Deep Learning Model to Evaluate Sensorimotor System Ability in Patients With Dizziness for Postural Control.

Balanced posture without dizziness is achieved via harmonious coordination of visual, vestibular, and somatosensory systems. Specific frequency bands of center of pressure (COP) signals during quiet standing are closely related to the sensory inputs of the sensorimotor system. In this study, we proposed a deep learning-based novel protocol using the COP signal frequencies to estimate the equilibrium score (ES), a sensory system contribution. Sensory organization test was performed with normal controls (n=125), patients with Meniere's disease (n=72) and vestibular neuritis (n=105). The COP signals preprocessed via filtering, detrending and augmenting during quiet standing were converted to frequency domains utilizing Short-time Fourier Transform. Four different types of CNN backbone including GoogleNet, ResNet-18, SqueezeNet, and VGG16 were trained and tested using the frequency transformed data of COP and the ES under conditions #2 to #6. Additionally, the 100 original output classes (1 to 100 ESs) were encoded into 50, 20, 10 and 5 sub-classes to improve the performance of the prediction model. Absolute difference between the measured and predicted ES was about 1.7 (ResNet-18 with encoding of 20 sub-classes). The average error of each sensory analysis calculated using the measured ES and predicted ES was approximately 1.0%. The results suggest that the sensory system contribution of patients with dizziness can be quantitatively assessed using only the COP signal from a single test of standing posture. This study has potential to reduce balance testing time (spent on six conditions with three trials each in sensory organization test) and the size of computerized dynamic posturography (movable visual surround and force plate), and helps achieve the widespread application of the balance assessment.

Microbiome and Prostate Cancer: Emerging Diagnostic and Therapeutic Opportunities.

This review systematically addresses the correlation between the microbiome and prostate cancer and explores its diagnostic and therapeutic implications. Recent research has indicated an association between the urinary and gut microbiome composition and prostate cancer incidence and progression. Specifically, the urinary microbiome is a potential non-invasive biomarker for early detection and risk evaluation, with altered microbial profiles in prostate cancer patients. This represents an advancement in non-invasive diagnostic approaches to prostate cancer. The role of the gut microbiome in the efficacy of various cancer therapies has recently gained attention. Gut microbiota variations can affect the metabolism and effectiveness of standard treatment modalities, including chemotherapy, immunotherapy, and hormone therapy. This review explores the potential of gut microbiome modification through dietary interventions, prebiotics, probiotics, and fecal microbiota transplantation for improving the treatment response and mitigating adverse effects. Moreover, this review discusses the potential of microbiome profiling for patient stratification and personalized treatment strategies. While the current research identifies the pivotal role of the microbiome in prostate cancer, it also highlights the necessity for further investigations to fully understand these complex interactions and their practical applications in improving patient outcomes in prostate cancer management.

HER2 positivity predicts BCG unresponsiveness and adaptive immune cell exhaustion in EORTC risk-stratified cohort of bladder cancer.

Introduction: Predicting the response to Bacillus Calmette-Guérin (BCG) therapy in high-risk patients with non-muscle invasive bladder cancer (NMIBC) is crucial, as failure may necessitate interventions, such as radical cystectomy or salvage therapy. With the recent classification of genetic class 2a (which has HER2 protein abundance as its signature mutation of ERBB2), evaluating its prognostic role and relationship with BCG response could yield important results. Methods: This retrospective study included 160 patients with NMIBC who underwent transurethral resection of bladder tumors at Gangneung Asan Hospital between 2000 and 2013 and were stratified based on the European Organization for Research and Treatment of Cancer (EORTC) risk criteria. In addition, we analyzed a subset of 67 patients who had received BCG induction therapy to identify factors predictive of BCG treatment response. Univariate and multivariate analyses were used to assess the impact of clinicopathological factors, HER2 positivity, and EORTC risk on recurrence, progression, survival, and BCG response. Each variable's prognostic significance was determined using the Kaplan-Meier analysis. The tumor microenvironments (TMEs) were evaluated in relation to HER2 and EORTC risk. Results: Patients with HER2+ had a higher median age, a greater prevalence of high-grade tumors, and more frequent recurrences. The univariate analysis demonstrated that the HER2+, intermediate (vs. low-risk) high (vs. low-risk), and EORTC recurrence risk groups were significantly associated with recurrence. In patients treated with BCG, only the HER2+ status predicted recurrence. In the univariate analysis for progression, age, high EORTC progression risk (vs. low-to-intermediate), HER2+, and programmed death-ligand 1 positive (PD-L1+) were significant factors. In multivariate analyses for progression, age, high EORTC progression risk, and PD-L1+ were significant factors for progression. HER2 expression was associated with the TME, influencing the proportion of PD-L1+ cells, as well as other markers of PD-1, CD8, and Ki67. Conclusion: The HER2+ status may be related to genetic characteristics that appear more frequently in older age, which suggests a potential for predicting the recurrence and response to BCG treatment. Additionally, analyzing TME trends of aggressive adaptive immune response characterized by HER2 expression provides insight into recurrence and BCG response mechanisms.

Deep Learning-Based Near-Fall Detection Algorithm for Fall Risk Monitoring System Using a Single Inertial Measurement Unit.

Proactively detecting falls and preventing injuries are among the primary keys to a healthy life for the elderly. Near-fall remote monitoring in daily life could provide key information to prevent future falls and obtain quantitative rehabilitation status for patients with weak balance ability. In this study, we developed a deep learning-based novel classification algorithm to precisely categorize three classes (falls, near-falls, and activities of daily living (ADLs)) using a single inertial measurement unit (IMU) device attached to the waist. A total of 34 young participants were included in this study. An IMU containing accelerometer and gyroscope sensors was fabricated to acquire acceleration and angular velocity signals. A comprehensive experiment including thirty-six types of activities (10 types of falls, 10 types of near-falls, and 16 types of ADLs) was designed based on previous studies. A modified directed acyclic graph-convolution neural network (DAG-CNN) architecture with hyperparameter optimization was proposed to predict fall, near-fall, and ADLs. Prediction results of the modified DAG-CNN structure were found to be approximately 7% more accurate than the traditional CNN structure. For the case of near-falls, the modified DAG-CNN demonstrated excellent prediction performance with accuracy of over 98% by combining gyroscope and accelerometer features. Additionally, by combining acceleration and angular velocity the trained model showed better performance than each model of acceleration and angular velocity. It is believed that information to preemptively handle the risk of falls and quantitatively evaluate the rehabilitation status of the elderly with weak balance will be provided by monitoring near-falls.

A Novel Optimization-Based Convolution Neural Network to Estimate the Contribution of Sensory Inputs to Postural Stability During Quiet Standing.

Adequate postural control is maintained by integrating signals from the visual, somatosensory, and vestibular systems. The purpose of this study is to propose a novel convolutional neural network (CNN)-based protocol that can evaluate the contributions of each sensory input for postural stability (calculated a sensory analysis index) using center of pressure (COP) signals in a quiet standing posture. Raw COP signals in the anterior/posterior and medial/lateral directions were extracted from 330 patients in a quiet standing with their eyes open for 20 seconds. The COP signals augmented using jittering and pooling techniques were transformed into the frequency domain. The sensory analysis indices were used as the output information from the deep learning models. A ResNet-50 CNN was combined with the k-nearest neighbor, random forest, and support vector machine classifiers for the training model. Additionally, a novel optimization process was proposed to include an encoding design variable that can group outputs into sub-classes along with hyperparameters. The results of optimization considering only hyperparameters showed low performance, with an accuracy of 55% or less and F-1 scores of 54% or less in all models. However, when optimization was performed using the encoding design variable, the performance was markedly increased in the CNN-classifier combined models (r = 0.975). These results suggest it is possible to evaluate the contribution of sensory inputs for postural stability using COP signals during a quiet standing. This study will facilitate the expanded dissemination of a system that can quantitatively evaluate the balance ability and rehabilitation progress of patients with dizziness.

Machine Learning Strategies for Low-Cost Insole-Based Prediction of Center of Gravity during Gait in Healthy Males.

Whole-body center of gravity (CG) movements in relation to the center of pressure (COP) offer insights into the balance control strategies of the human body. Existing CG measurement methods using expensive measurement equipment fixed in a laboratory environment are not intended for continuous monitoring. The development of wireless sensing technology makes it possible to expand the measurement in daily life. The insole system is a wearable device that can evaluate human balance ability by measuring pressure distribution on the ground. In this study, a novel protocol (data preparation and model training) for estimating the 3-axis CG trajectory from vertical plantar pressures was proposed and its performance was evaluated. Input and target data were obtained through gait experiments conducted on 15 adult and 15 elderly males using a self-made insole prototype and optical motion capture system. One gait cycle was divided into four semantic phases. Features specified for each phase were extracted and the CG trajectory was predicted using a bi-directional long short-term memory (Bi-LSTM) network. The performance of the proposed CG prediction model was evaluated by a comparative study with four prediction models having no gait phase segmentation. The CG trajectory calculated with the optoelectronic system was used as a golden standard. The relative root mean square error of the proposed model on the 3-axis of anterior/posterior, medial/lateral, and proximal/distal showed the best prediction performance, with 2.12%, 12.97%, and 12.47%. Biomechanical analysis of two healthy male groups was conducted. A statistically significant difference between CG trajectories of the two groups was shown in the proposed model. Large CG sway of the medial/lateral axis trajectory and CG fall of the proximal/distal axis trajectory is shown in the old group. The protocol proposed in this study is a basic step to have gait analysis in daily life. It is expected to be utilized as a key element for clinical applications.

Deep learning approach to estimate foot pressure distribution in walking with application for a cost-effective insole system.

BACKGROUND: Foot pressure distribution can be used as a quantitative parameter for evaluating anatomical deformity of the foot and for diagnosing and treating pathological gait, falling, and pressure sores in diabetes. The objective of this study was to propose a deep learning model that could predict pressure distribution of the whole foot based on information obtained from a small number of pressure sensors in an insole. METHODS: Twenty young and twenty older adults walked a straight pathway at a preferred speed with a Pedar-X system in anti-skid socks. A long short-term memory (LSTM) model was used to predict foot pressure distribution. Pressure values of nine major sensors and the remaining 90 sensors in a Pedar-X system were used as input and output for the model, respectively. The performance of the proposed LSTM structure was compared with that of a traditionally used adaptive neuro-fuzzy interference system (ANFIS). A low-cost insole system consisting of a small number of pressure sensors was fabricated. A gait experiment was additionally performed with five young and five older adults, excluding subjects who were used to construct models. The Pedar-X system placed parallelly on top of the insole prototype developed in this study was in anti-skid socks. Sensor values from a low-cost insole prototype were used as input of the LSTM model. The accuracy of the model was evaluated by applying a leave-one-out cross-validation. RESULTS: Correlation coefficient and relative root mean square error (RMSE) of the LSTM model were 0.98 (0.92 ~ 0.99) and 7.9 ± 2.3%, respectively, higher than those of the ANFIS model. Additionally, the usefulness of the proposed LSTM model for fabricating a low-cost insole prototype with a small number of sensors was confirmed, showing a correlation coefficient of 0.63 to 0.97 and a relative RMSE of 12.7 ± 7.4%. CONCLUSIONS: This model can be used as an algorithm to develop a low-cost portable smart insole system to monitor age-related physiological and anatomical alterations in foot. This model has the potential to evaluate clinical rehabilitation status of patients with pathological gait, falling, and various foot pathologies when more data of patients with various diseases are accumulated for training.

Acceleration Magnitude at Impact Following Loss of Balance Can Be Estimated Using Deep Learning Model.

Pre-impact fall detection can detect a fall before a body segment hits the ground. When it is integrated with a protective system, it can directly prevent an injury due to hitting the ground. An impact acceleration peak magnitude is one of key measurement factors that can affect the severity of an injury. It can be used as a design parameter for wearable protective devices to prevent injuries. In our study, a novel method is proposed to predict an impact acceleration magnitude after loss of balance using a single inertial measurement unit (IMU) sensor and a sequential-based deep learning model. Twenty-four healthy participants participated in this study for fall experiments. Each participant worn a single IMU sensor on the waist to collect tri-axial accelerometer and angular velocity data. A deep learning method, bi-directional long short-term memory (LSTM) regression, is applied to predict a fall's impact acceleration magnitude prior to fall impact (a fall in five directions). To improve prediction performance, a data augmentation technique with increment of dataset is applied. Our proposed model showed a mean absolute percentage error (MAPE) of 6.69 ± 0.33% with r value of 0.93 when all three different types of data augmentation techniques are applied. Additionally, there was a significant reduction of MAPE by 45.2% when the number of training datasets was increased by 4-fold. These results show that impact acceleration magnitude can be used as an activation parameter for fall prevention such as in a wearable airbag system by optimizing deployment process to minimize fall injury in real time.

Machine learning approach to predict center of pressure trajectories in a complete gait cycle: a feedforward neural network vs. LSTM network.

Center of pressure (COP) trajectories of human can maintain regulation of forward progression and stability of lateral sway during walking. The insole pressure system can only detect COP trajectories of each foot during single stance. In this study, we developed artificial neural network models that could present COP trajectories in an integrated coordinate system during a complete gait cycle using pressure information of the insole system. A feed forward artificial neural network (FFANN) and a long short-term memory (LSTM) model were developed. For FFANN, among 198 pressure sensors from Pedar-X insoles, proper input variables were selected using sequential forward selection to reduce input dimension. The LSTM model used all 198 signals as inputs because of its self-learning characteristic. As results of cross-validation, the FFANN model showed correlation coefficients of 0.98-0.99 and 0.93-0.95 in anterior/posterior and medial/lateral directions, respectively. For the LSTM model, correlation coefficients were similar to those of FFANN. However, the relative root mean square error (12.5%) of the FFANN model was higher than that (9.8%) of the LSTM model in medial/lateral direction (p = 0.03). This study can be used for quantitative evaluation of clinical diagnosis and rehabilitation status for patient with various diseases through further training using varied databases. Graphical abstract Architectures of neural networks developed in this study (a feed forward artificial neural network; b LSTM network).

Single Inertial Sensor-Based Neural Networks to Estimate COM-COP Inclination Angle During Walking.

A biomechanical understanding of gait stability is needed to reduce falling risk. As a typical parameter, the COM-COP (center of mass-center of pressure) inclination angle (IA) could provide valuable insight into postural control and balance recovery ability. In this study, an artificial neural network (ANN) model was developed to estimate COM-COP IA based on signals using an inertial sensor. Also, we evaluated how different types of ANN and the cutoff frequency of the low-pass filter applied to input signals could affect the accuracy of the model. An inertial measurement unit (IMU) including an accelerometer, gyroscope, and magnetometer sensors was fabricated as a prototype. The COM-COP IA was calculated using a 3D motion analysis system including force plates. In order to predict the COM-COP IA, a feed-forward ANN and long-short term memory (LSTM) network was developed. As a result, the feed-forward ANN showed a relative root-mean-square error (rRMSE) of 15% while the LSTM showed an improved accuracy of 9% rRMSE. Additionally, the LSTM displayed a stable accuracy regardless of the cutoff frequency of the filter applied to the input signals. This study showed that estimating the COM-COP IA was possible with a cheap inertial sensor system. Furthermore, the neural network models in this study can be implemented in systems to monitor the balancing ability of the elderly or patients with impaired balancing ability.

Machine Learning-Based Pre-Impact Fall Detection Model to Discriminate Various Types of Fall.

Pre-impact fall detection can send alarm service faster to reduce long-lie conditions and decrease the risk of hospitalization. Detecting various types of fall to determine the impact site or direction prior to impact is important because it increases the chance of decreasing the incidence or severity of fall-related injuries. In this study, a robust pre-impact fall detection model was developed to classify various activities and falls as multiclass and its performance was compared with the performance of previous developed models. Twelve healthy subjects participated in this study. All subjects were asked to place an inertial measuring unit module by fixing on a belt near the left iliac crest to collect accelerometer data for each activity. Our novel proposed model consists of feature calculation and infinite latent feature selection (ILFS) algorithm, auto labeling of activities, and application of machine learning classifiers for discrete and continuous time series data. Nine machine-learning classifiers were applied to detect falls prior to impact and derive final detection results by sorting the classifier. Our model showed the highest classification accuracy. Results for the proposed model that could classify as multiclass showed significantly higher average classification accuracy of 99.57 ± 0.01% for discrete data-based classifiers and 99.84 ± 0.02% for continuous time series-based classifiers than previous models (p < 0.01). In the future, multiclass pre-impact fall detection models can be applied to fall protector devices by detecting various activities for sending alerts or immediate feedback reactions to prevent falls.

Safety evaluation and consideration of 4 Pin Multi-needle for meso-therapy.

This study was conducted according to the method presented in the Republic of Korea Pharmacopoeia 11th Revision, aseptic test method to evaluate the suitability of sterilization for a sterile needle (4 Pin Multi-needle). In this study, four tests were conducted: sterility test, cytotoxicity test, acute toxicity test, skin sensitization test. First, in the aseptic test, the microorganism was not proliferated in the aseptic test of the medium. As a result of the performance test of the medium, it was confirmed that the microorganism developed within 3 days and the fungus was evident within 5 days. Based on this, it was confirmed that the medium was suitable, and as a result of the aseptic test, the development of microorganisms was not observed during the total culture period. Based on these results, tests were conducted which were confirmed to be suitable for aseptic testing because the development of bacteria on the provided samples was not recognized. For cytotoxicity tests ISO10993-5; 2009 (Biological Evaluation of Medical Devices, Part 5: Test for in vitro Cytotoxicity). As a result, the MEM eluate of the test substance caused very slight cytotoxicity to the fibroblasts of the mouse and was judged to be Grade 1 (Slightly cytotoxic) according to the judgment standard of ISO 10993-5. On the other hand, solvent control, negative control and positive control showed the expected results on the test. Acute Toxicity Test Results: It was judged that there was no systemic toxicity change when ICR mice were treated with 50 mL/kg B.W. of the eluate of sterile injectable needle for 72 hours. Skin sensitization test result: The Hartley guinea pig was evaluated as a substance which is evaluated as a substance which does not induce any skin reaction when skin sensitization is applied to the dissected material of the sterile injectable needle and is weak in skin sensitivity. Based on the above tests, we will study the stability and efficacy of more reliable medical devices based on the verification and performance of medical devices.

Visualization of plaque distribution in a curved artery: three-dimensional intravascular ultrasound imaging.

Intravascular ultrasound (IVUS) imaging provides an excellent tool for evaluation of the type, morphology, extent, and severity of an atheromatous plaque. 3 D IVUS imaging offers additive information pertaining to morphology of the arterial structures and volumetric plaque distributions. A new 3 D IVUS visualization technique was developed to provide 3 D structural information of a curved artery. A virtual 3 D curved arterial phantom consisting of varying cross-sectional shapes, wall thicknesses, and acoustic intensity information was utilized to validate the nonlinear interpolation technique to create intermediary 2 D IVUS images. IVUS imaging was performed for the iliofemoral arterial segment of an atherosclerotic Yucatan miniswine model. These in-vivo IVUS data were utilized for intermediary IVUS image generation and volumetric 3 D IVUS visualization. Smooth transitional changes of cross-sectional shape, wall thickness and grayscale intensity were found between the intermediary images and the original arterial phantom slices. The 3 D IVUS imaging of the unfolded curved iliofemoral artery provided realistic 3 D luminal surface images of the arteries with physiologic grayscale intensity information. This unique 3 D IVUS imaging technique may help with assessment of 3 D plaque distribution across the curved arterial structure, and improve 3 D visualization of atheromatous components.

Neochordoplasty versus leaflet resection for ruptured mitral chordae treatment: Virtual mitral valve repair.

Degenerative mitral valve (MV) disease involving mitral prolapse is one of the most prevalent MV pathologies. Quadrangular leaflet resection and neochordoplasty demonstrate excellent clinical outcomes for the treatment of posterior leaflet prolapse. We evaluated the functional and biomechanical characteristics of a virtual pathologic MV model suffering from chordal rupture, performed virtual neochordoplasty and quadrangular leaflet resection, and compared the two post-repair MV functions. The pre-repair MV demonstrated severe posterior leaflet prolapse due to the ruptured P2 chordae and excessive stress concentration. Both repair techniques revealed reduced leaflet prolapse, decreased stress concentration, and restored leaflet coaptation. While neochordoplasty demonstrated further improved leaflet coaptation and superior posterior leaflet mobility, leaflet resection showed more uniform leaflet stress distributions. Virtual MV repair simulation has the ability to predict and quantitate biomechanical and functional improvement following MV repair. This strategy has the potential to help determine the most effective repair technique to restore MV function.

Analysis of Pelvis-Thorax Coordination Patterns of Professional and Amateur Golfers during Golf Swing.

The aim of this research was to quantify the coordination pattern between thorax and pelvis during a golf swing. The coordination patterns were calculated using vector coding technique, which had been applied to quantify the coordination changes in coupling angle (γ) between two different segments. For this, fifteen professional and fifteen amateur golfers who had no significant history of musculoskeletal injuries. There was no significant difference in coordination patterns between the two groups for rotation motion during backswing (p = 0.333). On the other hand, during the downswing phase, there were significant differences between professional and amateur groups in all motions (flexion/extension: professional [γ] = 187.8°, amateur [γ] = 167.4°; side bending: professional [γ] = 288.4°, amateur [γ] = 245.7°; rotation: professional [γ] = 232.0°, amateur [γ] = 229.5°). These results are expected to be a discriminating measure to assess complex coordination of golfers' trunk movements and preliminary study for interesting comparison by golf skilled levels.

Computational virtual evaluation of the effect of annuloplasty ring shape.

Mitral regurgitation (MR) is a result of mitral valve (MV) pathology. Its etiology can be categorized as degenerative or functional MR. Ring annuloplasty aims to reconfigure a dilated mitral annulus to its normal size and shape. We investigated the effect of annuloplasty ring shape on MR outcome using our established 3-dimensional (3-D) echocardiography-based computational MV evaluation protocols. Virtual patient MV models were created from 3-D transesophageal echocardiographic data in patients with MR because of mitral annular dilation. Two distinct annuloplasty rings (Physio II and GeoForm) were designed and virtually implanted to the patient MVs. Dynamic finite element simulations of MV function were performed for each MV after virtual ring annuloplasty of either ring, and physiologic and biomechanical characteristics of MV function were compared. Excessive stress values appeared primarily in the midanterior and midposterior regions, and lack of leaflet coaptation was found in pre-annuloplasty patient MVs. Both rings demonstrated marked reduction of stresses and efficient leaflet coaptation. The Physio II ring demonstrated more evenly distributed stress reduction across the leaflets and annulus compared with the GeoForm ring. Conversely, the highly nonplanar curvature of the GeoForm ring more effectively increased leaflet coaptation compared with the Physio II ring. This indicates that the shape of annuloplasty ring affects post-annuloplasty physiologic and biomechanical conditions, which can lead to tissue alteration over a longer period after ring annuloplasty. This virtual ring annuloplasty simulation strategy provides detailed physiologic and biomechanical information and may help better plan the optimal ring selection and improved patient-specific MV repairs.

How to quantify the transition phase during golf swing performance: Torsional load affects low back complaints during the transition phase.

The transition phase of a golf swing is considered to be a decisive instant required for a powerful swing. However, at the same time, the low back torsional loads during this phase can have a considerable effect on golf-related low back pain (LBP). Previous efforts to quantify the transition phase were hampered by problems with accuracy due to methodological limitations. In this study, vector-coding technique (VCT) method was proposed as a comprehensive methodology to quantify the precise transition phase and examine low back torsional load. Towards this end, transition phases were assessed using three different methods (VCT, lead hand speed and X-factor stretch) and compared; then, low back torsional load during the transition phase was examined. As a result, the importance of accurate transition phase quantification has been documented. The largest torsional loads were observed in healthy professional golfers (10.23 ± 1.69 N · kg-1), followed by professional golfers with a history of LBP (7.93 ± 1.79 N · kg-1), healthy amateur golfers (1.79 ± 1.05 N · kg-1) and amateur golfers with a history of LBP (0.99 ± 0.87 N · kg-1), which order was equal to that of the transition phase magnitudes of each group. These results indicate the relationship between the transition phase and LBP history and the dependency of the torsional load magnitude on the transition phase.

Inter-joint coordination between hips and trunk during downswings: Effects on the clubhead speed.

Understanding of the inter-joint coordination between rotational movement of each hip and trunk in golf would provide basic knowledge regarding how the neuromuscular system organises the related joints to perform a successful swing motion. In this study, we evaluated the inter-joint coordination characteristics between rotational movement of the hips and trunk during golf downswings. Twenty-one right-handed male professional golfers were recruited for this study. Infrared cameras were installed to capture the swing motion. The axial rotation angle, angular velocity and inter-joint coordination were calculated by the Euler angle, numerical difference method and continuous relative phase, respectively. A more typical inter-joint coordination demonstrated in the leading hip/trunk than trailing hip/trunk. Three coordination characteristics of the leading hip/trunk reported a significant relationship with clubhead speed at impact (r < -0.5) in male professional golfers. The increased rotation difference between the leading hip and trunk in the overall downswing phase as well as the faster rotation of the leading hip compared to that of the trunk in the early downswing play important roles in increasing clubhead speed. These novel inter-joint coordination strategies have the great potential to use a biomechanical guideline to improve the golf swing performance of unskilled golfers.

Improved determination of dynamic balance using the centre of mass and centre of pressure inclination variables in a complete golf swing cycle.

Golf requires proper dynamic balance to accurately control the club head through a harmonious coordination of each human segment and joint. In this study, we evaluated the ability for dynamic balance during a golf swing by using the centre of mass (COM)-centre of pressure (COP) inclination variables. Twelve professional, 13 amateur and 10 novice golfers participated in this study. Six infrared cameras, two force platforms and SB-Clinic software were used to measure the net COM and COP trajectories. In order to evaluate dynamic balance ability, the COM-COP inclination angle, COM-COP inclination angular velocity and normalised COM-COP inclination angular jerk were used. Professional golfer group revealed a smaller COM-COP inclination angle and angular velocity than novice golfer group in the lead/trail direction (P < 0.01). In the normalised COM-COP inclination angular jerk, the professional golfer group showed a lower value than the other two groups in all directions. Professional golfers tend to exhibit improved dynamic balance, and this can be attributed to the neuromusculoskeletal system that maintains balance with proper postural control. This study has the potential to allow for an evaluation of the dynamic balance mechanism and will provide useful basic information for swing training and prevention of golf injuries.

Biomechanical evaluation of the pathophysiologic developmental mechanisms of mitral valve prolapse: effect of valvular morphologic alteration.

Mitral valve prolapse (MVP) refers to an excessive billowing of the mitral valve (MV) leaflets across the mitral annular plane into the left atrium during the systolic portion of the cardiac cycle. The underlying mechanisms for the development of MVP and mitral regurgitation in association with MV tissue remodeling are still unclear. We performed computational MV simulations to investigate the pathophysiologic developmental mechanisms of MVP. A parametric MV geometry model was utilized for this study. Posterior leaflet enlargement and posterior chordal elongation models were created by adjusting the geometry of the posterior leaflet and chordae, respectively. Dynamic finite element simulations of MV function were performed over the complete cardiac cycle. Computational simulations demonstrated that enlarging posterior leaflet area increased large stress concentration in the posterior leaflets and chordae, and posterior chordal elongation decreased leaflet coaptation. When MVP was accompanied by both posterior leaflet enlargement and chordal elongation simultaneously, the posterior leaflet was exposed to extremely large prolapse with a substantial lack of leaflet coaptation. These data indicate that MVP development is closely related to tissue alterations of the leaflets and chordae. This biomechanical evaluation strategy can help us better understand the pathophysiologic developmental mechanisms of MVP.