LncRNAs: the bridge linking RNA and colorectal cancer.

Long noncoding RNAs (lncRNAs) are transcribed by genomic regions (exceeding 200 nucleotides in length) that do not encode proteins. While the exquisite regulation of lncRNA transcription can provide signals of malignant transformation, lncRNAs control pleiotropic cancer phenotypes through interactions with other cellular molecules including DNA, protein, and RNA. Recent studies have demonstrated that dysregulation of lncRNAs is influential in proliferation, angiogenesis, metastasis, invasion, apoptosis, stemness, and genome instability in colorectal cancer (CRC), with consequent clinical implications. In this review, we explicate the roles of different lncRNAs in CRC, and the potential implications for their clinical application

Survival outcomes of stage I colorectal cancer:development and validation of the ACEPLY model using two prospective cohorts

BACKGROUND: Approximately 10% of stage I colorectal cancer (CRC) patients experience unfavorable clinical outcomes after surgery. However, little is known about the subset of stage I patients who are predisposed to high risk of recurrence or death. Previous evidence was limited by small sample sizes and lack of validation. METHODS: We aimed to identify early indicators and develop a risk stratification model to inform prognosis of stage I patients by employing two large prospective cohorts. Prognostic factors for stage II tumors, including T stage, number of nodes examined, preoperative carcinoma embryonic antigen (CEA), lymphovascular invasion, perineural invasion (PNI), and tumor grade were investigated in the discovery cohort, and significant findings were further validated in the other cohort. We adopted disease-free survival (DFS) as the primary outcome for maximum statistical power and recurrence rate and overall survival (OS) as secondary outcomes. Hazard ratios (HRs) were estimated from Cox proportional hazard models, which were subsequently utilized to develop a multivariable model to predict DFS. Predictive performance was assessed in relation to discrimination, calibration and net benefit. RESULTS: A total of 728 and 413 patients were included for discovery and validation. Overall, 6.7% and 4.1% of the patients developed recurrences during follow-up. We identified consistent significant effects of PNI and higher preoperative CEA on inferior DFS in both the discovery (PNI: HR = 4.26, 95% CI: 1.70–10.67, p = 0.002; CEA: HR = 1.46, 95% CI: 1.13–1.87, p = 0.003) and the validation analysis (PNI: HR = 3.31, 95% CI: 1.01–10.89, p = 0.049; CEA: HR = 1.58, 95% CI: 1.10–2.28, p = 0.014). They were also significantly associated with recurrence rate. Age at diagnosis was a prominent determinant of OS. A prediction model on DFS using Age at diagnosis, CEA, PNI, and number of LYmph nodes examined (ACEPLY) showed significant discriminative performance (C-index: 0.69, 95% CI:0.60–0.77) in the external validation cohort. Decision curve analysis demonstrated added clinical benefit of applying the model for risk stratification. CONCLUSIONS: PNI and preoperative CEA are useful indicators for inferior survival outcomes of stage I CRC. Identification of stage I patients at high risk of recurrence is feasible using the ACEPLY model, although the predictive performance is yet to be improved. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-022-02693-7

A Novel K-Means Clustering Algorithm with a Noise Algorithm for Capturing Urban Hotspots

With the development of cities, urban congestion is nearly an unavoidable problem for almost every large-scale city. Road planning is an effective means to alleviate urban congestion, which is a classical non-deterministic polynomial time (NP) hard problem, and has become an important research hotspot in recent years. A K-means clustering algorithm is an iterative clustering analysis algorithm that has been regarded as an effective means to solve urban road planning problems by scholars for the past several decades; however, it is very difficult to determine the number of clusters and sensitively initialize the center cluster. In order to solve these problems, a novel K-means clustering algorithm based on a noise algorithm is developed to capture urban hotspots in this paper. The noise algorithm is employed to randomly enhance the attribution of data points and output results of clustering by adding noise judgment in order to automatically obtain the number of clusters for the given data and initialize the center cluster. Four unsupervised evaluation indexes, namely, DB, PBM, SC, and SSE, are directly used to evaluate and analyze the clustering results, and a nonparametric Wilcoxon statistical analysis method is employed to verify the distribution states and differences between clustering results. Finally, five taxi GPS datasets from Aracaju (Brazil), San Francisco (USA), Rome (Italy), Chongqing (China), and Beijing (China) are selected to test and verify the effectiveness of the proposed noise K-means clustering algorithm by comparing the algorithm with fuzzy C-means, K-means, and K-means plus approaches. The compared experiment results show that the noise algorithm can reasonably obtain the number of clusters and initialize the center cluster, and the proposed noise K-means clustering algorithm demonstrates better clustering performance and accurately obtains clustering results, as well as effectively capturing urban hotspots

Experimental Study on the Strength and Hydration Products of Cement Mortar with Hybrid Recycled Powders Based Industrial-Construction Residue Cement Stabilization of Crushed Aggregate

The strength-formation mechanism for industrial-construction residue cement stabilization of crushed aggregate (IRCSCA) is not clear. To expand the application range for recycled micro-powders in road engineering, the dosages of eco-friendly hybrid recycled powders (HRPs) with different proportions of RBP and RCP affecting the strengths of cement-fly ash mortar at different ages, and the strength-formation mechanism, were studied with X-ray diffraction (XRD) and scanning electron microscopy (SEM). The results showed that the early strength of the mortar was 2.62 times higher than that of the reference specimen when a 3/2 mass ratio of brick powder and concrete powder was mixed to form the HRP and replace some of the cement. With increasing HRP content substituted for fly ash, the strength of the cement mortar first increased and then decreased. When the HRP content was 35%, the compressive strength of the mortar was 1.56 times higher than that of the reference specimen, and the flexural strength was 1.51 times higher; XRD and SEM studies of the hydrated cement mixed with HRP showed that the amount of CH in the cement paste was reduced by the pozzolanic reaction of HRP at later hydration ages, and it was very useful in improving the compactness of the mortar. The XRD spectrum of the cement paste made with HRP indicated that the CH crystal plane orientation index R, with a diffraction angle peak of approximately 34.0, was consistent with the cement slurry strength evolution law, and this research provides a reference for the application of HRP to produce IRCSCA

A Novel K-Means Clustering Method for Locating Urban Hotspots Based on Hybrid Heuristic Initialization

With rapid economic and demographic growth, traffic conditions in medium and large cities are becoming extremely congested. Numerous metropolitan management organizations hope to promote the coordination of traffic and urban development by formulating and improving traffic development strategies. The effectiveness of these solutions depends largely on an accurate assessment of the distribution of urban hotspots (centers of traffic activity). In recent years, many scholars have employed the K-Means clustering technique to identify urban hotspots, believing it to be efficient. K-means clustering is a sort of iterative clustering analysis. When the data dimensionality is large and the sample size is enormous, the K-Means clustering algorithm is sensitive to the initial clustering centers. To mitigate the problem, a hybrid heuristic “fuzzy system-particle swarm-genetic” algorithm, named FPSO-GAK, is employed to obtain better initial clustering centers for the K-Means clustering algorithm. The clustering results are evaluated and analyzed using three-cluster evaluation indexes (SC, SP and SSE) and two-cluster similarity indexes (CI and CSI). A taxi GPS dataset and a multi-source dataset were employed to test and validate the effectiveness of the proposed algorithm in comparison to the Random Swap clustering algorithm (RS), Genetic K-means algorithm (GAK), Particle Swarm Optimization (PSO) based K-Means, PSO based constraint K-Means, PSO based Weighted K-Means, PSO-GA based K-Means and K-Means++ algorithms. The comparison findings demonstrate that the proposed algorithm can achieve better clustering results, as well as successfully acquire urban hotspots

Research on correlation between dynamic resilient modulus and CBR of coarse-grained chlorine saline soil

Abstract A large area of coarse-grained saline soil is distributed in saline soil areas, and chlorine saline soil with a high salt content is a typical representative. The dynamic resilient modulus was accurately predicted using the California-bearing ratio (CBR) value to determine the relationship between the dynamic resilient modulus of coarse-grained chloride saline soil and its CBR value. Indoor dynamic triaxial tests and CBR tests were conducted to investigate the evolution of the dynamic resilient modulus (M R) and CBR of coarse-grained chlorine saline soil under the influence of the stress level, water content, and salt content. The test results showed that the dynamic resilient modulus increased with an increase in the confining pressure and bulk stress and decreased as the deviator stress increased; however, the CBR increased with an increase in the corresponding unit pressure. The higher the salt and water contents, the more obvious the influence of stress on the dynamic resilient modulus and CBR value. Under the same stress level, the decrease in the dynamic resilient modulus and CBR gradually increased with increasing salt and moisture content, and the effect of salt tended to be more significant than that of water. Based on the correlation between the dynamic resilient modulus and CBR revealed by the experiment, a more widely applicable model was selected from the existing theoretical models related to CBR for the regression analysis of the test data, and a prediction model of the dynamic resilient modulus based on the CBR value was proposed (M R = 21.06CBR 0.52). This prediction model had a high correlation coefficient (R 2 = 0.893) and could effectively predict the dynamic resilient modulus of coarse-grained chlorine saline soil using CBR values. The results provide a simple and reliable method for determining the design parameters of a coarse-grained saline soil subgrade

Enhanced Success History Adaptive DE for Parameter Optimization of Photovoltaic Models

In the past few decades, a lot of optimization methods have been applied in estimating the parameter of photovoltaic (PV) models and obtained better results, but these methods still have some deficiencies, such as higher time complexity and poor stability. To tackle these problems, an enhanced success history adaptive DE with greedy mutation strategy (EBLSHADE) is employed to optimize parameters of PV models to propose a parameter optimization method in this paper. In the EBLSHADE, the linear population size reduction strategy is used to gradually reduce population to improve the search capabilities and balance the exploitation and exploration capabilities. The less and more greedy mutation strategy is used to enhance the exploitation capability and the exploration capability. Finally, a parameter optimization method based on EBLSHADE is proposed to optimize parameters of PV models. The different PV models are selected to prove the effectiveness of the proposed method. Comparison results demonstrate that the EBLSHADE is an effective and efficient method and the parameter optimization method is beneficial to design, control, and optimize the PV systems

The diagnostic value of exosomal circular RNAs in cancer patients: A systematic review and meta‐analysis

Abstract Background Recently, serum exosomal circular RNAs (circRNAs) were applied to discriminate cancer patients from healthy individuals, indicating that exosomal circRNAs have the potential to be novel biomarkers for cancer diagnosis. This study aims to summarize the role of exosomal circRNAs in cancer diagnosis by a meta‐analysis. Methods A comprehensive literature search was conducted up to July 2021 in PubMed, Web of Science, EMBASE, and Cochrane Database. To evaluate the diagnostic value, the sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC) were pooled. Threshold effect followed by subgroup analysis and meta‐regression were performed to explore the source of heterogeneity. Sensitivity analysis was performed to assess the stability of this meta‐analysis model. Fagan plots and likelihood ratio scattergrams were used to explore the potential clinical significance. Results Ten eligible studies with 514 controls and 557 patients were included in this diagnostic meta‐analysis. The pooled sensitivity, specificity, PLR, NLR, and DOR were 0.75 (95% CI: 0.65–0.83), 0.84 (95% CI, 0.78–0.89), 5.87 (95% CI, 3.67–9.38), 0.28 (95% CI, 0.19–0.40), and 21.15 (95% CI, 10.25–43.68), respectively. The AUC was 0.89 (95% CI, 0.86–0.91). Sensitivity analysis showed that four studies had an impact on the pooled results and mainly contributed to the heterogeneity. Fagan's nomogram revealed that the prior probability of 20%, the post probability positive, the post probability negative were 59% and 6%, respectively. Conclusion Our results suggested that exosomal circRNAs might serve as powerful biomarkers in detecting cancers with high sensitivity and specificity. However, more well‐designed and multicenter diagnostic tests are needed to validate our results

Comprehensive Analysis of Potential Prognostic Values of ANGPTLs in Colorectal Cancer

Colorectal cancer (CRC) is one of the most common malignant tumors in the world. CRC recurrence and metastasis cause poor prognosis. ANGPTLs (angiopoietin-like proteins) are a family of proteins that are widely involved in metabolic disease and tumorigenesis. The roles of ANGPTLs in CRC are still controversial and deserve further research. In this study, several databases were employed to explore the expression profiles, prognostic values, genetic alterations, potential biological function, and immune infiltration correlation of ANGPTLs in CRC. The expression of ANGPTL4 was significantly positively correlated with the stage of CRC. Therefore, cell and molecular experiments were further performed to explore the roles of ANGPTL4. Our results showed that the transcriptions of ANGPTLs in colon cancer and rectal cancer tissues were lower than those in normal tissues, but the protein expression varied among different ANGPTLs. In addition, the high expression of ANGPTLs led to a relatively poor oncological outcome. Specifically, the expression of ANGPTL4 is significantly positively correlated with the stage of CRC. Further investigation revealed that ANGPTLs are mainly involved in signal transduction and the regulation of transcription, while KEGG pathway analyses demonstrated pathways in cancer. Additionally, we also observed that ANGPTL4 could promote the proliferation and migration of CRC cells, and four specific small molecule compounds had potential ANGPTL4-binding capabilities, suggesting the clinical application of these small molecule compounds on CRC treatment. Our findings imply the prognostic values and potential therapeutic targets of ANGPTLs in CRC

The effect of BMI on long-term outcome in patients with rectal cancer and establishment of a nomogram prediction model

Abstract Background The effects of body mass index (BMI) in patients with rectal cancer have been poorly studied and are still controversial. In this study, we aimed to assess the effect of BMI on the long-term outcome in patients with rectal cancer after radical surgery. Materials and methods Between April 2012 and December 2020, patients who received total mesorectal excision (TME) surgery were enrolled in the study. Patients were divided into four groups according to BMI level. Kaplan–Meier survival curves with log-rank tests were used to analyze overall survival (OS), Disease-free survival (DFS), local recurrence-free survival and distant metastasis-free survival. Univariate and multivariate analyses were performed to identify the risk factors associated with the long-term outcome. Nomograms were developed to predict the OS and DFS based on independent prognostic factors. Results A total of 688 patients were included in this study. The median follow-up time was 69 months. The 5-year OS rates of the control, underweight, overweight and obese groups were 79.2%, 62.2%, 88.7% and 86.3%, respectively. The 5-year DFS rates were 74.8%, 58.2%, 80.5% and 81.4%, respectively. Overweight (HR 0.534; 95% CI 0.332–0.860, p = 0.010) was an independent protective factor for OS and DFS (HR 0.675; 95% CI 0.461–0.989, p = 0.044). Underweight was an independent risk factor for DFS (HR = 1.623; 95% CI 1.034–2.548; p = 0.035), and had a trend to be an independent risk factor for OS (HR 1.594; 95% 0.954–2.663; p = 0.075). Nomograms were established to predict the 2-year OS, 5-year OS, 2-year DFS and 5-year DFS with an area under curve (AUC) of 0.767, 0.712, 0.746 and 0.734, respectively. Conclusions For rectal cancer patients after radical surgery, overweight was an independent protective factor for OS and DFS. Underweight was an independent risk factor for DFS and had a trend to be an independent risk factor for OS. Nomograms incorporating BMI and other prognostic factors could be helpful to predict long-term outcome