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Polyploid cells, which contain more than one set of chromosome pairs, are very common in nature. Polyploidy can provide cells with several potential benefits over their diploid counterparts, including an increase in cell size, contributing to organ growth and tissue homeostasis and improving cellular robustness via increased tolerance to genomic stress and apoptotic signals. Here, we focus on why polyploidy in the cell occurs and which stress responses and molecular signals trigger cells to become polyploid. Moreover, we discuss its crucial roles in cell growth and tissue regeneration in the heart, liver and other tissues.

Previous studies have confirmed microRNA (miRNA), small single-stranded non-coding RNA, participates in various biological processes and plays vital roles in many complex human diseases. Therefore, developing an efficient method to infer potential miRNA disease associations could greatly help understand operational mechanisms for diseases at the molecular level. However, during these early stages for miRNA disease prediction, traditional biological experiments are laborious and expensive. Therefore, this study proposes a novel method called AGAEMD (node-level Attention Graph Auto-Encoder to predict potential MiRNA Disease associations). We first create a heterogeneous matrix incorporating miRNA similarity, disease similarity, and known miRNA-disease associations. Then the matrix are input into a node-level attention encoder-decoder network which utilizes low dimensional dense embeddings to represent nodes and calculate association scores. To verify the effectiveness of the proposed method, we conduct a series of experiments on two benchmark datasets (the Human MicroRNA Disease Database v2.0 and v3.2) and report the averages over 10 runs in comparison with several state-of-the-art methods. Experimental results have demonstrated the excellent performance of AGAEMD in comparison with other methods. Three important diseases (Colon Neoplasms, Lung Neoplasms, Lupus Vulgaris) were applied in case studies. The results comfirm the reliable predictive performance of AGAEMD.

Background Ovarian cancer (OV) is one of the most common malignant tumors of gynecology oncology. The lack of effective early diagnosis methods and treatment strategies result in a low five-year survival rate. Also, immunotherapy plays an important auxiliary role in the treatment of advanced OV patient, so it is of great significance to find out effective immune-related tumor markers for the diagnosis and treatment of OV. Methods Based on the consensus clustering analysis of single-sample gene set enrichment analysis (ssGSEA) score transformed via The Cancer Genome Atlas (TCGA) mRNA profile, we obtained two groups with high and low levels of immune infiltration. Multiple machine learning methods were conducted to explore prognostic genes associated with immune infiltration. Simultaneously, the correlation between the expression of mark genes and immune cells components was explored. Results A prognostic classifier including 5 genes (CXCL11, S1PR4, TNFRSF17, FPR1 and DHRS95) was established and its robust efficacy for predicting overall survival was validated via 1129 OV samples. Some significant variations of copy number on gene loci were found between two risk groups and it showed that patients with fine chemosensitivity has lower risk score than patient with poor chemosensitivity (P = 0.013). The high and low-risk groups showed significantly different distribution (P Conclusion The present study identified five prognostic genes associated with immune infiltration of OV, which may provide some potential clinical implications for OV treatment.

Since errors in factor levels affect the traditional statistical properties of response surface designs, an important question to consider is robustness of design to errors. However, when the actua...

Background Adriamycin (ADR) is an effective antineoplastic drug; the clinical application of ADR is limited due to fatal heart dysfunction. Exenatide has antioxidant, anti-apoptotic and anti-inflammatory properties. It can alleviate heart damage induced by ischaemia-reperfusion injury. Thus, we assumed that exenatide would produce protective effects on ADR-induced heart dysfunction. Method Mice were treated with exenatide 1 h prior to every ADR treatment for 20 days. Left ventricular function and performance were assessed by echocardiography. Additionally, H9c2 cells were pretreated with exenatide followed by ADR, and intracellular reactive oxygen species (ROS) and cell viability, as well as the lactate dehydrogenase (LDH) and the creatine kinase MB (CK-MB), were subsequently measured. Flow cytometry and TUNEL staining were applied to assess the effect of exenatide on cardiac damage caused by ADR. Western blot and RT-PCR were performed to detect the effect of exenatide on apoptosis-related genes (Bcl-2 and Bax) and inflammation-related genes and/or proteins (tumour necrosis factor-α, interleukin-6, nuclear factor-κB, and p53). Result Echocardiography showed that cardiac dysfunction caused by ADR was significantly improved by treatment with exenatide. ADR mice had harmful changes in the levels of ROS and CK-MB/LDH production, as well as the targeted apoptotic and inflammatory molecules, and these effects were also reversed by exenatide. In vitro, exenatide mitigated ADR-induced oxidative stress and CK-MB/LDH production, as well as Annexin V+/PI+ and TUNEL+ apoptosis in H9c2 cells. Conclusion In conclusion, our research demonstrated the potential protective effects of exenatide on ADR-induced heart dysfunction through suppressing oxidative stress, apoptosis and inflammation.

BackgroundImmune check-point blockade (ICB) is one of the emerging therapeutic options for advanced hepatocellular carcinoma (HCC). However, low response rates amongst patients necessitates the development of robust predictive biomarkers that identify patients who likely benefit from ICB. Previously our group found that immunohistochemical scoring of CD38 in the tumour microenvironment predicts responsiveness to anti-PD-1/anti-PD-L1 immunotherapy in HCC.1 Recently BMS 4-gene inflammatory signature, comprising the 4 genes CD8, PD-L1, LAG-3 and STAT1, has been shown to be associated with better overall response to immunotherapy in various cancer types.2–4 In the present study, we examined the relationship between tissue expression of BMS 4-gene inflammatory signature and the responsiveness of HCC to immunotherapy, and whether BMS 4-gene inflammatory signature increases the predictive power of CD38.MethodsHCC tissue samples from 124 Asian patients that underwent conventional treatment and from 49 Asian patients that underwent ICB were analysed for CD8, PD-L1, LAG-3, STAT1, CD38 and CD68 tissue expression using immunohistochemistry and multiplex immunohistochemistry, followed by survival and statistical analysis.ResultsSurvival analysis of the 124 samples showed that high LAG-3 tissue expression was associated with shorter progression-free survival (PFS). On the other hand, immunohistochemical analyses on the 49 patient samples treated with ICB revealed that high LAG-3 density and high total LAG-3+CD8+ T cell proportion were associated with improved response to ICB (figure 1). However, CD8, PD-L1 and STAT1 levels did not significantly correlate with improved survival. The addition of total LAG-3+ cell proportion to total CD38+ cell proportion significantly increased the predictive value for both PFS (DeltaLRChi2=9.97; P=0.0016; table 1) and overall survival (OS) (DeltaLRChi2=8.84; P=0.0021; table 1), compared with total CD38+ cell proportion alone. Similarly findings were obtained after adding total LAG-3+CD8+ cell proportion to total CD38+ cell proportion (PFS: DeltaLRChi2=7.21; P=0.0072; OS: DeltaLRChi2=8.06; P=0.0045; table 1), compared with total CD38+ cell proportion alone. Lastly, when the total LAG-3+CD8+ cell proportion was added to total CD38+ and CD38+CD68+ cell proportion, the predictive value of the biomarker was significantly increased (PFS: DeltaLRChi2=6.10; P=0.0136; OS: DeltaLRChi2=6.18; P=0.0129; table 1). Ongoing works include further validation of the findings in various cohorts, and correlating with clinical outcome of the patients.Abstract 89 Table 1Log-likelihood of models with added predictive termsAbstract 89 Figure 1HCC patients‘ response to ICB in relation to LAG-3 density. (A) Kaplan-Meier curve showing the association between a high LAG-3 density and improved overall survival after treatment with ICB. (B) Kaplan-Meier curve showing the association between a high total LAG-3+CD8+ T cell proportion and improved overall survival after treatment with ICB. (C) Kaplan-Meier curve showing the association between a high LAG-3 density and improved progression-free survival after treatment with ICB. (D) Kaplan-Meier curve showing the association between a high total LAG-3+CD8+ T cell proportion and improved progression-free survival after treatment with ICB.ConclusionsHigh LAG-3 expression on tissue-infiltrating immune cells predicted greater response to ICB. LAG-3+ and LAG3+CD8+ cell proportion added predictive value to CD38+ cells for predicting survival outcome in immunotherapy-treated HCC. LAG-3 may be used in conjunction with CD38 to predict responsiveness to ICB in HCC.ReferencesNg HHM, Lee RY, Goh S, et al. Immunohistochemical scoring of CD38 in the tumor microenvironment predicts responsiveness to anti-PD-1/PD-L1 immunotherapy in hepatocellular carcinoma. J Immunother Cancer 2020;8.Hodi FS, Wolchok JD, Schadendorf D, et al. Abstract CT037: Genomic analyses and immunotherapy in advanced melanoma. AACR 2019.Lei M, Siemers NO, Pandya D, et al. Analyses of PD-L1 and Inflammatory Gene Expression Association with Efficacy of Nivolumab ± Ipilimumab in Gastric Cancer/Gastroesophageal Junction Cancer. Clinical Cancer Research 2021;27:3926–35.Sangro B, Melero I, Wadhawan S, et al. Association of inflammatory biomarkers with clinical outcomes in nivolumab-treated patients with advanced hepatocellular carcinoma. J Hepatol 2020.Ethics ApprovalThis study was approved by the Centralised Institutional Review Board of SingHealth (CIRB ref: 2009/907/B).ConsentWritten informed consent was obtained from the patient for publication of this abstract and any accompanying images. A copy of the written consent is available for review by the Editor of this journal.

Ovarian cancer (OV) is one of the most common malignant tumors of gynecology oncology. The lack of effective early diagnosis methods and treatment strategies result in a low five-year survival rate. Also, immunotherapy plays an important auxiliary role in the treatment of advanced OV patient, so it is of great significance to find out effective immune-related tumor markers for the diagnosis and treatment of OV.Based on the consensus clustering analysis of single-sample gene set enrichment analysis (ssGSEA) score transformed via The Cancer Genome Atlas (TCGA) mRNA profile, we obtained two groups with high and low levels of immune infiltration. Multiple machine learning methods were conducted to explore prognostic genes associated with immune infiltration. Simultaneously, the correlation between the expression of mark genes and immune cells components was explored.A prognostic classifier including 5 genes (CXCL11, S1PR4, TNFRSF17, FPR1 and DHRS95) was established and its robust efficacy for predicting overall survival was validated via 1129 OV samples. Some significant variations of copy number on gene loci were found between two risk groups and it showed that patients with fine chemosensitivity has lower risk score than patient with poor chemosensitivity (P = 0.013). The high and low-risk groups showed significantly different distribution (P 0.001) of five immune cells (Monocytes, Macrophages M1, Macrophages M2, T cells CD4 menory and T cells CD8).The present study identified five prognostic genes associated with immune infiltration of OV, which may provide some potential clinical implications for OV treatment.

Cardiovascular disease is the leading cause of death worldwide. Current palliative treatments can slow the progression of heart failure, but ultimately, the only curative treatment for end-stage heart failure is heart transplantation, which is only available for a minority of patients due to lack of donors’ hearts. Explorative research has shown the replacement of the damaged and lost myocardium by inducing cardiac regeneration from preexisting myocardial cells. Lower vertebrates, such as the newt and zebrafish, can regenerate lost myocardium through cardiomyocyte proliferation. The preexisting adult cardiomyocytes replace the lost cells through subsequent dedifferentiation, proliferation, migration, and re-differentiation. Similarly, neonatal mice show complete cardiac regeneration post-injury; however, this regenerative capacity is remarkably diminished one week after birth. In contrast, the adult mammalian heart presents a fibrotic rather than a regenerative response and only shows signs of partial pathological cardiomyocyte dedifferentiation after injury. In this review, we explore the cellular and molecular responses to myocardial insults in different adult species to give insights for future interventional directions by which one can promote or activate cardiac regeneration in mammals.

Myocardial infarction (MI) is one of the leading causes of mortality worldwide. It is caused by an acute imbalance between oxygen supply and demand in the myocardium, usually caused by an obstruction in the coronary arteries. The conventional therapy is based on the application of (a combination of) anti-thrombotics, reperfusion strategies to open the occluded artery, stents and bypass surgery. However, numerous patients cannot fully recover after these interventions. In this context, new therapeutic methods are explored. Three decades ago, the first biologicals were tested to improve cardiac regeneration. Angiogenic proteins gained popularity as potential therapeutics. This is not straightforward as proteins are delicate molecules that in order to have a reasonably long time of activity need to be stabilized and released in a controlled fashion requiring advanced delivery systems. To ensure long-term expression, DNA vectors-encoding for therapeutic proteins have been developed. Here, the nuclear membrane proved to be a formidable barrier for efficient expression. Moreover, the development of delivery systems that can ensure entry in the target cell, and also correct intracellular trafficking towards the nucleus are essential. The recent introduction of mRNA as a therapeutic entity has provided an attractive intermediate: prolonged but transient expression from a cytoplasmic site of action. However, protection of the sensitive mRNA and correct delivery within the cell remains a challenge. This review focuses on the application of synthetic delivery systems that target the myocardium to stimulate cardiac repair using proteins, DNA or RNA.

In large-scale replicated distributed storage systems consisting of hundreds to thousands of nodes, node failures are not rare and can cause data blocks to lose their replicas and become faulty. A simple but effective approach to prevent data loss from the node failures, i.e., ensuring reliability, is to shorten the identification time of the node failures and faulty blocks, which is determined by both timeouts and check intervals for node states. However, to maintain low repair network traffic, the identification time is actually relatively long and even dominates repair processes of critical blocks. In this paper, we propose a novel scheme, named RICK, to explore the potential in the identification time, and thus improve data reliability of replicated distributed storage systems while maintaining a low repair cost. First, by introducing an additional replica state, critical blocks (with two or more lost replicas) have individual short timeouts while sick blocks (with only one lost replica) preserve the long timeouts. Second, by replacing the static check intervals for node states with adaptive ones, the check intervals and the identification time of critical blocks are further shortened, which improves data reliability. Meanwhile, due to the low ratio of critical blocks in all faulty blocks, the repair network traffic remains low. The results from our simulation and prototype implementation show that RICK improves data reliability of replicated distributed storage systems by a factor of up to 14 in terms of mean time to data loss. Meanwhile, the extra repair network traffic caused by RICK is less than 1.5 percent of the total network traffic for data repairs.

Design of experiments has received much attention in the context of reliability improvement. Many engineers and researchers are considering how to use designed experiments to improve the reliability of products and processes. This work illustrates a 12-run Plackett-Burman design and its analysis to improve the reliability of a diamond drill bit. A detailed description and execution of the design are provided including how to choose the factors and determine the number of experimental factors. In addition, the results of the real-world experiment are presented and the predicted performance from statistical model fitting is verified by part of a confirmation experiment. The experimental data show that the recommended factor settings substantially improved the reliability of the diamond drill bits.

Pancreatic ductal adenocarcinoma (PDAC) is a malignant tumor with a specific tumor immune microenvironment (TIME). Therefore, investigating prognostic immune-related genes (IRGs) that are closely associated with TIME to predict PDAC clinical outcomes is necessary. In the present study, 459 samples of PDAC from the Genotype-Tissue Expression database, The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO) were included and a survival-associated module was identified using weighted gene co-expression network analysis. Based on the Cox regression analysis and least absolute shrinkage and selection operator analysis, four IRGs (2'-5'-oligoadenylate synthetase 1, MET proto-oncogene, receptor tyrosine kinase, interleukin 1 receptor type 2 and interleukin 20 receptor subunit β) were included in the prognostic model to calculate the risk score (RS), and patients with PDAC were divided into high- and low-RS groups. Kaplan-Meier survival and receiver operating characteristic curve analyses demonstrated that the low-RS group had significantly improved survival conditions compared with the high-RS group in TCGA training set. The prognostic function of the model was also validated using ICGC and GEO cohorts. To investigate the mechanism of different overall survival between the high- and low-RS groups, the present study included Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data and Cell Type Identification by Estimating Relative Subset of Known RNA Transcripts algorithms to investigate the state of the tumor microenvironment and immune infiltration inpatients in the cohort from TCGA. In summary, four genes associated with the TIME of PDAC were identified, which may provide a reference for clinical treatment.

Experimenters are often confronted with the problem that errors in setting factor levels cannot be measured. In the robust design scenario, the goal is to determine the design that minimizes the variability transmitted to the response from the variables’ errors. The prediction variance performance of response surface designs with errors is investigated using design efficiency and the maximum and minimum scaled prediction variance. The evaluation and comparison of response surface designs with and without errors in variables are developed for second order designs on spherical regions. The prediction variance and design efficiency results and recommendations for their use are provided.

The morbidity of lower extremity atherosclerotic disease(LEAD) has increased year by year. Chronic stress attenuates the ability of angiogenesis and deteriorates the prognosis of LEAD. Chronic stress increases plasma and tissue DPP-4 activities in mice. DPP-4 plays an important role in angiogenesis

High reliability is critical to large data centers consisting of hundreds to thousands of storage nodes where node failures are not rare. Data replication is a typical technique deployed to achieve high reliability. When a node failure is detected, blocks with lost replicas are identified and recovered. Long timeouts are usually used for node failure detection. For blocks with one lost replica, the long timeouts can significantly reduce network traffic induced by data recovery. However, for blocks with two or more lost replicas, which can be caused by concurrent node failures that are not rare in large data centers, the long timeouts will result in a high risk of loss of these blocks. In this paper, we propose MFR to separate the identification of the blocks with two or more lost replicas from that of the blocks with one lost replica in a way that the identification of the blocks with two or more replicas can be accelerated while that of the blocks with one lost replica stays the same. Consequently, MFR can significantly improve data reliability while keeping the network traffic induced by data recovery stable. The results from our simulation and prototype implementation show that MFR improves the reliability of storage systems by a factor of up to 4.0 in terms of mean time to data loss. As blocks with two or more lost replicas are far fewer than blocks with one lost replica, the extra network traffic caused by MFR is less than 0.54% of total network traffic for data recovery.

Profile monitoring is used to check the stability of the quality of a product over time when the product quality is best represented by a function at each time point. However, most previous monitoring approaches have not considered that the argument values may vary from profile to profile, which is common in practice. A novel nonparametric control scheme based on profile error is proposed for monitoring nonlinear profiles with varied argument values. The proposed scheme uses the metrics of profile error as the statistics to construct the control charts. More details about the design of this nonparametric scheme are also discussed. The monitoring performance of the combined control scheme is compared with that of alternative nonparametric methods via simulation. Simulation studies show that the combined scheme is effective in detecting parameter error and is sensitive to small shifts in the process. In addition, due to the properties of the charting statistics, the out-of-control signal can provide diagnostic information for the users. Finally, the implementation steps of the proposed monitoring scheme are given and applied for monitoring the blade manufacturing process. With the application in blade manufacturing of aircraft engines, the proposed nonparametric control scheme is effective, interpretable, and easy to apply.

This paper investigates the scaled prediction variances in the errors-in-variables model and compares the performance with those in classic model of response surface designs for three factors. The ordinary least squares estimators of regression coefficients are derived from a second-order response surface model with errors in variables. Three performance criteria are proposed. The first is the difference between the empirical mean of maximum value of scaled prediction variance with errors and the maximum value of scaled prediction variance without errors. The second is the mean squared deviation from the mean of simulated maximum scaled prediction variance with errors. The last performance measure is the mean squared scaled prediction variance change with and without errors. In the simulations, 1 000 random samples were performed following three factors with 20 experimental runs for central composite designs and 15 for Box-Behnken design. The independent variables are coded variables in these designs. Comparative results show that for the low level errors in variables, central composite face-centered design is optimal; otherwise, Box-Behnken design has a relatively better performance.

The regression model taking into account errors in the independent variables is seldom considered in response surface methodology. In response surface design models, independent variables are generally assumed to be measured without errors for given observation. However, in practice the independent variables cannot be exactly controlled and measured, and this situation is widespread in many fields. The aim of this paper is to investigate the regression coefficients, the stationary point and the optimum value in the errors-in-variables model and compare with the model without errors in response surface methodology. Here, as the original model, the independent variables are assumed to be fixed. This result has been obtained using one real data corresponding to a comparison analysis.