Progress of Targeted Therapy and Immunotherapy for Gastric Cancer

Gastric cancer (GC) is a common malignant tumor of the digestive tract in China. It is characterized by high morbidity, mortality, and proportion of patients in advanced stages. In the past years, chemotherapy was used as the main treatment for GC. Subsequently, targeted therapy with trastuzumab was approved to treat HER2-positive GC. However, the progress of drug development and clinical studies has been limited by the high heterogeneity of GC. In recent years, research on immunotherapy and new targets for therapeutic exploration in GC has made great strides. Herein, we provide a brief review of the progress of the research on targeted therapy and immunotherapy for GC

ViP-Mixer: A Convolutional Mixer for Video Prediction

Video prediction aims to predict future frames from a video's previous content. Existing methods mainly process video data where the time dimension mingles with the space and channel dimensions from three distinct angles: as a sequence of individual frames, as a 3D volume in spatiotemporal coordinates, or as a stacked image where frames are treated as separate channels. Most of them generally focus on one of these perspectives and may fail to fully exploit the relationships across different dimensions. To address this issue, this paper introduces a convolutional mixer for video prediction, termed ViP-Mixer, to model the spatiotemporal evolution in the latent space of an autoencoder. The ViP-Mixers are stacked sequentially and interleave feature mixing at three levels: frames, channels, and locations. Extensive experiments demonstrate that our proposed method achieves new state-of-the-art prediction performance on three benchmark video datasets covering both synthetic and real-world scenarios.Comment: Under revie

Deep Clustering Survival Machines with Interpretable Expert Distributions

Conventional survival analysis methods are typically ineffective to characterize heterogeneity in the population while such information can be used to assist predictive modeling. In this study, we propose a hybrid survival analysis method, referred to as deep clustering survival machines, that combines the discriminative and generative mechanisms. Similar to the mixture models, we assume that the timing information of survival data is generatively described by a mixture of certain numbers of parametric distributions, i.e., expert distributions. We learn weights of the expert distributions for individual instances according to their features discriminatively such that each instance's survival information can be characterized by a weighted combination of the learned constant expert distributions. This method also facilitates interpretable subgrouping/clustering of all instances according to their associated expert distributions. Extensive experiments on both real and synthetic datasets have demonstrated that the method is capable of obtaining promising clustering results and competitive time-to-event predicting performance

From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited

Graph-based semi-supervised learning (GSSL) has long been a hot research topic. Traditional methods are generally shallow learners, based on the cluster assumption. Recently, graph convolutional networks (GCNs) have become the predominant techniques for their promising performance. In this paper, we theoretically discuss the relationship between these two types of methods in a unified optimization framework. One of the most intriguing findings is that, unlike traditional ones, typical GCNs may not jointly consider the graph structure and label information at each layer. Motivated by this, we further propose three simple but powerful graph convolution methods. The first is a supervised method OGC which guides the graph convolution process with labels. The others are two unsupervised methods: GGC and its multi-scale version GGCM, both aiming to preserve the graph structure information during the convolution process. Finally, we conduct extensive experiments to show the effectiveness of our methods

Enumeration of spin-space groups: Towards a complete description of symmetries of magnetic orders

Symmetries of three-dimensional periodic scalar fields are described by 230 space groups (SGs). Symmetries of three-dimensional periodic (pseudo-) vector fields, however, are described by the spin-space groups (SSGs), which were initially used to describe the symmetries of magnetic orders. In SSGs, the real-space and spin degrees of freedom are unlocked in the sense that an operation could have different spacial and spin rotations. SSGs gives a complete symmetry description of magnetic structures, and have natural applications in the band theory of itinerary electrons in magnetically ordered systems with weak spin-orbit coupling.\textit{Altermagnetism}, a concept raised recently that belongs to the symmetry-compensated collinear magnetic orders but has non-relativistic spin splitting, is well described by SSGs. Due to the vast number and complicated group structures, SSGs have not yet been systematically enumerated. In this work, we exhaust SSGs based on the invariant subgroups of SGs, with spin operations constructed from three-dimensional (3D) real representations of the quotient groups for the invariant subgroups. For collinear and coplanar magnetic orders, the spin operations can be reduced into lower dimensional real representations. As the number of SSGs is infinite, we only consider SSGs that describe magnetic unit cells up to 12 times crystal unit cells. We obtain 157,289 non-coplanar, 24,788 coplanar-non-collinear, and 1,421 collinear SSGs. The enumerated SSGs are stored in an online database at \url{https://cmpdc.iphy.ac.cn/ssg} with a user-friendly interface. We also develop an algorithm to identify SSG for realistic materials and find SSGs for 1,626 magnetic materials. Our results serve as a solid starting point for further studies of symmetry and topology in magnetically ordered materials

The diagnostic role of resting myocardial blood flow in STEMI patients after revascularization

BackgroundThe value of semiquantitative resting myocardial perfusion imaging (MPI) in coronary artery disease (CAD) is limited. At present, quantitative MPI can be performed by a new cadmium zinc tellurium single-photon emission computed tomography (CZT-SPECT) scan. The quantitative index of resting myocardial blood flow (MBF) has received little attention, and its manifestations and clinical value in the presence of unstable coronary blood flow have not been clarified.PurposeIn patients with ST-segment elevation myocardial infarction (STEMI), whether resting MBF can provide additional value of blood flow than semi-quantitative resting MPI is not sure. We also explored the influencing factors of resting MBF.MethodsThis was a retrospective clinical study. We included 75 patients with STEMI in the subacute phase who underwent resting MPI and dynamic scans after reperfusion therapy. General patient information, STEMI-related data, MPI, gated MPI (G-MPI), and resting MBF data were collected and recorded. According to the clinically provided culprit vessels, the resting MBF was divided into ischemic MBF and non-ischemic MBF. The paired Wilcoxon signed-rank test was used for resting MBF. The receiver operating characteristic (ROC) curves were used to determine the optimal threshold for ischemia, and multiple linear regression analysis was used to analyze the influencing factors of resting MBF.ResultsThere was a statistically significant difference between the ischemic MBF and non-ischemic MBF [0.59 (0.47–0.72) vs. 0.76 (0.64–0.93), p < 0.0001]. The ROC curve analysis revealed that resting MBF could identify ischemia to a certain extent, with a cutoff value of 0.5975, area under the curve (AUC) = 0.666, sensitivity = 55.8%, and specificity = 68.7%. Male sex and summed rest score (SRS) were influencing factors for resting MBF.ConclusionTo a certain extent, resting MBF can suggest residual ischemia after reperfusion therapy in patients with STEMI. There was a negative correlation between male sex, SRS, and ischemic MBF. A lower resting MBF may be associated with more severe myocardial ischemia

Synthesis and tribological studies of nanoparticle additives for pyrolysis bio-oil formulated as a diesel fuel

AbstractThe tribological behaviour of pyrolysis bio-oil with a synthesized nano-Lanthanum oxide (La2O3) additive was evaluated using a point contact four ball tribometer under different frictional conditions. Results were compared against a micro (μ)-La2O3 additive and an un-additised bio-oil as a control. The results show that nano-La2O3 impregnated bio-oil had better tribological properties than the control groups. Under the operating loads, the optimum nanoparticle concentration within the bio-oil was investigated. At these levels, the combined action of adsorbed bio-oil films on the worn surfaces and the bearing effects of the nano-La2O3 minimized friction and wear. The tribo-mechanisms were ascribed to adhesive wear as a result of lubrication starvation under high loads, and abrasive wear at high rotational speeds as a result of combined deformation and aggregation of the nano-La2O3 particles

StoryAnalogy: Deriving Story-level Analogies from Large Language Models to Unlock Analogical Understanding

Analogy-making between narratives is crucial for human reasoning. In this paper, we evaluate the ability to identify and generate analogies by constructing a first-of-its-kind large-scale story-level analogy corpus, \textsc{StoryAnalogy}, which contains 24K story pairs from diverse domains with human annotations on two similarities from the extended Structure-Mapping Theory. We design a set of tests on \textsc{StoryAnalogy}, presenting the first evaluation of story-level analogy identification and generation. Interestingly, we find that the analogy identification tasks are incredibly difficult not only for sentence embedding models but also for the recent large language models (LLMs) such as ChatGPT and LLaMa. ChatGPT, for example, only achieved around 30% accuracy in multiple-choice questions (compared to over 85% accuracy for humans). Furthermore, we observe that the data in \textsc{StoryAnalogy} can improve the quality of analogy generation in LLMs, where a fine-tuned FlanT5-xxl model achieves comparable performance to zero-shot ChatGPT.Comment: Accepted by EMNLP 2023 main conferenc

Urease Breath Test and Stool Antigen Test Diagnose Helicobacter Pylori Infection in Patients with Bleeding Peptic Ulcer：a Meta-analysis

BackgroundIn patients with peptic ulcer bleeding (PUB) , intragastric blood and unavoidable medicine, including acid suppressive agent are suspected as limitary factors to diagnose Helicobacter pylori (H.pylori) infection correctly. The research conclusions about the accuracy of urease breath test (UBT) and stool antigen test (SAT) in patients with PUB are inconsistent.ObjectiveTo clarify the accuracy of UBT and SAT for H.pylori infection in PUB patients.MethodsPubMed, EMBase, the Cochrane Library, Web of Science, CNKI, Wanfang Data Knowledge Service Platform, China Biomedical Literature Database (CBM) were retrieved by computer for relevant articles related to the diagnosis of H.pylori infection by UBT and/or SAT published from the establishment of the database to March 31, 2021. The QUADAS-2 tool was used to evaluate the quality of the extracted literature. The bivariate mixed-effects regression model and network meta-analysis model (NMA) were used to synthesize diagnostic test data. Meta-regression and subgroup analysis were performed to explore the sources of heterogeneity.ResultsA total of 18 articles were included, with a total of 25 studies and 1 105 patients. Meta-analysis results showed that the combined sensitivity of UBT and SAT for diagnosing H.pylori infection in PUB patients were 0.90〔95%CI (0.79, 0.95) 〕 and 0.89〔95%CI (0.81, 0.94) 〕, the combined specificity were 0.91〔95%CI (0.86, 0.95) 〕 and 0.75〔95%CI (0.59, 0.87) 〕, the combined diagnostic odds ratio were 88.89〔95%CI (31.01, 254.82) 〕 and 24.35〔95%CI (13.76, 43.09) 〕, the combined positive likelihood ratio were 10.07〔95%CI (6.07, 16.71) 〕 and 3.60〔95%CI (2.11, 6.12) 〕, the combined negative likelihood ratio were 0.11〔95%CI (0.05, 0.24) 〕 and 0.15〔95%CI (0.09, 0.24) 〕, the area under the SROC curve were 0.93〔95%CI (0.90, 0.95) 〕 and 0.91〔95%CI (0.88, 0.93) 〕. Meta regression showed that the sampling time had an impact on the sensitivity heterogeneity of UBT and SAT, and the sampling time and the H.pylori infection criterion had an impact on the combined specificity heterogeneity of UBT. The Deek funnel chart indicated that there was no potential publication bias among the included researches (PUBT=0.53, PSAT=0.64) .ConclusionIn patients with PUB, UBT had a promising performance for the diagnosis of H.pylori infection. Because of the high number of false-positive results, SAT was not recommended for use in patients with PUB alone. At the same time, in order to avoid the impact of PPI on the detection results, it was recommended to perform the diagnostic tests as soon as possible under the condition of stable hemodynamics