Metabolic Profiling of VOCs Emitted by Bacteria Isolated from Pressure Ulcers and Treated with Different Concentrations of Bio-AgNPs

Considering the advent of antibiotic resistance, the study of bacterial metabolic behavior stimulated by novel antimicrobial agents becomes a relevant tool to elucidate involved adaptive pathways. Profiling of volatile metabolites was performed to monitor alterations of bacterial metabolism induced by biosynthesized silver nanoparticles (bio-AgNPs). Escherichia coli, Enterococcus faecalis, Klebsiella pneumoniae and Proteus mirabilis were isolated from pressure ulcers, and their cultures were prepared in the presence/absence of bio-AgNPs at 12.5, 25 and 50 µg mL⁻¹. Headspace solid phase microextraction associated to gas chromatography–mass spectrometry was the employed analytical platform. At the lower concentration level, the agent promoted positive modulation of products of fermentation routes and bioactive volatiles, indicating an attempt of bacteria to adapt to an ongoing suppression of cellular respiration. Augmented response of aldehydes and other possible products of lipid oxidative cleavage was noticed for increasing levels of bio-AgNPs. The greatest concentration of agent caused a reduction of 44 to 80% in the variety of compounds found in the control samples. Pathway analysis indicated overall inhibition of amino acids and fatty acids routes. The present assessment may provide a deeper understanding of molecular mechanisms of bio-AgNPs and how the metabolic response of bacteria is untangled.     

Two-step sinter-crystallization of K2O–CaO–P2O5–SiO2 (45S5-K) bioactive glass

Bioactive glasses and glass-ceramics (GCs) effectively regenerate bone tissue, however most GCs show improved mechanical properties. In this work, we developed and tested a rarely studied bioactive glass composition (24.4K₂O-26.9CaO-46.1SiO₂-2.6P₂O₅ mol%, identified as 45S5-K) with different particle sizes and heating rates to obtain a sintered GC that combines good fracture strength, low elastic modulus, and bioactivity. We analyzed the influence of the sintering processing conditions in the elastic modulus, Vickers microhardness, density, and crystal phase formation in the GC. The best GC shows improved properties compared with its parent glass. This glass achieves a good densification degree with a two-step viscous flow sintering approach and the resulting GC shows as high bioactivity as that of the standard 45S5 Bioglass®. Furthermore, the GC elastic modulus (56 GPa) is relatively low, minimizing stress shielding. Therefore, we unveiled the glass sintering behavior with concurrent crystallization of this complex bioactive glass composition and developed a potential GC for bone regeneration.     

Cyanogenic,carotenoids and protein composition in leaves and roots across seven diverse population found in the world cassava germplasm collection at CIAT,Colombia

The objective of this study was to characterise the nutritional potential of leaves and identify a diversity centre with low cyanide and high nutrient content among 178 Latin American cassava genotypes. This field-based collection represents the seven diversity centres, held at The International Center for Tropical Agriculture (CIAT Palmira, Colombia) by the Cassava Program. The cyanide, all-trans-β-carotene and lutein concentrations in cassava leaves ranged from 346 to 7484 ppm dry basis (db), from 174–547 μg g⁻¹ db and 15–181 μg g⁻¹ db, respectively. Cassava leaves also showed significant levels of essential amino acids leucine, lysine, phenylalanine, valine and threonine, and average total protein content of 26.24 g 100 g⁻¹ db. Among seven diversity centres, South American rainforest group showed low cyanide and high carotene content in leaves. In addition, VEN77 and PAN51 genotypes stood out for having low cyanide in leaves and roots and high carotene in leaves. This genetic diversity can be used to select high potential progenitors for breeding purposes.     

Piperacillin/tazobactam‐induced neurotoxicity in a hemodialysis patient: A case report

Antibiotics are potentially a cause of neurotoxicity in dialysis patients, the most common are the beta‐lactams as ceftazidime and cefepime, and few cases have been reported after piperacillin/tazobactam use. This report presents a case of a hypertensive and diabetic 67‐year‐old woman in regular hemodialysis, which previously had a stroke. She was hospitalized presenting pneumonia, which was initially treated with cefepime. Two days after treatment, she presented dysarthria, left hemiparesis, ataxia, and IX and X cranial nerves paresis. Computed tomography showed no acute lesions and cefepime neurotoxicity was hypothesized, and the antibiotic was replaced by piperacillin/tazobactam. The neurologic signs disappeared; however, 4 days after with piperacillin/tazobactam treatment, the neurological manifestations returned. A new computed tomography showed no new lesions, and the second antibiotic regimen withdrawn. After two hemodialysis sessions, the patient completely recovered from neurological manifestations. The patient presented sequentially neurotoxicity caused by two beta‐lactams antibiotics. This report meant to alert clinicians that these antibiotics have dangerous neurological effects in chronic kidney disease patients.     

Low standby power state storage for sub-130-nm technologies

Handheld and other battery-powered ICs require process scaling to increase functional integration and reduce active power consumption. Scaling also increases leakage current components to the point where standby power is frequently a limiting design factor. A scheme combining low-leakage thick-gate shadow latches and high-performance transistors is presented that decouples performance from standby power in sub-130-nm technologies. Circuit design and operation, including pulse-clocked latches, use of dynamic circuits, and inclusion of scan is presented. The approach is validated by experimental results on a 90-nm process.     

Stochastic molecular descriptors for polymers. 4. Study of complex mixtures with topological indices of mass spectra spiral and star networks: The blood proteome case

The Quantitative Structure-Property Relationships (QSPRs) based on Graph or Network Theory are important for predicting the properties of polymeric systems. In the three previous papers of this series (Polymer 45 (2004) 3845-3853; Polymer 46 (2005) 2791-2798; and Polymer 46 (2005) 6461-6473) we focused on the uses of molecular graph parameters called topological indices (TIs) to link the structure of polymers with their biological properties. However, there has been little effort to extend these TIs to the study of complex mixtures of artificial polymers or biopolymers such as nucleic acids and proteins. In this sense, Blood Proteome (BP) is one of the most important and complex mixtures containing protein polymers. For instance, outcomes obtained by Mass Spectrometry (MS) analysis of BP are very useful for the early detection of diseases and drug-induced toxicities. Here, we use two Spiral and Star Network representations of the MS outcomes and defined a new type of TIs. The new TIs introduced here are the spectral moments (π_k) of the stochastic matrix associated to the Spiral graph and describe non-linear relationships between the different regions of the MS characteristic of BP. We used the MARCH-INSIDE approach to calculate the π_k(SN) of different BP samples and S2SNet to determine several Star graph TIs. In the second step, we develop the corresponding Quantitative Proteome-Property Relationship (QPPR) models using the Linear Discriminant Analysis (LDA). QPPRs are the analogues of QSPRs in the case of complex biopolymer mixtures. Specifically, the new QPPRs derived here may be used to detect drug-induced cardiac toxicities from BP samples. Different Machine Learning classification algorithms were used to fit the QPPRs based on π_k(SN), showing J48 decision tree classifier to have the best performance. These results suggest that the present approach captures important features of the complex biopolymers mixtures and opens new opportunities to the application of the idea supporting classic QSPRs in polymer sciences.     

Impact of the addition of an enzyme pool on an activated sludge system treating dairy wastewater under fat shock loads

BACKGROUND: The application of lipase‐rich enzyme pools (such as the crude solid enzymatic preparation (SEP) obtained from Penicillium restrictum solid‐state fermentation of agro‐industrial wastes) to activated sludge systems may be an effective strategy for preventing various operational problems. The continuous addition of SEP to the treatment system can become cost‐prohibitive when in situ production and/or storage are factored in. The application of SEP to high‐fat wastewater treatment would only be justified as an emergency measure, such as a sudden increase in the fat content of the bioreactor influent. Therefore, the primary objective of this work was to investigate the efficiency of a crude SEP during fat shock loads, simulated through the periodic addition of dairy industry waste containing high fat concentrations to the feed stock of an activated sludge system, operated in continuous mode. RESULTS: The test bioreactor exhibited a higher average chemical oxygen demand (COD) removal efficiency than the control bioreactor (83% for control and 90% for test) and the fat accumulation in the biological flocs of the test bioreactor was 3.2 times lower than that in the control bioreactor. Turbidity was also lower in the effluent of the test bioreactor (123 and 66 FTU in control and test, respectively) and it had a shorter recovery time between shock loads, especially when the interval between loads was shorter than one month (biweekly and weekly shock loads). CONCLUSION: The addition of SEP during fat overloads in the reactor feed maintained efficient COD removal in the test bioreactor for 270 days without any operational problems. Copyright © 2008 Society of Chemical Industry     

Neural network systems to reproduce crash behavior of structural components

The use of neural networks as global approximation tool in crashworthiness problems is here investigated. Neural networks are not only asked to return some meaningful indices of the structural behavior but also to reproduce load-time curves during crash phenomena. To contain the number of examples required for the training process, parallel subsystems of small neural networks are designed. Design points for the training process are obtained by explicit finite element analyses performed by PAMCRASH. The settlement of the points in the design domain is defined using a maximum distance concept. The procedure is applied to different typical absorption structures made of aluminum alloy: riveted tubes, honeycomb structures, longitudinal keel beam and intersection elements of helicopter subfloors.