Prognostic value and nomograms of proximal margin distance in gastric cancer with radical distal gastrectomy

Objective:The proximal margin(PM)distance for distal gastrectomy(DG)of gastric cancer(GC)remains controversial.This study investigated the prognostic value of PM distance for survival outcomes,and aimed to combine clinicopathologic variables associated with survival outcomes after DG with different PM distance for GC into a prediction nomogram.Methods:Patients who underwent radical DG from June 2004 to June 2014 at Department of General Surgery,Nanfang Hospital,Southern Medical University were included.The first endpoints of the prognostic value of PM distance(assessed in 0.5 cm increments)for disease-free survival(DFS)and overall survival(OS)were assessed.Multivariate analysis by Cox proportional hazards regression was performed using the training set,and the nomogram was constructed,patients were chronologically assigned to the training set for dates from June 1,2004 to January 30,2012(n=493)and to the validation set from February 1,2012 to June 30,2014(n=211).Results:Among 704 patients with p TNM stage I,p TNM stage II,T1-2,T3-4,N0,differentiated type,tumor size≤5.0 cm,a PM of(2.1-5.0)cm vs.PM≤2.0 cm showed a statistically significant difference in DFS and OS,while a PM>5.0 cm was not associated with any further improvement in DFS and OS vs.a PM of 2.1-5.0 cm.In patients with p TNM stage III,N1,N2-3,undifferentiated type,tumor size>5.0 cm,the PM distance was not significantly correlated with DFS and OS between patients with a PM of(2.1-5.0)cm and a PM≤2 cm,or between patients with a PM>5.0 cm and a PM of(2.1-5.0)cm,so there were no significant differences across the three PM groups.In the training set,the C-indexes of DFS and OS,were 0.721 and 0.735,respectively,and in the validation set,the C-indexes of DFS and OS,were 0.752 and 0.751,respectively.Conclusions:It is necessary to obtain not less than 2.0 cm of PM distance in early-stage disease,while PM distance was not associated with long-term survival in later and more aggressive stages of disease because more advanced GC is a systemic disease.Different types of patients should be considered for removal of an individualized PM distance intra-operatively.We developed a universally applicable prediction model for accurately determining the 1-year,3-year and 5-year DFS and OS of GC patients according to their preoperative clinicopathologic characteristics and PM distance.

On applying stochastic network calculus

Performance evaluation plays a crucial role in the design of network systems. Many theoretical tools, including queueing theory, effective bandwidth and network calculus, have been proposed to provide modeling mechanisms and resuits. While these theories have been widely adopted for performance evaluation, each has its own limitation. With that network systems have become more complex and harder to describe, where a lot of uncertainty and randomness exists, to make performance evaluation of such systems tractable, some compromise is often necessary and helpful. Stochas- tic network calculus （SNC） is such a theoretical tool. While SNC is a relatively new theory, it is gaining increasing interest and popularity. In the current SNC literature, much attention has been paid on the development of the theory itself. In addition, researchers have also started applying SNC to performance analysis of various types of systems in recent years. The aim of this paper is to provide a tutorial on the new theoretical tool. Specifically, various SNC traffic models and SNC server models are reviewed. The focus is on how to apply SNC, for which, four critical steps are formalized and discussed. In addition, a list of SNC application topics/areas, where there may exist huge research potential, is presented.

Revealing the Distribution of Aggregation-Induced Emission Nanoparticles via Dual-Modality Imaging with Fluorescence and Mass Spectrometry

Aggregation-induced emission nanoparticles(AIE NPs)are widely used in the biomedical field.However,understanding the biological process of AIE NPs via fluorescence imaging is challenging because of the strong background and poor penetration depth.Herein,we present a novel dual-modality imaging strategy that combines fluorescence imaging and label-free laser desorption/ionization mass spectrometry imaging(LDI MSI)to map and quantify the biodistribution of AIE NPs(TPAFN-F127 NPs)by monitoring the intrinsic photoluminescence and mass spectrometry signal of the AIE molecule.We discovered that TPAFN-F127 NPs were predominantly distributed in the liver and spleen,and most gradually excreted from the body after 5 days.The accumulation and retention of TPAFN-F127 NPs in tumor sites were also confirmed in a tumor-bearing mouse model.As a proof of concept,the suborgan distribution of TPAFN-F127 NPs in the spleen was visualized by LDI MSI,and the results revealed that TPAFN-F127 NPs were mainly distributed in the red pulp of the spleen with extremely high concentrations within the marginal zone.The in vivo toxicity test demonstrated that TPAFN-F127 NPs are nontoxic for a long-term exposure.This dual-modality imaging strategy provides some insights into the fine distribution of AIE NPs and might also be extended to other polymeric NPs to evaluate their distribution and drug release behaviors in vivo.

人工智能在临床肿瘤学中的研究现状及展望

There has been an explosion of research activities and clinical investigations on the use of artificial intelligence(AI)in oncology during the past decade.This is driven primarily by technological advances in computing power and sophisticated AI algorithms,as well as the availability of a large amount of digitized data generated during routine cancer care.