Navigated liver surgery:State of the art and future perspectives

Background:In recent years,the development of digital imaging technology has had a significant influence in liver surgery.The ability to obtain a 3-dimensional(3D)visualization of the liver anatomy has provided surgery with virtual reality of simulation 3D computer models,3D printing models and more recently holograms and augmented reality(when virtual reality knowledge is superimposed onto reality).In addition,the utilization of real-time fluorescent imaging techniques based on indocyanine green(ICG)uptake allows clinicians to precisely delineate the liver anatomy and/or tumors within the parenchyma,applying the knowledge obtained preoperatively through digital imaging.The combination of both has transformed the abstract thinking until now based on 2D imaging into a 3D preoperative conception(virtual reality),enhanced with real-time visualization of the fluorescent liver structures,effectively facilitating intraoperative navigated liver surgery(augmented reality).Data sources:A literature search was performed from inception until January 2021 in MEDLINE(Pub Med),Embase,Cochrane library and database for systematic reviews(CDSR),Google Scholar,and National Institute for Health and Clinical Excellence(NICE)databases.Results:Fifty-one pertinent articles were retrieved and included.The different types of digital imaging technologies and the real-time navigated liver surgery were estimated and compared.Conclusions:ICG fluorescent imaging techniques can contribute essentially to the real-time definition of liver segments;as a result,precise hepatic resection can be guided by the presence of fluorescence.Furthermore,3D models can help essentially to further advancing of precision in hepatic surgery by permitting estimation of liver volume and functional liver remnant,delineation of resection lines along the liver segments and evaluation of tumor margins.In liver transplantation and especially in living donor liver transplantation(LDLT),3D printed models of the donor’s liver and models of the recipient’s hilar anatomy can contribute further to improving the results.In particular,pediatric LDLT abdominal cavity models can help to manage the largest challenge of this procedure,namely large-for-size syndrome.

基于Self-Attention-BiLSTM网络的西瓜种苗叶片氮磷钾含量高光谱检测方法

元素含量无损检测技术可以为植物生长发育的环境精准调控提供关键实时数据。以西瓜苗为例,提出了一种基于图谱特征融合的氮磷钾含量深度学习检测方法。首先,使用高光谱仪拍摄西瓜苗叶片的高光谱图像,使用连续流动化学分析仪测定叶片的3种元素含量。然后,采用基线偏移校正(BOC)叠加高斯平滑滤波(GF)的光谱预处理方法和随机森林算法(RF)建立预测模型,基于竞争性自适应重加权采样(CARS)和连续投影算法(SPA)2种算法初步筛选出特征波长,再综合考虑波长数和建模精度设计了一种最优波长评价方法,将波长数进一步减少到3~4个。最后,提取使用U-Net网络分割的彩色图像颜色和纹理特征,和光谱反射率特征一起作为输入,基于自注意力机制-双向长短时记忆(Self-Attention-BiLSTM)网络构建了3种元素含量的预测模型。实验结果表明,氮磷钾含量预测的R2分别为0.961、0.954、0.958,RMSE分别为0.294%、0.262%、0.196%,实现了很好的建模效果。使用该模型对另2个品种西瓜进行测试,R2超过0.899、RMSE小于0.498%,表明该模型具有很好的泛化性。该高光谱建模方法使用少量波长光谱即实现了高精度检测,在精度和效率上达成了很好的平衡,为后续便携式高光谱检测装备开发奠定了理论基础。

Chinese expert consensus on laparoscopic hepatic segmentectomy and subsegmentectomy navigated by augmented-and mixed-reality technology combined with indocyanine green fluorescence imaging

Augmented-and mixed-reality technologies have pioneered the realization of real-time fusion and interactive projection for laparoscopic surgeries.Indocyanine green fluorescence imaging technology has enabled anatomical,functional,and radical hepatectomy through tumor identification and localization of target hepatic segments,driving a transformative shift in themanagement of hepatic surgical diseases,moving away from traditional,empirical diagnostic and treatment approaches toward digital,intelligent ones.The Hepatic Surgery Group of the Surgery Branch of the Chinese Medical Association,Digital Medicine Branch of the Chinese Medical Association,Digital Intelligent Surgery Committee of the Chinese Society of ResearchHospitals,and Liver Cancer Committee of the Chinese Medical Doctor Association organized the relevant experts in China to formulate this consensus.This consensus provides a comprehensive outline of the principles,advantages,processes,and key considerations associated with the application of augmented reality and mixed-reality technology combined with indocyanine green fluorescence imaging technology for hepatic segmental and subsegmental resection.The purpose is to streamline and standardize the application of these technologies.

Analysis of Factors Influencing True Blood Loss in Navigated Total Knee Replacements

Title: Analysis of factors influencing true blood loss in navigated total knee replacements. Objectives: To evaluate true blood loss in total knee replacements and analyze the various factors such as gender, BMI, diagnosis, size of implants, duration of surgery, tourniquet usage etc. on calculated blood loss using formula by Nadler et al. All the cases included have been done using navigation system and no comparison with conventional jig based surgeries has been attempted. Methods: Retrospectively data of primary cemented total knee replacements performed from October 2012 to August 2013 were evaluated. All surgeries were performed using navigation system. The data collected included patient sex, height, weight and preoperative haemoglobin and hematocrit. The patients’ postoperative data of haemoglobin, hematocrit and drains were collected. All patients had their CBC done on 2nd post operative day. Any data on transfusions that patients received were also collected. We also collected data regarding the size of implant used. We calculated true blood based on formula given by Nadler, Hidalgo & Bloch. We excluded patients whose data were incomplete or who received tranexamic acid. Patients who needed stems (femoral or tibial) were also excluded from this study. Results: The average true calculated blood loss was 959.44 ml. BMI did not have any effect on blood loss. But larger size implants were associated with more blood loss. Conclusion: The preoperative haemoglobin is one of the most important factors in determining transfusion following the knee replacement. Male gender and larger implants are associated with more blood loss. BMI, diagnosis of OA or RA, tourniquet usage and time have no significant effect on blood loss. Our calculated blood loss compares favourably with published literature.

Free-walking:Pedestrian inertial navigation based on dual foot-mounted IMU

The inertial navigation system(INS),which is frequently used in emergency rescue operations and other situations,has the benefits of not relying on infrastructure,high positioning frequency,and strong real-time performance.However,the intricate and unpredictable pedestrian motion patterns lead the INS localization error to significantly diverge with time.This paper aims to enhance the accuracy of zero-velocity interval(ZVI)detection and reduce the heading and altitude drift of foot-mounted INS via deep learning and equation constraint of dual feet.Aiming at the observational noise problem of low-cost inertial sensors,we utilize a denoising autoencoder to automatically eliminate the inherent noise.Aiming at the problem that inaccurate detection of the ZVI detection results in obvious displacement error,we propose a sample-level ZVI detection algorithm based on the U-Net neural network,which effectively solves the problem of mislabeling caused by sliding windows.Aiming at the problem that Zero-Velocity Update(ZUPT)cannot suppress heading and altitude error,we propose a bipedal INS method based on the equation constraint and ellipsoid constraint,which uses foot-to-foot distance as a new observation to correct heading and altitude error.We conduct extensive and well-designed experiments to evaluate the performance of the proposed method.The experimental results indicate that the position error of our proposed method did not exceed 0.83% of the total traveled distance.

BDSec:Security Authentication Protocol for BeiDou-Ⅱ Civil Navigation Message

Due to the lack of authentication mechanism in BeiDou navigation satellite system(BDS),BD-Ⅱ civil navigation message(BDⅡ-CNAV) are vulnerable to spoofing attack and replay attack.To solve this problem,we present a security authentication protocol,called as BDSec,which is designed by using China’s cryptography Shangyong Mima(SM) series algorithms,such as SM2/4/9 and Zu Chongzhi(ZUC)algorithm.In BDSec protocol,both of BDⅡ-CNAV and signature information are encrypted using the SM4 algorithm(Symmetric encryption mechanism).The encrypted result is used as the subject authentication information.BDSec protocol applies SM9 algorithm(Identity-based cryptography mechanism) to protect the integrity of the BDⅡ-CNAV,adopts the SM2 algorithm(Public key cryptosystem) to guarantee the confidentiality of the important session information,and uses the ZUC algorithm(Encryption and integrity algorithm) to verify the integrity of the message authentication serial number and initial information and the information in authentication initialization sub-protocol respectively.The results of the SVO logic reasoning and performance analysis show that BDSec protocol meets security requirements for the dual user identity authentication in BDS and can realize the security authentication of BDⅡ-CNAV.

Method of improving pedestrian navigation performance based on chest card

With the development of positioning technology,loca-tion services are constantly in demand by people.As a primary location service pedestrian navigation has two main approaches based on radio and inertial navigation.The pedestrian naviga-tion based on radio is subject to environmental occlusion lead-ing to the degradation of positioning accuracy.The pedestrian navigation based on micro-electro-mechanical system inertial measurement unit(MIMU)is less susceptible to environmental interference,but its errors dissipate over time.In this paper,a chest card pedestrian navigation improvement method based on complementary correction is proposed in order to suppress the error divergence of inertial navigation methods.To suppress atti-tude errors,optimal feedback coefficients are established by pedestrian motion characteristics.To extend navigation time and improve positioning accuracy,the step length in subsequent movements is compensated by the first step length.The experi-mental results show that the positioning accuracy of the pro-posed method is improved by more than 47%and 44%com-pared with the pure inertia-based method combined with step compensation and the traditional complementary filtering com-bined method with step compensation.The proposed method can effectively suppress the error dispersion and improve the positioning accuracy.

Self-Healable and Stretchable PAAc/XG/Bi_(2)Se_(0.3)Te_(2.7) Hybrid Hydrogel Thermoelectric Materials

Thermoelectric power generators have attracted increasing interest in recent years owing to their great potential in wearable electronics power supply.It is noted that thermoelectric power generators are easy to damage in the dynamic service process,resulting in the formation of microcracks and performance degradation.Herein,we prepare a new hybrid hydrogel thermoelectric material PAAc/XG/Bi_(2)Se_(0.3)Te_(2.7)by an in situ polymerization method,which shows a high stretchable and self-healable performance,as well as a good thermoelectric performance.For the sample with Bi_(2)Se_(0.3)Te_(2.7)content of 1.5 wt%(i.e.,PAAc/XG/Bi2Se0.3Te27(1.5 wt%)),which has a room temperature Seebeck coefficient of-0.45 mV K^(-1),and exhibits an open-circuit voltage of-17.91 mV and output power of 38.1 nW at a temperature difference of 40 K.After being completely cut off,the hybrid thermoelectric hydrogel automatically recovers its electrical characteristics within a response time of 2.0 s,and the healed hydrogel remains more than 99%of its initial power output.Such stretchable and self-healable hybrid hydrogel thermoelectric materials show promising potential for application in dynamic service conditions,such as wearable electronics.

RepBoTNet-CESA:An Alzheimer’s Disease Computer Aided Diagnosis Method Using Structural Reparameterization BoTNet and Cubic Embedding Self Attention

Various deep learning models have been proposed for the accurate assisted diagnosis of early-stage Alzheimer’s disease(AD).Most studies predominantly employ Convolutional Neural Networks(CNNs),which focus solely on local features,thus encountering difficulties in handling global features.In contrast to natural images,Structural Magnetic Resonance Imaging(sMRI)images exhibit a higher number of channel dimensions.However,during the Position Embedding stage ofMulti Head Self Attention(MHSA),the coded information related to the channel dimension is disregarded.To tackle these issues,we propose theRepBoTNet-CESA network,an advanced AD-aided diagnostic model that is capable of learning local and global features simultaneously.It combines the advantages of CNN networks in capturing local information and Transformer networks in integrating global information,reducing computational costs while achieving excellent classification performance.Moreover,it uses the Cubic Embedding Self Attention(CESA)proposed in this paper to incorporate the channel code information,enhancing the classification performance within the Transformer structure.Finally,the RepBoTNet-CESA performs well in various AD-aided diagnosis tasks,with an accuracy of 96.58%,precision of 97.26%,and recall of 96.23%in the AD/NC task;an accuracy of 92.75%,precision of 92.84%,and recall of 93.18%in the EMCI/NC task;and an accuracy of 80.97%,precision of 83.86%,and recall of 80.91%in the AD/EMCI/LMCI/NC task.This demonstrates that RepBoTNet-CESA delivers outstanding outcomes in various AD-aided diagnostic tasks.Furthermore,our study has shown that MHSA exhibits superior performance compared to conventional attention mechanisms in enhancing ResNet performance.Besides,the Deeper RepBoTNet-CESA network fails to make further progress in AD-aided diagnostic tasks.

Effect of navigation endoscopy combined with threedimensional printing technology in the treatment of orbital blowout fractures

●AIM:To explore the combined application of surgical navigation nasal endoscopy(NNE)and three-dimensional printing technology(3DPT)for the adjunctive treatment of orbital blowout fractures(OBF).●METHODS:Retrospective analysis was conducted on the data of patients with OBF who underwent surgical treatment at the Affiliated Eye Hospital of Nanchang University between July 2012 and November 2022.The control group consisted of patients who received traditional surgical treatment(n=43),while the new surgical group(n=52)consisted of patients who received NNE with 3DPT.The difference in therapeutic effects between the two groups was evaluated by comparing the duration of the operation,best corrected visual acuity(BCVA),enophthalmos difference,recovery rate of eye movement disorder,recovery rate of diplopia,and incidence of postoperative complications.●RESULTS:The study included 95 cases(95 eyes),with 63 men and 32 women.The patients’age ranged from 5 to 67y(35.21±15.75y).The new surgical group and the control group exhibited no statistically significant differences in the duration of the operation,BCVA and enophthalmos difference.The recovery rates of diplopia in the new surgical group were significantly higher than those in the control group at 1mo[OR=0.03,95%CI(0.01–0.15),P<0.0000]and 3mo[OR=0.11,95%CI(0.03–0.36),P<0.0000]postoperation.Additionally,the recovery rates of eye movement disorders at 1 and 3mo after surgery were OR=0.08,95%CI(0.03–0.24),P<0.0000;and OR=0.01,95%CI(0.00–0.18),P<0.0000.The incidence of postoperative complications was lower in the new surgical group compared to the control group[OR=4.86,95%CI(0.95–24.78),P<0.05].●CONCLUSION:The combination of NNE and 3DPT can shorten the recovery time of diplopia and eye movement disorder in patients with OBF.

Anti-Spoofing:Integrated Information Authentication of BeiDou-ⅡCivil Navigation Message

The BeiDou-Ⅱcivil navigation message(BDⅡ-CNAV)is transmitted in an open environment and no information integrity protection measures are provided.Hence,the BDⅡ-CNAV faces the threat of spoofing attacks,which can lead to wrong location reports and time indication.In order to deal with this threat,we proposed a scheme of anti-spoofing for BDⅡ-CNAV based on integrated information authentication.This scheme generates two type authentication information,one is authentication code information(ACI),which is applied to confirm the authenticity and reliability of satellite time information,and the other is signature information,which is used to authenticate the integrity of satellite location information and other information.Both authentication information is designed to embed into the reserved bits in BDⅡ-CNAV without changing the frame structure.In order to avoid authentication failure caused by public key error or key error,the key or public key prompt information(KPKPI)are designed to remind the receiver to update both keys in time.Experimental results indicate that the scheme can successfully detect spoofing attacks,and the authentication delay is less than 1%of the transmission delay,which meets the requirements of BDⅡ-CNAV information authentication.

A dual adaptive unscented Kalman filter algorithm for SINS-based integrated navigation system

In this study, the problem of measuring noise pollution distribution by the intertial-based integrated navigation system is effectively suppressed. Based on nonlinear inertial navigation error modeling, a nested dual Kalman filter framework structure is developed. It consists of unscented Kalman filter (UKF)master filter and Kalman filter slave filter. This method uses nonlinear UKF for integrated navigation state estimation. At the same time, the exact noise measurement covariance is estimated by the Kalman filter dependency filter. The algorithm based on dual adaptive UKF (Dual-AUKF) has high accuracy and robustness, especially in the case of measurement information interference. Finally, vehicle-mounted and ship-mounted integrated navigation tests are conducted. Compared with traditional UKF and the Sage-Husa adaptive UKF (SH-AUKF), this method has comparable filtering accuracy and better filtering stability. The effectiveness of the proposed algorithm is verified.

MA-Res U-Net:Design of Soybean Navigation System with Improved U-Net Model

Traditional machine vision algorithms have difficulty handling the interference of light and shadow changes,broken rows,and weeds in the complex growth circumstances of soybean fields,which leads to erroneous navigation route segmentation.There are additional shortcomings in the feature extractFion capabilities of the conventional U-Net network.Our suggestion is to utilize an improved U-Net-based method to tackle these difficulties.First,we use ResNet’s powerful feature extraction capabilities to replace the original U-Net encoder.To enhance the concentration on characteristics unique to soybeans,we integrate a multi-scale high-performance attention mechanism.Furthermore,to do multi-scale feature extraction and capture a wider variety of contextual information,we employ atrous spatial pyramid pooling.The segmented image generated by our upgraded U-Net model is then analyzed using the CenterNet method to extract key spots.The RANSAC algorithm then uses these important spots to delineate the soybean seedling belt line.Finally,the navigation line is determined using the angle tangency theory.The experimental findings illustrate the superiority of our method.Our improved model significantly outperforms the original U-Net regarding mean Pixel Accuracy(mPA)and mean Intersection over Union(mIOU)indices,showing a more accurate segmentation of soybean routes.Furthermore,our soybean route navigation system’s outstanding accuracy is demonstrated by the deviation angle,which is only 3°between the actual deviation and the navigation line.This technology makes a substantial contribution to the sustainable growth of agriculture and shows potential for real-world applications.

Cognitive Navigation for Intelligent Mobile Robots:A Learning-Based Approach With Topological Memory Configuration

Autonomous navigation for intelligent mobile robots has gained significant attention,with a focus on enabling robots to generate reliable policies based on maintenance of spatial memory.In this paper,we propose a learning-based visual navigation pipeline that uses topological maps as memory configurations.We introduce a unique online topology construction approach that fuses odometry pose estimation and perceptual similarity estimation.This tackles the issues of topological node redundancy and incorrect edge connections,which stem from the distribution gap between the spatial and perceptual domains.Furthermore,we propose a differentiable graph extraction structure,the topology multi-factor transformer(TMFT).This structure utilizes graph neural networks to integrate global memory and incorporates a multi-factor attention mechanism to underscore elements closely related to relevant target cues for policy generation.Results from photorealistic simulations on image-goal navigation tasks highlight the superior navigation performance of our proposed pipeline compared to existing memory structures.Comprehensive validation through behavior visualization,interpretability tests,and real-world deployment further underscore the adapt-ability and efficacy of our method.

A data and physical model dual-driven based trajectory estimator for long-term navigation

Long-term navigation ability based on consumer-level wearable inertial sensors plays an essential role towards various emerging fields, for instance, smart healthcare, emergency rescue, soldier positioning et al. The performance of existing long-term navigation algorithm is limited by the cumulative error of inertial sensors, disturbed local magnetic field, and complex motion modes of the pedestrian. This paper develops a robust data and physical model dual-driven based trajectory estimation(DPDD-TE) framework, which can be applied for long-term navigation tasks. A Bi-directional Long Short-Term Memory(Bi-LSTM) based quasi-static magnetic field(QSMF) detection algorithm is developed for extracting useful magnetic observation for heading calibration, and another Bi-LSTM is adopted for walking speed estimation by considering hybrid human motion information under a specific time period. In addition, a data and physical model dual-driven based multi-source fusion model is proposed to integrate basic INS mechanization and multi-level constraint and observations for maintaining accuracy under long-term navigation tasks, and enhanced by the magnetic and trajectory features assisted loop detection algorithm. Real-world experiments indicate that the proposed DPDD-TE outperforms than existing algorithms, and final estimated heading and positioning accuracy indexes reaches 5° and less than 2 m under the time period of 30 min, respectively.

Navigation Finsler metrics on a gradient Ricci soliton

In this paper,we study a class of Finsler metrics defined by a vector field on a gradient Ricci soliton.We obtain a necessary and sufficient condition for these Finsler metrics on a compact gradient Ricci soliton to be of isotropic S-curvature by establishing a new integral inequality.Then we determine the Ricci curvature of navigation Finsler metrics of isotropic S-curvature on a gradient Ricci soliton generalizing result only known in the case when such soliton is of Einstein type.As its application,we obtain the Ricci curvature of all navigation Finsler metrics of isotropic S-curvature on Gaussian shrinking soliton.

Single-center experience with Knee+^(TM) augmented reality navigation system in primary total knee arthroplasty

BACKGROUND Computer-assisted systems obtained an increased interest in orthopaedic surgery over the last years,as they enhance precision compared to conventional hardware.The expansion of computer assistance is evolving with the employment of augmented reality.Yet,the accuracy of augmented reality navigation systems has not been determined.AIM To examine the accuracy of component alignment and restoration of the affected limb’s mechanical axis in primary total knee arthroplasty(TKA),utilizing an augmented reality navigation system and to assess whether such systems are conspicuously fruitful for an accomplished knee surgeon.METHODS From May 2021 to December 2021,30 patients,25 women and five men,under-went a primary unilateral TKA.Revision cases were excluded.A preoperative radiographic procedure was performed to evaluate the limb’s axial alignment.All patients were operated on by the same team,without a tourniquet,utilizing three distinct prostheses with the assistance of the Knee+™augmented reality navigation system in every operation.Postoperatively,the same radiographic exam protocol was executed to evaluate the implants’position,orientation and coronal plane alignment.We recorded measurements in 3 stages regarding femoral varus and flexion,tibial varus and posterior slope.Firstly,the expected values from the Augmented Reality system were documented.Then we calculated the same values after each cut and finally,the same measurements were recorded radiolo-gically after the operations.Concerning statistical analysis,Lin’s concordance correlation coefficient was estimated,while Wilcoxon Signed Rank Test was performed when needed.RESULTS A statistically significant difference was observed regarding mean expected values and radiographic mea-surements for femoral flexion measurements only(Z score=2.67,P value=0.01).Nonetheless,this difference was statistically significantly lower than 1 degree(Z score=-4.21,P value<0.01).In terms of discrepancies in the calculations of expected values and controlled measurements,a statistically significant difference between tibial varus values was detected(Z score=-2.33,P value=0.02),which was also statistically significantly lower than 1 degree(Z score=-4.99,P value<0.01).CONCLUSION The results indicate satisfactory postoperative coronal alignment without outliers across all three different implants utilized.Augmented reality navigation systems can bolster orthopaedic surgeons’accuracy in achieving precise axial alignment.However,further research is required to further evaluate their efficacy and potential.

Research on the navigation method of large-scale differential tail-control improvised guided munitions based on rotational speed constraints

In the process of launching guided projectile under the conventional system, it is difficult to effectively obtain the precise navigation parameters of the projectile in the high dynamic environment. Aiming at this problem, this paper describes a new system of guided ammunition based on tail spin reduction. After analyzing the mechanism of the ammunition's tail spin reduction, a navigation method of large scale difference tail control simple guided ammunition based on speed constraint is proposed. In this method,the corresponding navigation constraints can be carried out by combining the rotation speed state of the ammunition itself, and the optimal solution of navigation parameters during the flight of the missile can be obtained by Extended Kalman Filter(EKF). Finally, the performance of the proposed method was verified by the simulation environment, and the hardware-in-the-loop simulation test and flight test were carried out to verify the performance of the method in the real environment. The experimental results show that the proposed method can achieve the optimal estimation of navigation parameters for simple guided ammunition with large-scale difference tail control. Under the conditions of simulation test and hardware-in-loop simulation test, the position and velocity errors calculated by the method in this paper converged. Under the condition of flight test, the spatial average error calculated by the method described in this paper is 6.17 m, and the spatial error of the final landing point is 3.50 m.Through this method, the accurate acquisition of navigation parameters in the process of projectile launching is effectively realized.

uNavigator双导航系统在困难部位腹腔镜肝切除术的初步应用

目的探讨uNavigator双导航系统在腹腔镜肝切除术围手术期的应用价值。方法对1例53岁女性病人采用uNavigator双导航系统行腹腔镜肝切除术。该例病人因“体检发现肝脏占位2 d”就诊,有腰椎间盘突出症病史,术前血常规、凝血功能、肝功能和肾功能均正常,通过术前腹部增强CT诊断为11.2 cm的肝海绵状血管瘤,位于肝右叶,靠近右后、右前肝蒂及肝右静脉。根据腹部增强CT的DICOM数据重建病人肝脏的增强现实全息影像,用于医患沟通、术前手术规划和术中导航。于2024年1月25日行uNavigator双导航系统引导下腹腔镜肝右后叶及右前叶背侧段切除术。术后病理证实为肝海绵状血管瘤。结果利用三维图像与实际肝脏的融合图像标记血管瘤的左侧边界、右前和右后肝蒂的走行。使用超声刀沿边界离断肝实质,采用Endo-GIA切断右后肝蒂及肝右静脉主干。手术时间为180 min,出血量约300 mL。术后病人恢复良好,术后第8天出院。结论uNavigator双导航系统可促进医患沟通,帮助医生术前规划和术中导航。

Unified Description of the Three Stable Particles in Self-Action Allows Determination of Their Relative Masses

The Dirac equation γμ(δμ-eAμ)Ψ=mc2Ψ describes the bound states of the electron under the action of external potentials, Aμ. We assumed that the fundamental form of the Dirac equation γμ(δμ-Sμ)Ψ=0 should describe the stable particles (the electron, the proton and the dark-matter-particle (dmp)) bound to themselves under the action of their own potentials Sμ. The new equation reveals that self energy is consequence of self action, it also reveals that the spin angular momentum is consequence of the dynamic structure of the stable particles. The quantitative results are the determination of their relative masses as well as the determination of the electromagnetic coupling constant.