Reactive oxygen species (ROS) plays a key role in therapeutic effects as well as side effects of platinum drugs. Cisplatin mediates activation of nicotinamide adenine dinucleotide phosphate (NADPH) oxidase (NOX), which triggers oxygen (O) to superoxide radical (O) and its downstream HO. Through the Fenton's reaction, HO could be catalyzed by Fe/Fe to the toxic hydroxyl radicals (OH), which cause oxidative damages to lipids, proteins, and DNA. By taking the full advantage of Fenton's chemistry, we herein demonstrated tumor site-specific conversion of ROS generation induced by released cisplatin and Fe/Fe from iron-oxide nanocarriers with cisplatin(IV) prodrugs for enhanced anticancer activity but minimized systemic toxicity.

Many of today's machine learning (ML) systems are built by reusing an array of, often pre-trained, primitive models, each fulfilling distinct functionality (e.g., feature extraction). The increasing use of primitive models significantly simplifies and expedites the development cycles of ML systems. Yet, because most of such models are contributed and maintained by untrusted sources, their lack of standardization or regulation entails profound security implications, about which little is known thus far.In this paper, we demonstrate that malicious primitive models pose immense threats to the security of ML systems. We present a broad class of model-reuse attacks wherein maliciously crafted models trigger host ML systems to misbehave on targeted inputs in a highly predictable manner. By empirically studying four deep learning systems (including both individual and ensemble systems) used in skin cancer screening, speech recognition, face verification, and autonomous steering, we show that such attacks are (i) effective -the host systems misbehave on the targeted inputs as desired by the adversary with high probability, (ii) evasive -the malicious models function indistinguishably from their benign counterparts on non-targeted inputs, (iii) elastic -the malicious models remain effective regardless of various system design choices and tuning strategies, and (iv) easy -the adversary needs little prior knowledge about the data used for system tuning or inference. We provide analytical justification for the effectiveness of model-reuse attacks, which points to the unprecedented complexity of today's primitive models. This issue thus seems fundamental to many ML systems. We further discuss potential countermeasures and their challenges, which lead to several promising research directions.

There have been many incidents of prefix hijacking in the Internet. The hijacking AS can blackhole the hijacked traffic. Alternatively, it can transparently intercept the hijacked traffic by forwarding it onto the owner. This paper presents a study of such prefix hijacking and interception with the following contributions: (1). We present a methodology for prefix interception, (2). We estimate the fraction of traffic to any prefix that can be hijacked and intercepted in the Internet today, (3). The interception methodology is implemented and used to intercept real traffic to our prefix, (4). We conduct a detailed study to detect ongoing prefix interception.We find that: Our hijacking estimates are in line with the impact of past hijacking incidents and show that ASes higher up in the routing hierarchy can hijack a significant amount of traffic to any prefix, including popular prefixes. A less apparent result is that the same holds for prefix interception too. Further, our implementation shows that intercepting traffic to a prefix in the Internet is almost as simple as hijacking it. Finally, while we fail to detect ongoing prefix interception, the detection exercise highlights some of the challenges posed by the prefix interception problem.

Catheter-related infection is a great challenge to modern medicine, which causes significant economic burden and increases patient morbidity. Hence, there is a great requirement for functionalized surfaces with inherently antibacterial properties and biocompatibility that prevent bacterial colonization and attachment of blood cells. Herein, we developed a strategy for constructing polymer brushes with hierarchical architecture on polyurethane (PU) via surface-initiated atom-transfer radical polymerization (SI-ATRP). Surface-functionalized PU (PU-DMH) was readily prepared, which comprised of poly­(3-[dimethyl-[2-(2-methylprop-2-enoyloxy)­ethyl]­azaniumyl]­propane-1-sulfonate) (PDMAPS) brushes as the lower layer and antimicrobial peptide-conjugated poly­(methacrylic acid) (PMAA) brushes as the upper layer. The PU-DMH surface showed excellent bactericidal property against both Gram-positive and Gram-negative bacteria and could prevent accumulation of bacterial debris on surfaces. Simultaneously, the PU-DMH samples possessed good hemocompatibility and low cytotoxicity. Furthermore, the integrated antifouling and bactericidal properties of PU-DMH under hydrodynamic conditions were confirmed by an in vitro circulating model. The functionalized surface possessed persistent antifouling and bactericidal performances both under static and hydrodynamic conditions. The microbiological and histological results of animal experiments also verified the in vivo anti-infection performance. The present work might find promising clinical applications for preventing catheter-related infection.

Hierarchical CoMoO4@NiMoO4 core–shell nanosheet arrays synthesized via a two-step hydrothermal method with successive annealing exhibited outstanding electrochemical performance.

No abstract

No abstract

Self-adaptive antibacterial surfaces with bacterium-triggered antifouling-bactericidal switching properties were readily constructed for the therapy of catheter-associated infection.