Privacy-Preserving Consensus-Based Distributed Economic Dispatch of Smart Grids via State Decomposition

This paper studies the privacy-preserving distributed economic dispatch(DED)problem of smart grids.An autonomous consensus-based algorithm is developed via local data exchange with neighboring nodes,which covers both the islanded mode and the grid-connected mode of smart grids.To prevent power-sensitive information from being disclosed,a privacy-preserving mechanism is integrated into the proposed DED algorithm by randomly decomposing the state into two parts,where only partial data is transmitted.Our objective is to develop a privacy-preserving DED algorithm to achieve optimal power dispatch with the lowest generation cost under physical constraints while preventing sensitive information from being eavesdropped.To this end,a comprehensive analysis framework is established to ensure that the proposed algorithm can converge to the optimal solution of the concerned optimization problem by means of the consensus theory and the eigenvalue perturbation approach.In particular,the proposed autonomous algorithm can achieve a smooth transition between the islanded mode and the grid-connected mode.Furthermore,rigorous analysis is given to show privacy-preserving performance against internal and external eavesdroppers.Finally,case studies illustrate the feasibility and validity of the developed algorithm.

A Cloud-Fog Enabled and Privacy-Preserving IoT Data Market Platform Based on Blockchain

The dynamic landscape of the Internet of Things(IoT)is set to revolutionize the pace of interaction among entities,ushering in a proliferation of applications characterized by heightened quality and diversity.Among the pivotal applications within the realm of IoT,as a significant example,the Smart Grid(SG)evolves into intricate networks of energy deployment marked by data integration.This evolution concurrently entails data interchange with other IoT entities.However,there are also several challenges including data-sharing overheads and the intricate establishment of trusted centers in the IoT ecosystem.In this paper,we introduce a hierarchical secure data-sharing platform empowered by cloud-fog integration.Furthermore,we propose a novel non-interactive zero-knowledge proof-based group authentication and key agreement protocol that supports one-to-many sharing sets of IoT data,especially SG data.The security formal verification tool shows that the proposed scheme can achieve mutual authentication and secure data sharing while protecting the privacy of data providers.Compared with previous IoT data sharing schemes,the proposed scheme has advantages in both computational and transmission efficiency,and has more superiority with the increasing volume of shared data or increasing number of participants.

Ship recognition based on HRRP via multi-scale sparse preserving method

In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) based on the maximum margin criterion(MMC) is proposed for recognizing the class of ship targets utilizing the high-resolution range profile(HRRP). Multi-scale fusion is introduced to capture the local and detailed information in small-scale features, and the global and contour information in large-scale features, offering help to extract the edge information from sea clutter and further improving the target recognition accuracy. The proposed method can maximally preserve the multi-scale fusion sparse of data and maximize the class separability in the reduced dimensionality by reproducing kernel Hilbert space. Experimental results on the measured radar data show that the proposed method can effectively extract the features of ship target from sea clutter, further reduce the feature dimensionality, and improve target recognition performance.

Bipolar hip arthroplasty using conjoined tendon preserving posterior lateral approach in treatment of displaced femoral neck fractures

BACKGROUND Hip fractures account for 23.8%of all fractures in patients over the age of 75 years.More than half of these patients are older than 80 years.Bipolar hemiarthroplasty(BHA)was established as an effective management option for these patients.Various approaches can be used for the BHA procedure.However,there is a high risk of postoperative dislocation.The conjoined tendon-preserving posterior(CPP)lateral approach was introduced to reduce postoperative dislocation rates.AIM To evaluate the effectiveness and safety of the CPP lateral approach for BHA in elderly patients.METHODS We retrospectively analyzed medical data from 80 patients with displaced femoral neck fractures who underwent BHA.The patients were followed up for at least 1 year.Among the 80 patients,57(71.3%)were female.The time to operation averaged 2.3 d(range:1-5 d).The mean age was 80.5 years(range:67-90 years),and the mean body mass index was 24.9 kg/m^(2)(range:17-36 kg/m^(2)).According to the Garden classification,42.5%of patients were typeⅢand 57.5%of patients were typeⅣ.Uncemented bipolar hip prostheses were used for all patients.Torn conjoined tendons,dislocations,and adverse complications during and after surgery were recorded.RESULTS The mean postoperative follow-up time was 15.3 months(range:12-18 months).The average surgery time was 52 min(range:40-70 min)with an average blood loss of 120 mL(range:80-320 mL).The transfusion rate was 10%(8 of 80 patients).The gemellus inferior was torn in 4 patients(5%),while it was difficult to identify in 2 patients(2.5%)during surgery.The posterior capsule was punctured by the fractured femoral neck in 3 patients,but the conjoined tendon and the piriformis tendon remained intact.No patients had stem varus greater than 3 degrees or femoral fracture.There were no patients with stem subsidence more than 5 mm at the last follow-up.No postoperative dislocations were observed throughout the follow-up period.No significance was found between preoperative and postoperative mean Health Service System scores(87.30±2.98 vs 86.10±6.10,t=1.89,P=0.063).CONCLUSION The CPP lateral approach can effectively reduce the incidence of postoperative dislocation without increasing perioperative complications.For surgeons familiar with the posterior lateral approach,there is no need for additional surgical instruments,and it does not increase surgical difficulty.

Clinical Observation on the Treatment of Diabetic Kidney Disease with Damp-heat Stasis Syndrome in Clinical Proteinuria Stage by Kunkui Kidney Preserving Paste

[Objectives]To evaluate the clinical efficacy and safety of Kunkui Kidney Preserving Paste in the treatment of diabetic kidney disease(DKD)patients with damp-heat stasis syndrome in the clinical proteinuria stage.[Methods]Retrospective analysis was made on 30 patients with DKD who were diagnosed with damp-heat stasis syndrome in the clinical proteinuria stage from March 2021 to March 2023 in Jiangsu Province Hospital of Chinese Medicine,and who took Kunkui Kidney Preserving Paste continuously for six months.The urinary albumin/creatinine ratio(UACR),urinary complement C3,and urea nitrogen(BUN)of DKD patients before and after treatment were compared,and estimated glomerular filtration rate(eGFR),blood creatinine(Scr),and cystatin C(CysC)were estimated,and the therapeutic effects on renal function and urinary protein were evaluated.[Results]After treatment,UACR significantly decreased(P<0.01),and urinary complement C3 and Scr decreased(P<0.05),while other indicators showed no significant statistical difference(P>0.05).In terms of evaluating the efficacy of urinary protein therapy,8 cases showed recent relief;8 cases showed significant effect;9 cases were effective,and 5 cases were invalid after treatment,with a total effective rate of 83.33%.In terms of renal function efficacy evaluation,8 cases showed significant effect;4 cases were effective;11 cases were stable,and 7 cases were invalid,with a total effective rate of 76.67%.In the safety evaluation,there were no obvious adverse reactions.[Conclusions]The Kunkui Kidney Preserving Past has significant clinical efficacy and safety in treating DKD patients with damp-heat stasis syndrome in the clinical proteinuria period.It has significant advantages in reducing urinary protein and protecting renal function,which is worthy of clinical promotion.

2P3FL:A Novel Approach for Privacy Preserving in Financial Sectors Using Flower Federated Learning

The increasing data pool in finance sectors forces machine learning(ML)to step into new complications.Banking data has significant financial implications and is confidential.Combining users data from several organizations for various banking services may result in various intrusions and privacy leakages.As a result,this study employs federated learning(FL)using a flower paradigm to preserve each organization’s privacy while collaborating to build a robust shared global model.However,diverse data distributions in the collaborative training process might result in inadequate model learning and a lack of privacy.To address this issue,the present paper proposes the imple-mentation of Federated Averaging(FedAvg)and Federated Proximal(FedProx)methods in the flower framework,which take advantage of the data locality while training and guaranteeing global convergence.Resultantly improves the privacy of the local models.This analysis used the credit card and Canadian Institute for Cybersecurity Intrusion Detection Evaluation(CICIDS)datasets.Precision,recall,and accuracy as performance indicators to show the efficacy of the proposed strategy using FedAvg and FedProx.The experimental findings suggest that the proposed approach helps to safely use banking data from diverse sources to enhance customer banking services by obtaining accuracy of 99.55%and 83.72%for FedAvg and 99.57%,and 84.63%for FedProx.

Analysis of quality of life in patients after transgastric natural orifice transluminal endoscopic gallbladder-preserving surgery

BACKGROUND At present,laparoscopic cholecystectomy(LC)is the main surgical treatment for gallstones.But,after gallbladder removal,there are many complications.Therefore,it is hoped to remove stones while preserving the function of the gallbladder,and with the development of endoscopic technology,natural orifice transluminal endoscopic surgery came into being.AIM To compare the quality of life,perioperative indicators,adverse events after LC and transgastric natural orifice transluminal endoscopic gallbladder-preserving surgery(EGPS)in patients with gallstones.METHODS Patients who were admitted to The First Affiliated Hospital of Xinjiang Medical University from 2020 to 2022 were retrospectively collected.We adopted propen-sity score matching(1:1)to compare EGPS and LC patients.RESULTS A total of 662 cases were collected,of which 589 cases underwent LC,and 73 cases underwent EGPS.Propensity score matching was performed,and 40 patients were included in each of the groups.In the EGPS group,except the gastr-ointestinal defecation(P=0.603),the total score,physical well-being,mental well-being,and gastrointestinal digestion were statistically significant compared with the preoperative score after surgery(P<0.05).In the LC group,except the mental well-being,the total score,physical well-being,gastrointestinal digestion,the gastrointestinal defecation was statistically significant compared with the preoperative score after surgery(P<0.05).When comparing between groups,gastrointestinal defecation had significantly difference(P=0.002)between the two groups,there was no statistically significant difference in the total postoperative score and the other three subscales.In the surgery duration,hospital stay and cost,LC group were lower than EGPS group.The recurrence factors of gallstones after EGPS were analyzed:and recurrence was not correlated with gender,age,body mass index,number of stones,and preoperative score.CONCLUSION Whether EGPS or LC,it can improve the patient’s symptoms,and the EGPS has less impact on the patient’s defecation.It needed to,prospective,multicenter,long-term follow-up,large-sample related studies to prove.

LOCAL STRUCTURE-PRESERVING ALGORITHMS FOR THE KLEIN-GORDON-ZAKHAROV EQUATION

In this paper, using the concatenating method, a series of local structure-preserving algorithms are obtained for the Klein-Gordon-Zakharov equation, including four multisymplectic algorithms, four local energy-preserving algorithms, four local momentumpreserving algorithms;of these, local energy-preserving and momentum-preserving algorithms have not been studied before. The local structure-preserving algorithms mentioned above are more widely used than the global structure-preserving algorithms, since local preservation algorithms can be preserved in any time and space domains, which overcomes the defect that global preservation algorithms are limited to boundary conditions. In particular, under appropriate boundary conditions, local preservation laws are global preservation laws.Numerical experiments conducted can support the theoretical analysis well.

Loop Closure Detection via Locality Preserving Matching With Global Consensus

A critical component of visual simultaneous localization and mapping is loop closure detection(LCD),an operation judging whether a robot has come to a pre-visited area.Concretely,given a query image(i.e.,the latest view observed by the robot),it proceeds by first exploring images with similar semantic information,followed by solving the relative relationship between candidate pairs in the 3D space.In this work,a novel appearance-based LCD system is proposed.Specifically,candidate frame selection is conducted via the combination of Superfeatures and aggregated selective match kernel(ASMK).We incorporate an incremental strategy into the vanilla ASMK to make it applied in the LCD task.It is demonstrated that this setting is memory-wise efficient and can achieve remarkable performance.To dig up consistent geometry between image pairs during loop closure verification,we propose a simple yet surprisingly effective feature matching algorithm,termed locality preserving matching with global consensus(LPM-GC).The major objective of LPM-GC is to retain the local neighborhood information of true feature correspondences between candidate pairs,where a global constraint is further designed to effectively remove false correspondences in challenging sceneries,e.g.,containing numerous repetitive structures.Meanwhile,we derive a closed-form solution that enables our approach to provide reliable correspondences within only a few milliseconds.The performance of the proposed approach has been experimentally evaluated on ten publicly available and challenging datasets.Results show that our method can achieve better performance over the state-of-the-art in both feature matching and LCD tasks.We have released our code of LPM-GC at https://github.com/jiayi-ma/LPM-GC.

Lightweight privacy-preserving truth discovery for vehicular air quality monitoring

Air pollution has become a global concern for many years.Vehicular crowdsensing systems make it possible to monitor air quality at a fine granularity.To better utilize the sensory data with varying credibility,truth discovery frameworks are introduced.However,in urban cities,there is a significant difference in traffic volumes of streets or blocks,which leads to a data sparsity problem for truth discovery.Protecting the privacy of participant vehicles is also a crucial task.We first present a data masking-based privacy-preserving truth discovery framework,which incorporates spatial and temporal correlations to solve the sparsity problem.To further improve the truth discovery performance of the presented framework,an enhanced version is proposed with anonymous communication and data perturbation.Both frameworks are more lightweight than the existing cryptography-based methods.We also evaluate the work with simulations and fully discuss the performance and possible extensions.

Privacy Preserving Demand Side Management Method via Multi-Agent Reinforcement Learning

The smart grid utilizes the demand side management technology to motivate energy users towards cutting demand during peak power consumption periods, which greatly improves the operation efficiency of the power grid. However, as the number of energy users participating in the smart grid continues to increase, the demand side management strategy of individual agent is greatly affected by the dynamic strategies of other agents. In addition, the existing demand side management methods, which need to obtain users’ power consumption information,seriously threaten the users’ privacy. To address the dynamic issue in the multi-microgrid demand side management model, a novel multi-agent reinforcement learning method based on centralized training and decentralized execution paradigm is presented to mitigate the damage of training performance caused by the instability of training experience. In order to protect users’ privacy, we design a neural network with fixed parameters as the encryptor to transform the users’ energy consumption information from low-dimensional to high-dimensional and theoretically prove that the proposed encryptor-based privacy preserving method will not affect the convergence property of the reinforcement learning algorithm. We verify the effectiveness of the proposed demand side management scheme with the real-world energy consumption data of Xi’an, Shaanxi, China. Simulation results show that the proposed method can effectively improve users’ satisfaction while reducing the bill payment compared with traditional reinforcement learning(RL) methods(i.e., deep Q learning(DQN), deep deterministic policy gradient(DDPG),QMIX and multi-agent deep deterministic policy gradient(MADDPG)). The results also demonstrate that the proposed privacy protection scheme can effectively protect users’ privacy while ensuring the performance of the algorithm.

Feature Preserving Parameterization for Quadrilateral Mesh Generation Based on Ricci Flow and Cross Field

We propose a newmethod to generate surface quadrilateralmesh by calculating a globally defined parameterization with feature constraints.In the field of quadrilateral generation with features,the cross field methods are wellknown because of their superior performance in feature preservation.The methods based on metrics are popular due to their sound theoretical basis,especially the Ricci flow algorithm.The cross field methods’major part,the Poisson equation,is challenging to solve in three dimensions directly.When it comes to cases with a large number of elements,the computational costs are expensive while the methods based on metrics are on the contrary.In addition,an appropriate initial value plays a positive role in the solution of the Poisson equation,and this initial value can be obtained from the Ricci flow algorithm.So we combine the methods based on metric with the cross field methods.We use the discrete dynamic Ricci flow algorithm to generate an initial value for the Poisson equation,which speeds up the solution of the equation and ensures the convergence of the computation.Numerical experiments show that our method is effective in generating a quadrilateral mesh for models with features,and the quality of the quadrilateral mesh is reliable.

A Privacy-Preserving Distributed Subgradient Algorithm for the Economic Dispatch Problem in Smart Grid

Dear Editor,This letter aims to establish a privacy-preserving distributed optimization algorithm by combining the consensus iteration by subgradients, which not only enables the privacy preservation of optimization but also guarantees the optimality of solutions with some bias bounds.In the setting of distributed optimization, a network of nodes, having their own objective functions depending on the global agents' state, would like to distributedly optimize the sum of all objective functions through the local agent-to-agent information change.

A Novel Approach to Design Distribution Preserving Framework for Big Data

In several fields like financial dealing,industry,business,medicine,et cetera,Big Data(BD)has been utilized extensively,which is nothing but a collection of a huge amount of data.However,it is highly complicated along with time-consuming to process a massive amount of data.Thus,to design the Distribution Preserving Framework for BD,a novel methodology has been proposed utilizing Manhattan Distance(MD)-centered Partition Around Medoid(MD–PAM)along with Conjugate Gradient Artificial Neural Network(CG-ANN),which undergoes various steps to reduce the complications of BD.Firstly,the data are processed in the pre-processing phase by mitigating the data repetition utilizing the map-reduce function;subsequently,the missing data are handled by substituting or by ignoring the missed values.After that,the data are transmuted into a normalized form.Next,to enhance the classification performance,the data’s dimensionalities are minimized by employing Gaussian Kernel(GK)-Fisher Discriminant Analysis(GK-FDA).Afterwards,the processed data is submitted to the partitioning phase after transmuting it into a structured format.In the partition phase,by utilizing the MD-PAM,the data are partitioned along with grouped into a cluster.Lastly,by employing CG-ANN,the data are classified in the classification phase so that the needed data can be effortlessly retrieved by the user.To analogize the outcomes of the CG-ANN with the prevailing methodologies,the NSL-KDD openly accessible datasets are utilized.The experiential outcomes displayed that an efficient result along with a reduced computation cost was shown by the proposed CG-ANN.The proposed work outperforms well in terms of accuracy,sensitivity and specificity than the existing systems.

Evolutionary privacy-preserving learning strategies for edge-based IoT data sharing schemes

The fast proliferation of edge devices for the Internet of Things(IoT)has led to massive volumes of data explosion.The generated data is collected and shared using edge-based IoT structures at a considerably high frequency.Thus,the data-sharing privacy exposure issue is increasingly intimidating when IoT devices make malicious requests for filching sensitive information from a cloud storage system through edge nodes.To address the identified issue,we present evolutionary privacy preservation learning strategies for an edge computing-based IoT data sharing scheme.In particular,we introduce evolutionary game theory and construct a payoff matrix to symbolize intercommunication between IoT devices and edge nodes,where IoT devices and edge nodes are two parties of the game.IoT devices may make malicious requests to achieve their goals of stealing privacy.Accordingly,edge nodes should deny malicious IoT device requests to prevent IoT data from being disclosed.They dynamically adjust their own strategies according to the opponent's strategy and finally maximize the payoffs.Built upon a developed application framework to illustrate the concrete data sharing architecture,a novel algorithm is proposed that can derive the optimal evolutionary learning strategy.Furthermore,we numerically simulate evolutionarily stable strategies,and the final results experimentally verify the correctness of the IoT data sharing privacy preservation scheme.Therefore,the proposed model can effectively defeat malicious invasion and protect sensitive information from leaking when IoT data is shared.

Smart contract token-based privacy-preserving access control system for industrial Internet of Things

Due to mobile Internet technology's rapid popularization,the Industrial Internet of Things(IIoT)can be seen everywhere in our daily lives.While IIoT brings us much convenience,a series of security and scalability issues related to permission operations rise to the surface during device communications.Hence,at present,a reliable and dynamic access control management system for IIoT is in urgent need.Up till now,numerous access control architectures have been proposed for IIoT.However,owing to centralized models and heterogeneous devices,security and scalability requirements still cannot be met.In this paper,we offer a smart contract token-based solution for decentralized access control in IIoT systems.Specifically,there are three smart contracts in our system,including the Token Issue Contract(TIC),User Register Contract(URC),and Manage Contract(MC).These three contracts collaboratively supervise and manage various events in IIoT environments.We also utilize the lightweight and post-quantum encryption algorithm-Nth-degree Truncated Polynomial Ring Units(NTRU)to preserve user privacy during the registration process.Subsequently,to evaluate our proposed architecture's performance,we build a prototype platform that connects to the local blockchain.Finally,experiment results show that our scheme has achieved secure and dynamic access control for the IIoT system compared with related research.

“Parenchyma transection-first”strategy is superior to“tunnel-first”strategy in robotic spleen-preserving distal pancreatectomy with conservation of splenic vessels

Background: Creating a tunnel between the pancreas and splenic vessels followed by pancreatic parenchyma transection(“tunnel-first” strategy) has long been used in spleen-preserving distal pancreatectomy(SPDP) with splenic vessel preservation(Kimura’s procedure). However, the operation space is limited in the tunnel, leading to the risks of bleeding and difficulties in suturing. We adopted the pancreatic “parenchyma transection-first” strategy to optimize Kimura’s procedure. Methods: The clinical data of consecutive patients who underwent robotic SPDP with Kimura’s procedure between January 2017 and September 2022 at our center were retrieved. The cohort was classified into a “parenchyma transection-first” strategy(P-F) group and a “tunnel-first” strategy(T-F) group and analyzed. Results: A total of 91 patients were enrolled in this cohort, with 49 in the T-F group and 42 in the P-F group. Compared with the T-F group, the P-F group had significantly shorter operative time(146.1 ± 39.2 min vs. 174.9 ± 46.6 min, P < 0.01) and lower estimated blood loss [40.0(20.0–55.0) m L vs. 50.0(20.0–100.0) m L, P = 0.03]. Failure of splenic vessel preservation occurred in 10.2% patients in the TF group and 2.4% in the P-F group( P = 0.14). The grade 3/4 complications were similar between the two groups( P = 0.57). No differences in postoperative pancreatic fistula, abdominal infection or hemorrhage were observed between the two groups. Conclusions: The pancreatic “parenchyma transection-first” strategy is safe and feasible compared with traditional “tunnel-first strategy” in SPDP with Kimura’s procedure.

Spleen-preserving distal pancreatectomy from multi-port to reducedport surgery approach

BACKGROUND Minimally invasive pancreatic surgery via the multi-port approach has become a primary surgical method for distal pancreatectomy(DP)due to its advantages of lower wound pain and superior cosmetic results.Some studies have applied reduced-port techniques for DP in an attempt to enhance cosmetic outcomes due to the minimally invasive effects.Numerous recent review studies have compared multi-port laparoscopic DP(LDP)and multi-port robotic DP(RDP);most of these studies concluded multi-port RDP is more beneficial than multi-port LDP for spleen preservation.However,there have been no comprehensive reviews of the value of reduced-port LDP and reduced-port RDP.AIM To search for and review the studies on spleen preservation and the clinical outcomes of minimally invasive DP that compared reduced-port DP surgery with multi-port DP surgery.METHODS The PubMed medical database was searched for articles published between 2013 and 2022.The search terms were implemented using the following Boolean search algorithm:(“distal pancreatectomy”OR“left pancreatectomy”OR“peripheral pancreatic resection”)AND(“reduced-port”OR“single-site”OR“single-port”OR“dual-incision”OR“single-incision”)AND(“spleen-preserving”OR“spleen preservation”OR“splenic preservation”).A literature review was conducted to identify studies that compared the perioperative outcomes of reduced-port LDP and reduced-port RDP.RESULTS Fifteen articles published in the period from 2013 to 2022 were retrieved using three groups of search terms.Two studies were added after manually searching the related papers.Finally,10 papers were selected after removing case reports(n=3),non-English language papers(n=1),technique papers(n=1),reviews(n=1),and animal studies(n=1).The common items were defined as items reported in more than five papers,and data on these common items were extracted from all papers.The ten studies included a total of 337 patients(females/males:231/106)who underwent DP.In total,166 patients(females/males,106/60)received multi-port LDP,126(females/males,90/36)received reduced-port LDP,and 45(females/males,35/10)received reduced-port RDP.CONCLUSION Reduced-port RDP leads to a lower intraoperative blood loss,a lower postoperative pancreatic fistula rate,and shorter hospital stay and follow-up duration,but has a lower spleen preservation rate.

Reliable and Privacy-Preserving Federated Learning with Anomalous Users

Recently,various privacy-preserving schemes have been proposed to resolve privacy issues in federated learning(FL).However,most of them ignore the fact that anomalous users holding low-quality data may reduce the accuracy of trained models.Although some existing works manage to solve this problem,they either lack privacy protection for users’sensitive information or introduce a two-cloud model that is difficult to find in reality.A reliable and privacy-preserving FL scheme named reliable and privacy-preserving federated learning(RPPFL)based on a single-cloud model is proposed.Specifically,inspired by the truth discovery technique,we design an approach to identify the user’s reliability and thereby decrease the impact of anomalous users.In addition,an additively homomorphic cryptosystem is utilized to provide comprehensive privacy preservation(user’s local gradient privacy and reliability privacy).We give rigorous theoretical analysis to show the security of RPPFL.Based on open datasets,we conduct extensive experiments to demonstrate that RPPEL compares favorably with existing works in terms of efficiency and accuracy.

Meta-analysis of subtotal stomach-preserving pancreaticoduodenectomy vs pylorus preserving pancreaticoduodenectomy

AIM: To investigate the differences in outcome following pylorus preserving pancreaticoduodenectomy(PPPD) and subtotal stomach-preserving pancreaticoduodenectomy(SSPPD).METHODS: Major databases including Pub Med(Medline), EMBASE and Science Citation Index Expanded and the Cochrane Central Register of Controlled Trials(CENTRAL) in The Cochrane Library were searched for comparative studies between patients with PPPD and SSPPD published between January 1978 and July 2014. Studies were selected based on specific inclusion and exclusion criteria. The primary outcome was delayed gastric emptying(DGE). Secondary outcomes included operation time, intraoperative blood loss, pancreatic fistula, postoperative hemorrhage, intraabdominal abscess, wound infection, time to starting liquid diet, time to starting solid diet, period of nasogastric intubation, reinsertion of nasogastric tube, mortality and hospital stay. The pooled odds ratios(OR) or weighted mean difference(WMD) with 95% confidence intervals(95%CI) were calculated using either a fixed-effects or random-effects model. RESULTS: Eight comparative studies recruiting 650 patients were analyzed, which include two RCTs, one non-randomized prospective and 5 retrospective trial designs. Patients undergoing SSPPD experienced significantly lower rates of DGE(OR = 2.75; 95%CI: 1.75-4.30, P < 0.00001) and a shorter period of nasogastric intubation(OR = 2.68; 95%CI: 0.77-4.58,P < 0.00001), with a tendency towards shorter time to liquid(WMD = 2.97, 95%CI:-0.46-7.83; P = 0.09) and solid diets(WMD = 3.69, 95%CI:-0.46-7.83; P = 0.08) as well as shorter inpatient stay(WMD = 3.92, 95%CI:-0.37-8.22; P = 0.07), although these latter three did not reach statistical significance. PPPD, however, was associated with less intraoperative blood loss than SSPPD [WMD =-217.70, 95%CI:-429.77-(-5.63); P = 0.04]. There were no differences in other parameters between the two approaches, including operative time(WMD =-5.30, 95%CI:-43.44-32.84; P = 0.79), pancreatic fistula(OR = 0.91; 95%CI: 0.56-1.49; P = 0.70), postoperative hemorrhage(OR = 0.51; 95%CI: 0.15-1.74; P = 0.29), intraabdominal abscess(OR = 1.05; 95%CI: 0.54-2.05; P = 0.89), wound infection(OR = 0.88; 95%CI: 0.39-1.97; P = 0.75), reinsertion of nasogastric tube(OR = 1.90; 95%CI: 0.91-3.97; P = 0.09) and mortality(OR = 0.31; 95%CI: 0.05-2.01; P = 0.22).CONCLUSION: SSPPD may improve intraoperative and short-term postoperative outcomes compared to PPPD, especially DGE. However, these findings need to be further ascertained by well-designed randomized controlled trials.