Wi-CHAR: A WiFi Sensing Approach with Focus on Both Scenes and Restricted Data

Significant strides have been made in the field of WiFi-based human activity recognition, yet recent wireless sensing methodologies still grapple with the reliance on copious amounts of data. When assessed in unfamiliar domains, the majority of models experience a decline in accuracy. To address this challenge, this study introduces Wi-CHAR, a novel few-shot learning-based cross-domain activity recognition system. Wi-CHAR is meticulously designed to tackle both the intricacies of specific sensing environments and pertinent data-related issues. Initially, Wi-CHAR employs a dynamic selection methodology for sensing devices, tailored to mitigate the diminished sensing capabilities observed in specific regions within a multi-WiFi sensor device ecosystem, thereby augmenting the fidelity of sensing data. Subsequent refinement involves the utilization of the MF-DBSCAN clustering algorithm iteratively, enabling the rectification of anomalies and enhancing the quality of subsequent behavior recognition processes. Furthermore, the Re-PN module is consistently engaged, dynamically adjusting feature prototype weights to facilitate cross-domain activity sensing in scenarios with limited sample data, effectively distinguishing between accurate and noisy data samples, thus streamlining the identification of new users and environments. The experimental results show that the average accuracy is more than 93% (five-shot) in various scenarios. Even in cases where the target domain has fewer data samples, better cross-domain results can be achieved. Notably, evaluation on publicly available datasets, WiAR and Widar 3.0, corroborates Wi-CHAR's robust performance, boasting accuracy rates of 89.7% and 92.5%, respectively. In summary, Wi-CHAR delivers recognition outcomes on par with state-of-the-art methodologies, meticulously tailored to accommodate specific sensing environments and data constraints.

[Evaluation of anticoagulant effect and predicted dose of low molecular weight heparin in hemodialysis by anti-Ⅹa factor activity]

The purpose of this study was to explore the reasonable dose of low molecular weight heparin (LMWH) in hemodialysis (HD) and the strategy of preventing extracorporeal circuit coagulation (ECC). A retrospective case-control study included patients who used LMWH for anticoagulation during maintenance hemodialysis (MHD) in the Hemodialysis Center of Beijing Hospital from December 2020 to January 2021. Basic data such as weight, height, basic kidney disease, dialysis age and anti-Ⅹa factor activity before, during and after dialysis were collected. A total of 46 patients were enrolled in this study, including 5 patients in coagulation group (10.9%) and 41 patients in non-coagulation group (89.1%). The anti-Ⅹa factor activity reached its peak at 0.5 h after the start of HD. The level of anti-Ⅹa factor was incorporated into the receiver operating characteristic curve (ROC curve). The results showed that the area under the ROC curve (AUC) was 0.802 (95% confidence interval: 0.651-0.54, P=0.029), and the cutoff was 0.31 IU/ml (sensitivity 1, specificity 0.683). It is suggested that the body surface area should be used as the basis to estimate the anticoagulant dose of LMWH in HD, and the activity of HD 4 h anti-Ⅹa factor ≤0.31 IU/ml, which is of diagnostic value for ECC. In addition, the results of binary logistic regression analysis showed that dialysis age was an independent risk factor for ECC (OR value 1.319, 95%CI 1.052-1.654, P=0.017). In summary, this study reveals that dialysis age may be a risk factor for ECC and that the activity of HD 4 h anti-Ⅹa factor ≤0.31 IU/ml can be used as a potential diagnostic cut-off point for ECC in HD patients, which provides a scientific basis for monitoring strategies to prevent blood coagulation in HD filters.

PGGait: Gait Recognition Based on Millimeter-Wave Radar Spatio-Temporal Sensing of Multidimensional Point Clouds

Gait recognition, crucial in biometrics and behavioral analytics, has applications in human-computer interaction, identity verification, and health monitoring. Traditional sensors face limitations in complex or poorly lit settings. RF-based approaches, particularly millimeter-wave technology, are gaining traction for their privacy, insensitivity to light conditions, and high resolution in wireless sensing applications. In this paper, we propose a gait recognition system called Multidimensional Point Cloud Gait Recognition (PGGait). The system uses commercial millimeter-wave radar to extract high-quality point clouds through a specially designed preprocessing pipeline. This is followed by spatial clustering algorithms to separate users and perform target tracking. Simultaneously, we enhance the original point cloud data by increasing velocity and signal-to-noise ratio, forming the input of multidimensional point clouds. Finally, the system inputs the point cloud data into a neural network to extract spatial and temporal features for user identification. We implemented the PGGait system using a commercially available 77 GHz millimeter-wave radar and conducted comprehensive testing to validate its performance. Experimental results demonstrate that PGGait achieves up to 96.75% accuracy in recognizing single-user radial paths and exceeds 94.30% recognition accuracy in the two-person case. This research provides an efficient and feasible solution for user gait recognition with various applications.

Human Movement Recognition Based on 3D Point Cloud Spatiotemporal Information from Millimeter-Wave Radar

Human movement recognition is the use of perceptual technology to collect some of the limb or body movements presented. This practice involves the use of wireless signals, processing, and classification to identify some of the regular movements of the human body. It has a wide range of application prospects, including in intelligent pensions, remote health monitoring, and child supervision. Among the traditional human movement recognition methods, the widely used ones are video image-based recognition technology and Wi-Fi-based recognition technology. However, in some dim and imperfect weather environments, it is not easy to maintain a high performance and recognition rate for human movement recognition using video images. There is the problem of a low recognition degree for Wi-Fi recognition of human movement in the case of a complex environment. Most of the previous research on human movement recognition is based on LiDAR perception technology. LiDAR scanning using a three-dimensional static point cloud can only present the point cloud characteristics of static objects; it struggles to reflect all the characteristics of moving objects. In addition, due to its consideration of privacy and security issues, the dynamic millimeter-wave radar point cloud used in the previous study on the existing problems of human body movement recognition performance is better, with the recognition of human movement characteristics in non-line-of-sight situations as well as better protection of people's privacy. In this paper, we propose a human motion feature recognition system (PNHM) based on spatiotemporal information of the 3D point cloud of millimeter-wave radar, design a neural network based on the network PointNet++ in order to effectively recognize human motion features, and study four human motions based on the threshold method. The data set of the four movements of the human body at two angles in two experimental environments was constructed. This paper compares four standard mainstream 3D point cloud human action recognition models for the system. The experimental results show that the recognition accuracy of the human body's when walking upright can reach 94%, the recognition accuracy when moving from squatting to standing can reach 84%, that when moving from standing to sitting can reach 87%, and the recognition accuracy of falling can reach 93%.

mm-TPG: Traffic Policemen Gesture Recognition Based on Millimeter Wave Radar Point Cloud

Automatic driving technology refers to equipment such as vehicle-mounted sensors and computers that are used to navigate and control vehicles autonomously by acquiring external environmental information. To achieve automatic driving, vehicles must be able to perceive the surrounding environment and recognize and understand traffic signs, traffic signals, pedestrians, and other traffic participants, as well as accurately plan and control their path. Recognition of traffic signs and signals is an essential part of automatic driving technology, and gesture recognition is a crucial aspect of traffic-signal recognition. This article introduces mm-TPG, a traffic-police gesture recognition system based on a millimeter-wave point cloud. The system uses a 60 GHz frequency-modulated continuous-wave (FMCW) millimeter-wave radar as a sensor to achieve high-precision recognition of traffic-police gestures. Initially, a double-threshold filtering algorithm is used to denoise the millimeter-wave raw data, followed by multi-frame synthesis processing of the generated point cloud data and feature extraction using a ResNet18 network. Finally, gated recurrent units are used for classification to enable the recognition of different traffic-police gestures. Experimental results demonstrate that the mm-TPG system has high accuracy and robustness and can effectively recognize traffic-police gestures in complex environments such as varying lighting and weather conditions, providing strong support for traffic safety.

UltrasonicGS: A Highly Robust Gesture and Sign Language Recognition Method Based on Ultrasonic Signals

With the global spread of the novel coronavirus, avoiding human-to-human contact has become an effective way to cut off the spread of the virus. Therefore, contactless gesture recognition becomes an effective means to reduce the risk of contact infection in outbreak prevention and control. However, the recognition of everyday behavioral sign language of a certain population of deaf people presents a challenge to sensing technology. Ubiquitous acoustics offer new ideas on how to perceive everyday behavior. The advantages of a low sampling rate, slow propagation speed, and easy access to the equipment have led to the widespread use of acoustic signal-based gesture recognition sensing technology. Therefore, this paper proposed a contactless gesture and sign language behavior sensing method based on ultrasonic signals-UltrasonicGS. The method used Generative Adversarial Network (GAN)-based data augmentation techniques to expand the dataset without human intervention and improve the performance of the behavior recognition model. In addition, to solve the problem of inconsistent length and difficult alignment of input and output sequences of continuous gestures and sign language gestures, we added the Connectionist Temporal Classification (CTC) algorithm after the CRNN network. Additionally, the architecture can achieve better recognition of sign language behaviors of certain people, filling the gap of acoustic-based perception of Chinese sign language. We have conducted extensive experiments and evaluations of UltrasonicGS in a variety of real scenarios. The experimental results showed that UltrasonicGS achieved a combined recognition rate of 98.8% for 15 single gestures and an average correct recognition rate of 92.4% and 86.3% for six sets of continuous gestures and sign language gestures, respectively. As a result, our proposed method provided a low-cost and highly robust solution for avoiding human-to-human contact.

Wireless Sensing Technology Combined with Facial Expression to Realize Multimodal Emotion Recognition

Emotions significantly impact human physical and mental health, and, therefore, emotion recognition has been a popular research area in neuroscience, psychology, and medicine. In this paper, we preprocess the raw signals acquired by millimeter-wave radar to obtain high-quality heartbeat and respiration signals. Then, we propose a deep learning model incorporating a convolutional neural network and gated recurrent unit neural network in combination with human face expression images. The model achieves a recognition accuracy of 84.5% in person-dependent experiments and 74.25% in person-independent experiments. The experiments show that it outperforms a single deep learning model compared to traditional machine learning algorithms.

mmSafe: A Voice Security Verification System Based on Millimeter-Wave Radar

With the increasing popularity of smart devices, users can control their mobile phones, TVs, cars, and smart furniture by using voice assistants, but voice assistants are susceptible to intrusion by outsider speakers or playback attacks. In order to address this security issue, a millimeter-wave radar-based voice security authentication system is proposed in this paper. First, the speaker's fine-grained vocal cord vibration signal is extracted by eliminating static object clutter and motion effects; second, the weighted Mel Frequency Cepstrum Coefficients (MFCCs) are obtained as biometric features; and finally, text-independent security authentication is performed by the WMHS (Weighted MFCCs and Hog-based SVM) method. This system is highly adaptable and can authenticate designated speakers, resist intrusion by other unspecified speakers as well as playback attacks, and is secure for smart devices. Extensive experiments have verified that the system achieves a 93.4% speaker verification accuracy and a 5.8% miss detection rate for playback attacks.

mm-DSF: A Method for Identifying Dangerous Driving Behaviors Based on the Lateral Fusion of Micro-Doppler Features Combined

To address the dangerous driving behaviors prevalent among current car drivers, it is necessary to provide real-time, accurate warning and correction of driver's driving behaviors in a small, movable, and enclosed space. In this paper, we propose a method for detecting dangerous behaviors based on frequency-modulated continuous-wave radar (mm-DSF). The highly packaged millimeter-wave radar chip has good in-vehicle emotion recognition capability. The acquired millimeter-wave differential frequency signal is Fourier-transformed to obtain the intermediate frequency signal. The physiological decomposition of the local micro-Doppler feature spectrum of the target action is then used as the eigenvalue. Matrix signal intensity and clutter filtering are performed by analyzing the signal echo model of the input channel. The signal classification is based on the estimation and variety of the feature vectors of the target key actions using a modified and optimized level fusion method of the SlowFast dual-channel network. Nine typical risky driving behaviors were set up by the Dula Hazard Questionnaire and TEIQue-SF, and the accuracy of the classification results of the self-built dataset was analyzed to verify the high robustness of the method. The recognition accuracy of this method increased by 1.97% compared with the traditional method.

Horizontal Data Augmentation Strategy for Industrial Quality Prediction

In recent years, neural network-based soft sensor technology has been widely used in industrial production processes and has excellent optimization, monitoring, and quality prediction performance. This paper proposes a horizontal data augmentation strategy to provide highly available data for subsequent prediction models, called the combined autoencoder data augmentation (CADA) strategy. This paper has developed a CADA-based convolutional neural network (CADA-CNN) soft sensor model and applied it to the process of industrial debutanizer and industrial steam volume. In terms of method validation, this paper compares the output data of the proposed CADA by the Spearman correlation coefficient to verify the strategy's feasibility. Then, the output data of the CADA strategy is fed into the artificial neural network (NN), support vector regression (SVR), and convolutional neural network (CNN) for comparison experiments. The final experimental results show that our proposed CADA-CNN model has lower prediction error and better prediction error distribution.

A Non-Contact Detection Method for Multi-Person Vital Signs Based on IR-UWB Radar

With the vigorous development of ubiquitous sensing technology, an increasing number of scholars pay attention to non-contact vital signs (e.g., Respiration Rate (RR) and Heart Rate (HR)) detection for physical health. Since Impulse Radio Ultra-Wide Band (IR-UWB) technology has good characteristics, such as non-invasive, high penetration, accurate ranging, low power, and low cost, it makes the technology more suitable for non-contact vital signs detection. Therefore, a non-contact multi-human vital signs detection method based on IR-UWB radar is proposed in this paper. By using this technique, the realm of multi-target detection is opened up to even more targets for subjects than the more conventional single target. We used an optimized algorithm CIR-SS based on the channel impulse response (CIR) smoothing spline method to solve the problem that existing algorithms cannot effectively separate and extract respiratory and heartbeat signals. Also in our study, the effectiveness of the algorithm was analyzed using the Bland-Altman consistency analysis statistical method with the algorithm's respiratory and heart rate estimation errors of 5.14% and 4.87%, respectively, indicating a high accuracy and precision. The experimental results showed that our proposed method provides a highly accurate, easy-to-implement, and highly robust solution in the field of non-contact multi-person vital signs detection.

Millimeter-Wave Radar Localization Using Indoor Multipath Effect

The positioning of indoor electronic devices is an essential part of human-computer interaction, and the accuracy of positioning affects the level of user experience. Most existing methods for RF-based device localization choose to ignore or remove the impact of multipath effects. However, exploiting the multipath effect caused by the complex indoor environment helps to improve the model's localization accuracy. In response to this question, this paper proposes a multipath-assisted localization (MAL) model based on millimeter-wave radar to achieve the localization of indoor electronic devices. The model fully considers the help of the multipath effect when describing the characteristics of the reflected signal and precisely locates the target position by using the MAL area formed by the reflected signal. At the same time, for the situation where the radar in the traditional Single-Input Single-Output (SISO) mode cannot obtain the 3D spatial position information of the target, the advantage of the MAL model is that the 3D information of the target can be obtained after the mining process of the multipath effect. Furthermore, based on the original hardware, it can achieve a breakthrough in angular resolution. Experiments show that our proposed MAL model enables the millimeter-wave multipath positioning model to achieve a 3D positioning error within 15 cm.

Proteomics analysis of hip articular cartilage identifies differentially expressed proteins associated with osteonecrosis of the femoral head

OBJECTIVE

The cartilage degeneration that accompanies subchondral bone necrosis plays an important role in the development of osteonecrosis of femoral head (ONFH). To better understand the molecular basis of cartilage degradation in ONFH, we compared the proteomic profiles of ONFH cartilage with that of fracture control.

DESIGN

Hip cartilage samples were collected from 16 ONFH patients and 16 matched controls with femoral neck fracture. Proteomics analysis was conducted using tandem mass tag-based quantitation technique. Gene ontology (GO) analysis, KEGG pathway and protein-protein interaction analysis were used to investigate the functions of the altered proteins and biological pathways. Differentially expressed proteins including alpha-2-HS-glycoprotein (AHSG) and Cytokine-like protein 1 (Cytl1) were validated by Western blot (WB) and immunohistochemistry (IHC).

RESULTS

303 differentially expressed proteins were identified in ONFH cartilage with 72 up-regulated and 231 down-regulated. Collagen turnover, glycosaminoglycan biosynthesis, metabolic pathways, and complement and coagulation cascades were significantly modified in ONFH cartilage. WB and IHC confirmed the increased expression of AHSG and decreased expression of Cytl1 in ONFH cartilage.

CONCLUSIONS

Our results reveal the implication of altered protein expression in the development of ONFH, and provide novel clues for pathogenesis studies of cartilage degradation in ONFH.

Supervised learning in spiking neural networks: A review of algorithms and evaluations

As a new brain-inspired computational model of the artificial neural network, a spiking neural network encodes and processes neural information through precisely timed spike trains. Spiking neural networks are composed of biologically plausible spiking neurons, which have become suitable tools for processing complex temporal or spatiotemporal information. However, because of their intricately discontinuous and implicit nonlinear mechanisms, the formulation of efficient supervised learning algorithms for spiking neural networks is difficult, and has become an important problem in this research field. This article presents a comprehensive review of supervised learning algorithms for spiking neural networks and evaluates them qualitatively and quantitatively. First, a comparison between spiking neural networks and traditional artificial neural networks is provided. The general framework and some related theories of supervised learning for spiking neural networks are then introduced. Furthermore, the state-of-the-art supervised learning algorithms in recent years are reviewed from the perspectives of applicability to spiking neural network architecture and the inherent mechanisms of supervised learning algorithms. A performance comparison of spike train learning of some representative algorithms is also made. In addition, we provide five qualitative performance evaluation criteria for supervised learning algorithms for spiking neural networks and further present a new taxonomy for supervised learning algorithms depending on these five performance evaluation criteria. Finally, some future research directions in this research field are outlined.

A Device-Free Indoor Localization Method Using CSI with Wi-Fi Signals

Amid the ever-accelerated development of wireless communication technology, we have become increasingly demanding for location-based service; thus, passive indoor positioning has gained widespread attention. Channel State Information (CSI), as it can provide more detailed and fine-grained information, has been followed by researchers. Existing indoor positioning methods, however, are vulnerable to the environment and thus fail to fully reflect all the position features, due to limited accuracy of the fingerprint. As a solution, a CSI-based passive indoor positioning method was proposed, Wavelet Domain Denoising (WDD) was adopted to deal with the collected CSI amplitude, and the CSI phase information was unwound and transformed linearly in the offline phase. The post-processed amplitude and phase were taken as fingerprint data to build a fingerprint database, correlating with reference point position information. Results of experimental data analyzed under two different environments show that the present method boasts lower positioning error and higher stability than similar methods and can offer decimeter-level positioning accuracy.

A Delay Learning Algorithm Based on Spike Train Kernels for Spiking Neurons

Neuroscience research confirms that the synaptic delays are not constant, but can be modulated. This paper proposes a supervised delay learning algorithm for spiking neurons with temporal encoding, in which both the weight and delay of a synaptic connection can be adjusted to enhance the learning performance. The proposed algorithm firstly defines spike train kernels to transform discrete spike trains during the learning phase into continuous analog signals so that common mathematical operations can be performed on them, and then deduces the supervised learning rules of synaptic weights and delays by gradient descent method. The proposed algorithm is successfully applied to various spike train learning tasks, and the effects of parameters of synaptic delays are analyzed in detail. Experimental results show that the network with dynamic delays achieves higher learning accuracy and less learning epochs than the network with static delays. The delay learning algorithm is further validated on a practical example of an image classification problem. The results again show that it can achieve a good classification performance with a proper receptive field. Therefore, the synaptic delay learning is significant for practical applications and theoretical researches of spiking neural networks.

Human β-defensin-3 induces IL-8 release and apoptosis in airway smooth muscle cells

BACKGROUND

Human airway smooth muscle cells (ASMCs) may have a pro-inflammatory role through the release of inflammatory mediators. Increasing evidence indicates that human β-defensins (HBDs) are related to pathogenesis of asthma.

OBJECTIVES

To examine the plasma level of HBD-1, HBD-2 and HBD-3 in asthmatic patients and the expression of their mouse orthologues in the lung tissue of a mouse model of chronic severe asthma. Further to investigate the effect of HBD-3 on the release of the pro-inflammatory cytokine IL-8 and to explore the mechanisms.

METHODS

The plasma levels of HBD-1, HBD-2 and HBD-3 from 34 healthy controls and 25 asthmatic patients were determined by ELISA. The expression of mouse β-defensins MBD-1, MBD-3 and MBD-14 in the lung tissue of asthmatic mice was detected by Western blot. The ASMCs were cultured with HBD-3 for 24 hour, and then the supernatant level of IL-8 was evaluated by ELISA and the cell viability was examined by WST-1 assay. The signalling pathway was investigated with blocking antibodies or pharmacological inhibitors.

RESULTS

The plasma levels of HBD-1 and HBD-3 were elevated in asthmatic patients, and the expression of MBD-14, the mouse orthologue for HBD-3, was increased in asthmatic mice. HBD-3-induced IL-8 production in a CCR6 receptor-specific manner and was dependent on multiple signalling pathways. Moreover, HBD-3-induced cell apoptosis concurrently, which was dependent on the ERK1/2 MAPK pathway. Mitochondrial ROS regulated both HBD-3-induced IL-8 production and cell apoptosis.

CONCLUSIONS AND CLINICAL RELEVANCE

These observations provide clear evidence of an important new mechanism for the promotion of airway inflammation and tissue remodelling with potential relevance for the treatment of asthma.

Complex band structure of topological insulator Bi2Se3

Topological insulators are very interesting from a fundamental point of view, and their unique properties may be useful for electronic and spintronic device applications. From the point of view of applications it is important to understand the decay behavior of carriers injected in the band gap of the topological insulator, which is determined by its complex band structure (CBS). Using first-principles calculations, we investigate the dispersion and symmetry of the complex bands of Bi2Se3 family of three-dimensional topological insulators. We compare the CBS of a band insulator and a topological insulator and follow the CBS evolution in both when the spin-orbit interaction is turned on. We find significant differences in the CBS linked to the topological band structure. In particular, our results demonstrate that the evanescent states in Bi2Se3 are non-trivially complex, i.e. contain both the real and imaginary contributions. This explains quantitatively the oscillatory behavior of the band gap obtained from Bi2Se3 (0 0 0 1) slab calculations.

PKC-Dependent Phosphorylation May Regulate the Ability of Connexin43 to Inhibit DNA Synthesis

Phosphorylation affects several biological functions of connexin43 (Cx43), although its role on Cx43-mediated inhibition of DNA synthesis is not known. Previous studies showed increased Cx43 phosphorylation on serine in response to growth factor stimulation of cardiomyocytes, mediated by protein kinase C-epsilon (PKCepsilon). Here we report that activation of PKCepsilon is also necessary for stimulation of cardiomyocyte DNA synthesis and mitosis. We have investigated the participation of specific serine residues that are putative PKC targets in producing phosphorylated Cx43 species and also in regulating DNA synthesis in cardiomyocytes. Interference with the PKC signaling system and/or the phosphorylation of specific amino-acids of Cx43 may allow regulation of the mitogenic response.

[Preventive efficiency of IVIgG on nosocomial infection in the children with nephrotic syndrome]

In order to study the preventive efficiency of IVIgG on nosocomial infection(NI), 54 cases of the children with nephrotic syndrome(NS) were randomly divided into 2 groups, test group(n = 22) and control group(n = 32). Routine treatment was adopted to all cases. Besides the routine treatment, the test group was injected with IVIgG(100-300 mg.kg-1.d-1) for 2-3 days. The results showed that the NI rate of test group (13.6%) was significantly lower than that of the control(46.88%) (P < 0.05), and the hospital days of the test group(27.33 +/- 15.51) d was significantly shorter than that of the control(64.50 +/- 18.52) d (P < 0.05). These suggest that IVIgG, as one of the ways to prevent NI, can improve the immune state and effectively prevent the NI in the children with NS.

A prourokinase-RGDS chimera : Construction, expression and characterization

A tetrapeptide, RGDS, was inserted into proUK kringle domain G118-L119 by the construction of a mutant proUK-RGDS gene. The gene was expressed in the baculovirus expression system. Immunoaffinity chromatography was used to purify the chimera and protein with purity over 90% was achieved. The chimera was tested for its platelet membrane binding function and showed a calcium-dependent platelet binding activity. Amidolytic activity of the chimera was tested. The result indicated that specific amidolytic activity of plasmin activated chimera was 62 000 IU/mg, comparable to the previously reported 65 355 IU/mg of plasmin activated natural proUK([1]). Activation of plasminogen by the chimera after plasmin treatment followed Michieal-Menten kinetics, and the Km was 0.97 mumol/L, which was also comparable to 1.64 mumol/L of native urokinase. The chimera also showed intensive ability to inhibit platelet aggregationin vitro. These results indicate that this chimera might be useful as a bifunctional thrombolytic agent.

[Treatment of old femoral neck fracture with implantation of a vascularized fibular graft]

OBJECTIVE

To investigate the healing of old femoral neck fracture treated with transplantation of vascularized fibula graft accompanied with lag screw, and rehabilitation of and precaution against ischemic necrosis of femoral head.

METHOD

Vascularized fibula autograft was transplanted to the anterolateral side of the femoral neck with an internal fixator of lag screw. Fibular artery and vein were anastomosed with lateral circumflex artery and vein.

RESULT

186 of 229 cases subjected to systemic X-ray films and function follow-up (3 to 15 years). The percentage of fracture healing was 93.0% the sulass rate 86.5%.

CONCLUSION

Fibula with blood supply and compressed screw have a combined fixation effect of the old femoral neck fracture. Fibula is solid enough for supporting the femoral head and preventing it from collapse. Vascularized fibular grafting provides blood supply to the affected femoral head and neck, promoting femoral head restoration and reconstruction.

[Effect of +Gz-induced cerebral ischemia on the distribution of nitric oxide synthase in rabbit brain]

In order to study the effect of repeated short termed + Gz -induced cerebral ischemia on rabbit brain, rabbits were exposed to +4.0 Gz for 3 times (with 30 min interval in between) which caused the arterial pressure at eye level dropped to 0 kPa and lasted for 30 s. Changes of distribution of the nitric oxide synthase(NOS) positive neurons in rabbit brain were observed using the NADPH-diaphorase histochemical reaction. The results showed that NOS positive neurons in the parietal cortex, piriform cortex, hippocampus and dentate gyrus increased by 167%, 126%, 321%, 172% (P < 0.01) respectively, immediately after repeated + Gz exposures, as compared with those of controls. It still remained significantly more than that in controls (P < 0.01) 1 h and 6 h after exposure. It suggests that NO may have some effect on the neurons during cerebral ischemia.