A Novel Artificially Humanized Anti-Cripto-1 Antibody Suppressing Cancer Cell Growth

Cripto-1 is a member of the EGF-CFC/FRL1/Cryptic family and is involved in embryonic development and carcinogenesis. We designed a novel anti-Cripto-1 artificial antibody and assessed the recognition to the antigen and the potential to suppress the growth of cancer stem cells. First, single chain antibody clones were isolated by bio-panning with the affinity to recombinant Cripto-1 protein from our original phage-display library. Then, the variable regions of heavy chain VH and light chain VL in each clone were fused to constant regions of heavy chain CH and light chain CL regions respectively. These fused genes were expressed in ExpiCHO-S cells to produce artificial humanized antibodies against Cripto-1. After evaluation of the expression levels, one clone was selected and the anti-Cripto-1 antibody was produced and purified. The purified antibody showed affinity to recombinant Cripto-1 at 1.1 pmol and immunoreactivity to cancer tissues and cell lines. The antibody was available to detect the immunoreactivity in tissue microarrays of malignant tumors as well as in Cripto-1 overexpressing cells. Simultaneously, the antibody exhibited the potential to suppress the growth of human colon cancer derived GEO cells overexpressing Cripto-1 with IC₅₀ at approximately 110 nM. The artificially humanized antibody is proposed to be a good candidate to target cancer cells overexpressing Cripto-1.     

A review of machine learning applications in IoT-integrated modern power systems

This paper aims to provide a systematic and comprehensive survey of state-of-the-art machine learning techniques and their potential applications in IoT-integrated power systems.     

Machine-Learning based methods in short-term load forecasting

Short-term load forecasting is of great significance to the secure and efficient operation of power systems. However, loads can be affected by a variety of external impact factors and thus involve high levels of uncertainties. So it is a challenging task to achieve an accurate load forecast. This paper discusses three commonly-used machine-learning methods used for load forecasting, i.e., the support vector machine method, the random forest regression method, and the long short-term memory neural network method. The features and applications of these methods are analyzed and compared. By integrating the advantages of these methods, a fusion forecasting approach and a data preprocessing technique are proposed for improving the forecasting accuracy. A comparative study based on real load data is performed to verify that the proposed approach is capable of achieving a relatively higher forecasting accuracy.     

Analytical Modeling of Short-Channel Fully-Depleted Triple Work Function Metal Gate (TWFMG) SOI MESFET

Silicon - In this paper, a new structure: triple work function metal gate SOI MESFET, intended for integration into the deep-submicron CMOS technology, is proposed. The gate of the device consists...     

Using plasmonic cloaking method on infinite cylindrical structures and its applications

Journal of Computational Electronics - In recent years, cloaking using materials with negative electric permittivity or magnetic permeability has been studied and researched. It has been...     

Perpetual effect of laser surface texturing on tribological properties of Ti3SiC2/GNP composite—A fretting wear study

Tribological properties of Ti₃SiC₂ ceramic and Ti₃SiC₂/GNP composite were investigated using fretting sliding against Si₃N₄ ball counter body at a load of 50 N for a sliding distance of 900 m, under non-textured lubricated (NTL), textured lubricated (TL) condition, and textured lubricated high temperature (TLHT). This paper presents the influence of laser surface texturing (LST) on Ti₃SiC₂ and its graphene nanoplatelets (GNP) reinforced composite established through spark plasma sintering (SPS), tested under fretting wear conditions. The coefficient of friction and wear rate of Ti₃SiC₂ and its graphene nanoplatelets reinforced composites were incontrovertibly enhanced with laser surface texturing as compared to non-textured Ti₃SiC₂ and its GNP composite. The TL surface of Ti₃SiC₂ composite reinforced with 15% GNP showed the lowest COF and wear rate. Almost negligible changes were observed for TLHT tests. The LST proved to an efficient technique for enhancing the friction and wear properties of Ti₃SiC₂ and Ti₃SiC₂/GNP composite.     

A potential trackable bone filler: Preparation and characterization

This study reported the synthesis of fluorescent hydroxyapatite/alginate/carbon quantum dots (HA/Alg/CQDs) nanocomposites via the co-precipitation technique. The N-doped CQDs as a new class of fluorescent materials were prepared by the citric acid pyrolysis method, with an average size around 4 nm. Physical, chemical, and optical properties of the synthesized nanocomposites were investigated by X-ray diffraction (XRD), Fourier-transformed infrared spectroscopy (FTIR), atomic force microscopy (AFM), field-emission scanning electron microscopy (FESEM), UV–visible spectroscopy, and photoluminescence (PL) spectroscopy, respectively. The PL spectroscopy data verified the favorable in vitro luminescent emission of the HA/Alg/CQDs nanocomposites in comparison with HA/Alg and HA samples. The XRD patterns of the prepared samples confirmed the formation of crystalline HA in all composites, possessing a Ca/P ratio around 1.5 as obtained by EDX elemental analysis. The FESEM analysis exhibited HA nanoplates that homogeneously distributed throughout the alginate matrix. Therefore, the synthesized nanocomposites could be regarded as potential trackable drug carriers for hard tissue engineering applications.     

Preparation and characterization of new photoactive polyamides containing 4‐(4‐dimethylaminophenyl)urazole units

4‐(4‐dimethylaminophenyl)‐1,2,4‐triazolidine‐3,5‐dione ( DAPTD ) was prepared from 4‐dimethylaminobenzoic acid in five steps. The compound DAPTD was reacted with excess acetyl chloride in N,N‐dimethylacetamide (DMAc) solution and gave 1,2‐bisacetyl‐4‐[4‐(dimethylaminophenyl)]‐1,2,4‐triazolidine‐3,5‐dione as a model compound. Solution polycondensation reactions of monomer with succinyl chloride (SucC), suberoyl chloride (SubC), and sebacoyl chloride (SebC) were performed under conventional solution polymerization techniques in the presence of triethylamine and pyridine as a catalyst in N‐methylpyrrolidone (NMP) and led to the formation of novel aliphatic polyamides. These novel polyamides have inherent viscosities in the range of 0.09–0.21 dL/g in N,N‐dimethylformamide (DMF) at 25°C. Fluorimetric studies of the model compound as well as polymers were performed. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 103: 947–954, 2007     

Fundamental parameters affecting electrospinning of PAN nanofibers as uniaxially aligned fibers

Electrospinning with a collector consisting of two pieces of electrically conductive substrates separated by a gap has been used to prepare uniaxially aligned PAN nanofibers. Solution of 15 wt % of PAN/DMF was used tentatively for electrospinning. The effects of width of the gap and applied voltage on degree of alignment were investigated using image‐processing technique by Fourier power spectrum method. The electrospinning conditions that gave the best alignment of nanofibers for 10–15 wt % solution concentrations were experimentally obtained. Bundles like multifilament yarns of uniaxially aligned nanofibers were prepared using a new simple method. After‐treatments of these bundles were carried out in boiling water under tension. A comparison was made between the crystallinity and mechanical behavior of posttreated and untreated bundles. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 101: 4350–4357, 2006