Antimicrobial activity of leaf extracts of Euphorbia paralias L. and Melilotus sulcatus Desf. against some pathogenic microorganisms

The present study was aimed to investigate the antimicrobial potential of leaf extracts of Euphorbia paralias and Melilotus sulcatus against four bacterial species Staphylococcus aureus, Pseudomonas aeruginosa, Escherichia coli and Klebsiella sp. and two fungal species Asperigillus niger and Aspergillus flavus. The agar well diffusion assay was used to evaluate the antimicrobial activity. The effect of these extracts was most effective against the bacterial species compared to the fungal species at a used concentration (100 mg/ml). Methanolic extracts of selected plants displayed good antimicrobial activity against all tested microorganisms species, while, no activity for aqueous extracts against tested fungal species. Methanolic extracts were the most effective plant extracts against all tested bacterial species, with MIC and MBC reached 6.2 and 12.5 mg/ml, except Klebsiella sp. which was less sensitive to M. sulcatus methanolic extract and its MIC and MBC reached 12.5 and 25 mg/ml, respectively. These plant extracts which proved to be potentially effective can be used as bioactive agents to control microorganisms caused for diseases and they can be used naturally in the human and veterinary healthcare systems

A combining genetic learning algorithm and risk matrix model using in optimal production program

Jedan od veoma važnih ciljeva u svakom preduzeću je naći optimalno rešenje kod inverznih višekriterijumskih funkcija. Funkcija kojom se opisuju troškovi i funkcija kojom se opisuje profi t po jedinici proizvoda su dve inverzne funkcije sa mnogo konfliktnih informacija o proizvodnim parametrima. Pored toga, za donosioca odluke veoma važno je ukazati na rizik koje optimalno rešenje nosi sa sobom, Iz tog razloga u radu je razvijen model koji predstavlja kombinaciju primene genetskih algoritama (GA) i matrica rizika, radi poboljšanja kvaliteta odluke koja se bazira na kvantitativnim indikatorima, a ne samo na kvalitativnim. Rezultati istraživanja ukazuju da model integracije GA i RM ima veoma veliki značaj u olakšanju procesa odlučivanja o optimalnom proizvodnom programu uz istovremeno i povećanje kvaliteta donesenih odluka.One of the important issues for any enterprises is the compromise optimal solution between inverse of multi objective functions. The prediction of the production cost and/or profit per unit of a product and deal with two obverse functions at same time can be extremely difficult, especially if there is a lot of conflict information about production parameters. But the most important is how much risk of this compromise solution. For this reason, the research introduce and developed a strong and cabable model of genatic algorithim combining with risk management matrix to increase the quality of decisions as it is based on quantitive indicators, not on qualititive evaluation. Research results show that integration of genetic algorithm and risk management matrix model has strong significant in the decision making where it power and time to make the right decision and improve the quality of the decision making as well

Directed evolution to modify the substrate specificity of transketolase, a carbon-carbon bond-forming enzyme

Transketolase (TK) (E.C. 2.2.1.1) has significant potential as a biocatalyst in the production of pharmaceuticals and fine chemicals, catalysing the irreversible and stereospecific transfer of a C2 (1,2-dihydroxyethyl) moiety from the ketol donor substrate P-hydroxypyruvate (p-HPA) to a wide range of aldehyde acceptor substrates, such as glycoaldehyde (GA). Commercial application of TK is restricted by the limited availability and expense of P- HPA as well as its limited activity towards novel substrates. This project describes efforts to generate and identify variants of E. coli TK capable of accepting novel ketol donors and/or aldehyde acceptors. Variants were prepared by saturation site directed mutagenesis (SSDM), and characterised, using novel high-throughput HPLC and TLC screens. The model P-HPA and GA reaction, as well as a range of novel acceptors, such as propionaldehyde, benzaldehyde and hydroxybenzaldehyde were examined. HPLC assays were also developed for all aldehyde substrates, for the detailed analysis of enzyme variants identified from libraries by rapid HPLC or TLC. During such analysis a variety'of buffers were tested for suitability in novel screens. Mops and Hepes were both found to be capable of substrate conversion in the absence of transketolase, hence the discovery of the first ever mimetic reaction for transketolase. Two techniques were used to identify residues to target for random mutagenesis: phylogenetic library design (10 sites determined by S. Costelloe) and structural library design (10 site library generated and screened in collaboration with E. Hibbert). HPLC and TLC analysis identified variants with improved reaction rates towards the model HPA and GA reaction (A29E, A29D, I189Y and 46IS), and also towards propionaldehyde (H26A, H26T, H26K, R358I, H461S, D469S, and D469T), but failed to definitively identify a mutant capable of activity towards benzaldehyde

Interlaminar delamination in unidirectional carbon epoxy composites induced by static and fatigue loading

SIGLEAvailable from British Library Document Supply Centre- DSC:D73294/87 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Invasive and non-invasive diagnostic approaches for microbiological diagnosis of hospital-acquired pneumonia

Background: Data on the methods used for microbiological diagnosis of hospital-acquired pneumonia (HAP) are mainly extrapolated from ventilator-associated pneumonia. HAP poses additional challenges for respiratory sampling, and the utility of sputum or distal sampling in HAP has not been comprehensively evaluated, particularly in HAP admitted to the ICU. Methods: We analyzed 200 patients with HAP from six ICUs in a teaching hospital in Barcelona, Spain. The respiratory sampling methods used were divided into non-invasive [sputum and endotracheal aspirate (EAT)] and invasive [fiberoptic-bronchoscopy aspirate (FBAS), and bronchoalveolar lavage (BAL)]. Results: A median of three diagnostic methods were applied [range 2-4]. At least one respiratory sampling method was applied in 93% of patients, and two or more were applied in 40%. Microbiological diagnosis was achieved in 99 (50%) patients, 69 (70%) by only one method (42% FBAS, 23% EAT, 15% sputum, 9% BAL, 7% blood culture, and 4% urinary antigen). Seventy-eight (39%) patients underwent a fiberoptic-bronchoscopy when not receiving mechanical ventilation. Higher rates of microbiological diagnosis were observed in the invasive group (56 vs. 39%, p = 0.018). Patients with microbiological diagnosis more frequently presented changes in their empirical antibiotic scheme, mainly de-escalation. Conclusions: A comprehensive approach might be undertaken for microbiological diagnosis in critically ill nonventilated HAP. Sputum sampling determined one third of microbiological diagnosis in HAP patients who were not subsequently intubated. Invasive methods were associated with higher rates of microbiological diagnosis

Next-generation cell line selection methodology leveraging data lakes, natural language generation and advanced data analytics

Cell line development is an essential stage in biopharmaceutical development that often lies on the critical path. Failure to fully characterise the lead clone during initial screening can lead to lengthy project delays during scale-up, which can potentially compromise commercial manufacturing success. In this study, we propose a novel cell line development methodology, referenced as CLD4, which involves four steps enabling autonomous data-driven selection of the lead clone. The first step involves the digitalisation of the process and storage of all available information within a structured data lake. The second step calculates a new metric referenced as the cell line manufacturability index (MICL) quantifying the performance of each clone by considering the selection criteria relevant to productivity, growth and product quality. The third step implements machine learning (ML) to identify any potential risks associated with process operation and relevant critical quality attributes (CQAs). The final step of CLD4 takes into account the available metadata and summaries all relevant statistics generated in steps 1–3 in an automated report utilising a natural language generation (NLG) algorithm. The CLD4 methodology was implemented to select the lead clone of a recombinant Chinese hamster ovary (CHO) cell line producing high levels of an antibody-peptide fusion with a known product quality issue related to end-point trisulfide bond (TSB) concentration. CLD4 identified sub-optimal process conditions leading to increased levels of trisulfide bond that would not be identified through conventional cell line development methodologies. CLD4 embodies the core principles of Industry 4.0 and demonstrates the benefits of increased digitalisation, data lake integration, predictive analytics and autonomous report generation to enable more informed decision making

Development of a model for anemia of inflammation that is relevant to critical care

Background: Anemia of inflammation (AI) is common in critically ill patients. Although this syndrome negatively impacts the outcome of critical illness, understanding of its pathophysiology is limited. Also, new therapies that increase iron availability for erythropoiesis during AI are upcoming. A model of AI induced by bacterial infections that are relevant for the critically ill is currently not available. This paper describes the development of an animal model for AI that is relevant for critical care research. Results: In experiments with rats, the rats were inoculated either repeatedly or with a slow release of Streptococcus pneumoniae or Pseudomonas aeruginosa. Rats became ill, but their hemoglobin levels remained stable. The use of a higher dose of bacteria resulted in a lethal model. Then, we turned to a model with longer disease duration, using pigs that were supported by mechanical ventilation after inoculation with P. aeruginosa. The pigs became septic 12 to 24 h after inoculation, with a statistically significant decrease in mean arterial pressure and base excess, while heart rate tended to increase. Pigs needed resuscitation and vasopressor therapy to maintain a mean arterial pressure > 60 mmHg. After 72 h, the pigs developed anemia (baseline 9.9 g/dl vs. 72 h, 7.6 g/dl, p = 0.01), characterized by statistically significant decreased iron levels, decreased transferrin saturation, and increased ferritin. Hepcidin levels tended to increase and transferrin levels tended to decrease. Conclusions: Using pathogens commonly involved in pulmonary sepsis, AI could not be induced in rats. Conversely, in pigs, P. aeruginosa induced pulmonary sepsis with concomitant AI. This AI model can be applied to study the pathophysiology of AI in the critically ill and to investigate the effectivity and toxicity of new therapies that aim to increase iron availability. Keywords: Anemia of inflammation; Animal model; ICU; Infection; Iron