Nasopharyngeal carriage of community-acquired, antibiotic-resistant Streptococcus pneumoniae in a Zambian paediatric population

The present study was conducted to examine the response of amoeboid microglial cells in the postnatal rat brain to colchicine administration. One-day-old postnatal rats were given intraperitoneal injections of colchicine and sacrificed at 7, 14 and 21 days of age. In rats killed at 7 days age, the number of OX-42, OX-18 and ED1 positive amoeboid microglial cells was considerably reduced when compared with the control rats. At 14 and 21 days, the number of cells immunoreactive with the above antibodies was comparable to that of the control rats. The intensity of the immunoreaction with the various antibodies was also comparable in colchicine injected and control rats. When rhodamine isothiocyanate (RhIC) was administered, amoeboid microglial cells emitted a bright fluorescence in control rats as well as in colchicine-injected rats, although in the latter, the number of RhIC labelled cells was considerably reduced. With the antibody bromodeoxyuridine a large number of stained cells were observed in the control rats. On the other hand, occasional labelled cells were recognized in colchicine-injected rats. Apoptotic amoeboid microglial cells were observed in 4-day-old colchicine-injected rats. At the electron microscopic level, amoeboid microglial cells in colchicine-injected rats killed at 7 days of age showed a large number of phagosomes in their cytoplasm compared with the corresponding control rats. At 14 and 21 days, in colchicine-injected and control rats, amoeboid microglial cells did not display any noticeable differences. It is concluded from the present study that colchicine suppresses the number of amoeboid microglial cells, and that this may be attributed to the antimitotic effect of the drug as well as apoptosis induced by it; the phagocytic activity, however, was not affected. The cells returned to their normal population and morphological features once the drug was discontinued, indicating the reversible nature of the drug effect.     

Conditional Generative Adversarial Network Approach for Autism Prediction

Autism Spectrum Disorder (ASD) requires a precise diagnosis in order to be managed and rehabilitated. Non-invasive neuroimaging methods are disease markers that can be used to help diagnose ASD. The majority of available techniques in the literature use functional magnetic resonance imaging (fMRI) to detect ASD with a small dataset, resulting in high accuracy but low generality. Traditional supervised machine learning classification algorithms such as support vector machines function well with unstructured and semi structured data such as text, images, and videos, but their performance and robustness are restricted by the size of the accompanying training data. Deep learning on the other hand creates an artificial neural network that can learn and make intelligent judgments on its own by layering algorithms. It takes use of plentiful low-cost computing and many approaches are focused with very big datasets that are concerned with creating far larger and more sophisticated neural networks. Generative modelling, also known as Generative Adversarial Networks (GANs), is an unsupervised deep learning task that entails automatically discovering and learning regularities or patterns in input data in order for the model to generate or output new examples that could have been drawn from the original dataset. GANs are an exciting and rapidly changing field that delivers on the promise of generative models in terms of their ability to generate realistic examples across a range of problem domains, most notably in image-to-image translation tasks and hasn't been explored much for Autism spectrum disorder prediction in the past. In this paper, we present a novel conditional generative adversarial network, or cGAN for short, which is a form of GAN that uses a generator model to conditionally generate images. In terms of prediction and accuracy, they outperform the standard GAN. The proposed model is 74% more accurate than the traditional methods and takes only around 10 min for training even with a huge dataset.     

Hydrothermal fluorination of carbon nanotubes and its composite with metal ion-doped ceria for a non-gassing flow-in-a-cell application

A facile single step preparation method of fluorinated MWCNTs and metal ion co-doped gadolinium-doped ceria composite is reported. The fluorination was carried out by hydrothermal method using NaF as a fluorinating agent. Fluorination was confirmed by XPS and powder XRD with representative example F-MWCNT-CeO_2?x-Ag. The presence of 21.53 wt% or 18.72 at.% of fluoride was confirmed by EDX mapping along with 12.9 wt% or 1.52 at.% of ceria. Its utilisation in non-gassing electroosmotic pump is reported along with other material like F-MWCNT-CeO_2, F-MWCNT-CeO_2?x-Pt; F-MWCNT-CeO_2?x-Mn and F-MWCNT-CeO₂-H₂O₂. The pump with silver doping showed best performance with 36 μL min^?1 cm^?2 electroosmotic flux of water. The evaluation of electroosmotic flux in different electrolyte solutions of same concentration follow the trend of hydrated size of ions H⁺?>?Li⁺?>?Na⁺?>?K⁺. The stability of the pump was evaluated by continuous operation of the pump. The pump was stable till 90% coulombic capacity consumed. To assess the practical utility like use in insulin pump; the assembled electroosmotic pump was operated for five minutes three times a day to mimic the insulin delivery associated with meals. The pump was stable for 200 h within 10% of error suggesting its efficient practical utility.

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Post‐treatment selection for tailored fatigue performance of 18Ni300 maraging steel manufactured by laser powder bed fusion

This study investigates the fatigue behaviour of additively manufactured 18Ni300 maraging steel. Specifically, the surface and material parameters impacting fatigue performance are analysed through various post‐treatment combinations. Vertically built miniaturised test samples produced by laser powder bed fusion are tested in as‐built and age‐hardening heat‐treated conditions. To utilise the potential of using additive manufacturing for complex‐shaped parts in which conventional machining tools could have limited access, vibratory finishing and sand blasting are employed. The fatigue results show that in as‐built microstructural condition, both the surface treatments significantly enhanced the fatigue performance, with vibratory finishing outperforming sand blasting owing to better surface finish. After heat treatment, sand‐blasted samples performed better than vibratory‐finished ones because of higher residual stresses. This competing interaction between post‐treatments sheds light on identifying the relative influence of various factors. With systematic postfracture and microstructural analyses highlighting the fatigue influencing factors, recommendations are drawn to select post‐treatments to achieve the desired fatigue performance.