Lower limb joint loading during high-impact activities: implication for bone health.

Osteoporosis results in low-trauma fractures affecting millions globally, in particular elderly populations. Despite the inclusion of physical activity in fracture prevention strategies, the optimal bone-strengthening exercises remain uncertain, highlighting the need for a deeper understanding of lower limb joint loading dynamics across various exercise types and levels. This study examines lower limb joint loading during high-impact exercises across different intensities. A total of 40 healthy, active participants were recruited (mean ± SD: age of 40.3 ± 13.1 yr; height 1.71 ± 0.08 m; and mass 68.44 ± 11.67 kg). Motion capture data and ground reaction forces of 6 different exercises: a self-selected level of walking, running, countermovement jump, squat jump, unilateral hopping, and bilateral hopping were collected for each participant. Joint reaction forces were estimated using lower body musculoskeletal models developed in OpenSim. Running and hopping increased joint forces compared to walking, notably at the hip (83% and 21%), knee (134% and 94%), and ankle (94% and 77%), while jump exercises reduced hip and ankle loading compared to walking (36% and 19%). Joint loading varied with exercise type and intensity, with running faster increasing forces on all joints, particularly at the hip. Sprinting increased forces at the hip but lowered knee and ankle forces. Higher jumps intensified forces on all joints, while faster hopping reduced forces. The wide variation of lower limb joint loading observed across the exercises tested in this study underscores the importance of implementing diverse exercise routines to optimize overall bone health and strengthen the musculoskeletal structure. Practitioners must therefore ensure that exercise programs include movements that are specifically suitable for their intended purpose.

Canagliflozin Mitigates Diabetic Cardiomyopathy through Enhanced PINK1-Parkin Mitophagy.

Diabetic cardiomyopathy (DCM) is a major determinant of mortality in diabetic populations, and the potential strategies are insufficient. Canagliflozin has emerged as a potential cardioprotective agent in diabetes, yet its underlying molecular mechanisms remain unclear. We employed a high-glucose challenge (60 mM for 48 h) in vitro to rat cardiomyocytes (H9C2), with or without canagliflozin treatment (20 µM). In vivo, male C57BL/6J mice were subjected to streptozotocin and a high-fat diet to induce diabetes, followed by canagliflozin administration (10, 30 mg·kg-1·d-1) for 12 weeks. Proteomics and echocardiography were used to assess the heart. Histopathological alterations were assessed by the use of Oil Red O and Masson's trichrome staining. Additionally, mitochondrial morphology and mitophagy were analyzed through biochemical and imaging techniques. A proteomic analysis highlighted alterations in mitochondrial and autophagy-related proteins after the treatment with canagliflozin. Diabetic conditions impaired mitochondrial respiration and ATP production, alongside decreasing the related expression of the PINK1-Parkin pathway. High-glucose conditions also reduced PGC-1α-TFAM signaling, which is responsible for mitochondrial biogenesis. Canagliflozin significantly alleviated cardiac dysfunction and improved mitochondrial function both in vitro and in vivo. Specifically, canagliflozin suppressed mitochondrial oxidative stress, enhancing ATP levels and sustaining mitochondrial respiratory capacity. It activated PINK1-Parkin-dependent mitophagy and improved mitochondrial function via increased phosphorylation of adenosine monophosphate-activated protein kinase (AMPK). Notably, PINK1 knockdown negated the beneficial effects of canagliflozin on mitochondrial integrity, underscoring the critical role of PINK1 in mediating these protective effects. Canagliflozin fosters PINK1-Parkin mitophagy and mitochondrial function, highlighting its potential as an effective treatment for DCM.

Association between fat and fat-free body mass indices on shock attenuation during running.

High amplitudes of shock during running have been thought to be associated with an increased injury risk. This study aimed to quantify the association between dual-energy X-ray absorptiometry (DEXA) quantified body composition, and shock attenuation across the time and frequency domains. Twenty-four active adults participated. A DEXA scan was performed to quantify the fat and fat-free mass of the whole-body, trunk, dominant leg, and viscera. Linear accelerations at the tibia, pelvis, and head were collected whilst participants ran on a treadmill at a fixed dimensionless speed 1.00 Fr. Shock attenuation indices in the time- and frequency-domain (lower frequencies: 3-8 Hz; higher frequencies: 9-20 Hz) were calculated. Pearson correlation analysis was performed for all combinations of DEXA and attenuation indices. Regularised regression was performed to predict shock attenuation indices using DEXA variables. A greater power attenuation between the head and pelvis within the higher frequency range was associated with a greater trunk fat-free mass (r = 0.411, p = 0.046), leg fat-free mass (r = 0.524, p = 0.009), and whole-body fat-free mass (r = 0.480, p = 0.018). For power attenuation of the high-frequency component between the pelvis and head, the strongest predictor was visceral fat mass (ß = 48.79). Passive and active tissues could represent important anatomical factors aiding in shock attenuation during running. Depending on the type and location of these masses, an increase in mass may benefit injury risk reduction. Also, our findings could implicate the injury risk potential during weight loss programs.

Strategies to optimise machine learning classification performance when using biomechanical features.

Building prediction models using biomechanical features is challenging because such models may require large sample sizes. However, collecting biomechanical data on large sample sizes is logistically very challenging. This study aims to investigate if modern machine learning algorithms can help overcome the issue of limited sample sizes on developing prediction models. This was a secondary data analysis two biomechanical datasets - a walking dataset on 2295 participants, and a countermovement jump dataset on 31 participants. The input features were the three-dimensional ground reaction forces (GRFs) of the lower limbs. The outcome was the orthopaedic disease category (healthy, calcaneus, ankle, knee, hip) in the walking dataset, and healthy vs people with patellofemoral pain syndrome in the jump dataset. Different algorithms were compared: multinomial/LASSO regression, XGBoost, various deep learning time-series algorithms with augmented data, and with transfer learning. For the outcome of weighted multiclass area under the receiver operating curve (AUC) in the walking dataset, the three models with the best performance were InceptionTime with x12 augmented data (0.810), XGBoost (0.804), and multinomial logistic regression (0.800). For the jump dataset, the top three models with the highest AUC were the LASSO (1.00), InceptionTime with x8 augmentation (0.750), and transfer learning (0.653). Machine-learning based strategies for managing the challenging issue of limited sample size for biomechanical ML-based problems, could benefit the development of alternative prediction models in healthcare, especially when time-series data are involved.

SGLT2 inhibitor improves kidney function and morphology by regulating renal metabolism in mice with diabetic kidney disease.

BACKGROUND: Diabetic kidney disease (DKD) is a secondary complication of diabetes mellitus and a leading cause of chronic kidney disease. AIM: To investigate the impact of long-term canagliflozin treatment on DKD and elucidate its underlying mechanism. METHODS: DKD model was established using high-fat diet and streptozotocin in male C57BL/6J mice (n = 30). Mice were divided into five groups and treated for 12 weeks. 1) normal control mice, 2) DKD model, 3) mice treated low-dose of canagliflozin, 4) high-dose of canagliflozin and 5) ß-hydroxybutyrate. Mice kidney morphology and function were evaluated, and a metabolomics analysis was performed. RESULTS: Canagliflozin treatment reduced blood creatinine and urine nitrogen levels and improved systemic insulin sensitivity and glucose tolerance in diabetic mice. Additionally, a decrease in histological lesions including collagen and lipid deposition in the kidneys was observed. ß-hydroxybutyrate treatment did not yield a comparable outcome. The metabolomics analysis revealed that canagliflozin induced alterations in amino acid metabolism profiles in the renal tissue of diabetic mice. CONCLUSION: Canagliflozin protects the kidneys of diabetic mice by increasing the levels of essential amino acids, promoting mitochondrial homeostasis, mitigating oxidative stress, and stimulating the amino acid-dependent tricarboxylic acid cycle.

Insights into ageing rates comparison across tissues from recalibrating cerebellum DNA methylation clock.

DNA methylation (DNAm)-based age clocks have been studied extensively as a biomarker of human ageing and a risk factor for age-related diseases. Despite different tissues having vastly different rates of proliferation, it is still largely unknown whether they age at different rates. It was previously reported that the cerebellum ages slowly; however, this claim was drawn from a single clock using a relatively small sample size and so warrants further investigation. We collected the largest cerebellum DNAm dataset (N = 752) to date. We found the respective epigenetic ages are all severely underestimated by six representative DNAm age clocks, with the underestimation effects more pronounced in the four clocks whose training datasets do not include brain-related tissues. We identified 613 age-associated CpGs in the cerebellum, which accounts for only 14.5% of the number found in the middle temporal gyrus from the same population (N = 404). From the 613 cerebellum age-associated CpGs, we built a highly accurate age prediction model for the cerebellum named CerebellumClockspecific (Pearson correlation=0.941, MAD=3.18 years). Ageing rate comparisons based on the two tissue-specific clocks constructed on the 201 overlapping age-associated CpGs support the cerebellum has younger DNAm age. Nevertheless, we built BrainCortexClock to prove a single DNAm clock is able to unbiasedly estimate DNAm ages of both cerebellum and cerebral cortex, when they are adequately and equally represented in the training dataset. Comparing ageing rates across tissues using DNA methylation multi-tissue clocks is flawed. The large underestimation of age prediction for cerebellums by previous clocks mainly reflects the improper usage of these age clocks. There exist strong and consistent ageing effects on the cerebellar methylome, and we suggest the smaller number of age-associated CpG sites in cerebellum is largely attributed to its extremely low average cell replication rates.

Performance of multiple neural networks in predicting lower limb joint moments using wearable sensors.

Joint moment measurements represent an objective biomechemical parameter in joint health assessment. Inverse dynamics based on 3D motion capture data is the current 'gold standard' to estimate joint moments. Recently, machine learning combined with data measured by wearable technologies such electromyography (EMG), inertial measurement units (IMU), and electrogoniometers (GON) has been used to enable fast, easy, and low-cost measurements of joint moments. This study investigates the ability of various deep neural networks to predict lower limb joint moments merely from IMU sensors. The performance of five different deep neural networks (InceptionTimePlus, eXplainable convolutional neural network (XCM), XCMplus, Recurrent neural network (RNNplus), and Time Series Transformer (TSTPlus)) were tested to predict hip, knee, ankle, and subtalar moments using acceleration and gyroscope measurements of four IMU sensors at the trunk, thigh, shank, and foot. Multiple locomotion modes were considered including level-ground walking, treadmill walking, stair ascent, stair descent, ramp ascent, and ramp descent. We show that XCM can accurately predict lower limb joint moments using data of only four IMUs with RMSE of 0.046 ± 0.013 Nm/kg compared to 0.064 ± 0.003 Nm/kg on average for the other architectures. We found that hip, knee, and ankle joint moments predictions had a comparable RMSE with an average of 0.069 Nm/kg, while subtalar joint moments had the lowest RMSE of 0.033 Nm/kg. The real-time feedback that can be derived from the proposed method can be highly valuable for sports scientists and physiotherapists to gain insights into biomechanics, technique, and form to develop personalized training and rehabilitation programs.

Smooth and accurate predictions of joint contact force time-series in gait using over parameterised deep neural networks.

Alterations in joint contact forces (JCFs) are thought to be important mechanisms for the onset and progression of many musculoskeletal and orthopaedic pain disorders. Computational approaches to JCFs assessment represent the only non-invasive means of estimating in-vivo forces; but this cannot be undertaken in free-living environments. Here, we used deep neural networks to train models to predict JCFs, using only joint angles as predictors. Our neural network models were generally able to predict JCFs with errors within published minimal detectable change values. The errors ranged from the lowest value of 0.03 bodyweight (BW) (ankle medial-lateral JCF in walking) to a maximum of 0.65BW (knee VT JCF in running). Interestingly, we also found that over parametrised neural networks by training on longer epochs (>100) resulted in better and smoother waveform predictions. Our methods for predicting JCFs using only joint kinematics hold a lot of promise in allowing clinicians and coaches to continuously monitor tissue loading in free-living environments.

Monocular 3D Human Pose Markerless Systems for Gait Assessment.

Gait analysis plays an important role in the fields of healthcare and sports sciences. Conventional gait analysis relies on costly equipment such as optical motion capture cameras and wearable sensors, some of which require trained assessors for data collection and processing. With the recent developments in computer vision and deep neural networks, using monocular RGB cameras for 3D human pose estimation has shown tremendous promise as a cost-effective and efficient solution for clinical gait analysis. In this paper, a markerless human pose technique is developed using motion captured by a consumer monocular camera (800 × 600 pixels and 30 FPS) for clinical gait analysis. The experimental results have shown that the proposed post-processing algorithm significantly improved the original human pose detection model (BlazePose)'s prediction performance compared to the gold-standard gait signals by 10.7% using the MoVi dataset. In addition, the predicted T2 score has an excellent correlation with ground truth (r = 0.99 and y = 0.94x + 0.01 regression line), which supports that our approach can be a potential alternative to the conventional marker-based solution to assist the clinical gait assessment.

A Novel Model to Generate Heterogeneous and Realistic Time-Series Data for Post-Stroke Rehabilitation Assessment.

The application of machine learning-based tele-rehabilitation faces the challenge of limited availability of data. To overcome this challenge, data augmentation techniques are commonly employed to generate synthetic data that reflect the configurations of real data. One such promising data augmentation technique is the Generative Adversarial Network (GAN). However, GANs have been found to suffer from mode collapse, a common issue where the generated data fails to capture all the relevant information from the original dataset. In this paper, we aim to address the problem of mode collapse in GAN-based data augmentation techniques for post-stroke assessment. We applied the GAN to generate synthetic data for two post-stroke rehabilitation datasets and observed that the original GAN suffered from mode collapse, as expected. To address this issue, we propose a Time Series Siamese GAN (TS-SGAN) that incorporates a Siamese network and an additional discriminator. Our analysis, using the longest common sub-sequence (LCSS), demonstrates that TS-SGAN generates data uniformly for all elements of two testing datasets, in contrast to the original GAN. To further evaluate the effectiveness of TS-SGAN, we encode the generated dataset into images using Gramian Angular Field and classify them using ResNet-18. Our results show that TS-SGAN achieves a significant accuracy increase of classification accuracy (35.2%-42.07%) for both selected datasets. This represents a substantial improvement over the original GAN.

The Clinical Characteristics and Potential Molecular Mechanism of LMNA Mutation-Related Lipodystrophy.

This study aimed to enhance understanding of LMNA mutation-related lipodystrophy by elucidating genotype-phenotype correlations and potential molecular mechanisms. Clinical data from six patients with LMNA mutation-related lipodystrophy are analyzed, and four distinct LMNA mutations are identified. Associations between mutations and lipodystrophy phenotypes are assessed. Three LMNA mutation plasmids are constructed and transfected into HEK293 cells. Protein stability, degradation pathways, and binding proteins of mutant Lamin A/C are examined using Western blotting, co-immunoprecipitation, and mass spectrometry. Confocal microscopy is employed to observe nuclear structure. Four different LMNA mutations are identified in the six patients, all exhibiting lipodystrophy and metabolic disorders. Cardiac dysfunction is observed in two out of six patients. Metformin and pioglitazone are the primary treatments for glucose control. Confocal microscopy revealed nuclear blebbing and irregular cell membranes. Mutant Lamin A/C stability is significantly decreased, and degradation occurred primarily via the ubiquitin-proteasome system (UPS). Potential binding ubiquitination-related proteins of mutant Lamin A/C are identified. This study investigated LMNA mutation-related lipodystrophy, identifying four unique mutations and their connections to specific phenotypes. It is found to decreased mutant Lamin A/C stability and degradation primarily through the UPS, offering new insights into molecular mechanisms and potential therapeutic targets.

InterpolatedXY: a two-step strategy to normalize DNA methylation microarray data avoiding sex bias.

MOTIVATION: Data normalization is an essential step to reduce technical variation within and between arrays. Due to the different karyotypes and the effects of X chromosome inactivation, females and males exhibit distinct methylation patterns on sex chromosomes; thus, it poses a significant challenge to normalize sex chromosome data without introducing bias. Currently, existing methods do not provide unbiased solutions to normalize sex chromosome data, usually, they just process autosomal and sex chromosomes indiscriminately. RESULTS: Here, we demonstrate that ignoring this sex difference will lead to introducing artificial sex bias, especially for thousands of autosomal CpGs. We present a novel two-step strategy (interpolatedXY) to address this issue, which is applicable to all quantile-based normalization methods. By this new strategy, the autosomal CpGs are first normalized independently by conventional methods, such as funnorm or dasen; then the corrected methylation values of sex chromosome-linked CpGs are estimated as the weighted average of their nearest neighbors on autosomes. The proposed two-step strategy can also be applied to other non-quantile-based normalization methods, as well as other array-based data types. Moreover, we propose a useful concept: the sex explained fraction of variance, to quantitatively measure the normalization effect. AVAILABILITY AND IMPLEMENTATION: The proposed methods are available by calling the function 'adjustedDasen' or 'adjustedFunnorm' in the latest wateRmelon package (https://github.com/schalkwyk/wateRmelon), with methods compatible with all the major workflows, including minfi. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Predicting the Internal Knee Abduction Impulse During Walking Using Deep Learning.

Knee joint moments are commonly calculated to provide an indirect measure of knee joint loads. A shortcoming of inverse dynamics approaches is that the process of collecting and processing human motion data can be time-consuming. This study aimed to benchmark five different deep learning methods in using walking segment kinematics for predicting internal knee abduction impulse during walking. Three-dimensional kinematic and kinetic data used for the present analyses came from a publicly available dataset on walking (participants n = 33). The outcome for prediction was the internal knee abduction impulse over the stance phase. Three-dimensional (3D) angular and linear displacement, velocity, and acceleration of the seven lower body segment's center of mass (COM), relative to a fixed global coordinate system were derived and formed the predictor space (126 time-series predictors). The total number of observations in the dataset was 6,737. The datasets were split into training (75%, n = 5,052) and testing (25%, n = 1685) datasets. Five deep learning models were benchmarked against inverse dynamics in quantifying knee abduction impulse. A baseline 2D convolutional network model achieved a mean absolute percentage error (MAPE) of 10.80%. Transfer learning with InceptionTime was the best performing model, achieving the best MAPE of 8.28%. Encoding the time-series as images then using a 2D convolutional model performed worse than the baseline model with a MAPE of 16.17%. Time-series based deep learning models were superior to an image-based method when predicting knee abduction moment impulse during walking. Future studies looking to develop wearable technologies will benefit from knowing the optimal network architecture, and the benefit of transfer learning for predicting joint moments.

A comprehensive evaluation of state-of-the-art time-series deep learning models for activity-recognition in post-stroke rehabilitation assessment.

The recent COVID-19 pandemic has further high-lighted the need for improving tele-rehabilitation systems. One of the common methods is to use wearable sensors for monitoring patients and intelligent algorithms for accurate and objective assessments. An important part of this work is to develop an efficient evaluation algorithm that provides a high-precision activity recognition rate. In this paper, we have investigated sixteen state-of-the-art time-series deep learning algorithms with four different architectures: eight convolutional neural networks configurations, six recurrent neural networks, a combination of the two and finally a wavelet-based neural network. Additionally, data from different sensors' combinations and placements as well as different pre-processing algorithms were explored to determine the optimal configuration for achieving the best performance. Our results show that the XceptionTime CNN architecture is the best performing algorithm with normalised data. Moreover, we found out that sensor placement is the most important attribute to improve the accuracy of the system, applying the algorithm on data from sensors placed on the waist achieved a maximum of 42% accuracy while the sensors placed on the hand achieved 84%. Consequently, compared to current results on the same dataset for different classification categories, this approach improved the existing state of the art accuracy from 79% to 84%, and from 80% to 90% respectively.

Comparing shallow, deep, and transfer learning in predicting joint moments in running.

Joint moments are commonly calculated in biomechanics research and provide an indirect measure of muscular behaviors and joint loads. However, joint moments cannot be easily quantified clinically or in the field, primarily due to challenges measuring ground reaction forces outside the laboratory. The present study aimed to compare the accuracy of three different machine learning (ML) techniques - functional regression [ MLfregress ], a deep neural network (DNN) built from scratch [ MLDNN ], and transfer learning [ MLTL ], in predicting joint moments during running. Data for this study came from an open-source dataset and two studies on running with and without external loads. Three-dimensional (3D) joint moments of the hip, knee, and ankle, were derived using inverse dynamics. 3D joint angle, velocity, and acceleration of the three joints served as predictors for each of the three ML techniques. Prediction performance was generally the best using MLDNN, and the worse using MLfregress. Absolute predictive performance was the best for sagittal plane moments, which ranged from a RMSE of 0.16 Nm/kg at the ankle using MLDNN, to a RMSE of 0.49Nm/kg at the knee using MLfregress. MLDNN resulted in the greatest improvement in relative prediction performance (relRMSE) by 20% compared to MLfregress for the ankle adduction-abduction moment. DNN with or without transfer learning was superior in predicting joint moments using kinematic inputs compared to functional regression. Synergizing ML with kinematic inputs has the potential to solve the constraints of obtaining high fidelity biomechanics data normally only possible during laboratory studies.

Association between Serum Testosterone Levels and Body Composition among Men 20-59 Years of Age.

INTRODUCTION: Sex hormones play an important role in the development and maintenance of bone and muscle mass. However, studies regarding serum testosterone levels, osteoporosis, and sarcopenia in men are relatively sparse and have led to contradictory conclusions. Therefore, this study aimed to investigate the association between serum testosterone levels and body composition, including bone mineral density (BMD), appendicular lean mass index (ALMI), and appendicular fat mass index (AFMI), among men 20-59 years of age through a cross-sectional analysis of the National Health and Nutrition Examination Survey. MATERIALS AND METHODS: Our analysis was based on the data for 3,875 men, 20-59 years of age. Weighted multiple regression analyses were used to estimate the independent association between serum testosterone levels and body composition. Weighted generalized additive models and smooth curve fittings were used to characterize the nonlinear associations between them. RESULTS: The association between the serum testosterone level and lumbar BMD was positive in each multivariable linear regression model. In the model adjusted for age and race, the serum testosterone level was negatively associated with ALMI. However, in the models adjusted for body mass index, this association became positive. In addition, the association between the serum testosterone level and AFMI was negative in each multivariable linear regression model. CONCLUSION: Our study demonstrated a positive association of serum testosterone level with lumbar BMD and ALMI, and a negative association with AFMI, among men 20-59 years of age, suggesting that increasing testosterone levels may be beneficial to skeletal health in young and middle-aged men with low testosterone levels.

[Clinical efficacy of adductor canal blockade combined with transcutaneous electrical acupoint stimulation for total knee arthroplasty].

OBJECTIVE: To evaluate the efficacy of adductor canal block (ACB) combined with transcutaneous electrical acupoint stimulation (TEAS)for postoperative analgesia and early functional exercise after total knee arthroplasty (TKA). METHODS: A total of 84 patients underwent primary unilateral TKA from January 2019 to August 2020 were selected, including 45 males and 39 females, aged 66-77 (72.8±8.9) years;body mass index (BMI) was for 19-25 (23.6±3.5) kg /m2. They were divided into adductor canal block combined with transcutaneous electrical acupoint stimulation group (TEAS+ACB group)and simple adductor canal block group (ACB group) according to random number table method, 42 cases in each group. ACB was performed in ACB group during the operation. And TEAS was performed in TEAS+ACB group on bilateral lower limbs in 1-7 days postoperative on the basis of ACB. VAS scores at 6, 12, 24, 48, 72 h after surgery, knee function at 1, 2, 3, 7 days after surgery, knee motion at 7 days after surgery and length of hospitalization days were recorded and compared between the two groups. RESULTS: There were no significant differences in VAS of rest pain and activity pain in postoperative 6, 12 h between two groups (P>0.05), but the VAS of TEAS+ACB group was lower at 24, 48, 72 h after surgery(P<0.05). There was no significant difference in at 1 day postoperatively between two groups(P>0.05) , but the knee function of TEAS+ACB group was better than that of the ACB group in 2, 3, 7 days postoperatively (P<0.05). The length of hospitalization days in were less than in ACB group. On the 7th day after operation, the knee motion of TEAS+ACB group was greater than that of the ACB group (P<0.05). CONCLUSION: TEAS combined with ACB has a better postoperative analgesic efficacy than simple ACB, and can promote early functional exercise of patients. It is safe and effective for postoperative analgesia after TKA.

DNA methylation-based sex classifier to predict sex and identify sex chromosome aneuploidy.

BACKGROUND: Sex is an important covariate of epigenome-wide association studies due to its strong influence on DNA methylation patterns across numerous genomic positions. Nevertheless, many samples on the Gene Expression Omnibus (GEO) frequently lack a sex annotation or are incorrectly labelled. Considering the influence that sex imposes on DNA methylation patterns, it is necessary to ensure that methods for filtering poor samples and checking of sex assignment are accurate and widely applicable. RESULTS: Here we presented a novel method to predict sex using only DNA methylation beta values, which can be readily applied to almost all DNA methylation datasets of different formats (raw IDATs or text files with only signal intensities) uploaded to GEO. We identified 4345 significantly (p<0.01) sex-associated CpG sites present on both 450K and EPIC arrays, and constructed a sex classifier based on the two first principal components of the DNA methylation data of sex-associated probes mapped on sex chromosomes. The proposed method is constructed using whole blood samples and exhibits good performance across a wide range of tissues. We further demonstrated that our method can be used to identify samples with sex chromosome aneuploidy, this function is validated by five Turner syndrome cases and one Klinefelter syndrome case. CONCLUSIONS: This proposed sex classifier not only can be used for sex predictions but also applied to identify samples with sex chromosome aneuploidy, and it is freely and easily accessible by calling the 'estimateSex' function from the newest wateRmelon Bioconductor package ( https://github.com/schalkwyk/wateRmelon ).

A Deep Segmentation Network of Multi-Scale Feature Fusion Based on Attention Mechanism for IVOCT Lumen Contour.

Recently, coronary heart disease has attracted more and more attention, where segmentation and analysis for vascular lumen contour are helpful for treatment. And intravascular optical coherence tomography (IVOCT) images are used to display lumen shapes in clinic. Thus, an automatic segmentation method for IVOCT lumen contour is necessary to reduce the doctors' workload while ensuring diagnostic accuracy. In this paper, we proposed a deep residual segmentation network of multi-scale feature fusion based on attention mechanism (RSM-Network, Residual Squeezed Multi-Scale Network) to segment the lumen contour in IVOCT images. Firstly, three different data augmentation methods including mirror level turnover, rotation and vertical flip are considered to expand the training set. Then in the proposed RSM-Network, U-Net is contained as the main body, considering its characteristic of accepting input images with any sizes. Meanwhile, the combination of residual network and attention mechanism is applied to improve the ability of global feature extraction and solve the vanishing gradient problem. Moreover, the pyramid feature extraction structure is introduced to enhance the learning ability for multi-scale features. Finally, in order to increase the matching degree between the actual output and expected output, the cross entropy loss function is also used. A series of metrics are presented to evaluate the performance of our proposed network and the experimental results demonstrate that the proposed RSM-Network can learn the contour details better, contributing to strong robustness and accuracy for IVOCT lumen contour segmentation.

Lattice-Boltzmann interactive blood flow simulation pipeline.

PURPOSE: Cerebral aneurysms are one of the prevalent cerebrovascular disorders in adults worldwide and caused by a weakness in the brain artery. The most impressive treatment for a brain aneurysm is interventional radiology treatment, which is extremely dependent on the skill level of the radiologist. Hence, accurate detection and effective therapy for cerebral aneurysms still remain important clinical challenges. In this work, we have introduced a pipeline for cerebral blood flow simulation and real-time visualization incorporating all aspects from medical image acquisition to real-time visualization and steering. METHODS: We have developed and employed an improved version of HemeLB as the main computational core of the pipeline. HemeLB is a massive parallel lattice-Boltzmann fluid solver optimized for sparse and complex geometries. The visualization component of this pipeline is based on the ray marching method implemented on CUDA capable GPU cores. RESULTS: The proposed visualization engine is evaluated comprehensively and the reported results demonstrate that it achieves significantly higher scalability and sites updates per second, indicating higher update rate of geometry sites' values, in comparison with the original HemeLB. This proposed engine is more than two times faster and capable of 3D visualization of the results by processing more than 30 frames per second. CONCLUSION: A reliable modeling and visualizing environment for measuring and displaying blood flow patterns in vivo, which can provide insight into the hemodynamic characteristics of cerebral aneurysms, is presented in this work. This pipeline increases the speed of visualization and maximizes the performance of the processing units to do the tasks by breaking them into smaller tasks and working with GPU to render the images. Hence, the proposed pipeline can be applied as part of clinical routines to provide the clinicians with the real-time cerebral blood flow-related information.