Search

Although radiotherapy is one of the most important curative treatments for cancer, its clinical application is associated with undesired therapeutic effects on normal or healthy tissues. The use of targeted agents that can simultaneously achieve therapeutic and imaging functions could constitute a potential solution. Herein, we developed 2-deoxy-d-glucose (2DG)-labeled poly(ethylene glycol) (PEG) gold nanodots (2DG-PEG-AuD) as a tumor-targeted computed tomography (CT) contrast agent and radiosensitizer. The key advantages of the design are its biocompatibility and targeted AuD with excellent sensitivity in tumor detection via avid glucose metabolism. As a consequence, CT imaging with enhanced sensitivity and remarkable radiotherapeutic efficacy could be attained. Our synthesized AuD displayed linear enhancement of CT contrast as a function of its concentration. In addition, 2DG-PEG-AuD successfully demonstrated significant augmentation of CT contrast in both in vitro cell studies and in vivo tumor-bearing mouse models. In tumor-bearing mice, 2DG-PEG-AuD showed excellent radiosensitizing functions after intravenous injection. Results from this work indicate that 2DG-PEG-AuD could greatly potentiate theranostic capabilities by providing high-resolution anatomical and functional images in a single CT scan and therapeutic capability.

Background Dementia-friendly community has been promoted in Macao since 2016. There is no study investigating the understanding of nor attitudes towards dementia among public contact staff in Macao. This study aimed to (i) understand the level of knowledge of dementia, (ii) examine the attitudes towards people living with dementia, and (iii) explore the associated factors of the willingness to help people with dementia symptoms among police officers, bank officers, bus drivers, and building superintendents. Methods A cross-sectional survey was conducted between January and May 2019 using a structured questionnaire. Results A total of 351 valid questionnaires were received. Building superintendents had more knowledge while police officers and bank officers had more positive attitudes. All practitioners were more willing to help people with dementia symptoms when they were on official duty. Participants who had more knowledge about dementia were associated with a higher willingness to help people with dementia symptoms.

Background/aim Nuclear factor kappa B (NF-κB) inactivation and apoptosis activation have been shown to enhance the anticancer effect of cisplatin in oral squamous cell carcinoma (OSCC). Amentoflavone may suppress NF-κB activity and trigger apoptosis in different types of cancer. The aim of this study was to investigate the anticancer effect and mechanism of amentoflavone in combination with cisplatin in OSCC. Materials and methods We investigated the combination effect and mechanism of amentoflavone and cisplatin via cell viability analysis, flow cytometry-based apoptosis analyses, transwell migration/invasion assay, immunofluorescence staining and western blotting assay. Results Both amentoflavone and QNZ (NF-κB inhibitor) significantly increased cisplatin-induced cytotoxicity. Amentoflavone reduced cisplatin-triggered NF-κB activity and enhanced cisplatin-induced intrinsic caspase-dependent and independent apoptotic pathways. Moreover, amentoflavone augments cisplatin-suppressed invasion and migration ability of OSCC cells. Conclusion Inactivation of NF-κB and induction of apoptosis through intrinsic caspase-dependent and independent apoptotic pathways are associated with amentoflavone enhanced anti-OSCC efficacy of cisplatin.

Based on Construal Level Theory (CLT), the youth and older adults have different psychological distances towards dementia that may lead to different dementia knowledge and attitudes. A cross-sectional survey among 239 youth and 62 older adults using a two-step sampling approach in Macao aimed to examine the hypothesis. Results showed older adults had a higher score of dementia knowledge (F(1,299) = 45.692, p

Batch processes have been playing a crucial role for the flexibility in producing low-volume and high-value-added products. Due to the fluctuations of raw materials and operation conditions, the ba...

Photodynamic therapy (PDT), which involves the generation of reactive oxygen species (ROS) through interactions of a photosensitizer (PS) with light and oxygen, has been applied in oncology. Over the years, PDT techniques have been developed for the treatment of deep-seated cancers. However, (1) the tissue penetration limitation of excitation photon, (2) suppressed efficiency of PS due to multiple energy transfers, and (3) insufficient oxygen source in hypoxic tumor microenvironment still constitute major challenges facing the clinical application of PDT for achieving effective treatment. We present herein a PS-independent, ionizing radiation-induced PDT agent composed of yttrium oxide nanoscintillators core and silica shell (Y2O3:Eu@SiO2) with an annealing process. Our results revealed that annealed Y2O3:Eu@SiO2 could directly induce comprehensive photodynamic effects under X-ray irradiation without the presence of PS molecules. The crystallinity of Y2O3:Eu@SiO2 was demonstrated to enable the generation of electron-hole (e--h+) pairs in Y2O3 under ionizing irradiation, giving rise to the formation of ROS including superoxide, hydroxyl radical and singlet oxygen. In particular, combining Y2O3:Eu@SiO2 with fractionated radiation therapy increased radio-resistant tumor cell damage. Furthermore, photoacoustic imaging of tumors showed re-distribution of oxygen saturation (SO2) and reoxygenation of the hypoxia region. The results of this study support applicability of the integration of fractionated radiation therapy with Y2O3:Eu@SiO2, achieving synchronously in-depth and oxygen-insensitive X-ray PDT. Furthermore, we demonstrate Y2O3:Eu@SiO2 exhibited radioluminescence (RL) under X-ray irradiation and observed the virtually linear correlation between X-ray-induced radioluminescence (X-RL) and the Y2O3:Eu@SiO2 concentration in vivo. With the pronounced X-RL for in-vivo imaging and dosimetry, it possesses significant potential for utilization as a precision theranostics producing highly efficient X-ray PDT for deep-seated tumors.

Soft sensors attempt to predict the key quality variables that are infrequently available using the sensor and manipulated variables that are readily available. Since only limited amount of labeled data are available, there is always the concern whether the underlying physics were captured so that the model can be reasonably extrapolated. A sequence-to-sequence model in the form of a nonlinear state-observer/encoder and predictor/decoder was proposed. The observer can be trained using a large amount of unlabeled data, but in a supervised manner in which the process dynamics is tracked. The encoder output and manipulated variables are used to train the quality predictor. The model is applied to the product impurity predictions of an industrial column. Results show that good predictions and excellent consistency in the sign of estimated gains can be achieved even with limited amount of data. These findings indicated that the proposed sequence-to-sequence data-driven approach is able to capture the underlying physics of the process.

In chemical engineering applications, computational efficient meta-models have been successfully implemented in many instants to surrogate the high-fidelity computational fluid dynamics (CFD) simulators. Nevertheless, substantial simulation efforts are still required to generate representative training data for building meta-models. To solve this problem, in this research work an efficient meta-modeling method is developed based on the concept of transfer learning. First, a base model is built which roughly mimics the CFD simulator. With the help of this model, the feasible operating region of the simulated process is estimated, within which computer experiments are designed. After that, CFD simulations are run at the designed points for data collection. A transfer learning step, which is based on the Bayesian migration technique, is then conducted to build the final meta-model by integrating the information of the base model with the simulation data. Because of the incorporation of the base model, only a small number of simulation points are needed in meta-model training.

Resin transfer molding (RTM) is an efficient manufacturing process for fabricating polymer composites, in which liquid thermosetting resin is injected into a closed mold to saturate a fiber preform. In RTM, effective flow control is necessary to direct the resin to flow in the desired manner and to prevent the formation of defects. Most existing methods are based on numerical flow simulations, whose accuracy is directly tied to the fidelity of the physics and material models used in the codes. The control performance of these methods largely depends on the quality of the models. The traditional proportional–integral–differential controllers are unsuitable as well, because of the nonlinear and time-varying characteristics of the RTM system. In this research, a model predictive control strategy is proposed for adjusting the flow behavior of the resin inside the mold, and it does not rely on process simulators. Recursive least squares with an adaptive directional forgetting factor is adopted as a method to identify the input–output relationship of the process under control. Based on the identification results, both the flow velocity and the flow front profile can be controlled simultaneously. The feasibility of the proposed strategy are illustrated with experimental results. POLYM. ENG. SCI., 2017. © 2017 Society of Plastics Engineers

Use of computational fluid dynamics to model chemical process system has received much attention in recent years. However, even with state-of-the-art computing, it is still difficult to perform simulations with many physical factors taken into accounts. Hence, translation of such models into computationally easy surrogate models is necessary for successful applications of such high fidelity models to process design optimization, scale-up and model predictive control. In this work, the methodology, statistical background and past applications to chemical processes of meta-model development were reviewed. The objective is to help interested researchers be familiarized with the work that has been carried out and problems that remain to be investigated.

Herein, we report a new type of biodegradable, high surface-area gold nanodandelions (GNDs). This report possesses important features and some are the first of its kind: (1) the large scale green synthesis of GNDs with high monodispersity and a circa 100% yield with consistent chemistry, manufacturing and controls (CMC); (2) cellular/physiological degradability of GNDs leading to its disassembly into debris, which is indicative of the potential for possible body clearance; (3) precision control of the chemicophysical properties of the GNDs including shape, petal number and size, all can be judiciously fine-tuned by the synthetic parameters; (4) highly efficient radiotheranostics of GNDs encompassing better enhanced computed tomography (CT) contrast and pronounced X-ray induced reactive oxygen species (ROS) generation than conventional spherical gold nanoparticles (AuNP). It is noteworthy that the GNDs demonstrate a unique combinational effect of radiosensitization (production of superoxide anions and hydroxyl radicals) and type II photodynamic interaction (generation of singlet oxygen). Given the above, our reported GNDs are promising in clinical translation as radiotheranostics.

As the electronic devices getting lighter and smaller, a coreless substrate technology, called Embedded Trace Substrate (ETS) is developed to meet the market requirement. However, this design causes severe warpage due to the large difference in CTE (coefficient of thermal expansion) of buildup material and Cu plate. Recently, finite element analysis (FEA) is a popular and effective method used for substrate warpage prediction and mechanical studies. Manufacturers apply FEA simulation for substrate design improvements and provide substrate warpage that satisfying the customer’s specification. Nevertheless, the computational resources needed for high-fidelity simulation are extremely expensive and time-consuming. Hence the simulation study becomes a long and arduous task if it has to be performed many times, e.g., sensitivity analysis and warpage optimization.In this paper, we propose a new method for FEA modeling of mechanical behaviors of the substrate and present an optimization strategy for substrate warpage control. In the first step, the Gerber files of each layer of the substrate are converted into high-resolution bitmap images, and the copper area of each image is divided and scanned by a pre-sized block window. After that, the effective material properties for each block are calculated with a volume average micromechanics approach, and then all blocks are stacked-up to build a block-based analysis model for FEA simulation. As compared with conventional trace mapping simulation, the proposed method significantly decreases the demands of the computing resource. Besides, we gained accurate warpage prediction results as validated by a real substrate experiment. Finally, we presented an optimization strategy that manipulates the thickness of each layer for substrate warpage optimization in pre-processing steps of packaging. In conclusion, the results show that the methodology for substrate simulation in this paper is practical, effective, and costless.

A novel and efficient RCGA for constrained optimization has been proposed.The proposed RCGA integrates three effective and novel evolutionary operators named RS, DBX and DRM.The proposed RCGA is proven to have a small complexity index and outperform many state-of-the-art algorithms.The proposed RCGA has been successfully applied to optimize the GaAs film-growth performance of an MOCVD process. This paper presents a simple and efficient real-coded genetic algorithm (RCGA) for constrained real-parameter optimization. Different from some conventional RCGAs that operate evolutionary operators in a series framework, the proposed RCGA implements three specially designed evolutionary operators, named the ranking selection (RS), direction-based crossover (DBX), and the dynamic random mutation (DRM), to mimic a specific evolutionary process that has a parallel-structured inner loop. A variety of benchmark constrained optimization problems (COPs) are used to evaluate the effectiveness and the applicability of the proposed RCGA. Besides, some existing state-of-the-art optimization algorithms in the same category of the proposed algorithm are considered and utilized as a rigorous base of performance evaluation. Extensive comparison results reveal that the proposed RCGA is superior to most of the comparison algorithms in providing a much faster convergence speed as well as a better solution accuracy, especially for problems subject to stringent equality constraints. Finally, as a specific application, the proposed RCGA is applied to optimize the GaAs film growth of a horizontal metal-organic chemical vapor deposition reactor. Simulation studies have confirmed the superior performance of the proposed RCGA in solving COPs.

Background/aim A previous study indicated that amentoflavone inhibits tumor growth of breast cancer. However, the anti-cancer effects and mechanism of amentoflavone in hepatocellular carcinoma (HCC) have not been elucidated. The aim of the present study was to verify the effect of amentoflavone on tumor progression in HCC. Materials and methods HCC SK-Hep1 cells were treated with different concentrations of amentoflavone or 10 μM PD98059 (extracellular signal-regulated kinases (ERK) inhibitor) for 48 h, respectively, and then cell viability, NF-κB activation, levels of tumor progression-associated proteins, and cell invasion were evaluated with 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT), NF-κB reporter gene assay, western blotting, and cell invasion assay. Results The results demonstrated that both amentoflavone and PD98059 not only significantly reduced cell viability, NF-κB activation, and cell invasion, but also inhibited the expression of tumor progression-associated proteins. In addition, we found that amentoflavone suppresses ERK phosphorylation. Conclusion The results of the present study suggest that amentoflavone down-regulates ERK-modulated tumor progression in HCC.

>The goal of the present study was to investigate anticancer effect of amentoflavone on glioblastoma cells in vitro. Our results demonstrated that amentoflavone not only significantly reduced cell viability, nuclear factor-ĸappa B (NF-ĸB) activation, and protein expression of cellular Fas-associated protein with death domain-like interleukin 1 beta-converting enzyme inhibitory protein (C-FLIP) and myeloid cell leukemia 1 (MCL1), but significantly triggered cell accumulation at the sub-G 1 phase, loss of mitochondrial membrane potential, and expression of active caspase-3 and -8. In order to verify the effect of NF-ĸB inhibitor on expression of anti-apoptotic proteins, we performed western blotting. We found that the of NF-ĸB inhibitor or amentoflavone markedly diminished protein levels of MCL1 and C-FLIP. Taken all together, our findings show that amentoflavone induces intrinsic and extrinsic apoptosis and inhibits NF-ĸB-modulated anti-apoptotic signaling in U-87 MG cells in vitro.

Batch processing is a widely used method in process industry for its flexibility in manufacturing low-volume and high-value-added products. Due to inter-batch variations, the batch duration often varies, which may cause difficulties in operation scheduling and decision-making. The capability of predicting batch completion time offers valuable information to improved capacity utilisation, reduced workload, and reduced operating cost. To this end, several data-driven modelling methods have been reported. However, the uncertainty of the predicted completion time has not been well explored in previous research. In this paper, the challenges for batch-end prediction are discussed by stressing the importance of prediction uncertainty. This has been demonstrated by the application of probabilistic principal component analysis (PPCA) and quantitative sensitivity analysis to two batch processes. The prediction uncertainty tends to increase substantially, when the variable defining the completion time changes slowly towards the end of batch. Under such situations, we argue that the uncertainty should always be considered along with the mean prediction for practical use.

A novel dual functional theranosis platform is developed based on manganese magnetism-engineered iron oxide (MnMEIO) and gold nanorods (AuNRs) to combine magnetic resonance (MR) imaging and photothermal therapy in one nanocluster. The platform showed improved T2-weighted MR imaging and exhibited a near-infrared (NIR) induced temperature elevation due to the unique characteristics of AuNRs@MnMEIOs nanoclusters. The obtained dual functional spherical-shaped nanoclusters showed low cytotoxicity, and high cellular uptake efficiency. The AuNRs@MnMEIOs nanoclusters also demonstrated a 1.9 and 2.2 folds r2 relaxivity value higher than those of monodispersed MnMEIO and Resovist. In addition, in vivo MR imaging study found that the contrast enhancements were – 70.4 ± 4.3% versus – 7.5 ± 3.0% in Her-2/neu overexpression tumors as compared to the control tumors. More importantly, NIR laser irradiation to the tumor site resulted in outstanding photothermal therapeutic efficacy and without damage to the surrounding tissue. In additional, the prepared dual functional AuNRs@MnMEIOs display high stability and furthermore disperse even in the presence of external magnet, showing that AuNRs@MnMEIOs nanoclusters can be manipulated by an external magnetic field. Therefore, such nanoclusters combined MR imaging and photothermal therapeutic functionality can be developed as a promising nanosystem for effective cancer diagnosis and therapy.

This paper considers the mathematical modeling and optimal design of a horizontal metal-organic chemical vapor deposition (MOCVD) reactor for GaAs film growth. A detailed 3D model of the MOCVD reactor is developed, which includes a reaction kinetic mechanism to describe the gas-phase and surface reactions occurring in the reactor, and a comprehensive heat transfer scheme to express the heat transfer between the inner reactor wall and the outer tube cooling gas. To estimate the model parameter, a data-driven optimization scheme which incorporates a uniform design technique, a neural network auxiliary model and a real-coded genetic algorithm is developed. The proposed 3D MOCVD model is shown to be in a good agreement with experimental data and presents an excellent ability in predicting the GaAs film growth rate and uniformity. Besides, the comprehensive model enables the systematic investigation of the microscopic transfer phenomena and reaction dynamics that relate to the dimensionless groups such as the Reynolds number (Re), Prandtl number, Peclet number (Pe) and Grashof number (Gr). We found that the “cold finger” and buoyancy-driven transverse rolls can occur in the AIX 200/4 horizontal MOCVD reactor when Pe ≥ 20 and Gr/Re2 ≥ 4000. To improve the film growth performance and meet the high-quality demands, we further apply the proposed data-driven optimization scheme to search for a set of operating conditions that optimizes the film growth rate distribution and uniformity on the substrate. The optimized results indicate that a significantly better GaAs film growth performance can be achieved for the horizontal MOCVD reactor based on using the comprehensive 3D mathematical model and the proposed data-driven optimization scheme.

Biomedical imaging modalities in clinical practice have revolutionized oncology for several decades. State-of-the-art biomedical techniques allow visualizing both normal physiological and pathological architectures of the human body. The use of nanoparticles (NP) as contrast agents enabled visualization of refined contrast images with superior resolution, which assists clinicians in more accurate diagnoses and in planning appropriate therapy. These desirable features are due to the ability of NPs to carry high payloads (contrast agents or drugs), increased in vivo half-life, and disease-specific accumulation. We review the various NP-based interventions for treatments of deep-seated tumors, involving “seeing better” to precisely visualize early diagnosis and “going deeper” to activate selective therapeutics in situ.

Enthusiasm for photodynamic therapy (PDT) as a potential therapeutic intervention for cancer has increased exponentially in recent decades. Photodynamic therapy constitutes a clinically approved, minimally invasive treatment modality that uses a photosensitizer (light absorbing molecule) and light to kill cancer cells. The principle of PDT is, when irradiated with a light of a suitable wavelength, a photosensitizer absorbs the light energy and generates cytotoxic free radicals through various mechanisms. The overall efficiency of PDT depends on characteristics of activation light and in-situ dosimetry, including the choice of photosensitizer molecule, wavelength of the light, and tumor location and microenvironment, for instance, the use of two-photon laser or an X-ray irradiator as the light source increases tissue-penetration depth, enabling it to achieve deep PDT. In this mini-review, we discuss the various designs and strategies for single, two-photon, and X-ray-mediated PDT for improved clinical outcomes.

Resin transfer molding (RTM) is a popular manufacturing technique that produces fiber reinforced polymer (FRP) composites. In this paper, a model-assisted flow front control system is developed based on real-time estimation of permeability/porosity ratio using the information acquired by a visualization system. In the proposed control system, a radial basis function (RBF) network meta-model is utilized to predict the position of the future flow front by inputting the injection pressure, the current position of flow front, and the estimated ratio. By conducting optimization based on the meta-model, the value of injection pressure to be implemented at each step is obtained. Moreover, a cascade control structure is established to further improve the control performance. Experiments show that the developed system successfully enhances the performance of flow front control in RTM. Especially, the cascade structure makes the control system robust to model mismatch.

Recombinant Chlamydomonas reinhardtii chlorophyllase 1 (CrCLH1) that could catalyze chlorophyll hydrolysis to chlorophyllide and phytol in vitro was successfully expressed in Escherichia coli. The recombinant CrCLH1 was immobilized through covalent binding with a cubic (3-aminopropyl) triethoxysilane (APTES) coating on magnetic iron oxide nanoparticles (MIONPs), which led to markedly improved enzyme performance and decreased biocatalyst costs for potential industrial application. The immobilized enzyme exhibited a high immobilization yield (98.99 ± 0.91 mg/g of gel) and a chlorophyllase assay confirmed that the immobilized recombinant CrCLH1 retained enzymatic activity (722.3 ± 50.3 U/g of gel). Biochemical analysis of the immobilized enzyme, compared with the free enzyme, showed higher optimal pH and pH stability for chlorophyll-a hydrolysis in an acidic environment (pH 3-5). In addition, compared with the free enzyme, the immobilized enzyme showed higher activity in chlorophyll-a hydrolysis in a high temperature environment (50-60 °C). Moreover, the immobilized enzyme retained a residual activity of more than 64% of its initial enzyme activity after 14 cycles in a repeated-batch operation. Therefore, APTES-coated MIONP-immobilized recombinant CrCLH1 can be repeatedly used to lower costs and is potentially useful for the industrial production of chlorophyll derivatives.

Advanced glycation end products (AGEs) contribute to the pathogenesis of renal diseases and have become a new therapeutic target for treatment. Chronic renal failure (CRF) is the end-stage of chronic renal diseases and highly associated with increased incidence in cardiovascular complications, however, some of the pathogenesis has not yet been fully clarified. This study was designed to evaluate the therapeutic or preventive potential of pyridoxamine (PM) against CRF in a 5/6 subtotal nephrectomy rat model. Significant and beneficial contributions to body weight, cardiovascular parameters, clearances of creatinine and BUN and AGEs reduction were observed in CRF rats receiving PM for 8 weeks. PM supplementation might be considered as one of pharmacological strategies for preventing AGE-related pathologies and early stages of renal damage. Prospective trials are needed to elucidate a potential role for PM in adjunctive therapy and to confirm the adequate amount on cardiovascular and renal outcomes in CRF.

An optical gold nanoparticles (AuNPs)-based method was fabricated for the rapid detection of matrix metalloproteinase (MMP) activity and screening potential MMP inhibitors without sophisticated instruments. The diagnosis platform was composed of AuNPs, particular MMP substrates and 6-mercapto-1-hexanol (MCH). The functionalized AuNPs were subjected to specific MMP digestion, and the MMP found the substrate on AuNPs, such that the AuNPs lost shelter and MCH increased the attraction force between AuNPs. Consequently, AuNPs aggregation and a color change from red to purple with increasing MMP concentration were observed. The surface plasmon resonance (SPR) of the formed AuNPs allowed for the quantitative detection of MMP activity. A sensitive linear correlation existed between the absorbance and the activity of the MMPs, which ranged from 10 ng/mL to 700 ng/mL in NTTC buffer and plasma samples. The proposed colorimetric method could be accomplished in a homogeneous solution with one-step operation in 30 min and has been successfully applied to the determination of particular MMP activity in plasma samples, in which the results are consistent with substrate zymography. This technology may become a simple platform for parallel screening a number of inhibitors and offer an alternative method to studying the efficiency of inhibitors for suppressing MMP activity. The absorbance ratio at 625 nm and 525 nm (A(625)/A(525)) confirmed the efficiency of the inhibitors as observed in substrate zymography. The IC(50) of ONO-4817 and galardin for MMP-1, MMP-2 and MMP-7 determined by the proposed colorimetric method was similar to the results of substrate zymography.

This paper presents a novel intelligent run-to-run control strategy for chemical-mechanical polishing (CMP) processes. With the help of the recursive least squares identification method for model building, a real-coded genetic algorithm is applied to adaptively adjust the discount coefficients for double exponentially weighted moving average (EWMA) controller. The online intelligent scheme can effectively prevent the CMP processes from reaching unstable condition and can thus achieve high control performance. To demonstrate the effectiveness and applicability of the proposed intelligent run-to-run control strategy, two typical case studies are worked out in this paper. Extensive simulation comparisons with traditional double EWMA run-to-run control were performed. The simulation results show that the proposed intelligent run-to-run control is able to achieve better control performance than conventional schemes, especially for a process that has nonlinearities, process noise, and extra large metrology delays.

The importance of detecting and subtyping human papillomaviruses (HPVs) in clinical and epidemiological studies has been well addressed. In detecting the most common types of HPV, type 16 (HPV-16) and type 18 (HPV-18), in the cervical mucous of patients in a simple and rapid manner, the assay of a label- free colorimetric DNA sensing method based on sequence sandwich hybridization with oligonucleotide-functionalized Au nanoparticles (AuNPs) was fabricated in this study. Specific oligonucleotide probes were designed for the sequence detection within the LI gene of HPV-16 and HPV-18, and the probes were capped onto AuNPs, as AuNP probes. The target HPV sequences in clinical specimens were obtained by an asymmetric polymerase chain reaction (PCR) with universal primers, which can amplify the target sequences from several HPV serotypes, including HPV-16 and HPV-18. The DNA sandwich hybridization between the target sequences and the specific AuNP probes was performed at a temperature closer to the theoretical melting temperature of the DNA hybridization. Next, the procedure of increasing salt concentration and cooling the hybridizing solution was immediately utilized to discriminate the target sequences of HPV-16 or HPV-18. If the target sequences were not complementary to sequences of AuNP probes, the AuNPs would aggregate because no duplex DNA formation occurred such that the color of the reaction solution changed from red to purple. If the AuNP probes were a perfect match to the target sequences and a full DNA sandwich hybridization occurred, the reaction solution maintained its red color. A total of 70 mucous specimens from patients with cervical intraepithelial neoplasia were tested by the AuNP probes sandwich hybridization. The results show that there were 33, 16, 5, and 16 cases detected with HPV-16, HPV-18, both HPV-16 and HPV-18 (HPV-16 / HPV-18), and neither HPV-16 nor HPV-18, respectively.In comparison with the specific detection by TaqMan real-time PCR assays for HPV-16, the detection sensitivity and specificity of the AuNP probes sandwich hybridization reached 95% and 90%, respectively, for HPV-16 diagnosis.

In this paper we propose a simple and novel model‐based nonlinear control strategy for feedback linearizable nonlinear processes. The nonlinear controller, called NLC, has a forward static nonlinear element and, if the relative degree of the process is greater than unity, a linear dynamic feedback path. The nonlinear static element is a modified hyperbolic function which involves two tunable parameters used to adjust the linear gain and saturation level of the NLC. The NLC parameters are determined to achieve a desired tracking transfer function of the form (Kcs + 1)/(1 + ϵs) r+1 where Kc and ϵ are parameters relating to NLC controller settings and r the system's relative degree. The effectiveness and applicability of the proposed nonlinear control strategy are demonstrated through the controls of nonlinear chemical processes. Extensive simulation results show that the proposed nonlinear control strategy is a robust model‐based approach for the nonlinear control of dynamic processes.

A disposable amperometric immunosensing strip was fabricated for rapid detection of Escherichia coli O157:H7. The method uses an indirect sandwich enzyme-linked immunoassay with double antibodies. Screen-printed carbon electrodes (SPCEs) were framed by commercial silver and carbon inks. For electrochemical characterization the carbon electrodes were coupled with the first E. coli O157:H7-specific antibody, E. coli O157:H7 intact cells and the second E. coli O157:H7-specific antibody conjugated with horseradish peroxidase (HRP). Hydrogen peroxide and ferrocenedicarboxylic acid (FeDC) were used as the substrate for HRP and mediator, respectively, at a potential +300 mV vs. counter/reference electrode. The response current (RC) of the immunosensing strips could be amplified significantly by 13-nm diameter Au nanoparticles (AuNPs) attached to the working electrode. The results show that the combined effects of AuNPs and FeDC enhanced RC by 13.1-fold. The SPCE immunosensing strips were used to detect E. coli O157:H7 specifically. Concentrations of E. coli O157:H7 from 102 to 107 CFU/ml could be detected. The detection limit was approximately 6 CFU/strip in PBS buffer and 50 CFU/strip in milk. The SPCE modified with AuNPs and FeDC has the potential for further applications and provides the basis for incorporating the method into an integrated system for rapid pathogen detection.

Background It has been suggested that the antioxidant properties of olmesartan (OLM), an angiotensin II type 1 receptor (AT1R) blocker, contribute to renal protection rather than blood pressure lowering effects despite the fact that causal relationships between hypertension and renal artery disease exist. This study aimed to examine the hypothesis whether the antioxidative activities of OLM were correlated to arterial stiffness, reactive oxygen species and advanced glycation end products (AGEs) formation in rats with chronic renal failure (CRF). Methods CRF rats were induced by 5/6 nephrectomy and randomly assigned to an OLM (10 mg/day) group or a control group. Hemodynamic states, oxidative stress, renal function and AGEs were measured after 8 weeks of OLM treatment. Results All the hemodynamic derangements associated with renal and cardiovascular dysfunctions were abrogated in CRF rats receiving OLM. Decreased cardiac output was normalized compared to control (p p p p p p p Conclusion OLM treatment could ameliorate arterial stiffness in CRF rats with concomitant inhibition of MDA and AGEs levels through the reduction of oxidative stress in aortic wall.

In this paper, a novel and efficient real-coded genetic algorithm (RCGA) for process optimization is developed. The proposed RCGA is equipped with Ranking Selection (RS), Direction-Based Crossover (DBX) and Dynamic Random Mutation (DRM) operators. The RS operator is used to eliminate the bad solutions and reproduce good solutions, making the whole population to achieve a better average fitness. The DBX operator uses relative fitness information to direct the crossover toward a direction that significantly improves the objective fitness. The DRM operator prevents the premature convergence of RCGA and at the same time increases the precision of the searched solution. The effectiveness and application of the proposed RCGA are demonstrated through a variety of single-objective optimization benchmark problems. For comparative study, other existing RCGAs with different evolution operators are also performed to the same problem set. Extensive experiment results reveal that the proposed RCGA provides a significantly faster convergence speed and much better search performance than comparative methods.