors, and outcome. Model 1 was constructed using anthropometric variables to investigate the capacity for predicting outcomes. Model 2 was constructed using anthropometric and disc morphology variables. Age, body weight, body height, disc height, and disc depth were significantly associated with outcome. The area under the curve (AUC) statistics of Model 2 were significantly better than those of Model 1 at the L3-L4 and L4-L5 levels but not at the L5-S1 level. The results showed an association between disc morphology and disc bulging and/or protrusion at the L3-L4, L4-L5, and L5-S1 levels. The model utilizing both anthropometric factors and disc morphology factors had a better capacity to predict disc bulging and/or protrusion compared with the model using only anthropometric factors.