Ferroptosis is a newly defined programmed cell death process with the hallmark of the accumulation of iron‐dependent lipid peroxides. The term was first coined in 2012 by the Stockwell Lab, who ...described a unique type of cell death induced by the small molecules erastin or RSL3. Ferroptosis is distinct from other already established programmed cell death and has unique morphological and bioenergetic features. The physiological role of ferroptosis during development has not been well characterized. However, ferroptosis shows great potentials during the cancer therapy. Great progress has been made in exploring the mechanisms of ferroptosis. In this review, we focus on the molecular mechanisms of ferroptosis, the small molecules functioning in ferroptosis initiation and ferroptosis sensitivity in different cancers. We are also concerned with the new arising questions in this particular research area that remains unanswered.
Full text
Available for:
FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Advanced metering infrastructure (AMI) is the core component of the smart grid. As the wireless connection between smart meters in AMI is featured with high packet loss and low transmission rate, AMI ...is considered as a representative of the low power and lossy networks (LLNs). In such communication environment, the routing protocol in AMI network is essential to ensure the reliability and real-time of data transmission. The IPv6 routing protocol for low-power and lossy networks (RPL), proposed by IETF ROLL working group, is considered to be the best routing solution for the AMI communication environment. However, the performance of RPL can be seriously degraded due to jamming attack. In this paper, we analyze the performance degradation problem of RPL protocol under jamming attack. We propose a backup node selection mechanism based on the standard RPL protocol. The proposed mechanism chooses a predefined number of backup nodes that maximize the probability of successful transmission. We evaluation the proposed mechanism through MATLAB simulations, results show the proposed mechanism improves the performance of RPL under jamming attack prominently.
Full text
Available for:
CEKLJ, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
The processed lateral root of
(Aconiti Radix lateralis praeparata or Fuzi) is a potent traditional herbal medicine extensively used in treatment of cardiovascular diseases, rheumatism arthritis, and ...bronchitis in many Asian countries. Although Fuzi has promising therapeutic effects, its toxicities are frequently observed. Three main C
-diester-diterpenoid alkaloids (DDAs) are believed to be the principal toxins of the herb. Although toxicokinetic profiles of the toxic DDAs have already been examined in several studies, they have seldom been correlated with the toxicities of Fuzi. The current article aimed to investigate the relationship between the up-to-date toxicokinetic data of the toxic DDAs and the existing evidence of the toxic effects of Fuzi. Relationships between the cardiac toxicity and the plasma and heart concentration of DDAs in mice and rats were established. Based on our findings, clinical monitoring of the plasma concentrations of DDAs of Fuzi is recommended to prevent potential cardiac toxicities. Additionally, caution with respect to potential hepatic and renal toxicity induced by Fuzi should be exercised. In addition, further analyses focusing on the preclinical tissue distribution profile of DDAs and on the long-term toxicokinetic-toxicity correlation of DDAs are warranted for a better understanding of the toxic mechanisms and safer use of Fuzi.
Full text
Available for:
IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Abstract
Background
The unfolding protein response is a critical biological process implicated in a variety of physiological functions and disease states across eukaryotes. Despite its significance, ...the role and underlying mechanisms of the response in the context of ischemic stroke remain elusive. Hence, this study endeavors to shed light on the mechanisms and role of the unfolding protein response in the context of ischemic stroke.
Methods
In this study, mRNA expression patterns were extracted from the GSE58294 and GSE16561 datasets in the GEO database. The screening and validation of protein response-related biomarkers in stroke patients, as well as the analysis of the immune effects of the pathway, were carried out. To identify the key genes in the unfolded protein response, we constructed diagnostic models using both random forest and support vector machine-recursive feature elimination methods. The internal validation was performed using a bootstrapping approach based on a random sample of 1,000 iterations. Lastly, the target gene was validated by RT-PCR using clinical samples. We utilized two algorithms, CIBERSORT and MCPcounter, to investigate the relationship between the model genes and immune cells. Additionally, we performed uniform clustering of ischemic stroke samples based on expression of genes related to the UPR pathway and analyzed the relationship between different clusters and clinical traits. The weighted gene co-expression network analysis was conducted to identify the core genes in various clusters, followed by enrichment analysis and protein profiling for the hub genes from different clusters.
Results
Our differential analysis revealed 44 genes related to the UPR pathway to be statistically significant. The integration of both machine learning algorithms resulted in the identification of 7 key genes, namely ATF6, EXOSC5, EEF2, LSM4, NOLC1, BANF1, and DNAJC3. These genes served as the foundation for a diagnostic model, with an area under the curve of 0.972. Following 1000 rounds of internal validation via randomized sampling, the model was confirmed to exhibit high levels of both specificity and sensitivity. Furthermore, the expression of these genes was found to be linked with the infiltration of immune cells such as neutrophils and CD8 T cells. The cluster analysis of ischemic stroke samples revealed three distinct groups, each with differential expression of most genes related to the UPR pathway, immune cell infiltration, and inflammatory factor secretion. The weighted gene co-expression network analysis showed that all three clusters were associated with the unfolded protein response, as evidenced by gene enrichment analysis and the protein landscape of each cluster. The results showed that the expression of the target gene in blood was consistent with the previous analysis.
Conclusion
The study of the relationship between UPR and ischemic stroke can help to better understand the underlying mechanisms of the disease and provide new targets for therapeutic intervention. For example, targeting the UPR pathway by blocking excessive autophagy or inducing moderate UPR could potentially reduce tissue injury and promote cell survival after ischemic stroke. In addition, the results of this study suggest that the use of UPR gene expression levels as biomarkers could improve the accuracy of early diagnosis and prognosis of ischemic stroke, leading to more personalized treatment strategies. Overall, this study highlights the importance of the UPR pathway in the pathology of ischemic stroke and provides a foundation for future studies in this field.
Full text
Available for:
IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Epilepsy is characterized by repeated spontaneous bursts of neuronal hyperactivity and high synchronization in the central nervous system. It seriously affects the quality of life of epileptic ...patients, and nearly 30% of individuals are refractory to treatment of antiseizure drugs. Therefore, there is an urgent need to develop new drugs to manage and control refractory epilepsy. Cannabinoid ligands, including selective cannabinoid receptor subtype (CB1 or CB2 receptor) ligands and non-selective cannabinoid (synthetic and endogenous) ligands, may serve as novel candidates for this need. Cannabinoid appears to regulate seizure activity in the brain through the activation of CB1 and CB2 cannabinoid receptors (CB1R and CB2R). An abundant series of cannabinoid analogues have been tested in various animal models, including the rat pilocarpine model of acquired epilepsy, a pentylenetetrazol model of myoclonic seizures in mice, and a penicillin-induced model of epileptiform activity in the rats. The accumulating lines of evidence show that cannabinoid ligands exhibit significant benefits to control seizure activity in different epileptic models. In this review, we summarize the relationship between brain CB2 receptors and seizures and emphasize the potential mechanisms of their therapeutic effects involving the influences of neurons, astrocytes, and microglia cells. The unique features of CB2Rs, such as lower expression levels under physiological conditions and high inducibility under epileptic conditions, make it an important target for future research on drug-resistant epilepsy.
Full text
Available for:
IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Autonomous vehicles rely on sensors to measure road condition and make driving decisions, and their safety relies heavily on the reliability of these sensors. Out of all obstacle detection sensors, ...ultrasonic sensors have the largest market share and are expected to be increasingly installed on automobiles. Such sensors discover obstacles by emitting ultrasounds and analyzing their reflections. By exploiting the built-in vulnerabilities of sensors, we designed random spoofing, adaptive spoofing, and jamming attacks on ultrasonic sensors, and we managed to trick a vehicle to stop when it should keep moving, and let it fail to stop when it should. We validate our attacks on stand-alone sensors and moving vehicles, including a Tesla Model S with the "Autopilot" system. The results show that the attacks cause blindness and malfunction of not only sensors but also autonomous vehicles, which can lead to collisions. To enhance the security of ultrasonic sensors and autonomous vehicles, we propose two defense strategies, single-sensor-based physical shift authentication that verifies signals on the physical level, and multiple sensor consistency check that employs multiple sensors to verify signals on the system level. Our experiments on real sensors and MATLAB simulation reveal the validity of both schemes.
The design of product recovery network is one of the important and challenging problems in the field of reverse logistics. Some models have been formatted by researchers under deterministic ...environment. However, uncertainty is inherent during the process of the practical product recovery. In order to deal with uncertainty, this paper employs a fuzzy programming tool to design the product recovery network. Based on different criteria, three types of optimization models are proposed and some properties of them are investigated. To solve the proposed models, we design a hybrid intelligent algorithm which integrates fuzzy simulation and genetic algorithm. Finally, several numerical examples are presented to illustrate the effectiveness of the proposed models and algorithm.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Metastasis is the predominant reason for high mortality of hepatocellular carcinoma (HCC) patients. It is critical to explore the molecular mechanism underlying HCC metastasis. Here, we reported that ...transcription factor One Cut homeobox 2 (ONECUT2) functioned as an oncogene to facilitate HCC metastasis. Elevated ONECUT2 expression was positively correlated with increased tumor number, tumor encapsulation loss, microvascular invasion, poor tumor differentiation, and advanced TNM stage. Mechanistically, ONECUT2 directly bound to the promoters of fibroblast growth factor 2 (FGF2) and ATP citrate lyase (ACLY) and transcriptionally upregulated their expression. Knockdown of FGF2 and ACLY inhibited ONECUT2-mediated HCC metastasis, whereas upregulation of FGF2 and ACLY rescued ONECUT2 knockdown-induced suppression of HCC metastasis. ONECUT2 expression was positively correlated with FGF2 and ACLY expression in human HCC tissues. HCC patients with positive coexpression of ONECUT2/FGF2 or ONECUT2/ACLY exhibited the worst prognosis. In addition, FGF2 upregulated ONECUT2 expression through the FGFR1/ERK/ELK1 pathway, which formed an FGF2-FGFR1-ONECUT2 positive feedback loop. Knockdown of ONECUT2 inhibited FGF2-induced HCC metastasis. Furthermore, the combination of FGFR1 inhibitor PD173074 with ACLY inhibitor ETC-1002 markedly suppressed ONECUT2-mediated HCC metastasis. In summary, ONECUT2 was a potential prognostic biomarker in HCC and targeting this oncogenic signaling pathway may provide an efficient therapeutic strategy against HCC metastasis.
This paper investigates the problem of energy consumption in wireless sensor networks. Wireless sensor nodes deployed in harsh environment where the conditions change drastically suffer from sudden ...changes in link quality and node status. The end-to-end delay of each sensor node varies due to the variation of link quality and node status. On the other hand, the sensor nodes are supplied with limited energy and it is a great concern to extend the network lifetime. To cope with those problems, this paper proposes a novel and simple routing metric, predicted remaining deliveries (PRD), combining parameters, including the residual energy, link quality, end-to-end delay, and distance together to achieve better network performance. PRD assigns weights to individual links as well as end-to-end delay, so as to reflect the node status in the long run of the network. Large-scale simulation results demonstrate that PRD performs better than the widely used ETX metric as well as other two metrics devised recently in terms of energy consumption and end-to-end delay, while guaranteeing packet delivery ratio.
Muscle-invasive urothelial cancer (MUC), characterized by high aggressiveness and significant heterogeneity, is currently lacking highly precise individualized treatment options. We used a ...computational pipeline to synthesize multiomics data from MUC patients using 10 clustering algorithms, which were then combined with 10 machine learning algorithms to identify molecular subgroups of high resolution and develop a robust consensus machine learning-driven signature (CMLS). Through multiomics clustering, we identified three cancer subtypes (CSs) that are related to prognosis, with CS2 exhibiting the most favorable prognostic outcome. Subsequent screening enabled identification of 12 hub genes that constitute a CMLS with robust predictive power for prognosis. The low-CMLS group exhibited a more favorable prognosis and greater responsiveness to immunotherapy and was more likely to exhibit the “hot tumor” phenotype. The high-CMLS group had a poor prognosis and lower likelihood of benefitting from immunotherapy, but dasatinib and romidepsin may serve as promising treatments for them. Comprehensive analysis of multiomics data can offer important insights and further refine the molecular classification of MUC. Identification of CMLS represents a valuable tool for early prediction of patient prognosis and for screening potential candidates likely to benefit from immunotherapy, with broad implications for clinical practice.
Display omitted
Niu and colleagues first combined 5 types of multiomics data, 10 clustering algorithms, and 99 machine learning algorithm combinations to refine the molecular subtype of MUC and build a robust prognostic signature, which will improve precise treatment and treatment options for MUC.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
PDF
