Effects of Escapin Intermediate Products (EIP-K) on Biofilms of Pseudomonas aeruginosa

Escapin is an L-amino acid oxidase that produces antimicrobial metabolites collectively called “Escapin Intermediate Products” (EIP-K). EIP-K and H2O2 together were previously shown to be bactericidal towards diverse planktonic bacteria. The present work investigates the ability of EIP-K and H2O2 to antagonize bacterial biofilms, using Pseudomonas aeruginosa as a model. The project had three aims: 1) determine the most effective concentrations of EIP-K and H2O2 necessary to break down existing P. aeruginosa biofilms, using a crystal violet assay; 2) examine the ability of EIP-K + H2O2 to inhibit biofilm formation, using triphenyl tetrazolium chloride dye; and 3) determine the effect of EIP-K + H2O2 on the viability, biomass and structure of biofilms cultivated in flow cells using confocal laser scanning microscopy (CLSM). Results showed that EIP-K + H2O2 significantly reduced biofilm biomass relative to controls and that the compounds are effective at nanomolar concentrations

EFFECT OF GROWTH REGULATOR NAA AND IBA APPLICATIONS ON TOTAL PHENOLIC AND FLAVONOID COMPOUNDS EXTRACTED FROM IN VITRO PRODUCED CALLUS OF CHICORY PLANT (Cichorium intybus L.)

This research study was carried out in the plant tissue culture laboratory of the Agricultural Botany Department, Faculty of Agriculture, Ain Shams University, Shoubra El-khaima, Cairo, Egypt. Experiments were executed for the duration of two consecutive years 2017 and 2018 on chicory plant. Chicory (Cichorium intybus L.), which belongs to Asteraceae family, is considered as an important medicinal plant due to the presence of many bioactive substances such flavonoids, phenolic compounds, alkaloids, steroids, terpenoids, including( coumarines, cichoriin, esculetin, inulin, sesquiterpene lactones, chicoric acid, caffeic acid and some vitamins). In this research in vitro experiments were carried out using full strength Murashige and Skoog basal medium (MS) supplemented with different combinations of two plant growth regulators; Indole-3-butyric acid (IBA) including two concentrations (0.5 – 2.0 mg/l) and Naphthalene acetic acid (NAA) comprising four concentrations (0.5 – 2.0 – 3.0 – 5.0 mg/l). An abaxially (lower side) leaf explants (square pieces 0.5 × 0.5 cm) which were taken from 20 days old aseptic chicory seedlings were inoculated to (MS) surface. Initially, chicory seeds were aseptically germinated on half-strength MS medium, after surface sterilization by 70 % (v/v) ethanol for 60 seconds then soaking in 10 % Clorox (0.5% sodium hypochlorite NaOCl) for 10 min to produce the aseptic chicory seedlings which were the source of true leaf explants used in this research study. Total phenolic compounds and flavonoids content were extracted from six-week C. intybus friable callus produced under both light and dark in vitro culture conditions inside a growth chamber incubation room where temperature was adjusted at 25oC ±1. Total phenolic compounds and flavonoids were determined by spectrophotometric methods. The highest values for their contents were from chicory calli when MS callus induction medium was supplemented with 2 mg/l NAA under total dark condition when compared with the other remaining growth regulator treatment combinations and alternative light regime conditions

Immune-based strategies for treatment and prevention of hepatitis C virus infection

Hepatitis C virus (HCV) infection affects about 3% of the world’s population. Currently, the gold standard therapy does not work in a high percentage of patients and with all genotypes. In addition, it is costly, is associated with many side-effects. So, more convenient therapeutic strategies have been sought. These include, direct acting antivirals (DAAs), and immune-based therapy. Four DAA molecules have recently been approved by FDA.  Immune-based therapy aims at augmenting host immunity, thus prevention of infection or clearance of the virus with subsequent recovery can occur. Boosting T cell responses and activating humoral immune reactions have been targeted in the development of novel combating tools. The most intensively studied immune-therapeutic strategies are: 1) vaccines; either therapeutic or prophylactic, 2) dendritic cell immunotherapy, 3) antagonists of T cell inhibitory factors, 4) anti-HCV neutralizing antibodies, 4) cytokines and chemokines, 5) agonists for TLRs, and 6) caspase inhibitors

Hepcidin and its Related Hematological Biomarkers of Anemia in Children on Hemodialysis: Role of Carnitine Deficiency

BACKGROUND: Anemia is one of the most common complications in end-stage renal disease (ESRD) patients. Hepcidin is a hormone that regulates iron homeostasis in patients with ESRD. Carnitine deficiency is commonly seen in hemodialysis (HD) patients. AIM: This study aimed to investigate the relationship between hepcidin and inflammatory and other anemia markers in children with ESRD and to evaluate the association of carnitine deficiency with anemia in these patients. SUBJECTS AND METHODS: Thirty pediatric patients with ESRD undergoing HD, and thirty healthy, age- and sex-matched children served as controls were included in the study. Serum levels hepcidin, iron status, high-sensitivity C-reactive protein, and total carnitine were measured. RESULTS: Statistically significant increases in serum levels of hepcidin (100.7 ± 0.99 ng\ml vs. 77.43 ± 0.8 ng\ml, p = 0.000), was found in HD children as compared to healthy controls. Statistically significant increase in serum levels of hs-CRP (3.94 ± 0.19 mg/l vs. 1.36 ± 0.07 mg/l, p = 0.04) was found in HD children as compared to healthy controls. However, serum levels of carnitine (29.59 ± 2.46 μmol/L vs. 36 ± 2.39 μmol/L, p = 0.000) showed statistically significant decreases in HD children as compared to healthy controls positive correlation was found between hepcidin and hs-CRP (r = 0.059, p = 0.042). Furthermore, a positive correlation was present between serum carnitine levels and serum iron levels (r = 0.651, p = 0.042). CONCLUSION: Serum hepcidin may be a more useful biomarker of functional iron deficiency in children on HD. The efficacy of carnitine treatment for children on HD with carnitine deficiency and its effect on anemia needs to be studied

Bioactive metabolites of Streptomyces misakiensis display broad-spectrum antimicrobial activity against multidrug-resistant bacteria and fungi

BackgroundAntimicrobial resistance is a serious threat to public health globally. It is a slower-moving pandemic than COVID-19, so we are fast running out of treatment options.PurposeThus, this study was designed to search for an alternative biomaterial with broad-spectrum activity for the treatment of multidrug-resistant (MDR) bacterial and fungal pathogen-related infections.MethodsWe isolated Streptomyces species from soil samples and identified the most active strains with antimicrobial activity. The culture filtrates of active species were purified, and the bioactive metabolite extracts were identified by thin-layer chromatography (TLC), preparative high-performance liquid chromatography (HPLC), nuclear magnetic resonance (NMR) spectroscopy, and gas chromatography-mass spectrometry (GC-MS). The minimum inhibitory concentrations (MICs) of the bioactive metabolites against MDR bacteria and fungi were determined using the broth microdilution method.ResultsPreliminary screening revealed that Streptomyces misakiensis and S. coeruleorubidus exhibited antimicrobial potential. The MIC50 and MIC90 of S. misakiensis antibacterial bioactive metabolite (ursolic acid methyl ester) and antifungal metabolite (tetradecamethylcycloheptasiloxane) against all tested bacteria and fungi were 0.5 μg/ml and 1 μg/mL, respectively, versus S. coeruleorubidus metabolites: thiocarbamic acid, N,N-dimethyl, S-1,3-diphenyl-2-butenyl ester against bacteria (MIC50: 2 μg/ml and MIC90: 4 μg/mL) and fungi (MIC50: 4 μg/ml and MIC90: 8 μg/mL). Ursolic acid methyl ester was active against ciprofloxacin-resistant strains of Streptococcus pyogenes, S. agalactiae, Escherichia coli, Klebsiella pneumoniae, and Salmonella enterica serovars, colistin-resistant Aeromonas hydrophila and K. pneumoniae, and vancomycin-resistant Staphylococcus aureus. Tetradecamethylcycloheptasiloxane was active against azole- and amphotericin B-resistant Candida albicans, Cryptococcus neoformans, C. gattii, Aspergillus flavus, A. niger, and A. fumigatus. Ursolic acid methyl ester was applied in vivo for treating S. aureus septicemia and K. pneumoniae pneumonia models in mice. In the septicemia model, the ursolic acid methyl ester-treated group had a significant 4.00 and 3.98 log CFU/g decrease (P < 0.05) in liver and spleen tissue compared to the infected, untreated control group. Lung tissue in the pneumonia model showed a 2.20 log CFU/g significant decrease in the ursolic acid methyl ester-treated group in comparison to the control group. The haematological and biochemical markers in the ursolic acid methyl ester-treated group did not change in a statistically significant way. Moreover, no abnormalities were found in the histopathology of the liver, kidneys, lungs, and spleen of ursolic acid methyl ester-treated mice in comparison with the control group. ConclusionS. misakiensis metabolite extracts are broad-spectrum antimicrobial biomaterials that can be further investigated for the potential against MDR pathogen infections. Hence, it opens up new horizons for exploring alternative drugs for current and reemerging diseases

From Pixels to Diagnoses: Deep Learning's Impact on Medical Image Processing-A Survey

In healthcare, medical image processing is considered one of the most significant procedures used in diagnosing pathological conditions. Magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, and X-ray visualization have been used. Health institutions are seeking to use artificial intelligence techniques to develop medical image processing and reduce the burden on physicians and healthcare workers. Deep learning has occupied an important place in the healthcare field, supporting specialists in analysing and processing medical images. This article will present a comprehensive survey on the significance of deep learning in the areas of segmentation, classification, disease diagnosis, image generation, image transformation, and image enhancement. This survey seeks to provide an overview of the significance of deep learning in the early detection of diseases, studying tumor localization behaviors, predicting malignant diseases, and determining the suitable treatment for a patient. This article concluded that deep learning is of great significance in improving healthcare, enabling healthcare workers to make diagnoses quickly and more accurately, and improving patient outcomes by providing them with appropriate treatment strategies

Electrical power output prediction of combined cycle power plants using a recurrent neural network optimized by waterwheel plant algorithm

It is difficult to analyze and anticipate the power output of Combined Cycle Power Plants (CCPPs) when considering operational thermal variables such as ambient pressure, vacuum, relative humidity, and temperature. Our data visualization study shows strong non-linearity in the experimental data. We observe that CCPP energy production increases linearly with temperature but not pressure. We offer the Waterwheel Plant Algorithm (WWPA), a unique metaheuristic optimization method, to fine-tune Recurrent Neural Network hyperparameters to improve prediction accuracy. A robust mathematical model for energy production prediction is built and validated using anticipated and experimental data residuals. The residuals’ uniformity above and below the regression line suggests acceptable prediction errors. Our mathematical model has an R-squared value of 0.935 and 0.999 during training and testing, demonstrating its outstanding predictive accuracy. This research provides an accurate way to forecast CCPP energy output, which could improve operational efficiency and resource utilization in these power plants

A novel voting classifier for electric vehicles population at different locations using Al-Biruni earth radius optimization algorithm

The rising popularity of electric vehicles (EVs) can be attributed to their positive impact on the environment and their ability to lower operational expenses. Nevertheless, the task of determining the most suitable EV types for a specific site continues to pose difficulties, mostly due to the wide range of consumer preferences and the inherent limits of EVs. This study introduces a new voting classifier model that incorporates the Al-Biruni earth radius optimization algorithm, which is derived from the stochastic fractal search. The model aims to predict the optimal EV type for a given location by considering factors such as user preferences, availability of charging infrastructure, and distance to the destination. The proposed classification methodology entails the utilization of ensemble learning, which can be subdivided into two distinct stages: pre-classification and classification. During the initial stage of classification, the process of data preprocessing involves converting unprocessed data into a refined, systematic, and well-arranged format that is appropriate for subsequent analysis or modeling. During the classification phase, a majority vote ensemble learning method is utilized to categorize unlabeled data properly and efficiently. This method consists of three independent classifiers. The efficacy and efficiency of the suggested method are showcased through simulation experiments. The results indicate that the collaborative classification method performs very well and consistently in classifying EV populations. In comparison to similar classification approaches, the suggested method demonstrates improved performance in terms of assessment metrics such as accuracy, sensitivity, specificity, and F-score. The improvements observed in these metrics are 91.22%, 94.34%, 89.5%, and 88.5%, respectively. These results highlight the overall effectiveness of the proposed method. Hence, the suggested approach is seen more favorable for implementing the voting classifier in the context of the EV population across different geographical areas

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London