RGD-conjugated gold nanorods induce radiosensitization in melanoma cancer cells by downregulating αvβ3 expression

Background: Melanoma is known to be radioresistant and traditional treatments have been intractable. Therefore, novel approaches are required to improve the therapeutic efficacy of melanoma treatment. In our study, gold nanorods conjugated with Arg-Gly-Asp peptides (RGD-GNRs) were used as a sensitizer to enhance the response of melanoma cells to 6 mV radiation. Methods and materials: A375 melanoma cells were treated by gold nanorods or RGD-GNRs with or without irradiation. The antiproliferative impact of the treatments was measured by MTT assay. Radiosensitizing effects were determined by colony formation assay. Apoptosis and cell cycle data were measured by flow cytometry. Integrin alpha(v)beta(3) expression was also investigated by flow cytometry. Results: Addition of RGD-GNRs enhanced the radiosensitivity of A375 cells with a dose-modifying factor of 1.35, and enhanced radiation-induced apoptosis. DNA flow cytometric analysis indicated that RGD-GNRs plus irradiation induced significant G2/M phase arrest in A375 cells. Both spontaneous and radiation-induced expressions of integrin alpha(v)beta(3) were downregulated by RGD-GNRs. Conclusion: Our study indicated that RGD-GNRs could sensitize melanoma A375 cells to irradiation. It was hypothesized that this was mainly through downregulation of radiation-induced alpha(v)beta(3), in addition to induction of a higher proportion of cells within the G2/M phase. The combination of RGD-GNRs and radiation needs further investigation.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000302718200001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Nanoscience & NanotechnologyPharmacology & PharmacySCI(E)22ARTICLE915-924

An improved evolutionary approach-based hybrid algorithm for Bayesian network structure learning in dynamic constrained search space

Learning Bayesian network (BN) structures from data is a NP-hard problem due to the vastness of the solution space. To address this issue, hybrid approaches that integrate the constraint-based (CB) method and the score-and-search (SS) method have been developed in the literature, but when the constrained search space is fixed and inaccurate, it is very likely to lose the optimal solution, leading to low learning accuracy. Besides, due to the randomness and uncertainty of the search, it is difficult to preserve the superiority of the structures, resulting in low learning efficiency. Therefore, we propose a novel hybrid algorithm based on an improved evolutionary approach to explore BN structure with highest matching degree of data set in dynamic constrained search space. The proposed algorithm involves two phases, namely the CB phase and the SS phase. In the CB phase, the mutual information is utilized as the restriction to limit the search space, and a binding parameter is introduced to the novel encoding scheme so that the search space can be dynamically changed in the evolutionary process. In the SS phase, a new operator is developed to pass on the excellent genes from generation to generation, and an update principle for the binding parameter is exploited for the dynamic selection of the search space. We conduct the comparative experiments on the benchmark network data sets and provide performance and applicability analysis of our proposed method. The experimental results show that the new algorithm is effective in learning the BN structures

Structure-driven intercalated architecture of septuple-atomic-layer MA2Z4MA_2Z_4 family with diverse properties from semiconductor to topological insulator to Ising superconductor

Motivated by the fact that septuple-atomic-layer MnBi$_2$Te$_4$ can be structurally viewed as the combination of double-atomic-layer MnTe intercalating into quintuple-atomic-layer Bi$_2$Te$_3$, we present a general approach of constructing twelve septuple-atomic-layer $\alpha_i$- and $\beta_i$-$MA_2Z_4$ monolayer family (\emph{i} = 1 to 6) by intercalating MoS$_2$-type $MZ$$_2$ monolayer into InSe-type A$_2$Z$_2$ monolayer. Besides reproducing the experimentally synthesized $\alpha_1$-MoSi$_2$N$_4$, $\alpha_1$-WSi$_2$N$_4$ and $\beta_5$-MnBi$_2$Te$_4$ monolayer materials, another 66 thermodynamically and dynamically stable $MA_2Z_4$ were predicted, which span a wide range of properties upon the number of valence electrons (VEC). $MA_2Z_4$ with the rules of 32 or 34 VEC are mostly semiconductors with direct or indirect band gap and, however, with 33 VEC are generally metal, half-metal ferromagnetism, or spin-gapless semiconductor upon whether or not an unpaired electron is spin polarized. Moreover, we propose $\alpha_2$-WSi$_2$P$_4$ for the spin-valley polarization, $\alpha_1$-TaSi$_2$N$_4$ for Ising superconductor and $\beta_2$-SrGa$_2$Se$_4$ for topological insulator.Comment: Maintext 9 pages; 5 figures; Supplementary Materials 8 figures and 4 table

Aerosol Jet Printing of Graphene and Carbon Nanotube Patterns on Realistically Rugged Substrates

Direct-write additive manufacturing of graphene and carbon nanotube (CNT) patterns by aerosol jet printing (AJP) is promising for the creation of thermal and electrical interconnects in (opto)electronics. In realistic application scenarios, this however often requires deposition of graphene and CNT patterns on rugged substrates such as, for example, roughly machined and surface oxidized metal block heat sinks. Most AJP of graphene/CNT patterns has thus far however concentrated on flat wafer-or foil type substrates. Here, we demonstrate AJP of graphene and single walled CNT (SWCNT) patterns on realistically rugged plasma electrolytic-oxidized (PEO) Al blocks, which are promising heat sink materials. We show that AJP on the rugged substrates offers line resolution of down to similar to 40 mu m width for single AJP passes, however, at the cost of noncomplete substrate coverage including noncovered mu m-sized pores in the PEO Al blocks. With multiple AJP passes, full coverage including coverage of the pores is, however, readily achieved. Comparing archetypical aqueous and organic graphene and SWCNT inks, we show that the choice of the ink system drastically influences the nanocarbon AJP parameter window, deposit microstructure including crystalline quality, compactness of deposit, and inter/intrapass layer adhesion for multiple passes. Simple electrical characterization indicates aqueous graphene inks as the most promising choice for AJP-deposited electrical interconnect applications. Our parameter space screening thereby forms a framework for rational process development for graphene and SWCNT AJP on application-relevant, rugged substrates

Proton and Li-Ion Permeation through Graphene with Eight-Atom-Ring Defects

Defect-free graphene is impermeable to gases and liquids but highly permeable to thermal protons. Atomic-scale defects such as vacancies, grain boundaries and Stone-Wales defects are predicted to enhance graphene's proton permeability and may even allow small ions through, whereas larger species such as gas molecules should remain blocked. These expectations have so far remained untested in experiment. Here we show that atomically thin carbon films with a high density of atomic-scale defects continue blocking all molecular transport, but their proton permeability becomes ~1,000 times higher than that of defect-free graphene. Lithium ions can also permeate through such disordered graphene. The enhanced proton and ion permeability is attributed to a high density of 8-carbon-atom rings. The latter pose approximately twice lower energy barriers for incoming protons compared to the 6-atom rings of graphene and a relatively low barrier of ~0.6 eV for Li ions. Our findings suggest that disordered graphene could be of interest as membranes and protective barriers in various Li-ion and hydrogen technologies