The utility of single-chain Fv proteins as therapeutic agents would be realized if the circulating lives of these minimal antigen-binding polypeptides could be both prolonged and adjustable. We have developed a general strategy for creating tailored monoPEGylated single-chain antibodies. Free cysteine residues were engineered in an anti-TNF-alpha scFv at the C-terminus or within the linker segments of both scFv orientations, V(L)-linker-V(H) and V(H)-linker-V(L). High-level expression of 10 designed variant scFv proteins in Pichia pastoris allowed rapid purification. Optimization of site-specific conjugate preparation with 5, 20 and 40 kDa maleimide-PEG polymers was achieved and a comparison of the structural and functional properties of the scFv proteins and their PEGylated counterparts was performed. Peptide mapping and MALDI-TOF mass spectrometric analysis confirmed the unique attachment site for each PEG polymer. Independent biochemical and bioactivity analyses, including binding affinities and kinetics, antigenicity, flow cytometric profiling and cell cytotoxicity rescue, demonstrated that the functional activities of the 10 designed scFv conjugates are maintained, while scFv activity variations between these alternative assays can be correlated with conjugate and analytical designs. Pharmacokinetic studies of the PEGylated scFv in mice demonstrated up to 100-fold prolongation of circulating lives, in a range comparable to clinical antibodies.

Our previous study showed that tea polyphenols inhibited MAP kinase and AP-1 activities in mouse epidermal JB6 cells and the corresponding H-ras-transformed cell line 30.7b Ras 12. The present study investigated the mechanisms of this inhibition. The cells were incubated with (-)-epigallocatechin-3-gallate (EGCG) or theaflavin-3,3'-digallate (TFdiG) (20 mM) for different times, and the cell lysate was analyzed by immunoblotting. EGCG treatment decreased the levels of phospho-Erk1/2 and -MEK1/2 time-dependently (by 60% at 60 min). TFdiG lowered their levels by 38%-50% at 15 min. TFdiG effectively decreased total Raf-1 protein levels, most likely through lysosomal degradation. EGCG did not affect protein levels or the activity of Raf-1 significantly but decreased its association with MEK1 as determined by co-immunoprecipitation. In addition, EGCG and TFdiG (10 mM) inhibited the phosphorylation of Elk-1 by isolated phospho-Erk1/2 in vitro. This inhibition of Erk1/2 activity is Elk-1 concentration-dependent and ATP concentration-independent, which suggests that EGCG and TFdiG interfere with the binding of the protein substrate to the kinase. The presently demonstrated specific mechanisms of inhibition of MAP kinases by EGCG and TFdiG may help us to understand the effects of tea consumption on cancer, inflammatory diseases, and cardiovascular diseases.

We tested refugee camp residents on the Thailand–Myanmar border for Taenia solium infection. Taeniasis prevalence was consistent with that for other disease-endemic regions, but seropositivity indicating T. solium taeniasis was rare. Seropositivity indicating cysticercosis was 5.5% in humans, and 3.2% in pigs. Corralling pigs and providing latrines may control transmission of these tapeworms within this camp.

<p>5G and beyond networks are expected to support a wide range of services, with highly diverse requirements. Yet, the traditional “one-size-fits-all” network architecture lacks the flexibility to accommodate these services. In this respect, network slicing has been introduced as a promising paradigm for 5G and beyond networks, supporting not only traditional mobile services, but also vertical industries services, with very heterogeneous<br> requirements. Along with its benefits, the practical implementation of network slicing brings a lot of challenges. Thanks to the recent advances on Machine Learning (ML), some of these challenges have been addressed. In particular, the application of ML approaches is enabling the autonomous management of resources, in the network slicing paradigm. Accordingly, this paper presents a comprehensive survey on contributions on<br> ML in network slicing, identifying major categories and sub-categories in the literature. Key takeaways are also presented and open research challenges are discussed, together with potential solutions.</p>

<p>5G and beyond networks are expected to support a wide range of services, with highly diverse requirements. Yet, the traditional “one-size-fits-all” network architecture lacks the flexibility to accommodate these services. In this respect, network slicing has been introduced as a promising paradigm for 5G and beyond networks, supporting not only traditional mobile services, but also vertical industries services, with very heterogeneous<br> requirements. Along with its benefits, the practical implementation of network slicing brings a lot of challenges. Thanks to the recent advances on Machine Learning (ML), some of these challenges have been addressed. In particular, the application of ML approaches is enabling the autonomous management of resources, in the network slicing paradigm. Accordingly, this paper presents a comprehensive survey on contributions on<br> ML in network slicing, identifying major categories and sub-categories in the literature. Key takeaways are also presented and open research challenges are discussed, together with potential solutions.</p>