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BackgroundImmunotherapy offers new hope for improved survival in patients with advanced gastric cancer. Although large randomized controlled trials (RCTs) have been conducted to explore the efficacy and safety of first-line immunotherapy plus chemotherapy versus chemotherapy alone for advanced gastric cancer, the results are not completely consistent. And the strict inclusion criteria of RCTs lead to limited extrapolation. Therefore, it is of great significance to continue to conduct real-world studies comparing the clinical efficacy and safety of immunotherapy combined with chemotherapy versus chemotherapy alone in advanced gastric cancer.MethodsThis retrospective study included patients with HER-2 negative, unresectable advanced or recurrent gastric/gastroesophageal junction cancer (GC/GEJC) who received first-line immune checkpoint inhibitors (ICIs) in combination with chemotherapy or chemotherapy alone between January 1, 2018 to May 31, 2023. Progression-free survival (PFS), overall survival (OS), overall response rate (ORR), disease control rate (DCR) and adverse events (AEs) were compared between two groups.ResultsA total of 210 patients were enrolled in the combination treatment group (n=100) and chemotherapy alone group (n=110). After 12 months of follow-up, median PFS (mPFS) was 270 days (95%CI 177.510-362.490) in the chemotherapy alone group and 357 days (95%CI 250.103-463.897) in the combination treatment group (P0.05). There was no statistically significant difference in ORR between two groups (P=0.050). The DCR was 14.5% in the chemotherapy alone group and 38% in the combination treatment group (P

Background and ObjectiveEnhancing therapy choices for varying stages of esophageal cancer and improving patient survival depend on timely and precise diagnosis. Blood metabolites may play a role in either causing or preventing esophageal cancer, but further research is needed to determine whether blood metabolites constitute a genetic risk factor for the disease. In order to tackle these problems, we evaluated the causal association between esophageal cancer and 486 blood metabolites that functioned as genetic proxies using a two-sample Mendelian randomization (MR) study.MethodsWe utilized two-sample MR analyses to evaluate the causal links between blood metabolites and esophageal cancer. For the exposure, we used a genome-wide association study (GWAS) of 486 metabolites, and a GWAS study on esophageal cancer from Sakaue et al. was used for preliminary analyses. Causal analyses employed randomized inverse variance weighted (IVW) as the main method, supplemented by MR-Egger and weighted median (WM) analyses. Sensitivity analyses included the MR-Egger intercept test, Cochran Q test, MR-PRESSO, and leave-one-out analysis. Additionally, independent esophageal cancer GWAS data were utilized for replication and meta-analysis. FDR correction was applied to discern features with causal relationships. For conclusive metabolite identification, we conducted Steiger tests, linkage disequilibrium score regression, and colocalization analyses. Moreover, we utilized the program MetaboAnalyst 5.0 to analyze metabolic pathways.ResultsThis study found an important association between esophageal cancer and three metabolites: 1-linoleoylglycerophosphoethanolamine* [odds ratio (OR) = 3.21, 95% confidence interval (CI): 1.42–7.26, p < 0.01], pyroglutamine* (OR = 1.92, 95% CI: 1.17–3.17, p < 0.01), and laurate (12:0) (OR = 3.06, 95% CI: 1.38–6.78, p < 0.01).ConclusionThis study establishes a causal link between three defined blood metabolites and esophageal cancer, offering fresh insights into its pathogenesis.

The rapid dissemination of unverified information through social platforms like Twitter poses considerable dangers to societal stability. Identifying real versus fake claims is challenging, and previous work on rumor detection methods often fails to effectively capture propagation structure features. These methods also often overlook the presence of comments irrelevant to the discussion topic of the source post. To address this, we introduce a novel approach: the Structure-Aware Multilevel Graph Attention Network (SAMGAT) for rumor classification. SAMGAT employs a dynamic attention mechanism that blends GATv2 and dot-product attention to capture the contextual relationships between posts, allowing for varying attention scores based on the stance of the central node. The model incorporates a structure-aware attention mechanism that learns attention weights that can indicate the existence of edges, effectively reflecting the propagation structure of rumors. Moreover, SAMGAT incorporates a top-k attention filtering mechanism to select the most relevant neighboring nodes, enhancing its ability to focus on the key structural features of rumor propagation. Furthermore, SAMGAT includes a claim-guided attention pooling mechanism with a thresholding step to focus on the most informative posts when constructing the event representation. Experimental results on benchmark datasets demonstrate that SAMGAT outperforms state-of-the-art methods in identifying rumors and improves the effectiveness of early rumor detection.

Pancreatic cancer is one of the deadliest malignant tumors, which is a serious threat to human health and life, and it is expected that pancreatic cancer may be the second leading cause of cancer death in developed countries by 2030. Claudin18.2 is a tight junction protein expressed in normal gastric mucosal tissues, which is involved in the formation of tight junctions between cells and affects the permeability of paracellular cells. Claudin18.2 is highly expressed in pancreatic cancer and is associated with the initiation, progression, metastasis and prognosis of cancer, so it is considered a potential therapeutic target. Up to now, a number of clinical trials for Claudin18.2 are underway, including solid tumors such as pancreatic cancers and gastric cancers, and the results of these trials have not yet been officially announced. This manuscript briefly describes the Claudia protein, the dual roles of Cluadin18 in cancers, and summarizes the ongoing clinical trials targeting Claudin18.2 with a view to integrating the research progress of Claudin18.2 targeted therapy. In addition, this manuscript introduces the clinical research progress of Claudin18.2 positive pancreatic cancer, including monoclonal antibodies, bispecific antibodies, antibody-drug conjugates, CAR-T cell therapy, and hope to provide feasible ideas for the clinical treatment of Claudin18.2 positive pancreatic cancer.

Leptomeningeal metastasis (LM) is a significant complication that advances fast and has a poor prognosis for patients with advanced non-small cell lung cancer (NSCLC) who have epidermal growth factor receptor (EGFR) mutations. Current therapies for LM are inconsistent and ineffective, and established techniques such as radiation, chemotherapy, and surgery continue to fall short of potential outcomes. Nonetheless, EGFR tyrosine kinase inhibitors (TKIs) exhibit potent anti-tumor activity and hold considerable promise for NSCLC patients with EGFR mutations. Thus, assessing EGFR-TKIs effectiveness in treating these central nervous system (CNS) problems is crucial. This review integrates current literature on the intracranial efficacy of EGFR-TKIs to explore the varying impacts of approved EGFR-TKIs in LM patients and the therapeutic possibilities presented by other EGFR-TKIs in development. To delineate the optimal clinical treatment strategy, further exploration is needed regarding the optimal sequencing of EGFR-TKIs and the selection of alternative therapy options following initial treatment failure with EGFR-TKIs.

Image super-resolution (SR) significantly improves the quality of low-resolution images, and is widely used for image reconstruction in various fields. Although the existing SR methods have achieved distinguished results in objective metrics, most methods focus on real-world images and employ large and complex network structures, which are inefficient for medical diagnosis scenarios. To address the aforementioned issues, the distinction between pathology images and real-world images was investigated, and an SR Network with a wider and deeper attention module called Channel Attention Retention is proposed to obtain SR images with enhanced high-frequency features. This network captures contextual information within and across blocks via residual skips and balances the performance and efficiency by controlling the number of blocks. Meanwhile, a new linear loss was introduced to optimize the network. To evaluate the work and compare multiple SR works, a benchmark dataset bcSR was created, which forces a model training on wider and more critical regions. The results show that the proposed model outperforms state-of-the-art methods in both performance and efficiency, and the newly created dataset significantly improves the reconstruction quality of all compared models. Moreover, image classification experiments demonstrate that the suggested network improves the performance of downstream tasks in medical diagnosis scenarios. The proposed network and dataset provide effective priors for the SR task of pathology images, which significantly improves the diagnosis of relevant medical staff. The source code and the dataset are available on https://github.com/MoyangSensei/CARN-Pytorch.

Session-based recommendation (SBR) aims to recommend the next items based on anonymous behavior sequences over a short period of time. Compared with other recommendation paradigms, the information available in SBR is very limited. Therefore, capturing the item relations across sessions is crucial for SBR. Recently, many methods have been proposed to learn article transformation relationships over all sessions. Despite their success, these methods may enlarge the impact of noisy interactions and ignore the complex high-order relationship between non-adjacent items. In this study, we propose a self-supervised global context graph neural network (SGC-GNN) to model high-order transition relations between items over all sessions by using virtual context vectors, each of which connects to all items in a given session and enables to collect and propagation information beyond adjacent items. Moreover, to improve the robustness of the proposed model, we devise a contrastive self-supervised learning (SSL) module as an auxiliary task to jointly learn more robust representations of the items in sessions and train the model to fulfill the SBR task. Experimental results on three benchmark datasets demonstrate the superiority of our model over the state-of-the-art (SOTA) methods and validate the effectiveness of context vectors and the self-supervised module.

As a well-established multidrug combinations schema, traditional Chinese medicine (herbal prescription) has been used for thousands of years in real-world clinical settings. This paper uses a complex network approach to investigate the regularities underlying multidrug combinations in herbal prescriptions. Using five collected large-scale real-world clinical herbal prescription datasets, we construct five weighted herbal combination networks with herb as nodes and herbal combinational use in herbal prescription as links. We found that the weight distribution of herbal combinations displays a clear power law, which means that most herb pairs were used in low frequency and some herb pairs were used in very high frequency. Furthermore, we found that it displays a clear linear negative correlation between the clustering coefficients and the degree of nodes in the herbal combination network (HCNet). This indicates that hierarchical properties exist in the HCNet. Finally, we investigate the molecular network interaction patterns between herb related target modules (i.e., subnetworks) in herbal prescriptions using a network-based approach and further explore the correlation between the distribution of herb combinations and prescriptions. We found that the more the hierarchical prescription, the better the corresponding effect. The results also reflected a well-recognized principle called “Jun-Chen-Zuo-Shi” in TCM formula theories. This also gives references for multidrug combination development in the field of network pharmacology and provides the guideline for the clinical use of combination therapy for chronic diseases.

Abstract Background Complex diseases involve many genes, and these genes are often associated with several different illnesses. Disease similarity measurement can be based on shared genotype or phenotype. Quantifying relationships between genes can reveal previously unknown connections and form a reference base for therapy development and drug repurposing. Methods Here we introduce a method to measure disease similarity that incorporates the uniqueness of shared genes. For each disease pair, we calculated the uniqueness score and constructed disease similarity matrices using OMIM and Disease Ontology annotation. Results Using the Disease Ontology-based matrix, we identified several interesting connections between cancer and other disease and conditions such as malaria, along with studies to support our findings. We also found several high scoring pairwise relationships for which there was little or no literature support, highlighting potentially interesting connections warranting additional study. Conclusions We developed a co-occurrence matrix based on gene uniqueness to examine the relationships between diseases from OMIM and DORIF data. Our similarity matrix can be used to identify potential disease relationships and to motivate further studies investigating the causal mechanisms in diseases.

Abstract Community detection involves grouping the nodes of a network such that nodes in the same community are more densely connected to each other than to the rest of the network. Previous studies have focused mainly on identifying communities in networks using node connectivity. However, each node in a network may be associated with many attributes. Identifying communities in networks combining node attributes has become increasingly popular in recent years. Most existing methods operate on networks with attributes of binary, categorical, or numerical type only. In this study, we introduce kNN-enhance, a simple and flexible community detection approach that uses node attribute enhancement. This approach adds the k Nearest Neighbor (kNN) graph of node attributes to alleviate the sparsity and the noise effect of an original network, thereby strengthening the community structure in the network. We use two testing algorithms, kNN-nearest and kNN-Kmeans, to partition the newly generated, attribute-enhanced graph. Our analyses of synthetic and real world networks have shown that the proposed algorithms achieve better performance compared to existing state-of-the-art algorithms. Further, the algorithms are able to deal with networks containing different combinations of binary, categorical, or numerical attributes and could be easily extended to the analysis of massive networks.

ChIP-seq, which combines chromatin immunoprecipitation (ChIP) with next-generation parallel sequencing, allows for the genome-wide identification of protein-DNA interactions. This technology poses new challenges for the development of novel motif-finding algorithms and methods for determining exact protein-DNA binding sites from ChIP-enriched sequencing data. State-of-the-art heuristic, exhaustive search algorithms have limited application for the identification of short (l, d) motifs (l ≤ 10, d ≤ 2) contained in ChIP-enriched regions. In this work we have developed a more powerful exhaustive method (FMotif) for finding long (l, d) motifs in DNA sequences. In conjunction with our method, we have adopted a simple ChIP-enriched sampling strategy for finding these motifs in large-scale ChIP-enriched regions. Empirical studies on synthetic samples and applications using several ChIP data sets including 16 TF (transcription factor) ChIP-seq data sets and five TF ChIP-exo data sets have demonstrated that our proposed method is capable of finding these motifs with high efficiency and accuracy. The source code for FMotif is available at http://211.71.76.45/FMotif/.

Identification and characterization of gene regulatory binding motifs is one of the fundamental tasks toward systematically understanding the molecular mechanisms of transcriptional regulation. Recently, the problem has been abstracted as the challenge planted (l,d)-motif problem. Previous studies have developed numerous methods to solve the problem. But most of them need to specify the length l of a planted motif in advance and use depth first search strategy. In this study, we present an exact and efficient algorithm, called Apriori-Motif, without given the length l of a planted motif a priori. And a breadth first search strategy is used to prune search space quickly by the downward closure property utilized in Apriori, which is a classical algorithm for frequent pattern mining. Empirical study shows that Apriori-Motif is better than some existing methods.