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The aim: This study was set out to assess the potential protective impact of MK0752 (a gamma secretase inhibitor) on sepsis-induced renal injury through modulation of inflammatory and oxidative stress pathways. Materials and methods: Twenty-four Swiss-albino mice aged between eight and twelve week and weighted twenty to thirty-seven grams were randomly allocated into four groups (n=6 in each group). Sham group (laparotomy without cecal ligation and puncture (CLP), sepsis group (laparotomy with CLP), vehicle-treated group (equivalent volume of DMSO before the CLP), MK0752 treated group (5 mg/kg) single daily dose for three days before the CLP. Blood samples were used to assess the serum levels of urea and creatinine. The kidneys were used to assess tissue levels of the TNF-α, IL-10, IL-6, TNFR1, VEGF, notch1, jagged1 and tissue damage by histopathological analysis. Results: The current study shows that pretreatment with MK0752 ameliorates the renal damage by significantly reducing the proinflammatory cytokines and notch1 signaling. Conclusions: Taken together, these results suggest that MK0752 could be protective against the renal injury induced by sepsis through its ameliorative impact on renal architecture and modulating cytokines and Notch1 singling pathway. Further studies regarding the role of Notch signaling pathways would be worthwhile.

Data Availability Statement: NSL_KDD, and UNSW_NB15 Dataset free downloaded from the link: http://www.di.uniba.it/~andresini/datasets.html, accessed on 18 February 2022. CICIDS2017 Dataset free downloaded from the link: http://205.174.165.80/CICDataset/CIC-IDS-2017/Dataset/, accessed on 24 June 2022, and SCE_CIC_IDS18Dataset free downloaded from the link: https://www.unb.ca/cic/datasets/ids-2018.html, accessed on 12 January 2022. Copyright © 2023 by the authors. The rapid leap in wireless communication systems incorporated a plethora of new features and challenges that accompany the era of 6G and beyond being investigated and developed. Recently, machine learning techniques were widely deployed in many fields, especially wireless communications. It was used to improve network traffic performance regarding resource management, frequency spectrum optimization, latency, and security. The studies of modern wireless communications and anticipated features of ultra-densified ubiquitous wireless networks exposed a risky vulnerability and showed a necessity for developing a trustworthy intrusion detection system (IDS) with certain efficiency/standards that have not yet been achieved by current systems. IDSs lack acceptable immunity against repetitive, updatable, and intelligent attacks on wireless communication networks, significantly concerning the modern infrastructure of 6G communications, resulting in low accuracies/detection rates and high false-alarm/false-negative rates. For this objective principle, IDS system complexity was reduced by applying a unique meta-machine learning model for anomaly detection networks was developed in this paper. The five main stages of the proposed meta-model are as follows: the accumulated datasets (NSL KDD, UNSW NB15, CIC IDS17, and SCE CIC IDS18) comprise the initial stage. The second stage is preprocessing and feature selection, where preprocessing involves replacing missing values and eliminating duplicate values, leading to dimensionality minimization. The best-affected subset feature from datasets is selected using feature selection (i.e., Chi-Square). The third step is represented by the meta-model. In the training dataset, many classifiers are utilized (i.e., random forest, AdaBoosting, GradientBoost, XGBoost, CATBoost, and LightGBM). All the classifiers undergo the meta-model classifier (i.e., decision tree as the voting technique classifier) to select the best-predicted result. Finally, the classification and evaluation stage involves the experimental results of testing the meta-model on different datasets using binary-class and multi-class forms for classification. The results proved the proposed work’s high efficiency and outperformance compared to existing IDSs. This research received no external funding.

Introduction Meniere’s disease (MD) is characterized by episodic symptoms, including vertigo, hearing loss, and tinnitus. Objectives in this study, cases of unilateral tinnitus were investigated for MD. Method Twenty-two patients who presented with chronic unilateral tinnitus on puretone audiograms showing an average threshold better than 25 dB HL and thresholds in the lower frequencies worse than those in the higher frequencies by more than 10 dB were suspected to have early-stage MD and underwent electrocochleography (ECochG). Patients showing ECochG findings conclusive for MD were compared to a control group of patients presenting with chronic unilateral tinnitus on pure-tone audiograms showing an average threshold better than 25 dB and thresholds in the higher frequencies worse than those in the lower frequencies by more than 10 dB. Results Eighteen of the 22 patients included in this study showed elevated summating potential amplitude to action potential amplitude ratios in ECochG (suggestive of endolymphatic hydrops due to MD) and were followed up for 2 months; 14 of them experienced at least two attacks of vertigo or unsteadiness. In contrast, only one patient in the control group reported two or more attacks of vertigo or unsteadiness in the 2 month observation period. The incidence of this finding in the two groups was significantly different. Conclusion Patients with early-stage MD can present with only unilateral tinnitus. Thus, the addition of “pure-tone audiograms showing lower-frequency thresholds worse than higher-frequency thresholds” to the probable MD category in the globally. agreed diagnostic criteria for MD, may be useful.

Results from epidemiological and prospective studies indicate a close association between periodontitis and diabetes. However the mechanisms by which periodontal pathogens influence the development of prediabetes/diabetes are not clear. We previously reported that oral administration of a periodontal pathogen, Porphyromonas gingivalis (Pg) to WT mice results in insulin resistance, hyperinsulinemia, and glucose intolerance and that Pg translocates to the pancreas. In the current study, we determined the specific localization of Pg in relation to mouse and human pancreatic α- and β-cells using 3-D confocal and immunofluorescence microscopy and orthogonal analyses. Pg/gingipain is intra- or peri-nuclearly localized primarily in β-cells in experimental mice and also in human post-mortem pancreatic samples. We also identified bihormonal cells in experimental mice as well as human pancreatic samples. A low percentage of bihormonal cells has intracellular Pg in both humans and experimental mice. Our data show that the number of Pg translocated to the pancreas correlates with the number of bihormonal cells in both mice and humans. Our findings suggest that Pg/gingipain translocates to pancreas, particularly β-cells in both humans and mice, and this is strongly associated with emergence of bihormonal cells.

Quality of service metrics and differentiated service mechanism policies are the most important criteria to deliver essential Internet requirements, especially during user handover, due to the rapid growth of users, multimedia applications, and traffic. DiffServ routers provide per-hop behaviors to manage traffic for services, whereas their policies have been applied to several types of Internet traffic, such as hypertext transfer protocol, file transfer protocol, and content-based routing. Multihoming aims to improve the reliability, scalability, and performance of data communications networks. This paper evaluated DiffServ various policies compared in a systematic manner (in two stages) over the multihomed networks to utilize and adopt the best policy for communicating packets, and enhanced the overall performance in terms of throughput, end-to-end latency, and processing time. Moreover, the paper introduced an interface-selection technique for multihomed nodes to select a proper interface, which provides the best services and links the behaviors that this interface yields. The overall results showed how the introduced multihoming-based interface-selection mechanism managed to maintain communication with the multihomed node. Furthermore, our results showed that the DiffServ time-sliding window with a three-color marking policy achieved the best system performance compared with the remaining policies.

Recently, the oil and gas industry faced several crucial challenges affecting the global energy market, including the Covid-19 outbreak, fluctuations in oil prices with considerable uncertainty, dramatically increased environmental regulations, and digital cybersecurity challenges. Therefore, the industrial internet of things (IIoT) may provide needed hybrid cloud and fog computing to analyze huge amounts of sensitive data from sensors and actuators to monitor oil rigs and wells closely, thereby better controlling global oil production. Improved quality of service (QoS) is possible with the fog computing, since it can alleviate challenges that a standard isolated cloud can't handle, an extended cloud located near underlying nodes is being developed. The paradigm of cloud computing is not sufficient to meet the needs of the already extensively utilized IIoT (i.e., edge) applications (e.g., low latency and jitter, context awareness, and mobility support) for a variety of reasons (e.g., health care and sensor networks). Couple of paradigms just like mobile edge computing, fog computing, and mobile cloud computing, have arisen in recently to meet these criteria. Fog computing helps to optimize services and create better user experiences, such as faster responses for critical, time-sensitive needs. At the same time, it also invites problems, such as overload, underload, and disparity in resource usage, including latency, time responses, throughput, etc. The comprehensive review presented in this work shows that fog devices have highly constrained environments and limited hardware capabilities. The existing cloud computing infrastructure is not capable of processing all data in a centralized manner because of the network bandwidth costs and response latency requirements. Therefore, fog computing demonstrated, instead of edge computing, and referred to as "the enabling technologies allowing computation to be performed at the edge of the network, on downstream data on behalf of cloud services and upstream data on behalf of IIoT services" (Shi et al., 2016) is more effective for data processing when data sources are close together. A review of fog and cloud computing literature suggests that fog is better than cloud computing because fog computing performs time-dependent computations better than cloud computing. The cloud is inefficient for latency-sensitive multimedia services and other time-sensitive applications since it is accessible over the internet, like the real-time monitoring, automation, and optimization of petroleum industry operations. As a result, a growing number of IIoT projects are dispersing fog computing capacity throughout the edge network as well as through data centers and the public cloud. A comprehensive review of fog computing features is presented here, with the potential of using it in the petroleum industry. Fog computing can provide a rapid response for applications through preprocess and filter data. Data that has been trimmed can then be transmitted to the cloud for additional analysis and better service delivery.