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Liver fibrosis is a major risk factor for hepatocellular carcinoma origin, and its progression not only correlates with oxidative stress and inflammation, but also is encouraged by autophagy hold‐up. Therefore, new solutions to effectively attenuate oxidative stress and inflammation and coincidentally favor autophagy are highly demanded to reverse liver fibrosis, and even hamper its escalation into hepatocellular carcinoma. Herein, the porous manganese‐substituted Prussian blue (PMPB) analogs are harnessed to activate autophagy, scavenge reactive oxygen species (ROS), and suppress inflammation for liver fibrosis therapy. PMPB can effectively inhibit macrophage activation, facilitate macrophage autophagy, eradicate ROS, and blockade cellular cross‐talk, thus impeding further inflammation progression. Moreover, the favorable spontaneous capture of PMPB by Kupffer cells allows more PMPB accumulation in liver to significantly attenuate liver injury and collagen deposition, thereby inhibiting the progression of liver fibrosis. PMPB‐based nanomedicine shows great potentials in promoting autophagy activation, eliminating ROS, inhibiting inflammation, and protecting hepatocytes from oxidative stress‐arised damages, which eventually attenuate the extent of liver fibrosis, holding great promise in clinical translation for treating liver fibrosis. [ABSTRACT FROM AUTHOR]

Intratumoral or intestinal microbiota correlates with tumorigenesis and progression, and microbiota regulation for reinforcing various anti‐tumor approaches is of significant importance, which, however, suffers from no precise regulation method and unclear underlying mechanism. Herein, a microbiome metabolism‐engineered phototherapy strategy is established, wherein Nb2C/Au nanocomposite and the corresponding phototherapy are harnessed to realize "chemical" and "physical" bacterial regulations. Flora analysis and mass spectrometry (MS) and metabonomics combined tests demonstrate that the synergistic microbiota regulations can alter the abundance, diversity of intratumoral microbiome, and disrupt metabolic pathways of microbiome and tumor microenvironment, wherein the differential singling pathways and biosynthetic necessities or metabolites that can affect tumor progression are identified. As well, anti‐TNFα is introduced to unite with bacterial regulation to synergistically mitigate bacterial‐induced inflammation, which, along with the metabolism disruptions of intratumoral microbiota and tumor microenvironment, unfreezes tumor resistance and harvests significantly‐intensified phototherapy‐based anti‐tumor outcomes against 4T1 and CT26 tumors. The clear underlying principles of microbiome‐regulated tumorigenesis and the established microbiome metabolism regulation method provide distinctive insights into tumor therapy, and can be also extended to other gut microbiome‐associated lesions interference. [ABSTRACT FROM AUTHOR]

The authors present an Ag‐GeTe‐based conductive bridge memory that allows radio frequency (RF) input and transmission. The RF input modifies the resistance switching characteristics and the memory device can switch the order of harmonics of the transmitted RF wave. The transmittance for the RF input above 500 MHz was higher than 0.5 for both the low‐resistance state (LRS) and the high‐resistance state (HRS). Below 500 MHz the transmittance decreased with decreasing frequency and depended on the resistance state (LRS or HRS). At 20 MHz, the transmission for the HRS was 0.02, while that for the LRS was 0.2. An RF input of 20 MHz at 10 dBm modified the SET voltage from 0.19 to 0.05 V, and the RESET voltage from −0.17 to −0.05 V. The RF wave transmitted through the LRS consisted almost entirely of fundamental waves, while that in the HRS provided harmonics or frequency multiplication up to the fifth order at 150 MHz at 10 dBm. Heat cycle measurements with RF input revealed the difference between the non‐linear resistance producing the frequency multiplication and the DC resistance. [ABSTRACT FROM AUTHOR]

Abstract: Motivation: Predicting protein function accurately is an important issue in the post-genomic era. To achieve this goal, several approaches have been proposed deduce the function of unclassified proteins through sequence similarity, co-expression profiles, and other information. Among these methods, the global optimization method (GOM) is an interesting and powerful tool that assigns functions to unclassified proteins based on their positions in a physical interactions network [Vazquez, A., Flammini, A., Maritan, A. and Vespignani, A. (2003) Global protein function prediction from protein–protein interaction networks, Nat. Biotechnol., 21, 697–700]. To boost both the accuracy and speed of GOM, a new prediction method, MFGO (modified and faster global optimization) is presented in this paper, which employs local optimal repetition method to reduce calculation time, and takes account of topological structure information to achieve a more accurate prediction. Conclusion: On four proteins interaction datasets, including Vazquez dataset, YP dataset, DIP-core dataset, and SPK dataset, MFGO was tested and compared with the popular MR (majority rule) and GOM methods. Experimental results confirm MFGO’s improvement on both speed and accuracy. Especially, MFGO method has a distinctive advantage in accurately predicting functions for proteins with few neighbors. Moreover, the robustness of the approach was validated both in a dataset containing a high percentage of unknown proteins and a disturbed dataset through random insertion and deletion. The analysis shows that a moderate amount of misplaced interactions do not preclude a reliable function assignment. [Copyright &y& Elsevier]