Pharmacokinetic/Pharmacodynamic Correlation of Cefquinome Against Experimental Catheter-Associated Biofilm Infection Due to Staphylococcus aureus.

Biofilm formations play an important role in Staphylococcus aureus pathogenesis and contribute to antibiotic treatment failures in biofilm-associated infections. The aim of this study was to evaluate the pharmacokinetic/pharmacodynamic (PK/PD) profiles of cefquinome against an experimental catheter-related biofilm model due to S. aureus, including three clinical isolates and one non-clinical isolate. The minimal inhibitory concentration (MIC), minimal biofilm inhibitory concentration (MBIC), biofilm bactericidal concentration (BBC), minimal biofilm eradication concentration (MBEC) and biofilm prevention concentration (BPC) and in vitro time-kill curves of cefquinome were studied in both planktonic and biofilm cells of study S. aureus strains. The in vivo post-antibiotic effects (PAEs), PK profiles and efficacy of cefquinome were performed in the catheter-related biofilm infection model in murine. A sigmoid E max model was utilized to determine the PK/PD index that best described the dose-response profiles in the model. The MICs and MBICs of cefquinome for the four S. aureus strains were 0.5 and 16 μg/mL, respectively. The BBCs (32-64 μg/mL) and MBECs (64-256 μg/mL) of these study strains were much higher than their corresponding BPC values (1-2 μg/mL). Cefquinome showed time-dependent killing both on planktonic and biofilm cells, but produced much shorter PAEs in biofilm infections. The best-correlated PK/PD parameters of cefquinome for planktonic and biofilm cells were the duration of time that the free drug level exceeded the MIC (fT > MIC, R (2) = 96.2%) and the MBIC (fT > MBIC, R (2) = 94.7%), respectively. In addition, the AUC24h/MBIC of cefquinome also significantly correlated with the anti-biofilm outcome in this model (R (2) = 93.1%). The values of AUC24h/MBIC for biofilm-static and 1-log10-unit biofilm-cidal activity were 22.8 and 35.6 h; respectively. These results indicate that the PK/PD profiles of cefquinome could be used as valuable guidance for effective dosing regimens treating S. aureus biofilm-related infections

High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs

We present a new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks (conditional GANs). Conditional GANs have enabled a variety of applications, but the results are often limited to low-resolution and still far from realistic. In this work, we generate 2048x1024 visually appealing results with a novel adversarial loss, as well as new multi-scale generator and discriminator architectures. Furthermore, we extend our framework to interactive visual manipulation with two additional features. First, we incorporate object instance segmentation information, which enables object manipulations such as removing/adding objects and changing the object category. Second, we propose a method to generate diverse results given the same input, allowing users to edit the object appearance interactively. Human opinion studies demonstrate that our method significantly outperforms existing methods, advancing both the quality and the resolution of deep image synthesis and editing.Comment: v2: CVPR camera ready, adding more results for edge-to-photo example

C2FTrans: Coarse-to-Fine Transformers for Medical Image Segmentation

Convolutional neural networks (CNN), the most prevailing architecture for deep-learning based medical image analysis, are still functionally limited by their intrinsic inductive biases and inadequate receptive fields. Transformer, born to address this issue, has drawn explosive attention in natural language processing and computer vision due to its remarkable ability in capturing long-range dependency. However, most recent transformer-based methods for medical image segmentation directly apply vanilla transformers as an auxiliary module in CNN-based methods, resulting in severe detail loss due to the rigid patch partitioning scheme in transformers. To address this problem, we propose C2FTrans, a novel multi-scale architecture that formulates medical image segmentation as a coarse-to-fine procedure. C2FTrans mainly consists of a cross-scale global transformer (CGT) which addresses local contextual similarity in CNN and a boundary-aware local transformer (BLT) which overcomes boundary uncertainty brought by rigid patch partitioning in transformers. Specifically, CGT builds global dependency across three different small-scale feature maps to obtain rich global semantic features with an acceptable computational cost, while BLT captures mid-range dependency by adaptively generating windows around boundaries under the guidance of entropy to reduce computational complexity and minimize detail loss based on large-scale feature maps. Extensive experimental results on three public datasets demonstrate the superior performance of C2FTrans against state-of-the-art CNN-based and transformer-based methods with fewer parameters and lower FLOPs. We believe the design of C2FTrans would further inspire future work on developing efficient and lightweight transformers for medical image segmentation. The source code of this paper is publicly available at https://github.com/xianlin7/C2FTrans

In vitro evidence of baicalein’s inhibition of the metabolism of zidovudine (AZT)

Background: Herb-drug interaction (HDI) has been regarded as a key factor limiting the clinical application of herbs and drugs. Aims: Potential baicalein-zidovudine (AZT) interaction was predicted in the present study. Methods: In vitro evaluation of baicalein’s inhibition towards human liver microsomes (HLMs)-catalyzed metabolism of zidovudine (AZT) was performed. Dixon and Lineweaver-Burk plots were used to determine the inhibition kinetic type, and second plot with the slopes from Lineweaver-Burk plot versus the concentrations of baicalein was employed to calculate the inhibition parameter (Ki). In combination with the in vivo concentration of baicalein, in vitro-in vivo extrapolation (IVIVE) was carried out to predict in vivo baicalein-AZT interaction. Results: Competitive inhibition of baicalein towards AZT metabolism was demonstrated, and the Ki value was calculated to be 101.2 μM. The value of AUCi/AUC was calculated to be 2. Conclusion: Potential baicalein-AZT interaction was indicated in the present study, indicating the need for monitoring when AZT is co-administrated with baicalein or baicalein-containing herbs.Keywords: Baicalein, zidovudine (AZT), metabolism, herb-drug interactionAfrican Health sciences Vol 14 No. 1 March 201