Effect of negative pressure therapy on the treatment response to scar thickness and viscoelasticity.

Patients with scars face a grave threat to their mental and physical health. Negative pressure has been used for scar therapy in medical care and provides a microenvironment conducive to scar healing while stimulating cell regeneration. Negative pressure may disrupt scar tissue regeneration when the pressure is too high or too low, so finding a suitable negative pressure is important. We hypothesized that different negative pressure magnitudes would affect scar tissue properties differently. This research aimed to provide practical recommendations for scar therapy. This study used three negative pressures (-105 mmHg, -125 mmHg, and -145 mmHg) to compare scar material properties. We measured scar tissue thickness and viscoelasticity with a motor-driven ultrasound indentation system. According to the results of this study, scar thickness is most effectively reduced at a negative pressure of -105 mmHg. In comparison, scar viscoelasticity continuously increases at a negative pressure of -125 mmHg. Negative pressure therapy can be recommended to scar care clinics based on the results of this study.

Plantar pressure gradient and pressure gradient angle are affected by inner pressure of air insole.

Clinically, air insoles may be applied to shoes to decrease plantar pressure gradient (PPG) and increase plantar gradient angle (PGA) to reduce foot ulcers. PPG and PGA may cause skin breakdown. The effects of different inner pressures of inflatable air insoles on dynamic PPG and PGA distributions are largely unknown in non-diabetics and people with diabetes. This study aimed to explore the impact of varying inner air insole pressures on PPG and PGA to establish early mitigation strategies for people at risk of foot ulcers. A repeated measures study design, including three air insoles (80 mmHg, 160 mmHg, and 240 mmHg) and two walking durations (10 and 20 min) for a total of six walking protocols, was tested on 13 healthy participants (height, 165.8 ± 8.4 cm; age, 27.0 ± 7.3 years; and weight, 56.0 ± 7.9 kg, BMI: 20.3 ± 1.7 kg/m^2) over three consecutive weeks. PPG, a measurement of the spatial variation in plantar pressure around the peak plantar pressure (PPP) and PGA, a variation in the gradient direction values at the three plantar regions, big toe (T1), first metatarsal head (M1), and second metatarsal head (M2), were calculated. This study indicated that PPG was lower at 80 mmHg air insoles after 20 min of walking in the M1 region (p = 0.010). The PGA in the M2 increased at an air insole of 80 mmHg compared to 240 mmHg (p = 0.015). Compared to 20 min, the 10 min walking duration at 240 mmHg of air insole had the lowest PPG in the M1 (p = 0.015) and M2 (p = 0.034) regions. The 80 mmHg air insole significantly lowered the PPG compared to a 160 mmHg and 240 mmHg air insole. Moreover, the 80 mmHg air insole significantly decreased PPP and increased PGA compared to the 160 mmHg and 240 mmHg air insole. A shorter walking period (10 min) significantly lowered PPG. The findings of this study suggest that people with a higher risk of foot ulcers should wear softer air insoles to have a lower PPG, as well as an increased PGA.

Using cross-correlation analysis of multi-channel near infrared spectroscopy to assess the hemodynamic response to cupping therapy.

Cupping therapy is a common intervention for the management of musculoskeletal impairment. Previous studies have demonstrated that cupping therapy can improve muscle hemodynamic responses using single-channel near-infrared spectroscopy (NIRS). However, the effects of cupping therapy on spatial hemodynamic responses as well as the correlation between oxyhemoglobin and deoxy-hemoglobin are largely unknown. The cross-correlation function (CCF) algorithm was used to determine the correlation between time-series NIRS signals from inside and outside the cup as well as time-series oxyhemoglobin and deoxy-hemoglobin under 4 cupping intensities, including -225 and -300 mmHg for 5 and 10 min. The main finding was that the maximum CCF values of oxyhemoglobin was significantly higher than those in deoxy-hemoglobin (p < 0.05). Furthermore, it was found that there was a correlation between deoxy-hemoglobin with a longer duration and a larger magnitude of negative pressure. This is the first study investigating time-series hemodynamic responses after cupping therapy using cross-correlation function analysis of multi-channel NIRS signals.

The effects of different inner pressures of air insoles and walking durations on peak plantar pressure.

BACKGROUND: Exercise reduces chronic complications in individuals with diabetes and peripheral vascular diseases. In clinical practice, the use of air insole may reduce peak plantar pressure (PPP), and risk for diabetic foot ulcers (DFUs). However, there is no guideline on selecting air insole pressure for effectively reducing PPP. Therefore, this study aimed to investigate the effects of different air insole pressure on PPP at different walking durations. METHODS: We tested 13 participants using repeated measures study design, including 3 air insole pressures (80, 160, and 240 mm Hg) and 2 walking durations (10 and 20 minutes) for 6 walking conditions. PPP values at the first toe, first metatarsal head, and second metatarsal head were calculated. RESULTS: The one-way ANOVA showed significant pairwise differences of PPP at 20 minutes duration in the first metatarsal head between 80 and 240 mm Hg (P = .007) and between 160 and 240 mm Hg (P = .038); in the second metatarsal head between 80 and 240 mm Hg (P = .043). The paired t test confirmed that walking duration significantly has lower PPP at 10 minutes than 20 minutes with 240 mm Hg air insole in the first metatarsal head (P = .012) and the second metatarsal head (P = .027). CONCLUSION: People at risk of foot ulcers are suggested to wear shoes with 80 mm Hg of air insole for reducing PPP in the first metatarsal head and the second metatarsal head. Moreover, people may avoid wearing the stiffer insole (240 mm Hg) for more than 20 minutes.

An approach to the diagnosis of lumbar disc herniation using deep learning models.

Background: In magnetic resonance imaging (MRI), lumbar disc herniation (LDH) detection is challenging due to the various shapes, sizes, angles, and regions associated with bulges, protrusions, extrusions, and sequestrations. Lumbar abnormalities in MRI can be detected automatically by using deep learning methods. As deep learning models gain recognition, they may assist in diagnosing LDH with MRI images and provide initial interpretation in clinical settings. YOU ONLY LOOK ONCE (YOLO) model series are often used to train deep learning algorithms for real-time biomedical image detection and prediction. This study aims to confirm which YOLO models (YOLOv5, YOLOv6, and YOLOv7) perform well in detecting LDH in different regions of the lumbar intervertebral disc. Materials and methods: The methodology involves several steps, including converting DICOM images to JPEG, reviewing and selecting MRI slices for labeling and augmentation using ROBOFLOW, and constructing YOLOv5x, YOLOv6, and YOLOv7 models based on the dataset. The training dataset was combined with the radiologist's labeling and annotation, and then the deep learning models were trained using the training/validation dataset. Results: Our result showed that the 550-dataset with augmentation (AUG) or without augmentation (non-AUG) in YOLOv5x generates satisfactory training performance in LDH detection. The AUG dataset overall performance provides slightly higher accuracy than the non-AUG. YOLOv5x showed the highest performance with 89.30% mAP compared to YOLOv6, and YOLOv7. Also, YOLOv5x in non-AUG dataset showed the balance LDH region detections in L2-L3, L3-L4, L4-L5, and L5-S1 with above 90%. And this illustrates the competitiveness of using non-AUG dataset to detect LDH. Conclusion: Using YOLOv5x and the 550 augmented dataset, LDH can be detected with promising both in non-AUG and AUG dataset. By utilizing the most appropriate YOLO model, clinicians have a greater chance of diagnosing LDH early and preventing adverse effects for their patients.

Antiviral and Immunomodulatory Activities of Clinacanthus nutans (Burm. f.) Lindau.

Clinacanthus nutans (Burm. f.) Lindau has been used as a traditional herbal medicine for treating snake bites, scalds, burns, and viral and bacterial infections. It has been attracting an increasing amount of attention because of its biological activities, including its antidiabetic, antioxidant, antibacterial, anticancer, anti-inflammatory, antiviral, and immunoregulatory activities. Here, we conducted a panoramic survey of the literature regarding the immunoregulatory, anti-inflammatory, and antiviral activities of C. nutans. We discovered that C. nutans extracts have virucidal activities against herpes simplex virus types 1 and 2, varicella-zoster virus, cyprinid herpesvirus 3, porcine reproductive and respiratory syndrome virus, mosquito-borne chikungunya virus, and potentially SARS-CoV-2; such activities likely result from C. nutans interfering with the entry, penetration, infection, and replication of viruses. We also reviewed the phytochemicals in C. nutans extracts that exhibit anti-inflammatory and immunoregulatory activities. This updated review of the antiviral, anti-inflammatory, and immunoregulatory activities of C. nutans may guide future agricultural practices and reveal clinical applications of C. nutans.

Grey Relational Analysis and Grey Prediction Model (1, 6) Approach for Analyzing the Electrode Distance and Mechanical Properties of Tandem MIG Welding Distortion.

The tandem metal inert gas (MIG) process uses two wires that are continuously fed through a special welding torch and disbursed to form a single molten pool. Within the contact tip of the modern approach, the wires are electrically insulated from one another. This study identified the effect of welding electrode spacing on the distortion of AA5052 aluminum plates and different mechanical properties including hardness and thermal cycle using grey relational analysis. Plate distortion was subsequently predicted using the grey prediction model GM (1, 6). This research used a pair of 400 mm × 75 mm × 5 mm of AA5052 plates and electrode distances of 18, 27, and 36 mm. The welding current, voltage, welding speed, and argon flow rate were 130 A, 23 V, 7 mm/s, and 17 L/min, respectively. The temperature was measured using a type-K thermocouple at 10, 20, 30, and 40 mm from the center of the weld bead. The smallest distortion at an electrode distance of 27 mm was 1.4 mm. At an electrode distance of 27 mm, the plate may reach a proper peak temperature where the amount of heat input and dissipation rate are similar to those for electrode distances of 18 mm and 36 mm. The highest relative VHN of 57 was found in the BM, while the lowest, 46, was found in the WM, showing good agreement with their respective grain sizes. Six parameters were designed using grey relational analysis (GRA) and subsequently employed in the grey prediction model GM (1, 6). Process evaluation results show that predictions for welding distortions are consistent with actual results, thus, the GM (1, 6) model can be used as a predictive model for welding distortions of 5052 aluminum plates.

Effects of Walking Speeds and Durations on Peak Plantar Pressures.

BACKGROUND: Walking at various speeds and durations may result in different peak plantar pressure (PPP). However, there is no study comparing the effect of walking speeds and durations on PPP. The purpose of this study was to explore whether different walking speeds and durations significantly change PPP and establish a normal response in healthy people. METHODS: An in-shoe plantar pressure system was used to measure PPP under the first toe, first metatarsal, second metatarsal, and heel regions in 12 healthy, young people. All participants performed six walking trials at three speeds (3, 6, and 9 km/h) and for two durations (10 and 20 min). The 3 × 2 two-way analysis of variance was used to examine the main effects of speeds and durations and their interaction. RESULTS: The results showed that walking speeds significantly affected PPP and that walking duration did not. No interaction between the walking speed and duration was observed. Peak plantar pressure values under the first toe and the first metatarsal head were significantly higher (P < .05) at 9 km/h (509.1 ± 314.2 kPa and 591.4 ± 302.4 kPa, respectively) than at 3 km/h (275.4 ± 168.7 kPa and 369.4 ± 205.4 kPa, respectively) after 10-min walking. CONCLUSIONS: People at risk for foot ulcers may use slow and brisk walking for exercise to reduce PPP, thus reducing risk for foot ulcers. Our study demonstrated that slow running at 9 km/h significantly increases PPP.

Effects of walking speeds and durations on the plantar pressure gradient and pressure gradient angle.

BACKGROUND: Walking exercise has been demonstrated to improve health in people with diabetes. However, it is largely unknown the influences of various walking intensities such as walking speeds and durations on dynamic plantar pressure distributions in non-diabetics and diabetics. Traditional methods ignoring time-series changes of plantar pressure patterns may not fully capture the effect of walking intensities on plantar tissues. The purpose of this study was to investigate the effect of various walking intensities on the dynamic plantar pressure distributions. In this study, we introduced the peak pressure gradient (PPG) and its dynamic patterns defined as the pressure gradient angle (PGA) to quantify dynamic changes of plantar pressure distributions during walking at various intensities. METHODS: Twelve healthy participants (5 males and 7 females) were recruited in this study. The demographic data were: age, 27.1 ± 5.8 years; height, 1.7 ± 0.1 m; and weight, 63.5 ± 13.5 kg (mean ± standard deviation). An insole plantar pressure measurement system was used to measure plantar pressures during walking at three walking speeds (slow walking 1.8 mph, brisk walking 3.6 mph, and slow running 5.4 mph) for two durations (10 and 20 min). The gradient at a location is defined as the unique vector field in the two-dimensional Cartesian coordinate system with a Euclidean metric. PGA was calculated by quantifying the directional variation of the instantaneous peak gradient vector during stance phase of walking. PPG and PGA were calculated in the plantar regions of the first toe, first metatarsal head, second metatarsal head, and heel at higher risk for foot ulcers. Two-way ANOVA with Fisher's post-hoc analysis was used to examine the speed and duration factors on PPG and PGA. RESULTS: The results showed that the walking speeds significantly affect PPG (P < 0.05) and PGA (P < 0.05), and the walking durations does not. No interaction between the walking duration and speed was observed. PPG in the first toe region after 5.4 mph for either 10 or 20 min was significantly higher than 1.8 mph. Meanwhile, after 3.6 mph for 20 min, PPG in the heel region was significantly higher than 1.8 mph. Results also indicate that PGA in the forefoot region after 3.6 mph for 20 min was significantly narrower than 1.8 mph. CONCLUSIONS: Our findings indicate that people may walk at a slow speed at 1.8 mph for reducing PPG and preventing PGA concentrated over a small area compared to brisk walking at 3.6 mph and slow running at 5.4 mph.

Advanced Cross-Correlation Function Application to Identify Arterial Baroreflex Sensitivity Variations From Healthy to Diabetes Mellitus.

Diabetes mellitus (DM) is a chronic disease characterized by elevated blood glucose levels, which leads over time to serious damage to the heart, blood vessels, eyes, kidneys, and nerves. DM is of two types-types 1 or 2. In type 1, there is a problem with insulin secretion, and in type 2-insulin resistance. About 463 million people worldwide have diabetes, and 80% of the majority live in low- and middle-income countries, and 1.5 million deaths are directly attributed to diabetes each year. Autonomic neuropathy (AN) is one of the common diabetic complications, leading to failure in blood pressure (BP) control and causing cardiovascular disease. Therefore, early detection of AN becomes crucial to optimize treatment. We propose an advanced cross-correlation function (ACCF) between BP and heart rate with suitable threshold parameters to analyze and detect early changes in baroreflex sensitivity (BRS) in DM with AN (DM+). We studied heart rate (HR) and systolic BP responses during tilt in 16 patients with diabetes mellitus only (DM-), 19 diabetes mellitus with autonomic dysfunction (DM+), and 10 healthy subjects. The ACCF analysis revealed that the healthy and DM groups had different filtered percentages of significant maximum cross-correlation function (CCF) value (p < 0.05), and the maximum CCF value after thresholds was significantly reduced during tilt in the DM+ group (p < 0.05). The maximum CCF index, a parameter for the phase between HR and BP, separated the healthy group from the DM groups (p < 0.05). Due to the maximum CCF index in DM groups being located in the positive range and significantly different from healthy ones, it could be speculated that BRS dysfunction in DM and AN could cause a phase change from lead to lag. ACCF could detect and separate DM+ from DM groups. This fact could represent an advantage of the ACCF algorithm. A common cross-correlation analysis was not easy to distinguish between DM- and DM+. This pilot study demonstrates that ACCF analysis with suitable threshold parameters could explore hidden changes in baroreflex control in DM+ and DM-. Furthermore, the superiority of this ACCF algorithm is useful in distinguishing whether AN is present or not in DM.

A Deep Learning Method for Foot Progression Angle Detection in Plantar Pressure Images.

Foot progression angle (FPA) analysis is one of the core methods to detect gait pathologies as basic information to prevent foot injury from excessive in-toeing and out-toeing. Deep learning-based object detection can assist in measuring the FPA through plantar pressure images. This study aims to establish a precision model for determining the FPA. The precision detection of FPA can provide information with in-toeing, out-toeing, and rearfoot kinematics to evaluate the effect of physical therapy programs on knee pain and knee osteoarthritis. We analyzed a total of 1424 plantar images with three different You Only Look Once (YOLO) networks: YOLO v3, v4, and v5x, to obtain a suitable model for FPA detection. YOLOv4 showed higher performance of the profile-box, with average precision in the left foot of 100.00% and the right foot of 99.78%, respectively. Besides, in detecting the foot angle-box, the ground-truth has similar results with YOLOv4 (5.58 ± 0.10° vs. 5.86 ± 0.09°, p = 0.013). In contrast, there was a significant difference in FPA between ground-truth vs. YOLOv3 (5.58 ± 0.10° vs. 6.07 ± 0.06°, p < 0.001), and ground-truth vs. YOLOv5x (5.58 ± 0.10° vs. 6.75 ± 0.06°, p < 0.001). This result implies that deep learning with YOLOv4 can enhance the detection of FPA.

Effectiveness of self-management of dry and wet cupping therapy for low back pain: A systematic review and meta-analysis.

BACKGROUND: Low back pain (LBP) can significantly affect a person's quality of life. Cupping has been used to treat LBP. However, various cupping methods are typically included in evaluating the efficacy of cupping therapy. Therefore, the objectives of this study were to evaluate the evidence from the literature regarding the effects of dry and wet cupping therapy on LBP in adults. Dry and wet cupping therapy are analyzed categorically in this study. METHODS: We searched for randomized clinical trials with cupping in LBP published between 2008 and 2022. In dry or wet cupping clinical studies, pain intensity was assessed using the Visual Analogue Scale and present pain intensity, and the quality of life intensity was measured using the Oswestry disability index. RESULTS: The 656 studies were identified, of which 10 studies for 690 patients with LBP were included in the meta-analysis. There was a significant reduction in the pain intensity score with present pain intensity using wet cupping therapy (P < .01). In addition, both cupping therapy groups displayed significant Oswestry disability index score reduction compared to the control group (both P < .01). The patients with LBP have a substantial reduction by using wet cupping but have not shown a considerable decrease by using dry cupping (P = .19). In addition, only wet cupping therapy groups displayed a significantly improved quality of life compared to the control group. The study had a very high heterogeneity (I2 > 50%). It means there is no standardization in the treatment protocol in randomized clinical trials. In the meta-regression, there was statistically significant evidence that the number of treatment times and intercepts were related (P < .01). CONCLUSION: The present meta-analysis shows that wet cupping therapy effectively reduces the pain intensity of LBP. Furthermore, both dry wet cupping therapy improved patients with LBP quality of life.

Hypertension and Stroke Cardiovascular Control Evaluation by Analyzing Blood Pressure, Cerebral Blood Flow, Blood Vessel Resistance and Baroreflex.

Cardiovascular diseases have been the leading causes of mortality in Taiwan and the world at large for decades. The composition of cardiovascular and cerebrovascular systems is quite complicated. Therefore, it is difficult to detect or trace the related signs of cardiovascular and cerebrovascular diseases. The characteristics and changes in cardiopulmonary system disease can be used to track cardiovascular and cerebrovascular disease prevention and diagnosis. This can effectively reduce the occurrence of cardiovascular and cerebrovascular diseases. This study analyzes the variability in blood pressure, cerebral blood flow velocity and the interaction characteristics using linear and nonlinear approaches in stroke, hypertension and healthy groups to identify the differences in cardiovascular control in these groups. The results showed that the blood pressure and cerebral blood flow of stroke patients and hypertensive patients were significantly higher than those of healthy people (statistical differences (p < 0.05). The cerebrovascular resistance (CVR) shows that the CVR of hypertensive patients is higher than that of healthy people and stroke patients (p < 0.1), indicating that the cerebral vascular resistance of hypertensive patients is slightly higher. From the patient's blood flow and vascular characteristics, it can be observed that the cardiovascular system is different from those in healthy people. Baroreflex sensitivity (BRS) decreased in stroke patients (p < 0.05). Chaotic analysis revealed that the blood pressure disturbance in hypertensive patients has a higher chaotic behavior change and the difference in initial state sensitivity. Cross-correlation (CCF) analysis shows that as the course of healthy→hypertension→stroke progresses, the maximum CCF value decreases significantly (p < 0.05). That means that blood pressure and cerebral blood flow are gradually not well controlled by the self-regulation mechanism. In conclusion, cardiovascular control performance in hypertensive and stroke patients displays greater variation. This can be observed by the bio-signal analysis. This analysis could identify a measure for detecting and preventing the risk for hypertension and stroke in clinical practice. This is a pilot study to analyze cardiovascular control variation in healthy, hypertensive and stroke groups.

Estimation of Various Walking Intensities Based on Wearable Plantar Pressure Sensors Using Artificial Neural Networks.

Walking has been demonstrated to improve health in people with diabetes and peripheral arterial disease. However, continuous walking can produce repeated stress on the plantar foot and cause a high risk of foot ulcers. In addition, a higher walking intensity (i.e., including different speeds and durations) will increase the risk. Therefore, quantifying the walking intensity is essential for rehabilitation interventions to indicate suitable walking exercise. This study proposed a machine learning model to classify the walking speed and duration using plantar region pressure images. A wearable plantar pressure measurement system was used to measure plantar pressures during walking. An Artificial Neural Network (ANN) was adopted to develop a model for walking intensity classification using different plantar region pressure images, including the first toe (T1), the first metatarsal head (M1), the second metatarsal head (M2), and the heel (HL). The classification consisted of three walking speeds (i.e., slow at 0.8 m/s, moderate at 1.6 m/s, and fast at 2.4 m/s) and two walking durations (i.e., 10 min and 20 min). Of the 12 participants, 10 participants (720 images) were randomly selected to train the classification model, and 2 participants (144 images) were utilized to evaluate the model performance. Experimental evaluation indicated that the ANN model effectively classified different walking speeds and durations based on the plantar region pressure images. Each plantar region pressure image (i.e., T1, M1, M2, and HL) generates different accuracies of the classification model. Higher performance was achieved when classifying walking speeds (0.8 m/s, 1.6 m/s, and 2.4 m/s) and 10 min walking duration in the T1 region, evidenced by an F1-score of 0.94. The dataset T1 could be an essential variable in machine learning to classify the walking intensity at different speeds and durations.

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 various walking intensities on leg muscle fatigue and plantar pressure distributions.

BACKGROUND: Physical activity may benefit health and reduce risk for chronic complications in normal and people with diabetes and peripheral vascular diseases. However, it is unclear whether leg muscle fatigue after weight-bearing physical activities, such as brisk walking, may increase risk for plantar tissue injury. In the literature, there is no evidence on the effect of muscle fatigue on plantar pressure after various walking intensities. The objectives of this study were to investigate the effects of various walking intensities on leg muscle fatigue and plantar pressure patterns. METHODS: A 3 × 2 factorial design, including 3 walking speeds (1.8 (slow and normal walking), 3.6 (brisk walking), and 5.4 (slow running) mph) and 2 walking durations (10 and 20 min) for a total of 6 walking intensities, was tested in 12 healthy participants in 3 consecutive weeks. The median frequency and complexity of electromyographic (EMG) signals of tibialis anterior (TA) and gastrocnemius medialis (GM) were used to quantify muscle fatigue. Fourier transform was used to compute the median frequency and multiscale entropy was used to calculate complexity of EMG signals. Peak plantar pressure (PPP) values at the 4 plantar regions (big toe, first metatarsal head, second metatarsal head, and heel) were calculated. RESULTS: Two-way ANOVA showed that the walking speed (at 1.8, 3.6, 5.4 mph) significantly affected leg muscle fatigue, and the duration factor (at 10 and 20 min) did not. The one-way ANOVA showed that there were four significant pairwise differences of the median frequency of TA, including walking speed of 1.8 and 3.6 mph (185.7 ± 6.1 vs. 164.9 ± 3.0 Hz, P = 0.006) and 1.8 and 5.4 mph (185.7 ± 6.1 vs. 164.5 ± 5.5 Hz, P = 0.006) for the 10-min duration; and walking speed of 1.8 and 3.6 mph (180.0 ± 5.9 vs. 163.1 ± 4.4 Hz, P = 0.024) and 1.8 and 5.4 mph (180.0 ± 5.9 vs. 162.8 ± 4.9 Hz, P = 0.023) for the 20-min duration. The complexity of TA showed a similar trend with the median frequency of TA. The median frequency of TA has a significant negative correlation with PPP on the big toe ( r = -0.954, P = 0.003) and the first metatarsal head ( r = -0.896, P = 0.016). CONCLUSIONS: This study demonstrated that brisk walking and slow running speeds (3.6 and 5.4 mph) cause an increase in muscle fatigue of TA compared to slow walking speed (1.8 mph); and the increased muscle fatigue is significantly related to a higher PPP.

Recent Advancement in Anticancer Activity of Clinacanthus nutans (Burm. f.) Lindau.

Clinacanthus nutans is a traditional medicinal herb that is applied for the therapy of snake bites, skin infection, herpes infection, burns, scalds, dysentery, and diabetes. Clinacanthus nutans is also used to treat several cancers, including breast, cervical, colon, gastric, head and neck, liver, lung, pancreatic, and skin cancers, as well as lymphoma and leukemia; however, the underlying mechanisms of its anticancer activity remained undetermined. We searched PubMed and Google with key words "Clinacanthus nutans and cancer" and collected recent papers of Clinacanthus nutans with anticancer activity. We focused on the preparation, effects, and action mechanisms of Clinacanthus nutans extracts on various types of cancers. We hope that this mini review can help update our knowledge about active components, effects, and molecular mechanisms of extracts from this promising herb Clinacanthus nutans for ongoing studies and speed up its clinical application in the future.

Using Bidimensional Multiscale Entropy Analysis of Ultrasound Images to Assess the Effect of Various Walking Intensities on Plantar Soft Tissues.

Walking performance is usually assessed by linear analysis of walking outcome measures. However, human movements consist of both linear and nonlinear complexity components. The purpose of this study was to use bidimensional multiscale entropy analysis of ultrasound images to evaluate the effects of various walking intensities on plantar soft tissues. Twelve participants were recruited to perform six walking protocols, consisting of three speeds (slow at 1.8 mph, moderate at 3.6 mph, and fast at 5.4 mph) for two durations (10 and 20 min). A B-mode ultrasound was used to assess plantar soft tissues before and after six walking protocols. Bidimensional multiscale entropy (MSE2D) and the Complexity Index (CI) were used to quantify the changes in irregularity of the ultrasound images of the plantar soft tissues. The results showed that the CI of ultrasound images after 20 min walking increased when compared to before walking (CI4: 0.39 vs. 0.35; CI5: 0.48 vs. 0.43, p < 0.05). When comparing 20 and 10 min walking protocols at 3.6 mph, the CI was higher after 20 min walking than after 10 min walking (CI4: 0.39 vs. 0.36, p < 0.05; and CI5: 0.48 vs. 0.44, p < 0.05). This is the first study to use bidimensional multiscale entropy analysis of ultrasound images to assess plantar soft tissues after various walking intensities.

Emerging technologies for the prevention and management of diabetic foot ulcers.

Diabetic foot ulcers (DFUs) are one of the most serious complications of diabetes mellitus (DM). Although research has improved understanding of DFU etiology, an effective clinical prevention and management of DFUs remains undetermined. Knowledge of recent technologies may enable clinicians and researchers to provide appropriate interventions to prevent and treat DFUs. This paper discusses how diabetes causes peripheral neuropathy and peripheral arterial diseases, which contribute to increased risk of DFUs. Then, emerging technologies that could be used to quantify risks of DFUs are discussed, including laser Doppler flowmetry for assessing plantar tissue viability, infrared thermography for early detection of plantar tissue inflammation, plantar pressure and pressure gradient system for identification of specific site at risk for DFUs, and ultrasound indentation tests (elastography) to quantify plantar tissue mechanical property. This paper also reviews how physical activity reduces risks of DFUs and how technology promotes adherence of physical activity. The clinician should encourage people with DM to exercise (brisk walking) at least 150 min per week and assess their exercise log along with the blood glucose log for providing individualized exercise prescription. Last, rehabilitation interventions such as off-loading devices, thermotherapy and electrotherapy are discussed. Although the exact etiology of DFUs is unclear, the emerging technologies discussed in this paper would enable clinicians to closely monitor the change of risk of DFUs and provide timely intervention. An integrated approach using all these emerging technologies should be promoted and may lead to a better outcome of preventing and managing DFUs.