This numerical study proposes a cell sorting technique based on dielectrophoresis (DEP) in a microfluidic chip. Under the joint effect of DEP and fluid drag, white blood cells and circulating tumor cells are separated because of different dielectric properties. First, the mathematical models of device geometry, single cell, DEP force, electric field, and flow field are established to simulate the cell motion. Based on the simulation model, important boundary parameters are discussed to optimize the cell sorting ability of the device. A proper matching relationship between voltage and flow rate is then provided. The inlet and outlet conditions are also investigated to control the particle motion in the flow field. The significance of this study is to verify the cell separating ability of the microfluidic chip, and to provide a logistic design for the separation of rare diseased cells.

The long-term reliability of the neural electrode is closely related to its implantation behavior. In orderto realize the quantitative research of the implantation behavior in a low-cost and accurate way, a refined brainmodel containing meninges is proposed. First, the expected simulation material was selected through measuringthe elastic modulus based on the method of atomic force microscope indentation technique. As a result, the 2%(mass fraction) agarose gel simulated the gray and white matter, the 7 : 1 (volume ratio) polydimethylsiloxane(PDMS) sheet simulated the pia mater, and the polyvinyl chloride (PVC) film simulated the dura mater. Second,based on designing a three-layer structure mold, the brain model was prepared by inverted pouring to realizea flat implantation surface. Finally, the simulation behavior of the brain model was investigated with the ratbrain as a reference. For mechanical behavior of implantation, the implantation force experienced two peaks bothin the brain model and the rat brain, maximum values of which were 10.17 mN and 7.69 mN respectively. Thelarger implantation force in the brain model will increase the strength requirement for the electrode, but reducethe risk of buckling of that in practical application. For humoral dissolution behavior, the dissolution rates ofthe polyethylene glycol (PEG) coating of the electrode in the brain model and rat brain were 7 000 μm3/s and5 600 μm3/s, respectively. The faster dissolution rate in the brain model will cause the larger thickness of thecoating design but provide sufficient implantable time in practical application. The establishment of the brainmodel and the research of its simulated behavior are beneficial to the size design of the electrode substrate andcoating, and research of the implantation mechanism, and further increase the functional life of the electrode.

Precise temperature control and temperature distribution prediction are of great significance forradiofrequency ablation. This research proposes a real-time calculation method for the temperature distribution of radiofrequency ablation combined with proportional-integral temperature control. The thermo-electricalcoupling was simplified into a linear relationship based on the study of the influence of temperature-dependentelectrical conductivity and thermal conductivity on the PI-controlled radiofrequency ablation temperature distribution, which increases the computational efficiency by 150 times. The average calculation time for radiofrequencyablation of 10 min is about 23 s, and the difference between the calculation results of this method and that fromCOMSOL Multiphysics is no more than 1 ?C. This method is not only used for single-probe, but also for doubleprobe radiofrequency ablation in this paper.

Identifying essential proteins from protein-protein interaction networks is important for studies onbiological evolution and new drug’s development. Most of the presented criteria for prioritizing essential proteinsonly focus on a certain attribute of the proteins in the network, which suffer from information loss. In order toovercome this problem, a relatively comprehensive and effective novel method for essential proteins identificationbased on improved multicriteria decision making (MCDM), called essential proteins identification-technique fororder preference by similarity to ideal solution (EPI-TOPSIS), is proposed. First, considering different attributes ofproteins, we propose three methods from different aspects to evaluate the significance of the proteins: gene-degreecentrality (GDC) for gene expression sequence; subcellular-neighbor-degree centrality (SNDC) and subcellular-indegree centrality (SIDC) for subcellular location information and protein complexes. Then, betweenness centrality(BC) and these three methods are considered together as the multiple criteria of the decision-making model.Analytic hierarchy process is used to evaluate the weights of each criterion, and the essential proteins are prioritizedby an ideal solution of MCDM, i.e., TOPSIS. Experiments are conducted on YDIP, YMIPS, Krogan and BioGRIDnetworks. The results indicate that EPI-TOPSIS outperforms several state-of-the-art approaches for identifyingthe essential proteins through the performance measures.

Colorectal cancer (CRC) is one of the most common malignancies worldwide. Development of predictivemolecular markers may help to achieve the best outcome in clinic. The purpose of this study is to identify thedifferentially expressed genes and new potential predictive and prognostic molecular biomarkers for CRC. Inthis study, CRC and matched normal tissues acquired from the same patient were used to extract total RNA.The NanoString nCounter assay was applied to determine the differentially expressed genes. The results werethen validated by using the Cancer Genome Atlas data. Finally, we identified 27 genes that revealed significantcorrelation with CRC in the tumor tissue. Several genes in the pan-cancer panel showed significant differentialexpression, which were more universal than others in the CRC tissue. Since some of them have not been reportedas being directly related to CRC yet, future mechanism studies can be designed based on this study. Our studydemonstrated NanoString nCounter assay could serve as an alternative approach for gene expression analysis andidentified several unreported differently expressed genes in CRC patients, which may provide some importantclues for more in-depth study of CRC and serve as potential predictive molecular biomarkers for clinical diagnosisapplication.

Colorectal cancer is one of the biggest health threats to humans and takes thousands of lives every year.Colonoscopy is the gold standard in clinical practice to inspect the intestinal wall, detect polyps and remove polypsin early stages, preventing polyps from becoming malignant and forming colorectal cancer instances. In recentyears, computer-aided polyp detection systems have been widely used in colonoscopies to improve the qualityof colonoscopy examination and increase the polyp detection rate. Currently, the most efficient computer-aidedsystems are built with machine learning methods. However, developing such a computer-aided detection systemrequires experienced doctors to label a large number of image data from colonoscopy videos, which is extremelytime-consuming, laborious and expensive. One possible solution is to adopt a semi-supervised learning, which canbuild a detection system on a dataset where part of its data is not necessary to be labeled. In this paper, on thebasis of state-of-the-art object detection method and semi-supervised learning technique, we design and implementa semi-supervised colonoscopy polyp detection system containing four main steps: running standard supervisedtraining with all labeled data; running inference on unlabeled data to obtain pseudo labels; applying a set ofstrong augmentation to both unlabeled data and pseudo label; combining labeled data, and unlabeled data withits pseudo labels to retrain the detector. The semi-supervised learning system is evaluated both on public datasetand our original private dataset and proves its effectiveness. Also, the inference speed of the semi-supervisedlearning system can meet the requirement of real-time operation.

This study aimed to analyze the hemodynamic effects of bifurcated vessels using different bloodviscosity models. Three-dimensional models of bifurcated vessels in the popliteal artery were constructed basedon CT images, and hemodynamic parameters of the Newtonian, Casson, and two-phase models were calculatedby the computational fluid dynamics method. Blood flowed through the popliteal artery. Blood flow velocitychanged after the bifurcated vessel, with accelerated blood flow velocity in the anterior tibial artery. A lowvelocity vortex region with a region of low wall shear stress (WSS) was generated outside the bifurcated vessel.Local non-Newtonian importance factors of great than 1 (i.e., IL > 1) occurred during the cardiac cycle, andIL > 1.75 occurred at the beginning and end of the cycle. Compared with the Casson and two-phase models, theNewtonian model has a larger vortex region and lower WSS. Low-velocity vortex regions and low WSS regionsin the bifurcated vessels may contribute to the development of atherosclerosis. Blood exhibited non-Newtonianfluid properties in bifurcated vessels (IL > 1), and the effect of non-Newtonian properties was more pronouncedat the beginning and end of heartbeats (IL > 1.75). The Newtonian model predicts a higher risk of atherosclerosisformation and the effect of non-Newtonian properties of blood should be considered in hemodynamic studies.

Turner syndrome (TS) is a chromosomal disorder disease that only affects the growth of female patients. Prompt diagnosis is of high significance for the patients. However, clinical screening methods are time-consuming and cost-expensive. Some researchers used machine learning-based methods to detect TS, the performance of which needed to be improved. Therefore, we propose an ensemble method of two-path capsule networks (CapsNets) for detecting TS based on global-local facial images. Specifically, the TS facial images are preprocessed and segmented into eight local parts under the direction of physicians; then, nine two-path CapsNets are respectively trained using the complete TS facial images and eight local images, in which the few-shot learning is utilized to solve the problem of limited data; finally, a probability-based ensemble method is exploited to combine nine classifiers for the classification of TS. By studying base classifiers, we find two meaningful facial areas are more related to TS patients, i.e., the parts of eyes and nose. The results demonstrate that the proposed model is effective for the TS classification task, which achieves the highest accuracy of 0.924 1.

Articular cartilage defects are considered to be associated with the development of osteoarthritis. Research on relevant tissue regeneration is important in the treatment of osteoarthritis. The scaffolds applied incartilage regeneration should have good histocompatibility and mechanical properties, as well as no cytotoxicity,and promote the proliferation and differentiation of seed cells. Different combinations of peptide sequences inpolypeptide hydrogels endow them with unique characteristics including excellent biodegradability and accuratesimulation of the extracellular matrix of chondrocytes to maintain the stability of the chondrogenic phenotypeand facilitate articular hyaline cartilage regeneration. Thus, the application of polypeptide hydrogels for cartilage regeneration has a bright future. In this study, the research progress of polypeptide hydrogels used incartilage-regeneration engineering is systematically reviewed. The characteristics, limitations, and prospects ofthese materials are evaluated.

Adolescent idiopathic scoliosis seriously affects the physical and mental health of adolescents. In thepast, the research on therapeutic orthosis ignored the influence of muscle factors. Aimed at this problem, basedon the principle of reverse engineering, through the spine computed tomography data model of three-dimensionalreconstruction, muscle forces around the spine are imported into the spinal muscle force model and AnyBodysoftware is used for simulation. The geometric similarity and biomechanical effectiveness of the established modelare verified. In order to obtain the relationship among the applied orthopedic force, Cobb angle and vertebraldisplacement, a finite element model conforming to spinal anatomy is established, and then the biomechanicalanalysis of the finite element model of the scoliosis is carried out. Reasonable control of paravertebral muscles canplay a positive role in orthopedic treatment, and the fitting equation can provide a reference for doctors to applythe orthopedic force on patient.

Hyperplastic scar is a common fibrotic disease that may ultimately lead to severe dysfunction anddeformity, causing physical and psychological distress. Therefore, we aim to evaluate the effect of the mechanicalmicroenvironment of scar substrates on the morphology of human fibroblasts (HFbs). The micro-modular fabrication technique was used to design a new cross-groove topology and to construct four elastic substrates withthe stiffness of 19.3 kPa and 90.1 kPa coupled with parallel groove and cross groove, respectively, to simulate themechanical microenvironment of skin wounds and scar tissues. The morphological changes in HFbs in differentsubstrates were observed, and the changes in the cell-long axis length, area, and the angle between cell-long axisand grooves were recorded. Immunofluorescence staining was performed to observe the distribution of microfilaments. The results indicated that substrate stiffness and topography affected the morphology of HFbs. The cellswere elongated in parallel grooves as well as in the area where cross grooves restricted groove length, the celllength was restricted, and the angle between the long axis and the groove was increased. The topography exertedno significant effect on the cell area, but the cell area increased with increasing the stiffness. The parallel groovepromoted the expression of the F-actin to a certain extent, and the fluorescence intensity of F-actin decreased withincreasing the stiffness. Studying the effect of the mechanical microenvironment of substrates on HFb morphologyis of great importance for understanding the mechanisms of scar formation and prevention.

Flexible ureteroscopy (FURS) has been widely used in the diagnosis and treatment of upper urinarytract diseases. The key operation of FURS is that the surgeon manipulates the distal shaft of flexible ureteroscopeto a specific target for diagnosis and treatment. However, the hysteresis of flexible ureteroscope may be one ofthe most important factors that degrade the manipulation accuracy and the surgeon usually spends a long timenavigating the distal shaft during surgery. In this study, we obtained hysteresis curves of distal shaft deflectionfor the flexible ureteroscope through extensive repeated experiments. Then, two methods based on piecewiselinear approximation and long short-term memory neural network were employed to model the hysteresis curves.On this basis, we proposed two hysteresis compensation strategies for the distal shaft deflection. Finally, wecarried out hysteresis compensation experiments to verify the two proposed compensation strategies. Experimentalresults showed that the hysteresis compensation strategies can significantly improve position accuracy with meancompensation errors of no more than 5?.

We aim to develop a novel visualization tool for percutaneous renal puncture training based on augmented reality (AR) and compare the needle placement performance of this AR system with ultrasound-guidedfreehand navigation in phantoms. A head-mounted display-based AR navigation system was developed usingthe Unity3D software and Visual Studio to enable the overlay of the preoperative needle path and the complexanatomical structures onto a phantom in real time. The spatial location of the stationary phantom and the percutaneous instrument motion were traced by a Qualisys motion capture system. To evaluate the tracking accuracy,15 participants (7 males and 8 females) performed a single needle insertion using AR navigation (the number ofpunctures n = 75) and ultrasound-guided freehand navigation (n = 75). The needle placement error was measuredas the Euclidean distance between the actual needle tip and the virtual target by MicronTracker. All participantsdemonstrated a superior needle insertion efficiency when using the AR-assisted puncture method compared withthe ultrasound-guided freehand method. The needle insertion error of the ultrasound-guided method showed anincreased error compared with the AR method (5.54 mm ± 2.59 mm, 4.34 mm ± 2.10 mm, respectively, p < 0.05).The ultrasound-guided needle placements showed an increased time compared with the AR method (19.08 s ±3.59 s, 15.14 s ± 2.72 s, respectively, p < 0.000 1). Our AR training system facilitates the needle placement performance and solves hand-eye coordination problems. The system has the potential to increase efficiency andeffectiveness of percutaneous renal puncture training.

Deep tissue pressure injuries (DTPIs) have witnessed a growing prevalence in hospitals and other health care units especially among individuals with pathological conditions that give rise to restricted mobility, impaired sensation, and reduced tissue tolerance. The etiology of DTPIs has been a subject of controversy, to which several explanatory models have been proposed, including direct mechanical insult, ischemia-reperfusion, lymphatic occlusion, and inflammatory cytokines. In line with these pathophysiological scenarios, ultrasound, subepidermal moisture detection, and biomarker technologies have been proposed as potential early detection methods of DTPIs. This paper provides a systematic review involving these three methods. The conclusion is that combining and implementing these methods at different time periods during DTPIs development and progression respectively is likely to be the most universal, effective and promising way for DTPIs diagnosis.

Lung image registration plays an important role in lung analysis applications, such as respiratory motion modeling. Unsupervised learning-based image registration methods that can compute the deformation without the requirement of supervision attract much attention. However, it is noteworthy that they have two drawbacks: they do not handle the problem of limited data and do not guarantee diffeomorphic (topologypreserving) properties, especially when large deformation exists in lung scans. In this paper, we present an unsupervised few-shot learning-based diffeomorphic lung image registration, namely Dlung. We employ fine-tuning techniques to solve the problem of limited data and apply the scaling and squaring method to accomplish the diffeomorphic registration. Furthermore, atlas-based registration on spatio-temporal (4D) images is performed and thoroughly compared with baseline methods. Dlung achieves the highest accuracy with diffeomorphic properties. It constructs accurate and fast respiratory motion models with limited data. This research extends our knowledge of respiratory motion modeling.

The high frequency ventilation (HFV) can well support the breathing of respiratory patient with 20%—40% of normal tidal volume. Now as a therapy of rescue ventilation when conversional ventilation failed, the HFV has been applied in the treatments of severe patients with acute respiratory failure (ARF), acute respiratory distress syndrome (ARDS), etc. However, the gas exchange mechanism (GEM) of HFV is still not fully understood by researchers. In this paper, the GEM of HFV is reviewed to track the studies in the last decades and prospect for the next likely studies. And inspired by previous studies, the GEM of HFV is suggested to be continually developed with various hypotheses which will be testified in simulation, experiment and clinic trail. One of the significant measures is to study the GEM of HFV under the cross-disciplinary integration of medicine and engineering. Fully understanding the GEM can theoretically support and expand the applications of HFV, and is helpful in investigating the potential indications and contraindications of HFV.