Successional Changes of Microbial Communities and Host-Microbiota Interactions Contribute to Dietary Adaptation in Allodiploid Hybrid Fish.

Host-microbiota interactions play critical roles in host development, immunity, metabolism, and behavior. However, information regarding host-microbiota interactions is limited in fishes due to their complex living environment. In the present study, an allodiploid hybrid fish derived from herbivorous Megalobrama amblycephala (♀) × carnivorous Culter alburnus (♂) was used to investigate the successional changes of the microbial communities and host-microbiota interactions during herbivorous and carnivorous dietary adaptations. The growth level was not significantly different in any developmental stage between the two diet groups of fish. The diversity and composition of the dominant microbial communities showed similar successional patterns in the early developmental stages, but significantly changed during the two dietary adaptations. A large number of bacterial communities coexisted in all developmental stages, whereas the abundance of some genera associated with metabolism, including Acinetobacter, Gemmobacter, Microbacterium, Vibrio, and Aeromonas, was higher in either diet groups of fish. Moreover, the abundance of phylum Firmicutes, Actinobacteria, and Chloroflexi was positively correlated with the host growth level. In addition, Spearman's correlation analysis revealed that the differentially expressed homologous genes in the intestine associated with cell growth, immunity, and metabolism were related to the dominant gut microbiota. Our results present evidence that host genetics-gut microbiota interactions contribute to dietary adaptation in hybrid fish, which also provides basic data for understanding the diversity of dietary adaptations and evolution in fish.

3D dense connectivity network with atrous convolutional feature pyramid for brain tumor segmentation in magnetic resonance imaging of human heads.

The existing deep convolutional neural networks (DCNNs) based methods have achieved significant progress regarding automatic glioma segmentation in magnetic resonance imaging (MRI) data. However, there are two main problems affecting the performance of traditional DCNNs constructed by simply stacking convolutional layers, namely, exploding/vanishing gradients and limitations to the feature computations. To address these challenges, we propose a novel framework to automatically segment brain tumors. First, a three-dimensional (3D) dense connectivity architecture is used to build the backbone for feature reuse. Second, we design a new feature pyramid module using 3D atrous convolutional layers and add this module to the end of the backbone to fuse multiscale contexts. Finally, a 3D deep supervision mechanism is equipped with the network to promote training. On the multimodal brain tumor image segmentation benchmark (BRATS) datasets, our method achieves Dice similarity coefficient values of 0.87, 0.72, and 0.70 on the BRATS 2013 Challenge, 0.84, 0.70, and 0.61 on the BRATS 2013 LeaderBoard, 0.83, 0.70, and 0.62 on the BRATS 2015 Testing, 0.8642, 0.7738, and 0.7525 on the BRATS 2018 Validation in terms of whole tumors, tumor cores, and enhancing cores, respectively. Compared to the published state-of-the-art methods, the proposed method achieves promising accuracy and fast processing, demonstrating good potential for clinical medicine.

Pharmacokinetics, pharmacodynamics, and immunogenicity of belatacept in adult kidney transplant recipients.

BACKGROUND AND OBJECTIVES: Belatacept is a first-in-class, selective co-stimulation blocker recently approved for the prophylaxis of organ rejection in adult kidney transplant recipients. The objective of this study was to report the pharmacokinetics, pharmacodynamics, and immunogenicity of belatacept. METHODS: The pharmacokinetics, pharmacodynamics (CD86 receptor occupancy), and immunogenicity of belatacept were studied in de novo adult kidney transplant recipients in phase II and III clinical studies. RESULTS: Following multiple doses of 5 or 10 mg/kg, the geometric mean (percentage coefficient of variation) maximum serum concentration and area under the serum concentration-time curve over one dosing interval of belatacept were 136 (20%) and 238 (27%) µg/mL, and 13,587 (27%) and 21,241 (35%) µg·h/mL, respectively. The median belatacept elimination half-life was 8-9 days. Belatacept exhibited concentration-dependent binding to CD86 receptors. The pre-dose CD86 receptor occupancy by belatacept decreased from 94 to 65% between day 5 and 1 year post-transplant, with corresponding pre-dose trough serum concentrations of belatacept decreasing from ~35 to 4 µg/mL during this period. The cumulative incidence of developing anti-belatacept antibodies was 5.3% up to 3 years post-transplant and had no impact on belatacept exposure. CONCLUSIONS: Belatacept in adult kidney transplant demonstrated linear pharmacokinetics with low variability, concentration-dependent pharmacodynamics, and a low incidence of anti-drug antibodies.

Quantifying the impact of nonadherence patterns on exposure to oral immunosuppressants.

BACKGROUND AND OBJECTIVES: Nonadherence to oral immunosuppressive drugs in renal transplant patients remains a major challenge. The objective of this study was to develop an adherence-exposure model that 1) quantifies the impact of nonadherence patterns on cyclosporine levels and 2) identifies nonadherence patterns that are associated with unfavorable transplantation outcomes. DESIGN, SETTING, PARTICIPANTS, AND MEASUREMENTS: This model quantified variability in drug exposure, expressed as the coefficient of variation (CV%), for time-averaged and trough cyclosporine levels (C(avg) and C(min), respectively), and percentage of days spent below the therapeutic C(min) target. Simulated patterns of nonadherence closely matched those observed in clinical practice for four nonadherence clusters and an "Others" category. RESULTS: Patients in simulated nonadherence clusters 1-3 spent a mean (standard deviation) 5.8% (4.9), 9.0% (5.0), and 6.5% (3.4) of days below the C(min) target, compared with 76.8% (6.5) for cluster 4 and 38.3% (6.4) for the "Others" category. Mean (standard deviation) CV% values for C(min) were 24.1 (7.9), 35.4 (11.7), and 34.1 (10.6) for clusters 1-3, compared with 136.4 (23.6) for cluster 4 and 64.8 (10.3) for the "Others" category. Findings for C(avg) were similar. CONCLUSION: Based on nonadherence patterns and known relationships between CV% for C(min) and C(avg), and transplantation outcomes, patients in cluster 4 and the "Others" category are expected to be at high risk of allograft rejection. The proposed drug adherence-exposure model is useful to identify high-risk patients who can be targeted for interventions aimed at enhancing drug adherence to optimize clinical long-term outcomes.

Model for intracellular Lamivudine metabolism in peripheral blood mononuclear cells ex vivo and in human immunodeficiency virus type 1-infected adolescents.

The pharmacologic variability of nucleoside reverse transcriptase inhibitors such as lamivudine (3TC) includes not only systemic pharmacokinetic variability but also interindividual differences in cellular transport and metabolism. A modeling strategy linking laboratory studies of intracellular 3TC disposition with clinical studies in adolescent patients is described. Data from ex vivo laboratory experiments using peripheral blood mononuclear cells (PBMCs) from uninfected human subjects were first used to determine a model and population parameter estimates for 3TC cellular metabolism. Clinical study data from human immunodeficiency virus type 1-infected adolescents were then used in a Bayesian population analysis, together with the prior information from the ex vivo analysis, to develop a population model for 3TC systemic kinetics and cellular kinetics in PBMCs from patients during chronic therapy. The laboratory results demonstrate that the phosphorylation of 3TC is saturable under clinically relevant concentrations, that there is a rapid equilibrium between 3TC monophosphate and diphosphate and between 3TC diphosphate and triphosphate, and that 3TC triphosphate is recycled to 3TC monophosphate through a 3TC metabolite that remains to be definitively characterized. The resulting population model shows substantial interindividual variability in the cellular kinetics of 3TC with population coefficients of variation for model parameters ranging from 47 to 87%. This two-step ex vivo/clinical modeling approach using Bayesian population modeling of 3TC that links laboratory and clinical data has potential application for other drugs whose intracellular pharmacology is a major determinant of activity and/or toxicity.