Clinical, biochemical and inflammatory predictors of mortality in patients with spontaneous bacterial peritonitis

Background: Spontaneous bacterial peritonitis (SBP) is a serious complication of liver cirrhosis. It contributes to high morbidity and mortality in this population. In-hospital mortality of SBP ranges between 20% and 40%, suggesting that further refinements are essential in managing SBP. Early recognition of high-risk patients would enable us to reduce the short-term mortality.Objective: The current study aimed to evaluate the value of clinical, biochemical and inflammatory markers in the prediction of 1-month and 3-month cumulative mortality in patients with SBP.Patients and methods: Two hundred patients with a confirmed diagnosis of SBP were enrolled. They were admitted and received the proper treatment at the National Liver Institute Hospital-Menoufia University, Egypt. Patients were prospectively followed up for mortality over a period of three months. Predictors of mortality were assessed and analyzed.Results: Mortality rates were 20% and 41% at 1 month and 3 month respectively. Our findings showed that low blood pressure, abdominal pain, fever, higher Child-Pugh score, MELD score, serum bilirubin, INR, serum creatinine, C-reactive protein to albumin (CRP/Albumin) ratio, neutrophil–lymphocyte ratio (NLR), massive splenomegaly and large ascites have been demonstrated as risk factors associated with short-term mortality.Conclusion: SBP carries a high risk of mortality among cirrhotic patients. Clinical parameters (low blood pressure, abdominal pain, fever, massive splenomegaly and large ascites), prognostic scores (Child-Pugh and MELD) and inflammatory markers (CRP, CRP/albumin ratio, and NLR) seem to be accurate and reliable tools that could independently predict short-term mortality in patients with SBP

Aggregate production planning considering organizational learning with case based analysis

Responding rapidly to customer needs is one of the main targets of industrial organizations that want to survive in the current market competition. This objective can be attained through robust planning. Workforce productivity is considered one of the important entities in production planning. However, it has a dynamic nature, i.e. the productivity growths thanks to on-job training or learning phenomenon. Considering this fact in manufacturing planning enhances the robustness of the developed plans. The present paper presents a mathematical model for medium-range production planning that is used to find the optimal aggregate production plan. The model aims to optimize the total production costs while respecting most of the operational constraints and considering the process of organizational learning. The presented model is constructed relying on the real industrial practices; the outcome is a mixed-integer linear program. The model was validated and checked using real data collected from an Egyptian factory that produces electric motors for home appliances. The proposed mathematical model was optimally solved using “ILOG-CPLEX 12.6”. By comparing the results obtained versus that of the method adopted in the factory, a cost reduction of 6.3% is achieved for the presented data set. A set of managerial aspects are concluded after the model analysis. Moreover, the impact of using detailed learning rates on the production cost is discussed

Determining the Stationary Enablers of Resilient and Sustainable Supply Chains

One of an organization’s significant challenges in a globalized world is reducing risk by building resilient supply chains (SCs). It is required to realize a competitive advantage in a volatile and fast changing environment. Conversely, the key enablers of such sustainable and resilient supply chain management are not fully analyzed in building projects. This study aims at determining the stationary enablers of resilient and sustainable supply chains. For this to happen, a questionnaire survey comprising 32 enablers of resilient and sustainable supply chains has been conducted with Egyptian engineers to appraise their degree of importance. The results show that the five most important enablers of resilient and sustainable supply chains are: top management support, adaptability, visibility, quality awareness, and responsiveness. This research’s results will allow building administrators to create diverse SCs, while being mindful of how the characteristics of a supply chain decrease or increase its resilience and eventually affect the exposure to risk in the building’s SCs

In Vitro Inhibition of Hepatitis C Virus by Antisense Oligonucleotides in PBMC Compared to Hepatoma Cells

Aim. To assess the efficiency of phosphorothioate antisense oligodeoxynucleotide 1 (S-ODN1) on HCV translation inhibition in PBMC compared to hepatoma cells in vitro for the first time. Materials and Methods. The study included 34 treatment naive HCV patients. IRES domain III and IV sequence variations were tested in 45 clones from 9 HCV patients. PBMC of HCV positive patients were subjected to S-ODN in vitro. Concomitantly HepG2 cells infected by the same patient’s serum were also treated with S-ODN1 for 24 and 48 hours. Cellular RNA was tested for HCV plus and minus strands by reverse transcription polymerase chain reaction (RT-PCR). Results. Sequence variations were seen in HCV IRES domain III only while domain IV was conserved among all the tested patient’s clones. S-ODN1 successfully inhibited HCV translation in HepG2 cells, while in PBMC inhibition was partial. Conclusion. HCV IRES domain IV is more conserved than domain IIId in genotype 4 HCV patients. S-ODN against HCV IRES domain IV was not efficient to inhibit HCV translation in PBMC under the study conditions. Further studies testing other S-ODN targeting other HCV IRES domains in PBMC should be done

Face Detection & Recognition from Images & Videos Based on CNN & Raspberry Pi

The amount of multimedia content is growing exponentially and a major portion of multimedia content uses images and video. Researchers in the computer vision community are exploring the possible directions to enhance the system accuracy and reliability, and these are the main requirements for robot vision-based systems. Due to the change of facial expressions and the wearing of masks or sunglasses, many face recognition systems fail or the accuracy in recognizing the face decreases in these scenarios. In this work, we contribute a real time surveillance framework using Raspberry Pi and CNN (Convolutional Neural Network) for facial recognition. We have provided a labeled dataset to the system. First, the system is trained upon the labeled dataset to extract different features of the face and landmark face detection and then it compares the query image with the dataset on the basis of features and landmark face detection. Finally, it compares faces and votes between them and gives a result that is based on voting. The classification accuracy of the system based on the CNN model is compared with a mid-level feature extractor that is Histogram of Oriented Gradient (HOG) and the state-of-the-art face detection and recognition methods. Moreover, the accuracy in recognizing the faces in the cases of wearing a mask or sunglasses or in live videos is also evaluated. The highest accuracy achieved for the VMU, face recognition, and 14 celebrity datasets is 98%, 98.24%, 89.39%, and 95.71%, respectively. Experimental results on standard image benchmarks demonstrate the effectiveness of the proposed research in accurate face recognition compared to the state-of-the-art face detection and recognition methods

Face Detection & Recognition from Images & Videos Based on CNN & Raspberry Pi

The amount of multimedia content is growing exponentially and a major portion of multimedia content uses images and video. Researchers in the computer vision community are exploring the possible directions to enhance the system accuracy and reliability, and these are the main requirements for robot vision-based systems. Due to the change of facial expressions and the wearing of masks or sunglasses, many face recognition systems fail or the accuracy in recognizing the face decreases in these scenarios. In this work, we contribute a real time surveillance framework using Raspberry Pi and CNN (Convolutional Neural Network) for facial recognition. We have provided a labeled dataset to the system. First, the system is trained upon the labeled dataset to extract different features of the face and landmark face detection and then it compares the query image with the dataset on the basis of features and landmark face detection. Finally, it compares faces and votes between them and gives a result that is based on voting. The classification accuracy of the system based on the CNN model is compared with a mid-level feature extractor that is Histogram of Oriented Gradient (HOG) and the state-of-the-art face detection and recognition methods. Moreover, the accuracy in recognizing the faces in the cases of wearing a mask or sunglasses or in live videos is also evaluated. The highest accuracy achieved for the VMU, face recognition, and 14 celebrity datasets is 98%, 98.24%, 89.39%, and 95.71%, respectively. Experimental results on standard image benchmarks demonstrate the effectiveness of the proposed research in accurate face recognition compared to the state-of-the-art face detection and recognition methods

Insight into antioxidant and anti-inflammatory effects of marine bacterial natural exopolysaccharide (EPSSM) using carrageenan-induced paw edema in rats

Abstract Inflammation is a part of the body’s intricate biological reaction to noxious stimuli and defensive reactions. So, the aim of this investigation was to study the anti-inflammatory activity of exopolysaccharide (EPSSM) using carrageenan-induced paw edema in rats. A halophilic bacterial strain was isolated from marine sediments in the Red Sea in Egypt. The isolate has been visually and physiologically recognized, as well as by analyzing its 16S rRNA gene, which confirms Kocuria sp. clone Asker4. This particular isolate can be referenced using the accession number OL798051.1. EPSSM was subjected to purification and fractionation by a DEAE-cellulose column. Preliminary chemical analysis of EPSSM indicated that the monosaccharides were fructose, glucuronic acid, and xylose, with 2.0, 0.5, and 1.0, respectively. The antioxidant potential of EPSSM was investigated, and it was discovered that the level of activity increased independently of the concentrations, reaching a maximum threshold of 94.13% at 100 µg/mL of EPSSM for 120 min. Also, EPSSM at 50 mg/kg orally produced a significant anti-inflammatory effect on the carrageenan model at 2, 3, and 4 intervals. The EPSSM intervention resulted in reductions in the levels of catalase and superoxide dismutase enzymes, as well as a decrease in glutathione. Furthermore, the levels of nitric oxide, lipid peroxidation, and reactive oxygen species resulting from carrageenan-induced edema showed a significant reduction subsequent to the administration of EPSSM. Moreover, the findings indicated that the protein expression levels of cyclooxygenase-2 and interleukin-6 were reduced following treatment with EPSSM, resulting in a reduction of paw edema

Anti-Alzheimer Activity of Combinations of Cocoa with Vinpocetine or Other Nutraceuticals in Rat Model: Modulation of Wnt3/β-Catenin/GSK-3β/Nrf2/HO-1 and PERK/CHOP/Bcl-2 Pathways

Alzheimer’s disease (AD) is a devastating illness with limited therapeutic interventions. The aim of this study is to investigate the pathophysiological mechanisms underlying AD and explore the potential neuroprotective effects of cocoa, either alone or in combination with other nutraceuticals, in an animal model of aluminum-induced AD. Rats were divided into nine groups: control, aluminum chloride (AlCl3) alone, AlCl3 with cocoa alone, AlCl3 with vinpocetine (VIN), AlCl3 with epigallocatechin-3-gallate (EGCG), AlCl3 with coenzyme Q10 (CoQ10), AlCl3 with wheatgrass (WG), AlCl3 with vitamin (Vit) B complex, and AlCl3 with a combination of Vit C, Vit E, and selenium (Se). The animals were treated for five weeks, and we assessed behavioral, histopathological, and biochemical changes, focusing on oxidative stress, inflammation, Wnt/GSK-3β/β-catenin signaling, ER stress, autophagy, and apoptosis. AlCl3 administration induced oxidative stress, as evidenced by elevated levels of malondialdehyde (MDA) and downregulation of cellular antioxidants (Nrf2, HO-1, SOD, and TAC). AlCl3 also upregulated inflammatory biomarkers (TNF-α and IL-1β) and GSK-3β, leading to increased tau phosphorylation, decreased brain-derived neurotrophic factor (BDNF) expression, and downregulation of the Wnt/β-catenin pathway. Furthermore, AlCl3 intensified C/EBP, p-PERK, GRP-78, and CHOP, indicating sustained ER stress, and decreased Beclin-1 and anti-apoptotic B-cell lymphoma 2 (Bcl-2) expressions. These alterations contributed to the observed behavioral and histological changes in the AlCl3-induced AD model. Administration of cocoa, either alone or in combination with other nutraceuticals, particularly VIN or EGCG, demonstrated remarkable amelioration of all assessed parameters. The combination of cocoa with nutraceuticals attenuated the AD-mediated deterioration by modulating interrelated pathophysiological pathways, including inflammation, antioxidant responses, GSK-3β-Wnt/β-catenin signaling, ER stress, and apoptosis. These findings provide insights into the intricate pathogenesis of AD and highlight the neuroprotective effects of nutraceuticals through multiple signaling pathways