Predicting Vertical Ground Reaction Force during Treadmill Running Using Principal Component Analysis and Wavelet Neural Network / 医用生物力学

Objective To establish the method of predicting the vertical ground reaction force (vGRF) during treadmill running based on principal component analysis and wavelet neural network (PCA-WNN). Methods Nine rearfoot strikers were selected and participated in running experiment on an instrumented treadmill at the speed of 12, 14 and 16 km/h. The kinematics data and vGRF were collected using infrared motion capture system and dynamometer treadmill. A three-layer neural network framework was constructed, in which the activation function of the hidden layers was the Morlet function. Velocities of mass center of the thigh, shank and foot as well as joint angles of the hip, knee and ankle were input into the WNN model. The prediction accuracy of the model was evaluated by the coefficient of multiple correlation (CMC) and error. The consistencies between predicted and measured peak GRF were analyzed by Bland-Altman method. Results The CMC between the predicted and measured GRF at different speeds were all greater than 0.99; the root mean square error (RMSE) between the predicted and measured vGRF was 0.18-0.28 BW; and the normalized root mean square error (NRMSE) was 6.20%-8.42%; the NRMSE between the predicted and measured impact forces and propulsive forces were all smaller than 15%. Bland-Altman results showed that the predicted peak errors of propulsive force at 12 km/h and that of impact force and propulsive force at 14 km/h were within the 95% agreement interval. Conclusions The PCA-WNN model constructed in this study can accurately predict the vGRF during treadmill running. The results provide a new method to obtain kinetic data and perform real-time monitoring on a treadmill, which is of great significance for studying running injuries and rehabilitation treatment.

[The potential role of mirnas in the pathogenesis of hemorrhagic fever with renal syndrome].

Hemorrhagic fever with renal syndrome (HFRS) is an acute natural focal viral disease caused by viruses of the genus hantavirus, characterized by damage to small blood vessels, kidneys, lungs and other organs of a person. MicroRNAs (miRNAs) are 18-22 nucleotide endogenously expressed RNA molecules that inhibit gene expression at the post-transcriptional level by binding to the 3-untranslated region of the target mRNA. It has been proven that miRNAs play a significant role in various biological processes, including the cell cycle, apoptosis, cell proliferation and differentiation. It has been proven that miRNAs may be involved in the pathogenesis of infectious diseases, including HFRS. Hantavirus infection predominantly affects endothelial cells and causes dysfunction of the endothelium of capillaries and small vessels. It is known that the immune response induced by Hantavirus infection plays an important role in disrupting the endothelial barrier. In a few studies, both in vitro and in vivo, it has been shown that endothelial dysfunction and the immune response after infection with Hantavirus can be partially regulated by miRNAs by acting on certain genes. Most of the miRNAs is expressed within the cells themselves. However, in some biological fluids of the human body, for example, plasma or blood serum, numerous miRNAs, called circulating miRNAs, have been found. Circulating miRNAs can be secreted by cells into human biological fluids as part of extracellular vesicles as exosomes or be part of an RNA-bound protein complex as miRNA-Argonaute 2 (Ago2). These miRNAs are resistant to nucleases, which makes them attractive as potential biomarkers in various human diseases. There is no specific antiviral therapy for HFRS, and the determination of laboratory parameters that are used to diagnose, assess the severity, and predict the course of the disease remains a challenge due to the peculiarities of the pathophysiology and clinical course of the disease. Studying the role of miRNAs in HFRS seems to be expedient for the development of specific and effective therapy, as well as for use as diagnostic and prognostic biomarkers (in relation to circulating miRNAs).

Severity detection for the coronavirus disease 2019 (COVID-19) patients using a machine learning model based on the blood and urine tests

The recent outbreak of the coronavirus disease-2019 (COVID-19) caused serious challenges to the human society in China and across the world. COVID-19 induced pneumonia in human hosts and carried a highly inter-person contagiousness. The COVID-19 patients may carry severe symptoms, and some of them may even die of major organ failures. This study utilized the machine learning algorithms to build the COVID-19 severeness detection model. Support vector machine (SVM) demonstrated a promising detection accuracy after 32 features were detected to be significantly associated with the COVID-19 severeness. These 32 features were further screened for inter-feature redundancies. The final SVM model was trained using 28 features and achieved the overall accuracy 0.8148. This work may facilitate the risk estimation of whether the COVID-19 patients would develop the severe symptoms. The 28 COVID-19 severeness associated biomarkers may also be investigated for their underlining mechanisms how they were involved in the COVID-19 infections.

Visual Fixation Assessment in Patients with Disorders of Consciousness Based on Brain-Computer Interface / 神经科学通报·英文版

Visual fixation is an item in the visual function subscale of the Coma Recovery Scale-Revised (CRS-R). Sometimes clinicians using the behavioral scales find it difficult to detect because of the motor impairment in patients with disorders of consciousness (DOCs). Brain-computer interface (BCI) can be used to improve clinical assessment because it directly detects the brain response to an external stimulus in the absence of behavioral expression. In this study, we designed a BCI system to assist the visual fixation assessment of DOC patients. The results from 15 patients indicated that three showed visual fixation in both CRS-R and BCI assessments and one did not show such behavior in the CRS-R assessment but achieved significant online accuracy in the BCI assessment. The results revealed that electroencephalography-based BCI can detect the brain response for visual fixation. Therefore, the proposed BCI may provide a promising method for assisting behavioral assessment using the CRS-R.

Preliminary study on the application of upper-airway model construction with 3DMIA in children with OSAHS / 中国耳鼻咽喉头颈外科

OBJECTIVE To investigate the applicability of 3DMIA software to upper airway modeling in children with obstructive sleep apnea hypopnea syndrome (OSAHS). METHODS A total of 30 children diagnosed with OSAHS by polysomnography were included in this study. Data regarding upper airway structure were collected via spiral CT while sleeping and awake, from which a three-dimensional model of the upper respiratory tract from the nasopharynx to the supraglottic region using 3DMIA software was constructed. The upper airway volume and airway minimum cross-sectional area were measured employing software algorithms. RESULTS The upper airway volume and airway minimum cross-sectional area of the 30 children during sleep were significantly less than those while awake (P<0.01). CONCLUSION 3DMIA software modeling and software algorithm measurement are more objective than traditional radiology (e.g. Fujioka) with respect to evaluation of the extent of the upper airway narrowing in OSAHS children, and has good applicability to study upper airway morphology and function in children with OSAHS.

Construction of digital three-dimensional models of renal stones and virtual surgery simulation / 南方医科大学学报

<p><b>OBJECTIVE</b>To construct three-dimensional (3D) models of renal stones and perform percutaneous nephrolithotomy (PCNL) virtual surgery simulation. Methods CT images were obtained from 8 patients with renal stones. Images segmentation and reconstruction were performed using MIMICS 10.0 software to construct the 3D model of the renal stones, which provided the anatomical relationships between the kidney and the adjacent organs. The optimal PCNL virtual surgery simulation for each individual case was performed using FreeForm Modeling System on the basis of the 3D model.</p><p><b>RESULTS</b>Eight 3D models of renal stone were constructed. The 3D model of the renal stones represented the interrelationships of the stones, intrarenal vessel, and the collecting system with the adjacent anatomical structures. Individualized PCNL virtual surgery simulations including percutaneous puncture, dilatation and pneumatic lithotripsy were performed successfully in all the 8 3D models.</p><p><b>CONCLUSION</b>Digital 3D model of renal stone provides the reliable and comprehensive imaging information for surgical design, and PCNL virtual surgery simulation has important clinical significance to improve the stone clearance rate and reduce the surgical complications.</p>

Virtual surgery for choledocholithiasis / 中华消化外科杂志

Objective To study the image segmentation, three-dimensional (3D) reconstruction and simulation operation of choledocholithotomy and T-tube drainage based on the computed tomagraphy (CT) data of patient with choledocholithiasis. Methods Patient with choledocholithiasis underwent 64-slice spiral CT imaging. The images segmentation and 3D reconstruction were performed by Medical Image Process System (MIPS) to construct the 3D model of the hepatobiliary system. The model was modified by FreeForm Modeling System. The virtual surgical instruments were developed by GHOST SDK software, and were imported to the virtual surgery. Results The data of plain, arterial phase, venous phase and portal venous phase scanning were collected, and the data stored in DICOM format were transformed to BMP format. A model of the hepatobiliary system was constructed after the data was segmented by MIPS, and then the model was exported in the STL format. The data in STL format were imported to FreeForm Modeling System for smoothing the model. Different structures were assigned different colors to make the model more vivid. The self-developed virtual surgical instruments were imported to the system, and the virtual surgery for choledocholithiasis was performed with PHANToM. Conclusions With the help of MIPS, the image segmentation and 3D reconstruction of the model are finished rapidly and effectively. After the virtual surgical instruments are developed in FreeForm Modeling System, the virtual surgery can be achieved in the 3D model with the assistance of PHANToM.

Virtual piggyback liver transplantation using three-dimensional imaging technique / 中华消化外科杂志

Objective To study the value of three-dimensional (3D) visualization and virtual surgery system in piggyback liver transplantation. Methods Two patients who suffered from choledocholithiasis were scanned by 64-slice spiral CT and the data were collected. The segmentation of the hepatic CT images was carried out. The 3D model of the liver and the intrahepatic vessels was reconstructed, and was imported to the FreeForm Modeling System in STL format for smoothing and modifying. Piggyback liver transplantation was simulated with the force-feedback equipment (PHANToM). Results The reconstructed 3D model of the liver was vivid, and the process of the virtual piggyback liver transplantation was verisimilar. Conclusions The 3D model of the liver enables the simulation of piggyback liver transplantation. It can reduce the risk and complications of the surgery, and enhance the communication between doctor and patient through designing surgical plan and demonstrating visualized operation before surgery. Virtual liver transplantation is also helpful during the training of medical workers.