Brain and the gastrointestinal (GI) tract are intimately connected to form a bidirectional neurohumoral communication system. The communication between gut and brain, knows as the gut-brain axis, is so well established that the functional status of gut is always related to the condition of brain. The researches on the gut-brain axis were traditionally focused on the psychological status affecting the function of the GI tract. However, recent evidences showed that gut microbiota communicates with the brain via the gut-brain axis to modulate brain development and behavioral phenotypes. These recent fi ndings on the new role of gut microbiota in the gut-brain axis implicate that gut microbiota could associate with brain functions as well as neurological diseases via the gut-brain axis. To elucidate the role of gut microbiota in the gut-brain axis, precise identification of the composition of microbes constituting gut microbiota is an essential step. However, identifi cation of microbes constituting gut microbiota has been the main technological challenge currently due to massive amount of intestinal microbes and the diffi culties in culture of gut microbes. Current methods for identifi cation of microbes constituting gut microbiota are dependent on omics analysis methods by using advanced high tech equipment. Here, we review the association of gut microbiota with the gut-brain axis, including the pros and cons of the current high throughput methods for identifi cation of microbes constituting gut microbiota to elucidate the role of gut microbiota in the gut-brain axis.

Resveratrol has been clinically shown to possess a number of human health benefits. As a result, many attempts have been made to engineer resveratrol production in major cereal grains but have been largely unsuccessful. In this study, we report the creation of a transgenic rice plant that accumulates 1.9 µg resveratrol/g in its grain, surpassing the previously reported anti-metabolic syndrome activity of resveratrol through a synergistic interaction between the transgenic resveratrol and the endogenous properties of the rice. Consumption of our transgenic resveratrol-enriched rice significantly improved all aspects of metabolic syndrome and related diseases in animals fed a high-fat diet. Compared with the control animals, the resveratrol-enriched rice reduced body weight, blood glucose, triglycerides, total cholesterol, and LDL-cholesterol by 24.7%, 22%, 37.4%, 27%, and 59.6%, respectively. The resveratrol-enriched rice from our study may thus provide a safe and convenient means of preventing metabolic syndrome and related diseases without major lifestyle changes or the need for daily medications. These results also suggest that future transgenic plants could be improved if the synergistic interactions of the transgene with endogenous traits of the plant are considered in the experimental design.

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Obesity is the most prevalent disease in the world which poses a serious risk for various chronic diseases. However, currently there are not any therapeutic agents that reduce body weight without causing serious side effects. In order to prevent and/or treat obesity and related diseases through a nutraceutical approach, we created a resveratrol-enriched transgenic rice accumulating 1.4 μg/g of resveratrol in its grain, DJ-526. Feeding of mice with the resveratrol-enriched rice DJ-526 showed excellent anti-obesity effect with reduction of body weights and abdominal fat volumes compared to the control by 20.0% and 31.3%, respectively. Also, the consumption of the resveratrol-enriched rice DJ526 significantly improved the blood lipid profiles and glucose levels in the animal experiments. Our resveratrol-enriched rice DJ-526 rice could provide both safe and convenient way for people with obesity and related diseases without major change of lifestyle or unwanted side effects from medication.

Outbreaks of infection occur more often than they are reported in most developing countries, largely due to poor diagnostic services. A Klebsiella species bacteremia outbreak in a newborn unit with high mortality was recently encountered at a location being surveilled for childhood bacteremia. These surveillance efforts offered the opportunity to determine the cause of this neonatal outbreak. In this report, we present the whole-genome sequences of New Delhi metallo-β-lactamase (NDM-5)-containing Klebsiella quasipneumoniae subsp. similipneumoniae bloodstream isolates from a neonatal bacteremia outbreak at a tertiary hospital in Nigeria and as part of the largest collection of K. pneumoniae bloodstream isolates from children in Africa. Comparative analysis of the genetic environment surrounding the NDM-5 genes revealed nearly perfect sequence identity to blaNDM-5-bearing IncX3-type plasmids from other members of the Enterobacteriaceae. IMPORTANCE Carbapenem-resistant Klebsiella pneumoniae is of global health importance, yet there is a paucity of genome-based studies in Africa. Here we report fatal blood-borne NDM-5-producing K. quasipneumoniae subsp. similipneumoniae infections from Nigeria, Africa. New Delhi metallo-β-lactamase (NDM)-producing Klebsiella spp. are responsible for high mortality and morbidity, with the NDM-5 variant showing elevated carbapenem resistance. The prevalence of NDM-5 in Klebsiella has been limited primarily to K. pneumoniae, with only one isolate being collected from Africa. During an outbreak of sepsis in a teaching hospital in Nigeria, five NDM-5-producing K. quasipneumoniae subsp. similipneumoniae sequence type 476 isolates were identified. Given the increased resistance profile of these strains, this study highlights the emerging threat of blaNDM-5 dissemination in hospital environments. The observation of these NDM-5-producing isolates in Africa stresses the urgency to improve monitoring and clinical practices to reduce or prevent the further spread of resistance.

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Time resolved phase-contrast magnetic resonance imaging 4D-PCMR (also called 4D Flow MRI) data while capable of non-invasively measuring blood velocities, can be affected by acquisition noise, flow artifacts, and resolution limits. In this paper, we present a novel method for merging 4D Flow MRI with computational fluid dynamics (CFD) to address these limitations and to reconstruct de-noised, divergence-free high-resolution flow-fields. Proper orthogonal decomposition (POD) is used to construct the orthonormal basis of the local sampling of the space of all possible solutions to the flow equations both at the low-resolution level of the 4D Flow MRI grid and the high-level resolution of the CFD mesh. Low-resolution, de-noised flow is obtained by projecting in-vivo 4D Flow MRI data onto the low-resolution basis vectors. Ridge regression is then used to reconstruct high-resolution de-noised divergence-free solution. The effects of 4D Flow MRI grid resolution, and noise levels on the resulting velocity fields are further investigated. A numerical phantom of the flow through a cerebral aneurysm was used to compare the results obtained using the POD method with those obtained with the state-of-the-art de-noising methods. At the 4D Flow MRI grid resolution, the POD method was shown to preserve the small flow structures better than the other methods, while eliminating noise. Furthermore, the method was shown to successfully reconstruct details at the CFD mesh resolution not discernible at the 4D Flow MRI grid resolution. This method will improve the accuracy of the clinically relevant flow-derived parameters, such as pressure gradients and wall shear stresses, computed from in-vivo 4D Flow MRI data.

Near-wall blood flow and wall shear stress (WSS) regulate major forms of cardiovascular disease, yet they are challenging to quantify with high fidelity. Patient-specific computational and experimental measurement of WSS suffers from uncertainty, low resolution, and noise issues. Physics-informed neural networks (PINNs) provide a flexible deep learning framework to integrate mathematical equations governing blood flow with measurement data. By leveraging knowledge about the governing equations (herein, Navier–Stokes), PINN overcomes the large data requirement in deep learning. In this study, it was shown how PINN could be used to improve WSS quantification in diseased arterial flows. Specifically, blood flow problems where the inlet and outlet boundary conditions were not known were solved by assimilating very few measurement points. Uncertainty in boundary conditions is a common feature in patient-specific computational fluid dynamics models. It was shown that PINN could use sparse velocity measurements away from the wall to quantify WSS with very high accuracy even without full knowledge of the boundary conditions. Examples in idealized stenosis and aneurysm models were considered demonstrating how partial knowledge about the flow physics could be combined with partial measurements to obtain accurate near-wall blood flow data. The proposed hybrid data-driven and physics-based deep learning framework has high potential in transforming high-fidelity near-wall hemodynamics modeling in cardiovascular disease.