Role of MBL2 polymorphisms in sepsis and survival: A pilot study and in silico analysis

Sepsis is a serious infection-induced syndrome with serious ramifications, especially in intensive care units. Global concern motivated the investigation of the role of related genes’ polymorphism in predicting the liability to infection, sepsis, septic shock and survival. Among these genes is the gene encoding mannose-binding lectin (MBL), with its remarkable importance in the immune system. However, the previous studies showed conflicting results and ambiguity that urged us to engage with this issue in the Egyptian population. Prediction of functional and structural impacts of single nucleotide polymorphisms (SNPs) was done using in silico methods. A prospective observational study was conducted in intensive care units; one hundred and thirty patients were followed up. Genotyping was performed using real-time polymerase chain reaction (RT-PCR) technology. MBL SNPs showed a remarkable high frequency in our population, as well. No significant association was found between MBL2 genotypes and any of our analyses (sepsis, septic shock and survival). Only septic shock and age were independently associated with time of survival by Cox regression analysis. Our study may confirm the redundancy of MBL and the absence of significant impact on sepsis liability and mortality in adult patients

Studying the Effect of Adding Titanium Dioxide (TiO2) Nanoparticles on the Compressive Strength of Chemical and Heat-activated Acrylic Denture Base Resins

Problem: The commonly used acrylic resins for fabricating denture base suffer from poor mechanical properties. Aim: This study aimed to assess the effect of incorporating Titanium Dioxide (TiO2) nanoparticles (NPs) as a reinforcement agent on the compressive strength of acrylic denture base materials. Materials and methods: Thirty-two cylindrical specimens (22 mm in height and 12 mm in diameter) were prepared from PMMA resins with and without TiO2 NPs. They were allocated into two main groups according to the materials used such as cold cure and heat cure denture base resins and then subdivided into two subgroups each containing eight specimens: control (without nanoparticles) and experimental (with 2 wt.% TiO2 NPs). TiO2 NPs were synthesized via a chemical processing route and particle morphology and size distribution were assessed using SEM and AFM while XRD technique was employed to determine the crystalline structure of the NPs. Compression test was performed on the specimens using a universal Instron testing machine to compare the compressive strength. Results: Size of crystalline TiO2 NPs varied between 40-80 nm. The mean compressive strength for the cold cure acrylic resin (control group) and its nanocomposite (experimental group) were found as 15.37 MPa and 17.42 MPa while for the heat cure acrylic resin and its nanocomposite were 23.04 MPa and 24.30 MPa. A statistically significant difference was recorded in the compressive strength between the cold cure acrylic resin and its nanocomposite. However, the difference was non-significant in the case of heat cure acrylic resin. Conclusion: The compressive strength of both cold cure and heat cure acrylic resins increased after incorporation TiO2 NPs

Direct enzymatic esterification of cotton and Avicel with wild-type and engineered cutinases

In this work, the surface of cellulose, either Avicel or cotton fabric, was modified using cutinases without any previous treatment to swell or to solubilise the polymer. Aiming further improvement of cutinase ester synthase activity on cellulose, an engineered cutinase was investigated. Wild-type cutinase from Fusarium solani and its fusion with the carbohydrate-binding module N1 from Cellulomonas fimi were able to esterify the hydroxyl groups of cellulose with distinct efficiencies depending on the acid substrate/solvent system used, as shown by titration and by ATR-FTIR. The carbonyl stretching peak area increased significantly after enzymatic treatment during 72 h at 30 °C. Cutinase treatment resulted in relative increases of 31 and 9 % when octanoic acid and vegetable oil were used as substrates, respectively. Cutinase-N1 treatment resulted in relative increases of 11 and 29 % in the peak area when octanoic acid and vegetable oil were used as substrates, respectively. The production and application of cutinase fused with the domain N1 as a cellulose ester synthase, here reported for the first time, is therefore an interesting strategy to pursuit.This work was co-funded by the European Social Fund through the management authority POPH and FCT, Postdoctoral fellowship reference: SFRH/BPD/47555/2008. The authors also want to thank Doctor Raul Machado for his valuable help on FTIR spectral data treatment

Constrained pre-equalization accounting for multi-path fading emulated using large RC networks: applications to wireless and photonics communications

Multi-path propagation is modelled assuming a multi-layer RC network with randomly allocated resistors and capacitors to represent the transmission medium. Due to frequency-selective attenuation, the waveforms associated with each propagation path incur path-dependent distortion. A pre-equalization procedure that takes into account the capabilities of the transmission source as well as the transmission properties of the medium is developed. The problem is cast within a Mixed Integer Linear Programming optimization framework that uses the developed nominal RC network model, with the excitation waveform customized to optimize signal fidelity from the transmitter to the receiver. The objective is to match a Gaussian pulse input accounting for frequency regions where there would be pronounced fading. Simulations are carried out with different network realizations in order to evaluate the sensitivity of the solution with respect to changes in the transmission medium mimicking the multi-path propagation. The proposed approach is of relevance where equalization techniques are difficult to implement. Applications are discussed within the context of emergent communication modalities across the EM spectrum such as light percolation as well as emergent indoor communications assuming various modulation protocols or UWB schemes as well as within the context of space division multiplexing

Clinico-radiological features, molecular spectrum, and identification of prognostic factors in developmental and epileptic encephalopathy due to inosine triphosphate pyrophosphatase (ITPase) deficiency

Developmental and epileptic encephalopathy 35 (DEE 35) is a severe neurological condition caused by biallelic variants in ITPA, encoding inosine triphosphate pyrophosphatase, an essential enzyme in purine metabolism. We delineate the genotypic and phenotypic spectrum of DEE 35, analyzing possible predictors for adverse clinical outcomes. We investigated a cohort of 28 new patients and reviewed previously described cases, providing a comprehensive characterization of 40 subjects. Exome sequencing was performed to identify underlying ITPA pathogenic variants. Brain MRI (magnetic resonance imaging) scans were systematically analyzed to delineate the neuroradiological spectrum. Survival curves according to the Kaplan–Meier method and log-rank test were used to investigate outcome predictors in different subgroups of patients. We identified 18 distinct ITPA pathogenic variants, including 14 novel variants, and two deletions. All subjects showed profound developmental delay, microcephaly, and refractory epilepsy followed by neurodevelopmental regression. Brain MRI revision revealed a recurrent pattern of delayed myelination and restricted diffusion of early myelinating structures. Congenital microcephaly and cardiac involvement were statistically significant novel clinical predictors of adverse outcomes. We refined the molecular, clinical, and neuroradiological characterization of ITPase deficiency, and identified new clinical predictors which may have a potentially important impact on diagnosis, counseling, and follow-up of affected individuals

Custom Hardware Versus Cloud Computing in Big Data

The computational and data handling challenges in big data are immense yet a market is steadily growing traditionally supported by technologies such as Hadoop for management and processing of huge and unstructured datasets. With this ever increasing deluge of data we now need the algorithms, tools and computing infrastructure to handle the extremely computationally intense data analytics, looking for patterns and information pertinent to creating a market edge for a range of applications. Cloud computing has provided opportunities for scalable high-performance solutions without the initial outlay of developing and creating the core infrastructure. One vendor in particular, Amazon Web Services, has been leading this field. However, other solutions exist to take on the computational load of big data analytics. This chapter provides an overview of the extent of applications in which big data analytics is used. Then an overview is given of some of the high-performance computing options that are available, ranging from multiple Central Processing Unit (CPU) setups, Graphical Processing Units (GPUs), Field Programmable Gate Arrays (FPGAs) and cloud solutions. The chapter concludes by looking at some of the state of the art solutions for deep learning platforms in which custom hardware such as FPGAs and Application Specific Integrated Circuits (ASICs) are used within a cloud platform for key computational bottlenecks