A randomized phase 2 study of HLX22 plus trastuzumab biosimilar HLX02 and XELOX as first-line therapy for HER2-positive advanced gastric cancer.

BACKGROUND: Gastric cancer is the fifth most common cancer and the fourth most common cause of cancer death worldwide, yet the prognosis of advanced disease remains poor. METHODS: This was a randomized, double-blinded, phase 2 trial (ClinicalTrials.gov: NCT04908813). Patients with locally advanced/metastatic HER2-positive gastric/gastroesophageal junction cancer and no prior systemic antitumor therapy were randomized 1:1:1 to 25 mg/kg HLX22 (a novel anti-HER2 antibody) + HLX02 (trastuzumab biosimilar) + oxaliplatin and capecitabine (XELOX) (group A), 15 mg/kg HLX22 + HLX02 + XELOX (group B), or placebo + HLX02 + XELOX (group C) in 3-week cycles. Primary endpoints were progression-free survival (PFS) and objective response rate (ORR) assessed by independent radiological review committee (IRRC). FINDINGS: Between November 29, 2021, and June 6, 2022, 82 patients were screened; 53 were randomized to group A (n = 18), B (n = 17), and C (n = 18). With 14.3 months of median follow-up, IRRC-assessed median PFS was prolonged with the addition of HLX22 (A vs. C, 15.1 vs. 8.2 months, hazard ratio [HR] 0.5 [95% confidence interval (CI) 0.17-1.27]; B vs. C, not reached vs. 8.2 months, HR 0.1 [95% CI 0.04-0.52]). Confirmed ORR was comparable among groups (A vs. B vs. C, 77.8% vs. 82.4% vs. 88.9%). Treatment-related adverse events (TRAEs) were observed in 18 (100%), 16 (94.1%), and 17 (94.4%) patients, respectively. One (5.6%) patient in group C reported a grade 5 TRAE. CONCLUSIONS: Adding HLX22 to HLX02 and XELOX prolonged PFS and enhanced antitumor response in the first-line treatment of HER2-positive gastric cancer, with manageable safety. FUNDING: Shanghai Henlius Biotech, Inc.

Experience sharing on perioperative clinical management of gastric cancer patients based on the "China Robotic Gastric Cancer Surgery Guidelines".

BACKGROUND: With the popularization of robotic surgical systems in the field of surgery, robotic gastric cancer surgery has also been fully applied and promoted in China. The Chinese Guidelines for Robotic Gastric Cancer Surgery was published in the Chinese Journal of General Surgery in August 2021. METHODS: We have made a detailed interpretation of the process of robotic gastric cancer surgery regarding the indications, contraindications, perioperative preparation, surgical steps, complication, and postoperative management based on the recommendations of China's Guidelines for Robotic Gastric Cancer Surgery and supplemented by other surgical guidelines, consensus, and single-center experience. RESULTS: Twenty experiences of perioperative clinical management of robotic gastric cancer surgery were described in detail. CONCLUSION: We hope to bring some clinical reference values to the front-line clinicians in treating robotic gastric cancer surgery. TRIAL REGISTRATION: The guidelines were registered on the International Practice Guideline Registration Platform ( http://www.guidelines-registry.cn ) (registration number: IPGRP-2020CN199).

Winter wheat ear counting based on improved YOLOv7x and Kalman filter tracking algorithm with video streaming.

Accurate and real-time field wheat ear counting is of great significance for wheat yield prediction, genetic breeding and optimized planting management. In order to realize wheat ear detection and counting under the large-resolution Unmanned Aerial Vehicle (UAV) video, Space to depth (SPD) module was added to the deep learning model YOLOv7x. The Normalized Gaussian Wasserstein Distance (NWD) Loss function is designed to create a new detection model YOLOv7xSPD. The precision, recall, F1 score and AP of the model on the test set are 95.85%, 94.71%, 95.28%, and 94.99%, respectively. The AP value is 1.67% higher than that of YOLOv7x, and 10.41%, 39.32%, 2.96%, and 0.22% higher than that of Faster RCNN, SSD, YOLOv5s, and YOLOv7. YOLOv7xSPD is combined with the Kalman filter tracking and the Hungarian matching algorithm to establish a wheat ear counting model with the video flow, called YOLOv7xSPD Counter, which can realize real-time counting of wheat ears in the field. In the video with a resolution of 3840×2160, the detection frame rate of YOLOv7xSPD Counter is about 5.5FPS. The counting results are highly correlated with the ground truth number (R2 = 0.99), and can provide model basis for wheat yield prediction, genetic breeding and optimized planting management.

Short-term and long-term outcomes after robotic versus open hepatectomy in patients with large hepatocellular carcinoma: a multicenter study.

BACKGROUND: Robotic hepatectomy (RH) is currently widely accepted and it is associated with some benefits when compared to open hepatectomy (OH). However, whether such benefits can still be achieved for patients with large hepatocellular carcinoma (HCC) remain unclear. This study aimed to evaluate the short-term and long-term outcomes of patients undergoing RH or OH. METHODS: Perioperative and survival data from patients with large HCC who underwent RH or OH between January 2010 and December 2020 were collected from eight centres. Propensity score matching (PSM) was performed to minimise potential biases. RESULTS: Using predefined inclusion criteria, 797 patients who underwent OH and 309 patients who underwent RH were enroled in this study. After PSM, 280 patients in the robotic group had shorter operative time (median 181 vs. 201 min, P <0.001), lower estimated blood loss (median 200 vs. 400 ml, P <0.001), and shorter postoperative length of stay (median 6 vs. 9 days, P <0.001) than 465 patients in the open group. There were no significant differences between the two groups in overall survival and recurrence-free survival. Cox analysis showed AFP greater than 400 ng/ml, tumour size greater than 10 cm, and microvascular invasion were independent risk factors for overall survival and recurrence-free survival. After PSM, subgroup analysis showed that patients with a huge HCC (diameter >10 cm) who underwent RH had significantly lower estimated blood loss (median 200.0 vs. 500.0 min, P <0.001), and shorter length of stay (median 7 vs. 10 days, P <0.001) than those who underwent OH. CONCLUSION: Safety and feasibility of RH and OH for patients with large HCC were comparable. RH resulted in similar long-term survival outcomes as OH.

Outcomes of Robotic Versus Laparoscopic Pancreatoduodenectomy Following Learning Curves of Surgeons: A Multicenter Study on 2255 Patients.

OBJECTIVE: This study aimed to compare robotic pancreatoduodenectomy (RPD) with laparoscopic pancreatoduodenectomy (LPD) in operative and oncologic outcomes. BACKGROUND: Previous studies comparing RPD with LPD have only been carried out in small, single-center studies with variable quality. METHODS: Consecutive patients from nine centers in China who underwent RPD or LPD between 2015 and 2022 were included. A 1:1 propensity score matching (PSM) was used to minimize bias. RESULTS: Of the 2,255 patients, 1158 underwent RPD and 1097 underwent LPD. Following PSM, 1006 patients were enrolled in each group. The RPD group had significantly shorter operative time (270.0 vs. 305.0 minutes, P<0.001), lower intraoperative blood transfusion rate (5.9% vs. 12.0%, P<0.001), lower conversion rate (3.8% vs. 6.7%, P=0.004), and higher vascular reconstruction rate (7.9% vs. 5.6%, P=0.040) than the LPD group. There were no significant differences in estimated blood loss, postoperative length of stay, perioperative complications, and 90-day mortality. Patients who underwent vascular reconstruction had similar outcomes between the two groups, although they had significantly lower estimated blood loss (300.0 vs. 360.0 mL; P=0.021) in the RPD group. Subgroup analysis on pancreatic ductal adenocarcinoma (PDAC) found no significant differences between the two groups in median recurrence-free survival (14.3 vs. 15.3 mo, P=0.573) and overall survival (24.1 vs. 23.7 mo, P=0.710). CONCLUSIONS: In experienced hands, both RPD and LPD are safe and feasible procedures with similar surgical outcomes. RPD had the perioperative advantage over LPD especially in vascular reconstruction. For PDAC patients, RPD resulted in similar oncological and survival outcomes as LPD.

ECA-TFUnet: A U-shaped CNN-Transformer network with efficient channel attention for organ segmentation in anatomical sectional images of canines.

Automated organ segmentation in anatomical sectional images of canines is crucial for clinical applications and the study of sectional anatomy. The manual delineation of organ boundaries by experts is a time-consuming and laborious task. However, semi-automatic segmentation methods have shown low segmentation accuracy. Deep learning-based CNN models lack the ability to establish long-range dependencies, leading to limited segmentation performance. Although Transformer-based models excel at establishing long-range dependencies, they face a limitation in capturing local detail information. To address these challenges, we propose a novel ECA-TFUnet model for organ segmentation in anatomical sectional images of canines. ECA-TFUnet model is a U-shaped CNN-Transformer network with Efficient Channel Attention, which fully combines the strengths of the Unet network and Transformer block. Specifically, The U-Net network is excellent at capturing detailed local information. The Transformer block is equipped in the first skip connection layer of the Unet network to effectively learn the global dependencies of different regions, which improves the representation ability of the model. Additionally, the Efficient Channel Attention Block is introduced to the Unet network to focus on more important channel information, further improving the robustness of the model. Furthermore, the mixed loss strategy is incorporated to alleviate the problem of class imbalance. Experimental results showed that the ECA-TFUnet model yielded 92.63% IoU, outperforming 11 state-of-the-art methods. To comprehensively evaluate the model performance, we also conducted experiments on a public dataset, which achieved 87.93% IoU, still superior to 11 state-of-the-art methods. Finally, we explored the use of a transfer learning strategy to provide good initialization parameters for the ECA-TFUnet model. We demonstrated that the ECA-TFUnet model exhibits superior segmentation performance on anatomical sectional images of canines, which has the potential for application in medical clinical diagnosis.

Research on weed identification in soybean fields based on the lightweight segmentation model DCSAnet.

Weeds can compete with crops for sunlight, water, space and various nutrients, which can affect the growth of crops.In recent years, people have started to use self-driving agricultural equipment, robots, etc. for weeding work and use of drones for weed identification and spraying of weeds with herbicides, and the effectiveness of these mobile weeding devices is largely limited by the superiority of weed detection capability. To improve the weed detection capability of mobile weed control devices, this paper proposes a lightweight weed segmentation network model DCSAnet that can be better applied to mobile weed control devices. The whole network model uses an encoder-decoder structure and the DCA module as the main feature extraction module. The main body of the DCA module is based on the reverse residual structure of MobileNetV3, effectively combines asymmetric convolution and depthwise separable convolution, and uses a channel shuffle strategy to increase the randomness of feature extraction. In the decoding stage, feature fusion utilizes the high-dimensional feature map to guide the aggregation of low-dimensional feature maps to reduce feature loss during fusion and increase the accuracy of the model. To validate the performance of this network model on the weed segmentation task, we collected a soybean field weed dataset containing a large number of weeds and crops and used this dataset to conduct an experimental study of DCSAnet. The results showed that our proposed DCSAnet achieves an MIoU of 85.95% with a model parameter number of 0.57 M and the highest segmentation accuracy in comparison with other lightweight networks, which demonstrates the effectiveness of the model for the weed segmentation task.

A Pathway to Assess Genetic Variation of Wheat Germplasm by Multidimensional Traits with Digital Images.

In this paper, a new pathway was proposed to assess the germplasm genetic variation by multidimensional traits of wheat seeds generated from digital images. A machine vision platform was first established to reconstruct wheat germplasm 3D model from omnidirectional image sequences of wheat seeds. Then, multidimensional traits were conducted from the wheat germplasm 3D model, including seed length, width, thickness, surface area, volume, maximum projection area, roundness, and 2 new defined traits called cardioid-derived area and the index of adjustment (J index). To assess genetic variation of wheat germplasm, phenotypic coefficients of variation (PCVs), analysis of variance (ANOVA), clustering, and the defined genetic variation factor (GVF) were calculated using the extracted morphological traits of 15 wheat accessions comprising 13 offspring and 2 parents. The measurement accuracy of 3D reconstruction model is demonstrated by the correlation coefficient (R) and root mean square errors (RMSEs). Results of PCVs among all the traits show importance of multidimensional traits, as seed volume (22.4%), cardioid-derived area (16.97%), and maximum projection area (14.67%). ANOVA shows a highly significance difference among all accessions. The results of GVF innovatively reflect the connection between genotypic variance and phenotypic traits from parents to offspring. Our results confirmed that extracting multidimensional traits from digital images is a promising high-throughput and cost-efficient pathway that can be included as a valuable approach in genetic variation assessment, and it can provide useful information for genetic improvement, preservation, and evaluation of wheat germplasm.

Crop/Plant Modeling Supports Plant Breeding: II. Guidance of Functional Plant Phenotyping for Trait Discovery.

Observable morphological traits are widely employed in plant phenotyping for breeding use, which are often the external phenotypes driven by a chain of functional actions in plants. Identifying and phenotyping inherently functional traits for crop improvement toward high yields or adaptation to harsh environments remains a major challenge. Prediction of whole-plant performance in functional-structural plant models (FSPMs) is driven by plant growth algorithms based on organ scale wrapped up with micro-environments. In particular, the models are flexible for scaling down or up through specific functions at the organ nexus, allowing the prediction of crop system behaviors from the genome to the field. As such, by virtue of FSPMs, model parameters that determine organogenesis, development, biomass production, allocation, and morphogenesis from a molecular to the whole plant level can be profiled systematically and made readily available for phenotyping. FSPMs can provide rich functional traits representing biological regulatory mechanisms at various scales in a dynamic system, e.g., Rubisco carboxylation rate, mesophyll conductance, specific leaf nitrogen, radiation use efficiency, and source-sink ratio apart from morphological traits. High-throughput phenotyping such traits is also discussed, which provides an unprecedented opportunity to evolve FSPMs. This will accelerate the co-evolution of FSPMs and plant phenomics, and thus improving breeding efficiency. To expand the great promise of FSPMs in crop science, FSPMs still need more effort in multiscale, mechanistic, reproductive organ, and root system modeling. In summary, this study demonstrates that FSPMs are invaluable tools in guiding functional trait phenotyping at various scales and can thus provide abundant functional targets for phenotyping toward crop improvement.

The Importance of Using Realistic 3D Canopy Models to Calculate Light Interception in the Field.

Quantifying canopy light interception provides insight into the effects of plant spacing, canopy structure, and leaf orientation on radiation distribution. This is essential for increasing crop yield and improving product quality. Canopy light interception can be quantified using 3-dimensional (3D) plant models and optical simulations. However, virtual 3D canopy models (VCMs) have often been used to quantify canopy light interception because realistic 3D canopy models (RCMs) are difficult to obtain in the field. This study aims to compare the differences in light interception between VCMs and RCM. A realistic 3D maize canopy model (RCM) was reconstructed over a large area of the field using an advanced unmanned aerial vehicle cross-circling oblique (CCO) route and the structure from motion-multi-view stereo method. Three types of VCMs (VCM-1, VCM-4, and VCM-8) were then created by replicating 1, 4, and 8 individual realistic plants constructed by CCO in the center of the corresponding RCM. The daily light interception per unit area (DLI), as computed for the 3 VCMs, exhibited marked deviation from the RCM, as evinced by the relative root mean square error (rRMSE) values of 20.22%, 17.38%, and 15.48%, respectively. Although this difference decreased as the number of plants used to replicate the virtual canopy increased, rRMSE of DLI for VCM-8 and RCM still reached 15.48%. It was also found that the difference in light interception between RCMs and VCMs was substantially smaller in the early stage (48 days after sowing [DAS]) than in the late stage (70 DAS). This study highlights the importance of using RCM when calculating light interception in the field, especially in the later growth stages of plants.

Modulation of taxane binding to tubulin curved and straight conformations by systematic 3'N modification provides for improved microtubule binding, persistent cytotoxicity and in vivo potency.

The taxane class of microtubule stabilizers are some of the most effective and widely used chemotherapeutics. The anticancer activity of taxanes arises from their ability to induce tubulin assembly by selectively recognizing the curved (c-) conformation in unassembled tubulin as compared to the straight (s-) conformation in assembled tubulin. We first designed and synthesized a series of 3'N-modified taxanes bearing covalent groups. Instead of discovering covalent taxanes, we found a series of non-covalent taxanes 2, in which the 3'N side chain was found to be essential for cytotoxicity due to its role in locking tubulin in the s-conformation. A representative compound bearing an acrylamide moiety (2h) exhibited increased binding affinity to the unassembled tubulin c-conformation and less cytotoxicity than paclitaxel. Further exploration of chemical space around 2h afforded a new series 3, in which derivatives such as 3l bind more tightly to both the s- and c-conformations of tubulin compared to paclitaxel, leading to more efficient promotion of tubulin polymerization and a greater persistence of in vitro efficacy against breast cancer cells after drug washout. Although 3l also had improved in vivo potency as compared to paclitaxel, it was also associated with increased systemic toxicity that required localized, intratumoral injection to observe potent and prolonged antitumor efficacy.

Structural insight into the stabilization of microtubules by taxanes.

Paclitaxel (Taxol) is a taxane and a chemotherapeutic drug that stabilizes microtubules. While the interaction of paclitaxel with microtubules is well described, the lack of high-resolution structural information on a tubulin-taxane complex precludes a comprehensive description of the binding determinants that affect its mechanism of action. Here, we solved the crystal structure of baccatin III the core moiety of paclitaxel-tubulin complex at 1.9 Å resolution. Based on this information, we engineered taxanes with modified C13 side chains, solved their crystal structures in complex with tubulin, and analyzed their effects on microtubules (X-ray fiber diffraction), along with those of paclitaxel, docetaxel, and baccatin III. Further comparison of high-resolution structures and microtubules' diffractions with the apo forms and molecular dynamics approaches allowed us to understand the consequences of taxane binding to tubulin in solution and under assembled conditions. The results sheds light on three main mechanistic questions: (1) taxanes bind better to microtubules than to tubulin because tubulin assembly is linked to a ßM-loopconformational reorganization (otherwise occludes the access to the taxane site) and, bulky C13 side chains preferentially recognize the assembled conformational state; (2) the occupancy of the taxane site has no influence on the straightness of tubulin protofilaments and; (3) longitudinal expansion of the microtubule lattices arises from the accommodation of the taxane core within the site, a process that is no related to the microtubule stabilization (baccatin III is biochemically inactive). In conclusion, our combined experimental and computational approach allowed us to describe the tubulin-taxane interaction in atomic detail and assess the structural determinants for binding.

Quantification of the three-dimensional root system architecture using an automated rotating imaging system.

BACKGROUND: Crop breeding based on root system architecture (RSA) optimization is an essential factor for improving crop production in developing countries. Identification, evaluation, and selection of root traits of soil-grown crops require innovations that enable high-throughput and accurate quantification of three-dimensional (3D) RSA of crops over developmental time. RESULTS: We proposed an automated imaging system and 3D imaging data processing pipeline to quantify the 3D RSA of soil-grown individual plants across seedlings to the mature stage. A multi-view automated imaging system composed of a rotary table and an imaging arm with 12 cameras mounted with a combination of fan-shaped and vertical distribution was developed to obtain 3D image data of roots grown on a customized root support mesh. A 3D imaging data processing pipeline was developed to quantify the 3D RSA based on the point cloud generated from multi-view images. The global architecture of root systems can be quantified automatically. Detailed analysis of the reconstructed 3D root model also allowed us to investigate the Spatio-temporal distribution of roots. A method combining horizontal slicing and iterative erosion and dilation was developed to automatically segment different root types, and identify local root traits (e.g., length, diameter of the main root, and length, diameter, initial angle, and the number of nodal roots or lateral roots). One maize (Zea mays L.) cultivar and two rapeseed (Brassica napus L.) cultivars at different growth stages were selected to test the performance of the automated imaging system and 3D imaging data processing pipeline. CONCLUSIONS: The results demonstrated the capabilities of the proposed imaging and analytical system for high-throughput phenotyping of root traits for both monocotyledons and dicotyledons across growth stages. The proposed system offers a potential tool to further explore the 3D RSA for improving root traits and agronomic qualities of crops.

Perioperative and Oncological Outcomes of Robotic Versus Open Pancreaticoduodenectomy in Low-Risk Surgical Candidates: A Multicenter Propensity Score-Matched Study.

OBJECTIVES: This study aimed to perform a multicenter comparison between robotic pancreaticoduodenectomy (RPD) and open pancreaticoduodenectomy (OPD). BACKGROUND: Previous comparisons of RPD versus OPD have only been carried out in small, single-center studies of variable quality. METHODS: Consecutive patients who underwent RPD (n = 1032) or OPD (n = 1154) at 7 centers in China between July 2012 and July 2020 were included. A 1:1 propensity score matching (PSM) was performed. RESULTS: After PSM, 982 patients in each group were enrolled. The RPD group had significantly lower estimated blood loss (EBL) (190.0 vs 260.0 mL; P < 0.001), and a shorter postoperative 1length of hospital stay (LOS) (12.0 (9.0-16.0) days vs 14.5 (11.0-19.0) days; P < 0.001) than the OPD group. There were no significant differences in operative time, major morbidity including clinically relevant postoperative pancreatic fistula (CR-POPF), bile leakage, delayed gastric emptying, postoperative pancreatectomy hemorrhage (PPH), reoperation, readmission or 90-day mortality rates. Multivariable analysis showed R0 resection, CR-POPF, PPH and reoperation to be independent risk factors for 90-day mortality. Subgroup analysis on patients with pancreatic ductal adenocarcinoma (PDAC) (n = 326 in each subgroup) showed RPD had advantages over OPD in EBL and postoperative LOS. There were no significant differences in median disease-free survival (15.2 vs 14.3 months, P = 0.94) or median overall survival (24.2 vs 24.1 months, P = 0.88) between the 2 subgroups. CONCLUSIONS: RPD was comparable to OPD in feasibility and safety. For patients with PDAC, RPD resulted in similar oncologic and survival outcomes as OPD.

UAV-based multi-sensor data fusion and machine learning algorithm for yield prediction in wheat.

Early prediction of grain yield helps scientists to make better breeding decisions for wheat. Use of machine learning (ML) methods for fusion of unmanned aerial vehicle (UAV)-based multi-sensor data can improve the prediction accuracy of crop yield. For this, five ML algorithms including Cubist, support vector machine (SVM), deep neural network (DNN), ridge regression (RR) and random forest (RF) were used for multi-sensor data fusion and ensemble learning for grain yield prediction in wheat. A set of thirty wheat cultivars and breeding lines were grown under three irrigation treatments i.e., light, moderate and high irrigation treatments to evaluate the yield prediction capabilities of a low-cost multi-sensor (RGB, multi-spectral and thermal infrared) UAV platform. Multi-sensor data fusion-based yield prediction showed higher accuracy compared to individual-sensor data in each ML model. The coefficient of determination (R 2) values for Cubist, SVM, DNN and RR models regarding grain yield prediction were observed from 0.527 to 0.670. Moreover, the results of ensemble learning through integrating the above models illustrated further increase in accuracy. The predictions of ensemble learning showed high R 2 values up to 0.692, which was higher as compared to individual ML models across the multi-sensor data. Root mean square error (RMSE), residual prediction deviation (RPD) and ratio of prediction performance to inter-quartile range (RPIQ) were calculated to be 0.916 t ha-1, 1.771 and 2.602, respectively. The results proved that low altitude UAV-based multi-sensor data can be used for early grain yield prediction using data fusion and an ensemble learning framework with high accuracy. This high-throughput phenotyping approach is valuable for improving the efficiency of selection in large breeding activities. Supplementary Information: The online version contains supplementary material available at 10.1007/s11119-022-09938-8.

Ginkgolide B inhibits the malignant biological behaviors of gastric cancer HGC-27 cells by blocking the PI3K/Akt/mTOR signaling pathway / 中国肿瘤生物治疗杂志

@#[摘 要] 目的：探讨银杏内酯B（GKB）是否通过阻抑PI3K/Akt/mTOR信号通路抑制胃癌HGC-27细胞的增殖、凋亡、迁移及侵袭。方法：将HGC-27细胞分为对照、GKB低剂量（100 mg/L）、GKB高剂量（200 mg/L）、GKB高剂量（200 mg/L）+740Y-P（PI3K激活剂）、Ly294002（PI3K抑制剂）组。采用MTT、Edu、FCM、Transwell实验分别检测各组细胞的增殖、凋亡、迁移和侵袭能力，qPCR和WB法分别检测各组细胞中PI3K mRNA、Akt mRNA、mTOR mRNA和Ki-67、caspase-3、p-PI3K/PI3K、p-Akt/Akt、p-mTOR/mTOR蛋白的表达。构建胃癌HGC-27细胞裸鼠移植瘤模型，观察GKB对移植瘤生长的影响，WB法检测移植瘤组织中Ki-67、caspase-3、p-PI3K/PI3K、p-Akt/Akt、p-mTOR/mTOR蛋白的表达。结果：体外实验结果表明，与对照组相比，GKB低剂量组、GKB高剂量组、Ly294002组HGC-27细胞的增殖活力及细胞增殖率、迁移和侵袭细胞数，PI3K、Akt、mTOR mRNA表达，以及Ki-67、p-PI3K/PI3K、p-Akt/Akt、p-mTOR/mTOR蛋白表达均显著降低（均P<0.05）；细胞凋亡率、caspase-3蛋白表达均显著升高（均P<0.05）；740Y-P可部分逆转GKB对HGC-27细胞的抑制作用（均P<0.05）。荷瘤裸鼠实验结果显示，GKB可显著抑制HGC-27细胞裸鼠移植瘤的生长（P<0.05），且可下调PI3K/Akt/mTOR通路相关蛋白的表达。结论：GKB可通过阻抑PI3K/Akt/mTOR信号通路而抑制胃癌HGC-27细胞增殖、迁移与侵袭并促进其凋亡。

The role of IL-35 and IL-37 in breast cancer - potential therapeutic targets for precision medicine.

Breast cancer is still a major concern due to its relatively poor prognosis in women, although there are many approaches being developed for the management of breast cancer. Extensive studies demonstrate that the development of breast cancer is determined by pro versus anti tumorigenesis factors, which are closely related to host immunity. IL-35 and IL-37, anti-inflammatory cytokines, play an important role in the maintenance of immune homeostasis. The current review focuses on the correlation between clinical presentations and the expression of IL-35 and IL-37, as well as the potential underlying mechanism during the development of breast cancer in vitro and in vivo. IL-35 is inversely correlated the differentiation and prognosis in breast cancer patients; whereas IL-37 shows dual roles during the development of breast cancer, and may be breast cancer stage dependent. Such information might be useful for both basic scientists and medical practitioners in the management of breast cancer patients.