Hyperthermic intraperitoneal chemotherapy for gastric cancer with peritoneal metastasis:A multicenter propensity scorematched cohort study

Objective:Systemic chemotherapy has limited efficacy in the treatment of peritoneal metastasis(PM)in gastric cancer(GC).Hyperthermic intraperitoneal chemotherapy(HIPEC)combined with complete cytoreductive surgery(CRS)has shown promising outcomes but remains controversial.The present study aimed to evaluate the safety and efficacy of HIPEC without CRS in GC patients with PM.Methods:This retrospective propensity score-matched multicenter cohort study included GC patients with PM treated with either chemotherapy alone(Cx group)or with HIPEC combined with chemotherapy(HIPEC-Cx group)in four Chinese high-volume gastric medical centers between 2010 and 2017.The primary outcomes were median survival time(MST)and 3-year overall survival(OS).Propensity score matching was performed to compensate for controlling potential confounding effects and selection bias.Results:Of 663 eligible patients,498 were matched.The MST in the Cx and HIPEC-Cx groups was 10.8 and 15.9 months,respectively[hazard ratio(HR)=0.71,95%confidence interval(95%CI),0.58-0.88;P=0.002].The 3-year OS rate was 10.1%(95%CI,5.4%-14.8%)and 18.4%(95%CI,12.3%-24.5%)in the Cx and HIPEC-Cx groups,respectively(P=0.017).The complication rates were comparable.The time to first flatus and length of hospital stay for patients undergoing HIPEC combined with chemotherapy was longer than that of chemotherapy alone(4.6±2.4 d vs.2.7±1.8 d,P<0.001;14.2±5.8 d vs.11.4±7.7 d,P<0.001),respectively.The median follow-up period was 33.2 months.Conclusions:Compared with standard systemic chemotherapy,HIPEC combined with chemotherapy revealed a statistically significant survival benefit for GC patients with PM,without compromising patient safety.

Raltitrexed as a synergistic hyperthermia chemotherapy drug screened in patient-derived colorectal cancer organoids

Objective:Organoids have recently been used as in vitro models to screen chemotherapy drugs in combination with hyperthermia treatment in colorectal cancer.Our research aimed to establish a library of patient-derived colorectal cancer organoids to evaluate synergism between chemotherapy drugs and hyperthermia;validate an index of the hyperthermia chemotherapy sensitization enhancement ratio(HCSER)to identify the chemotherapeutics most enhanced by hyperthermia;and recommend chemotherapy drugs for hyperthermic intraperitoneal treatment.Methods:Organoids were grown from cells extracted from colorectal cancer patient samples or colorectal cancer cell lines.Cells from both sources were encapsulated in 3 D Matrigel droplets,which were formulated in microfluidics and phase-transferred into identical cell-laden Matrigel microspheres.The microspheres were seeded in 96-well plates,with each well containing a single microsphere that developed into an organoid after 7 days.The organoids were used to evaluate the efficacy of chemotherapy drugs at both 37℃ as a control and 43℃ for 90 min to examine hyperthermia synergism.Cell viability was counted with 10%CCK8.Results:We successfully established a library of colorectal cancer organoids from 22 patient parental tumors.We examined the hyperthermia synergism of 7 commonly used hyperthermic intraperitoneal chemotherapy drugs.In 11 of the 22 patient organoids,raltitrexed had significant hyperthermia synergism,which was indexed as the highest HCSER score within each patient group.Conclusions:Our results primarily demonstrated the use of patient-derived colorectal cancer organoids as in vitro models to evaluate hyperthermia synergistic chemotherapeutics.We found that hyperthermia enhanced the effect of raltitrexed the most among the common anti-colorectal cancer drugs.

A universal Wi-Fi fingerprint localization method based on machine learning and sample differences

Wi-Fi technology has become an important candidate for localization due to its low cost and no need of additional installation.The Wi-Fi fingerprint-based positioning is widely used because of its ready hardware and acceptable accuracy,especially with the current fingerprint localization algorithms based on Machine Learning(ML)and Deep Learning(DL).However,there exists two challenges.Firstly,the traditional ML methods train a specific classification model for each scene;therefore,it is hard to deploy and manage it on the cloud.Secondly,it is difficult to train an effective multi-classification model by using a small number of fingerprint samples.To solve these two problems,a novel binary classification model based on the samples’differences is proposed in this paper.We divide the raw fingerprint pairs into positive and negative samples based on each pair’s distance.New relative features(e.g.,sort features)are introduced to replace the traditional pair features which use the Media Access Control(MAC)address and Received Signal Strength(RSS).Finally,the boosting algorithm is used to train the classification model.The UJIndoorLoc dataset including the data from three different buildings is used to evaluate our proposed method.The preliminary results show that the floor success detection rate of the proposed method can reach 99.54%(eXtreme Gradient Boosting,XGBoost)and 99.22%(Gradient Boosting Decision Tree,GBDT),and the positioning error can reach 3.460 m(XGBoost)and 4.022 m(GBDT).Another important advantage of the proposed algorithm is that the model trained by one building’s data can be well applied to another building,which shows strong generalizable ability.