Purpose Vulvovaginal candidiasis (VVC) is a mucosal infection of the female lower genital tract for which treatment using conventional antifungal drugs shows limited effectiveness. Herein, amphotericin B-loaded poly(lactic-co-glycolic acid)-polyethylene glycol (PLGA-PEG) nanoparticles (AmB-NPs) were fabricated and combined with low intensity ultrasound (US) to mediate AmB-NPs intravaginal drug delivery to achieve productive synergistic antifungal activity in a rabbit model of VVC. Methods Polymeric AmB-NPs were fabricated by a double emulsion method and the physical characteristics and biosafety of nanoparticles were analyzed. The distribution and tissue permeability of nanoparticles after intravaginal ultrasound irradiation (1.0 MHz, 1.0 W/cm2, 5 min, 50% duty ratio) were observed in the vagina. The synergistic therapeutic activity of US-mediated AmB-NPs treatment was evaluated using an experimental rabbit model of VVC. Vaginal C. albicans colony counts, the pathological structure of the vagina epithelium, and Th1/Th2/Th17-type cytokine and oxidative stress levels were analyzed to investigate the therapeutic effect in vivo. Results The prepared AmB-NPs showed an obvious shell and core structure with uniform size and good dispersion and displayed high biosafety and US-sensitive slow drug release. Ultrasound significantly enhanced nanoparticle transport through the mucus and promoted permeability in the vaginal tissue. US-mediated AmB-NPs treatment effectively increased drug sensitivity, even in the presence of the vaginal mucus barrier in vitro. On the seventh day after treatment in vivo, the combination treatment of AmB-NPs and US significantly reduced the fungal load in the vagina, achieving over 95% clearance rates, and also improved the pathological epithelium structural damage and glycogen secretion function. The expression of Th1 (IFN-γ, IL-2) and Th17 (IL-17) cytokines were significantly increased and Th2 (IL-6, IL-10) cytokines significantly decreased in the US + AmB-NP group. Furthermore, US-mediated AmB-NPs treatment effectively increased C. albicans intracellular reactive oxygen species (ROS) levels and promoted vaginal oxidation and antioxidants to normal levels. Conclusion US-mediated drug-loaded nanoparticles with intravaginal drug delivery exhibited a productive synergistic antifungal effect, which may provide a new non-invasive, safe, and effective therapy for acute or recurrent fungal vaginitis. Graphical Abstract

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Graph embedding maps graph nodes to low-dimensional vectors, and is widely adopted in machine learning tasks. The increasing availability of billion-edge graphs underscores the importance of learning efficient and effective embeddings on large graphs, such as link prediction on Twitter with over one billion edges. Most existing graph embedding methods fall short of reaching high data scalability. In this paper, we present a general-purpose, distributed, information-centric random walk-based graph embedding framework, DistGER, which can scale to embed billion-edge graphs. DistGER incrementally computes information-centric random walks. It further leverages a multi-proximity-aware, streaming, parallel graph partitioning strategy, simultaneously achieving high local partition quality and excellent workload balancing across machines. DistGER also improves the distributed Skip-Gram learning model to generate node embeddings by optimizing the access locality, CPU throughput, and synchronization efficiency. Experiments on real-world graphs demonstrate that compared to state-of-the-art distributed graph embedding frameworks, including KnightKing, DistDGL, and Pytorch-BigGraph, DistGER exhibits 2.33×--129× acceleration, 45% reduction in cross-machines communication, and >10% effectiveness improvement in downstream tasks.

Disaggregated memory architecture has risen in popularity for large datacenters with the advantage of improved resource utilization, failure isolation, and elasticity. Replicated state machines (RSMs) have been extensively used for reliability and consistency. In traditional RSM protocols, each replica stores replicated data and has the computing power to participate in some part of the protocols. However, traditional RSM protocols fail to work in the disaggregated memory architecture due to asymmetric resources on CPU nodes and memory nodes. This paper proposes ECHO, a fast one-sided RDMA-based RSM protocol with lightweight log replication and remote applying, efficient linearizability guarantee, and fast coordinator failure recovery. ECHO enables all operations in the protocol to be efficiently executed using only one-sided RDMA, without the participation of any computing resource in the memory pool. To provide lightweight log replication and remote applying, ECHO couples the replicated log and the state machine to avoid dual-copy and performs remote applying by updating pointers. To enable efficient remote log state management, ECHO leverages a hitchhiked log state updating scheme to eliminate extra network round trips. To provide efficient linearizability guarantee, ECHO performs immediate remote applying after log replication and leverages the local locks at the coordinator to ensure linear consistency. Moreover, ECHO adopts a commit-aware log cache to make data visible immediately after being committed. To achieve fast failure recovery, ECHO leverages a commit point identification scheme to reduce the overhead of log consistency recovery. Experimental results demonstrate that ECHO outperforms the state-of-the-art RSM protocol (namely Sift) in multiple scenarios. For example, ECHO achieves 27%-52% higher throughput on typical write-intensive workloads. Moreover, ECHO reduces the consistency recovery time by 3 orders of magnitude for coordinator failure.