Search

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. [ABSTRACT FROM AUTHOR]

Data reliability and availability, and serviceability (RAS) of erasure-coded data centers are highly affected by data repair induced by node failures. In a traditional failure identification scheme, all chunks share the same identification time threshold, thus losing opportunities to further improve the RAS. To solve this problem, we propose RAFI, a novel risk-aware failure identification scheme. In RAFI, chunk failures in stripes experiencing different numbers of failed chunks are identified using different time thresholds. For those chunks in a high-risk stripe, a shorter identification time is adopted, thus improving the overall data reliability and availability. For those chunks in a low-risk stripe, a longer identification time is adopted, thus reducing the repair network traffic. Therefore, RAS can be improved simultaneously. We also propose three optimization techniques to reduce the additional overhead that RAFI imposes on management nodes and to ensure that RAFI can work properly under large-scale clusters. We use simulation, emulation, and prototyping implementation to evaluate RAFI from multiple aspects. Simulation and prototype results prove the effectiveness and correctness of RAFI, and the performance improvement of the optimization techniques on RAFI is demonstrated by running the emulator. [ABSTRACT FROM AUTHOR]

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. Delivery strategies for enhanced myocardial repair. Unlabelled Image [ABSTRACT FROM AUTHOR]

Background: Myocardial infarction (MI) is caused by occlusion of the coronary artery and induces ischemia in the myocardium and eventually a massive loss in cardiomyocytes. Studies have shown many factors or treatments that can affect the healing and remodeling of the heart upon infarction, leading to better cardiac performance and clinical outcome. Previously, miR-132/212 has been shown to play an important role in arteriogenesis in a mouse model of hindlimb ischemia and in the regulation of cardiac contractility in hypertrophic cardiomyopathy in mice. In this study, we explored the role of miR-132/212 during ischemia in a murine MI model. Methods and Results: miR-132/212 knockout mice show enhanced cardiac contractile function at baseline compared to wild-type controls, as assessed by echocardiography. One day after induction of MI by permanent occlusion, miR-132/212 knockout mice display similar levels of cardiac damage as wild-type controls, as demonstrated by infarction size quantification and LDH release, although a trend toward more cardiomyocyte cell death was observed in the knockout mice as shown by TUNEL staining. Four weeks after MI, miR-132/212 knockout mice show no differences in terms of cardiac function, expression of cardiac stress markers, and fibrotic remodeling, although vascularization was reduced. In line with these in vivo observation, overexpression of miR-132 or miR-212 in neonatal rat cardiomyocyte suppress hypoxia induced cardiomyocyte cell death. Conclusion: Although we previously observed a role in collateral formation and myocardial contractility, the absence of miR-132/212 did not affect the overall myocardial performance upon a permanent occlusion of the coronary artery. This suggests an interplay of different roles of this miR-132/212 before and during MI, including an inhibitory effect on cell death and angiogenesis, and a positive effect on cardiac contractility and autophagic response. Thus, spatial or tissue specific manipulation of this microRNA family may be essential to fully understand the roles and to develop interventions to reduce infarct size. [ABSTRACT FROM AUTHOR]

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 actual design could be observed in the experimental settings, its optimality and prediction are of interest. Various numerical and graphical methods are useful tools for understanding the behavior of the designs. The D- and G-efficiencies and the fraction of design space plot are adapted to assess second-order response surface designs where the predictor variables are disturbed by a random error. Our study shows that the D-efficiencies of the competing designs are considerably low for big variance of the error, while the G-efficiencies are quite good. Fraction of design space plots display the distribution of the scaled prediction variance through the design space with and without errors in factor levels. The robustness of experimental designs against factor errors is explored through comparative study. The construction and use of the D- and G-efficiencies and the fraction of design space plots are demonstrated with several examples of different designs with errors. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Introduction: Immune checkpoint blockade (ICB) is a systemic therapeutic option for advanced hepatocellular carcinoma (HCC). However, low patient response rates necessitate the development of robust predictive biomarkers that identify individuals who will benefit from ICB. A 4-gene inflammatory signature, comprising CD8 , PD-L1 , LAG-3 , and STAT1 , was recently shown to be associated with a better overall response to ICB in various cancer types. Here, we examined whether tissue protein expression of CD8, PD-L1, LAG-3, and STAT1 predicts response to ICB in HCC., Methods: HCC samples from 191 Asian patients, comprising resection specimens from 124 patients (ICB-naïve) and pre-treatment specimens from 67 advanced HCC patients treated with ICB (ICB-treated), were analyzed for CD8, PD-L1, LAG-3, and STAT1 tissue expression using multiplex immunohistochemistry followed by statistical and survival analyses., Results: Immunohistochemical and survival analyses of ICB-naïve samples showed that high LAG-3 expression was associated with shorter median progression-free survival (mPFS) and overall survival (mOS). Analysis of ICB-treated samples revealed that high proportions of LAG-3 + and LAG-3 + CD8 + cells pre-treatment were most closely associated with longer mPFS and mOS. Using a log-likelihood model, adding the total LAG-3 + cell proportion to the total CD8 + cell proportion significantly increased the predictive values for mPFS and mOS, compared with the total CD8 + cell proportion alone. Moreover, levels of CD8 and STAT1, but not PD-L1, were significantly correlated with better responses to ICB. After analyzing viral-related and non-viral HCC samples separately, only the LAG3 + CD8 + cell proportion was significantly associated with responses to ICB regardless of viral status., Conclusion: Immunohistochemical scoring of pre-treatment levels of LAG-3 and CD8 in the tumor microenvironment may help predict ICB benefits in HCC patients. Furthermore, immunohistochemistry-based techniques offer the advantage of being readily translatable in the clinical setting., Competing Interests: DT is in the advisory board in Novartis, Celgene, Sirtex, MSD, BMS, Eisai, Ipsen, and Bayer; and as research support in Novartis, Sirtex, and BMS. JJL received research funding from Bayer, has participated in the advisory boards for Eisai, Ipsen, and Bayer, and Taiho; and received honorarium from Ipsen and BMS. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be constructed as a potential conflict of interest., (Copyright © 2023 Cheung, Seah, Fang, Orpilla, Lau, Lim, Lim, Lee, Lim, Lim, Cheng, Toh, Choo, Lee, Lee, Liu, Lim, Tai and Yeong.)

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 these matrixes 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.