Serum prohepcidin concentrations in rheumatoid arthritis and its relation to disease activity

Objectives The aim of this study was to assess the possible relations between serum level of prohepcidin in patients with rheumatoid arthritis (RA) and their rheumatoid anemia profiles and disease activity. Patients and methods A total of 80 patients with RA (34 male and 46 female) were enrolled. Their mean age was 43.3 ± 11.5 years, and the mean duration of the disease was 7.7 ± 7.0 years. RA disease activities were measured using Disease Activity Score 28 (DAS28). Anemia profiles were measured. Serum concentration of prohepcidin, the prohormone of hepcidin, was measured using enzyme-linked immunosorbent assay. Results The patients′ mean concentration of serum prohepcidin was 211.4 ± 5.88 ng/ml, which was significantly higher than in the control group (167 ± 5.2 ng/ml). Serum level of interleukin-6 and tumor necrosis factor-α were significantly higher in RA patients than in the healthy control group (21.11 ± 5.88 vs. 3.36 ± 1.3 pg/ml and 17.8 ± 3.7 vs. 3.7 ± 1.1 pg/ml, respectively). The prohepcidin concentration was correlated with rheumatoid factor, C-reactive protein, erythrocyte sedimentation rate, and DAS28. There was a significant correlation between prohepcidin with tumor necrosis factor-α and interleukin-6. The prohepcidin concentration was significantly higher in the patients with active RA (DAS28 > 5.1) than those with inactive-to-moderate RA (DAS28≤5.1). Serum prohepcidin concentration in patients negatively correlated with serum iron (r = −0.23, P = 0.04). However, the prohepcidin concentration did not correlate with other anemia profiles. There was no difference of prohepcidin concentration between the patients with anemia of chronic disease and those without. Conclusion Serum concentration of prohepcidin reflects the disease activity, regardless of the anemia states in RA patients, and thus prohepcidin could be used as another useful marker for RA disease activity

Autonomous Short-Term Traffic Flow Prediction Using Pelican Optimization with Hybrid Deep Belief Network in Smart Cities

Accurate and timely traffic flow prediction not just allows traffic controllers to evade traffic congestion and guarantee standard traffic functioning, it even assists travelers to take advantage of planning ahead of schedule and modifying travel routes promptly. Therefore, short-term traffic flow prediction utilizing artificial intelligence (AI) techniques has received significant attention in smart cities. This manuscript introduces an autonomous short-term traffic flow prediction using optimal hybrid deep belief network (AST2FP-OHDBN) model. The presented AST2FP-OHDBN model majorly focuses on high-precision traffic prediction in the process of making near future prediction of smart city environments. The presented AST2FP-OHDBN model initially normalizes the traffic data using min–max normalization. In addition, the HDBN model is employed for forecasting the traffic flow in the near future, and makes use of DBN with an adaptive learning step approach to enhance the convergence rate. To enhance the predictive accuracy of the DBN model, the pelican optimization algorithm (POA) is exploited as a hyperparameter optimizer, which in turn enhances the overall efficiency of the traffic flow prediction process. For assuring the enhanced predictive outcomes of the AST2FP-OHDBN algorithm, a wide-ranging experimental analysis can be executed. The experimental values reported the promising performance of the AST2FP-OHDBN method over recent state-of-the-art DL models with minimal average mean-square error of 17.19132 and root-mean-square error of 22.6634

Protein tyrosine phosphatase 1B inhibition improves endoplasmic reticulum stress-impaired endothelial cell angiogenic response: A critical role for cell survival

Endoplasmic reticulum (ER) stress contributes to endothelial dysfunction, which is the initial step in atherogenesis. Blockade of protein tyrosine phosphatase (PTP)1B, a negative regulator of insulin receptors that is critically located on the surface of ER membrane, has been found to improve endothelial dysfunction. However, the role of ER stress and its related apoptotic sub-pathways in PTP1B-mediated endothelial dysfunction, particularly its angiogenic capacity, have not yet been fully elucidated. Thus, the present study aimed to investigate the impact of PTP1B suppression on ER stress-mediated impaired angiogenesis and examined the contribution of apoptotic signals in this process. Endothelial cells were exposed to pharmacological ER stressors, including thapsigargin (TG) or 1,4-dithiothreitol (DTT), in the presence or absence of a PTP1B inhibitor or small interfering (si)RNA duplexes. Then, ER stress, angiogenic capacity, cell cycle, apoptosis and the activation of key apoptotic signals were assessed. It was identified that the inhibition of PTP1B prevented ER stress caused by DTT and TG. Moreover, ER stress induction impaired the activation of endothelial nitric oxide synthase (eNOS) and the angiogenic capacity of endothelial cells, while PTP1B inhibition exerted a protective effect. The results demonstrated that blockade or knockdown of PTP1B prevented ER stress-induced apoptosis and cell cycle arrest. This effect was associated with reduced expression levels of caspase-12 and poly (ADP-Ribose) polymerase 1. PTP1B blockade also suppressed autophagy activated by TG. The current data support the critical role of PTP1B in ER stress-mediated endothelial dysfunction, characterized by reduced angiogenic capacity, with an underlying mechanism involving reduced eNOS activation and cell survival. These findings provide evidence of the therapeutic potential of targeting PTP1B in cardiovascular ischemic conditions

Intelligent Intrusion Detection Using Arithmetic Optimization Enabled Density Based Clustering with Deep Learning

Rapid advancements in the internet and communication domains have led to a massive rise in the network size and the equivalent data. Consequently, several new attacks have been created and pose several challenging issues for network security. In addition, the intrusions can launch several attacks and can be handled by the use of intrusion detection system (IDS). Though several IDS models are available in the literature, there is still a need to improve the detection rate and decrease the false alarm rate. The recent developments of machine learning (ML) and deep learning (DL)-based IDS systems are being deployed as possible solutions for effective intrusion detection. In this work, we propose an arithmetic optimization-enabled density-based clustering with deep learning (AOEDBC-DL) model for intelligent intrusion detection. The presented AOEDBC-DL technique follows a data clustering process to handle the massive quantity of network data traffic. To accomplish this, the AOEDBC-DL technique applied a density-based clustering technique and the initial set of clusters are initialized using the arithmetic optimization algorithm (AOA). In order to recognize and classify intrusions, a bidirectional long short term memory (BiLSTM) mechanism was exploited in this study. Eventually, the AOA was applied as a hyperparameter tuning procedure of the BiLSTM model. The experimental result analysis of the AOEDBC-DL algorithm was tested using benchmark IDS datasets. Extensive comparison studies highlighted the enhancements of the AOEDBC-DL technique over other existing approaches

Endoplasmic Reticulum (ER) Stress-Generated Extracellular Vesicles (Microparticles) Self-Perpetuate ER Stress and Mediate Endothelial Cell Dysfunction Independently of Cell Survival.

Circulating extracellular vesicles (EVs) are recognized as biomarkers and effectors of endothelial dysfunction, the initiating step of cardiovascular abnormalities. Among these EVs, microparticles (MPs) are vesicles directly released from the cytoplasmic membrane of activated cells. MPs were shown to induce endothelial dysfunction through the activation of endoplasmic reticulum (ER) stress. However, it is not known whether ER stress can lead to MPs release from endothelial cells and what biological messages are carried by these MPs. Therefore, we aimed to assess the impact of ER stress on MPs shedding from endothelial cells, and to investigate their effects on endothelial cell function. EA.hy926 endothelial cells or human umbilical vein endothelial cells (HUVECs) were treated for 24 h with ER stress inducers, thapsigargin or dithiothreitol (DTT), in the presence or absence of 4-Phenylbutyric acid (PBA), a chemical chaperone to inhibit ER stress. Then, MPs were isolated and used to treat cells (10-20 μg/mL) for 24-48 h before assessing ER stress response, angiogenic capacity, nitric oxide (NO) release, autophagy and apoptosis. ER stress (thapsigargin or DDT)-generated MPs did not differ quantitatively from controls; however, they carried deleterious messages for endothelial function. Exposure of endothelial cells to ER stress-generated MPs increased mRNA and protein expression of key ER stress markers, indicating a vicious circle activation of ER stress. ER stress (thapsigargin)-generated MPs impaired the angiogenic capacity of HUVECs and reduced NO release, indicating an impaired endothelial function. While ER stress (thapsigargin)-generated MPs altered the release of inflammatory cytokines, they did not, however, affect autophagy or apoptosis in HUVECs. This work enhances the general understanding of the deleterious effects carried out by MPs in medical conditions where ER stress is sustainably activated such as diabetes and metabolic syndrome

Artificial Ecosystem-Based Optimization with an Improved Deep Learning Model for IoT-Assisted Sustainable Waste Management

Increasing waste generation has become a key challenge around the world due to the dramatic expansion in industrialization and urbanization. This study focuses on providing effective solutions for real-time monitoring garbage collection systems via the Internet of things (IoT). It is limited to controlling the bad odor of blowout gases and the spreading of overspills by using an IoT-based solution. The inadequate and poor dumping of waste produces radiation and toxic gases in the environment, creating an adversarial effect on global warming, human health, and the greenhouse system. The IoT and deep learning (DL) confer active solutions for real-time data monitoring and classification, correspondingly. Therefore, this paper presents an artificial ecosystem-based optimization with an improved deep learning model for IoT-assisted sustainable waste management, called the AEOIDL-SWM technique. The presented AEOIDL-SWM technique exploits IoT-based camera sensors for collecting information and a microcontroller for processing the data. For waste classification, the presented AEOIDL-SWM technique applies an improved residual network (ResNet) model-based feature extractor with an AEO-based hyperparameter optimizer. Finally, the sparse autoencoder (SAE) algorithm is exploited for waste classification. To depict the enhancements of the AEOIDL-SWM system, a widespread simulation investigation is performed. The comparative analysis shows the enhanced outcomes of the AEOIDL-SWM technique over other DL models

Artificial Ecosystem-Based Optimization with an Improved Deep Learning Model for IoT-Assisted Sustainable Waste Management

Increasing waste generation has become a key challenge around the world due to the dramatic expansion in industrialization and urbanization. This study focuses on providing effective solutions for real-time monitoring garbage collection systems via the Internet of things (IoT). It is limited to controlling the bad odor of blowout gases and the spreading of overspills by using an IoT-based solution. The inadequate and poor dumping of waste produces radiation and toxic gases in the environment, creating an adversarial effect on global warming, human health, and the greenhouse system. The IoT and deep learning (DL) confer active solutions for real-time data monitoring and classification, correspondingly. Therefore, this paper presents an artificial ecosystem-based optimization with an improved deep learning model for IoT-assisted sustainable waste management, called the AEOIDL-SWM technique. The presented AEOIDL-SWM technique exploits IoT-based camera sensors for collecting information and a microcontroller for processing the data. For waste classification, the presented AEOIDL-SWM technique applies an improved residual network (ResNet) model-based feature extractor with an AEO-based hyperparameter optimizer. Finally, the sparse autoencoder (SAE) algorithm is exploited for waste classification. To depict the enhancements of the AEOIDL-SWM system, a widespread simulation investigation is performed. The comparative analysis shows the enhanced outcomes of the AEOIDL-SWM technique over other DL models

Impact of Mo-Doping on the Structural, Optical, and Electrocatalytic Degradation of ZnO Nanoparticles: Novel Approach

Pure and Molybdenum (Mo)-doped zinc oxide (ZnO) nanoparticles were prepared by a cost-effective combustion synthesis route. XRD results revealed the decrement in crystallite size of ZnO with an increase in Mo-doping concentration. Optical bandgap (Eg) values were determined using optical reflectance spectra of these films measured in the range of 190–800 nm. The Eg values decreased with increasing the Mo-doping concentration. The dielectric properties of these samples were studied to determine the dielectric constant values. Raman spectra of these samples were recorded to know the structure. These sample absorption spectra were recorded for electrocatalytic applications. All the prepared samples were subjected to electrocatalytic degradation of Rhodamine B. The 0.01 wt% Mo doped ZnO showed 100% in 7 min electrocatalytic degradation