Objectives
To evaluate gender differences in initial presentation, pathology and outcomes with GC (GC).
Methods
The 1973–2013 Surveillance Epidemiology and End Results (SEER) 17-registry database was ...analysed for renal tumours from 1973 to 2013 coded as primary site “stomach”. After various exclusions, a final study group of 99,922 cases with complete data was obtained. Demographic variables analysed included age, sex, marital status and race. Tumour variables included size, stage at diagnosis, grade, primary site, treatment and histology. Primary outcome variables included overall survival (OS) and cancer-specific survival (CSS).
Results
Overall, 96,501 gastric cancer patients were identified. Of those, 34,862 (36.2%) were women. For woman, log-rank test showed that OS and CSS were significantly longer in man (
p
< 0.0001). In Cox regression analysis, woman was associated with a significantly improved OS (HR of death in 1973 to 2003 = 0.87, 95% CI = 0.85–0.89,
P
< 0.001) (HR of death in 2004 to 2013 = 0.94, 95% CI = 0.91–0.97,
P
< 0.001) and cancer-specific survival (HR of death in 1973 to 2003 = 0.90, 95% CI = 0.87–0.92,
P
< 0.001) (HR of death in 2004 to 2013 = 0.90, 95% CI = 0.87–0.93,
P
< 0.001). When performing a Kaplan-Meier curve analysis after propensity score matching, gender persisted to be a significant survival of woman for OS and CSS.
Conclusions
Men present with larger, higher stage, higher grade GC than women. OS and CSS are better in women, which is significantly different.
The matching of similar pairs of objects, called similarity join, is fundamental functionality in data management. We consider the case of trajectory similarity join (TS-Join), where the objects are ...trajectories of vehicles moving in road networks. Thus, given two sets of trajectories and a threshold
θ
, the TS-Join returns all pairs of trajectories from the two sets with similarity above
θ
. This join targets applications such as trajectory near-duplicate detection, data cleaning, ridesharing recommendation, and traffic congestion prediction.
With these applications in mind, we provide a purposeful definition of similarity. To enable efficient TS-Join processing on large sets of trajectories, we develop search space pruning techniques and take into account the parallel processing capabilities of modern processors. Specifically, we present a two-phase divide-and-conquer algorithm. For each trajectory, the algorithm first finds similar trajectories. Then it merges the results to achieve a final result. The algorithm exploits an upper bound on the spatiotemporal similarity and a heuristic scheduling strategy for search space pruning. The algorithm's per-trajectory searches are independent of each other and can be performed in parallel, and the merging has constant cost. An empirical study with real data offers insight in the performance of the algorithm and demonstrates that is capable of outperforming a well-designed baseline algorithm by an order of magnitude.
Autonomous agents have long been a research focus in academic and industry communities. Previous research often focuses on training agents with limited knowledge within isolated environments, which ...diverges significantly from human learning processes, and makes the agents hard to achieve human-like decisions. Recently, through the acquisition of vast amounts of Web knowledge, large language models (LLMs) have shown potential in human-level intelligence, leading to a surge in research on LLM-based autonomous agents. In this paper, we present a comprehensive survey of these studies, delivering a systematic review of LLM-based autonomous agents from a holistic perspective. We first discuss the construction of LLM-based autonomous agents, proposing a unified framework that encompasses much of previous work. Then, we present a overview of the diverse applications of LLM-based autonomous agents in social science, natural science, and engineering. Finally, we delve into the evaluation strategies commonly used for LLM-based autonomous agents. Based on the previous studies, we also present several challenges and future directions in this field.
Long non-coding RNA H19 was demonstrated to be significantly correlated with tumor metastasis. However, the specific functions of H19 in colorectal cancer (CRC) metastasis and the underlying ...mechanism are still largely unclear.
Use public database to screen the potential lncRNA crucial for metastasis in colorectal cancer. The expression of H19 in clinical CRC specimens was detected by qRT-PCR. The effect of H19 on the metastasis of CRC cells was investigated by transwell, wound healing assays, CCK-8 assays and animal studies. The potential proteins binding to H19 were identified by LC-MS and verified by RNA immunoprecipitation (RIP). The expression of indicated RNA and proteins were measured by qRT-PCR or western blot.
We found the expression of lncRNA H19 was significantly upregulated in primary tumor and metastatic tissues, correlated with poor prognosis in CRC. Ectopic H19 expression promoted the metastasis of colorectal cancer cells in vitro and in vivo, and induced epithelial-to-mesenchymal transition (EMT). Mechanistically, H19 directly bound to hnRNPA2B1. Knockdown of hnRNPA2B1 attenuated the H19-induce migration and invasion in CRC cells. Furthermore, H19 stabilized and upregulated the expression of Raf-1 by facilitated the interaction between hnRNPA2B1 and Raf-1 mRNA, resulting in activation of Raf-ERK signaling.
Our findings demonstrate the role of H19/hnRNPA2B1/EMT axis in regulation CRC metastasis, suggested H19 could be a potential biomarker to predict prognosis as well as a therapeutic strategy for CRC.
C-X-C chemokine receptor type 2 (CXCR2) is a key regulator that drives immune suppression and inflammation in tumor microenvironment. CXCR2-targeted therapy has shown promising results in several ...solid tumors. However, the underlying mechanism of CXCR2-mediated cross-talk between gastric cancer cells and macrophages still remains unclear.
The expression of CXCR2 and its ligands in 155 human gastric cancer tissues was analyzed via immunohistochemistry, and the correlations with clinical characteristics were evaluated. A coculture system was established, and functional assays, including ELISA, transwell, cell viability assay, and qPCR, were performed to determine the role of the CXCR2 signaling axis in promoting gastric cancer growth and metastasis. A xenograft gastric cancer model and a lymph node metastasis model were established to study the function of CXCR2
.
CXCR2 expression is associated with the prognosis of patients with gastric cancer (
= 0.002). Of all the CXCR2 ligands, CXCL1 and CXCL5 can significantly promote migration of gastric cancer cells. Macrophages are the major sources of CXCL1 and CXCL5 in the gastric cancer microenvironment, and promote migration of gastric cancer cells through activating a CXCR2/STAT3 feed-forward loop. Gastric cancer cells secrete TNF-α to induce release of CXCL1 and CXCL5 from macrophages. Inhibiting CXCR2 pathway of gastric cancer cells can suppress migration and metastasis of gastric cancer
and
.
Our study suggested a previously uncharacterized mechanism through which gastric cancer cells interact with macrophages to promote tumor growth and metastasis, suggesting that CXCR2 may serve as a promising therapeutic target to treat gastric cancer.
Esophageal squamous cell carcinoma (ESCC) is the major subtype of esophageal cancer with high aggressiveness and poor prognosis. There is an urgent need for understanding the molecular mechanism ...underlying the development and progression of ESCC.
ESCC tissues and corresponding non-neoplastic tissues were collected. The expression and function of miR-124-3p and BCAT1 in two cell lines KYSE-150 and Eca109 were determined.
We show downregulation of miR-124-3p expression in ESCC tissues, which is highly correlated with proliferation and migration of ESCC cell lines KYSE-150 and Eca109. miR-124-3p show high correlation with TNM stage and differentiation grade. Furthermore, miR-124-3p directly targets mRNA 3'UTR region of BCAT1, which results in upregulation of BCAT1 expression as observed in ESCC tissues and cell lines. Also, our data indicates that BCAT1 high expression is strongly linked to the disease-free survival, tumor size, pathologic stage, T classification and differentiation grade. On the other hand, we clarified the upstream mechanism regulating miR-124-3p expression in ESCC, which involves in the hypermethylation-silencing regulation mediated by DNA methyltransferase 1(DNMT1), which is of high expression in ESCC tissues and cell lines in the present study. In addition, DNMT1 knockdown or inhibition of DNMT1 function contributes to downregulation of miR-124-3p and BCAT1 expression.
Our study thus clarifies a new mechanism that DNMT1/miR-124/BCAT1 axis regulates the development and progression of ESCC.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
We study the
mergeability
of data summaries. Informally speaking, mergeability requires that, given two summaries on two datasets, there is a way to merge the two summaries into a single summary on ...the two datasets combined together, while preserving the error and size guarantees. This property means that the summaries can be merged in a way akin to other algebraic operators such as sum and max, which is especially useful for computing summaries on massive distributed data. Several data summaries are trivially mergeable by construction, most notably all the
sketches
that are linear functions of the datasets. But some other fundamental ones, like those for heavy hitters and quantiles, are not (known to be) mergeable. In this article, we demonstrate that these summaries are indeed mergeable or can be made mergeable after appropriate modifications. Specifically, we show that for ε-approximate heavy hitters, there is a deterministic mergeable summary of size
O
(1/ε); for ε-approximate quantiles, there is a deterministic summary of size
O
((1/ε) log(ε
n
)) that has a restricted form of mergeability, and a randomized one of size
O
((1/ε) log
3/2
(1/ε)) with full mergeability. We also extend our results to geometric summaries such as ε-approximations which permit approximate multidimensional range counting queries. While most of the results in this article are theoretical in nature, some of the algorithms are actually very simple and even perform better than the previously best known algorithms, which we demonstrate through experiments in a simulated sensor network.
We also achieve two results of independent interest: (1) we provide the best known randomized streaming bound for ε-approximate quantiles that depends only on ε, of size
O
((1/ε) log
3/2
(1/ε)), and (2) we demonstrate that the MG and the SpaceSaving summaries for heavy hitters are isomorphic.
Gastric cancer (GC) is a highly heterogeneous disease. In recent years, the prognostic value of the mRNA expression-based stemness index (mRNAsi) across cancers has been reported. We intended to ...identify stemness index-associated genes (SI-genes) for clinical characteristic, gene mutation status, immune response, and tumor microenvironment evaluation as well as risk stratification and survival prediction.
The correlations between the mRNAsi and GC prognosis, clinical characteristics, gene mutation status, immune cell infiltration and tumor microenvironment were evaluated. Weighted gene correlation network analysis (WGCNA) was performed to identify SI-genes from differentially expressed genes (DEGs) in The Cancer Genome Atlas (TCGA). Single-sample gene set enrichment analysis (ssGSEA) was employed to calculate the sample SI-gene-based ssGSEA score according to the SI-genes. Then, the correlations between the ssGSEA score and GC prognosis, clinical characteristics, gene mutation status, immune cell infiltration and tumor microenvironment were analyzed. Finally, the least absolute shrinkage and selection operator (LASSO) Cox regression algorithm was used to construct a prognostic signature with prognostic SI-genes. The ssGSEA score and prognostic signature were validated using the Gene Expression Omnibus (GEO) database.
The mRNAsi could predict overall survival (OS), clinical characteristics, the gene mutation status, immune cell infiltration, and the tumor microenvironment composition. Fourteen positive SI-genes and 178 negative SI-genes were screened out using WGCNA. The ssGSEA score, similar to the mRNAsi, was found to be closely related to OS, clinical characteristics, the gene mutation status, immune cell infiltration, and the tumor microenvironment composition. Finally, a prognostic signature based on 18 prognostic SI-genes was verified to more accurately predict GC 1-year, 3-year, and 5-year OS than traditional clinical prediction models.
The ssGSEA score and prognostic signature based on 18 prognostic SI-genes are of great value for immune response evaluation, risk stratification and survival prediction in GC and suggest that stemness features are crucial drivers of GC progression.
SimRank
is a popular measurement for evaluating the node-to-node similarities based on the graph topology. In recent years, single-source and top-
k
SimRank queries have received increasing attention ...due to their applications in web mining, social network analysis, and spam detection. However, a fundamental obstacle in studying SimRank has been the lack of ground truths. The only exact algorithm, Power Method, is computationally infeasible on graphs with more than
10
6
nodes. Consequently, no existing work has evaluated the actual accuracy of various single-source and top-
k
SimRank algorithms on large real-world graphs. In this paper, we present ExactSim, the first algorithm that computes the exact single-source and top-
k
SimRank results on large graphs. This algorithm produces ground truths with precision up to 7 decimal places with high probability. With the ground truths computed by ExactSim, we present the first experimental study of the accuracy/cost trade-offs of existing approximate SimRank algorithms on large real-world graphs and synthetic graphs. Finally, we use the ground truths to exploit various properties of SimRank distributions on large graphs.
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