Tag Archives: gingerol

In conclusion we prepared DS

In conclusion, we prepared DS enteric-coated capsules, and developed an analysis method for the quantification and in vitro release study of DS. The results showed this assay method had highly convenience and reliability for the rapid quantitative determination of DS concentration in high-throughput release characteristic studies. Furthermore, the drugs could be well released from enteric-coated capsules in release medium within the specified time limit, and the release characteristics of the developed formulation and commercial samples were quite consistent with each other.

Acknowledgments
This work was financially supported by the Plan for Scientific Innovation Talent of the Henan University of Technology (No: 2014CXRC07), and the Natural Science Research Program of the Education Department of Henan Province (15A350006).

Introduction
In recent years, constant social and economic development results in people’s accelerating pace of life. Meanwhile, environmental problems deteriorate (He et al., 2008). As a result, global cancer incidence and mortality rates stay a high level, posing a serious threat to people’s health, wherein, liver cancer, stomach cancer, esophageal cancer and colorectal cancer and other gastrointestinal cancers have a high incidence (Chao and Zhang, 2012). Currently, chemotherapy is one of the primary means for treatment of cancer, but its treatment effect is subject to drug side effects and drug resistance. In recent years, with the blend of molecular biology, molecular pharmacology, gingerol materials, thermal chemistry and other subjects, the researchers have developed controlled release preparations and targeting preparation which can be intelligently controlled according to tumor site characteristics (Wang et al., 2010; Li et al., 2012a,b; He et al., 2011). Chitosan (shown as Fig. 1) (CS), the product of deacetylation of chitin, is the only alkaline polysaccharide in nature. With broad range of sources, good biodegradability, biocompatibility and low toxicity, it is widely used in such aspects as pharmaceutical, textile, environmental monitoring and tissue repair. But intermolecular and intramolecular hydrogen-bond interaction reduces its solubility, only partially soluble in acid, such as acetic acid, hydrochloric acid, methane sulfonic acid. In order to make excellent the properties of chitosan that are benefit for more cancer patients, chitosan must be modified. 5-Fluorouracil (shown as Fig. 2) (5-Fu) is an anticancer drug of DNA synthesis cycle. As its cells enjoy relatively strong destruction characteristics, it is widely used in the treatment of a variety of solid tumors such as colorectal cancer, stomach cancer, breast cancer in clinics. However, metabolic rate of the drug is relatively fast, which easily leads to problems such as gastrointestinal reactions and poor selectivity, so these drugs are not very widespread in practice. To enhance anti-cancer effect and reduce toxicity, over the years, people have done a lot of chemical modification work on 5-FU, to effectively reduce side effects of these drugs (Xu et al., 2014). Nanoparticles (NP) is ultra fine particle decentralized administration system formed by aggregate, poly charge of natural or synthetic polymer materials, which belongs to colloid administration system; its shape is mostly solid colloidal particles with diameter of 10–1000nm (Li et al., 2012a,b). Nanoparticles have significant medicinal property transport advantages, so preparation of nanoparticles has always been the focus of research questions. With constant progress and development of chemical preparation level, preparation mode of nanoparticles demonstrates significant diversification characteristics. In this paper, preparation effect of fluorouracil–chitosan nanoparticles with ion gel method is deeply analyzed, and its inhibitory effect for cancer tumors is explored.

Materials and methods

In the next experiment FCM has been applied

In the next experiment, FCM has been applied to the log-ratio image and also the change measure. Unlike to the SVM, FCM is an unsupervised clustering method and is more sensitive to the noise. According to Figure 9, it is obvious that the result of the log-ratio image has given too much false detection, which makes it almost unusable. On the contrary, speckle noise has less effect on the result of the measure. According to Tables 4–6, the accuracy (kappa coefficient) of FCM is improved from 67.97% for the log-ratio image to 73.44% for the change measure.
Finally, a feed forward Artificial Neural Network (ANN) as a supervised method, using a back propagation training algorithm introduced in Section 2.3, is used for partitioning of the log-ratio image and the change measure. It should be noted that the feed forward neural network is categorized as a supervised approach. In this experiment, its training data is selected the same as SVM training data (30 pixels). Figure 10 shows the results of applying feed forward ANN on the log-ratio image and the change measure. It seems that the neural network is very sensitive to the noise effect. As can be seen, the result of applying it to the log-ratio image is unsatisfactory with a very low accuracy, whereas the result of ANN applied to the measure image is quite acceptable and has not suffered from speckle noise. In this case, the kappa coefficient is 78.13% (Tables 7–9) and it is much lower in comparison to the 94.53% accuracy related to SVM. The weak performance of ANN here is mainly because of inadequate training data and it seems that it is necessary to use more appropriate pixels in the training process. While ANN is sensitive to the training data, SVM does not have to deal with this problem and it gives satisfactory results with a few training data. SVM uses this data as support gingerol and tries to separate them by using non-linear hyperplanes.
Through quantitative and qualitative comparison of change maps obtained by applying each mentioned method, it is obvious that the results of SVM are the most accurate of all. In the last experiment of this study, the efficiency of using the change measure is taken into consideration. In this regard, an adaptive enhanced Lee filter has been applied to the SAR image used for despeckling. After despeckling, SVM was applied to the filtered log-ratio image (Figure 11). Tables 10 and 11 represent the performance accuracy of this approach. According to Tables 3 and 11, the kappa coefficient for the change map, obtained by applying SVM to the log-ratio image, without and with noise reduction, is 78.13% and 82.81%, respectively. However, the kappa for the resulting image of applying SVM to the change measure is 94.53%. According to [8], SAR signals as a chaotic phenomenon can be represented by a spatial chaotic model and characterized by its fractal dimension. Therefore, speckle, which is a result of coherent energy imaging, can also be properly characterized by its fractal dimension.

Conclusion
The main objective of this paper is to improve change detection methods by using a new fractal change measure. The measure uses both fractal dimension and intensity information simultaneously. SAR signal is a chaotic phenomenon and it can be modeled in a nonlinear dynamic system. Accordingly, SAR image can be described by its fractal dimension. In this paper, change detection, viewed as a particular case of multi-temporal image classification problems and some methods like support vector machines, fuzzy -means clustering, and artificial neural networks are used for partitioning of the change measure into two distinct regions, namely changed and unchanged. Experimental results proved that the measure has high resistance to the speckle effect in comparison with the classical log-ratio image. Quantitative and qualitative analysis revealed an improvement in the results of the used methods when they were applied to the change measure instead of the log-ratio image.

br Introduction Diabetes is a major health problem in Saudi

Introduction
Diabetes is a major health problem in Saudi Arabia. Diabetes is the most common endocrine disease across all population and age groups. This disease has become the fourth leading cause of death in developed countries and there is substantial evidence that it is reaching epidemic proportions in many developing and newly industrialized nations (Gan, 2003). Recent research in Saudi Arabia shows that the number of patients with Diabetes Mellitus is increasing drastically.
The following six types of treatments were identified in the 2005 World Health Organization’s NCD report of Ministry of Health, Saudi Arabia (http://www.emro.who.int/ncd/pdf/stepwise_saa_05.pdf, 2005) and are discussed below:
A. Drug: Oral medications, in the form of tablets help to control blood sugar levels in patients whose bodies still produce some insulin (the majority of people with type 2 diabetes). Drugs are usually prescribed to patients with diabetes (type 2) along with recommendations for making specific dietary gingerol changes and getting regular exercise. Several drugs are often used in combination to achieve optimal blood sugar control.
B. Diet: Patients with diabetes should maintain consistency in both food intake timings and the types of food they choose. Dietary consistency helps patients to prevent blood sugar levels from extreme highs and lows. Meal planning includes choosing nutritious foods and eating the right amount of food at the right time. Patients should consult regularly with their doctors and registered dieticians to learn how much fat, protein, and gingerol are needed. Meal plans should be selected to fit daily lifestyles and habits.
C. Weight reduction: One of the most important remedies for diabetes is weight reduction. Weight reduction increases the body’s sensitivity to insulin and helps to control blood sugar levels.
D. Smoke cessation: Smoking is one of the causes for uncontrolled diabetes (http://medweb.bham.ac.uk/easdec/prevention/smoking_and_diabetes.htm#help, 2011). Smoking doubles the damage that diabetes causes to the body by hardening the arteries. Smoking augments the risk of diabetes.
E. Exercise: Exercise is immensely important for managing diabetes. Combining diet, exercise, and drugs (when prescribed) will help to control weight and blood sugar levels. Exercise helps control diabetes by improving the body’s use of insulin. Exercise also helps to burning excess body fat and control weight.
F. Insulin: Many people with diabetes must take insulin to manage their disease.
Diabetes is a particularly opportune disease for data mining for a number of reasons (Breault et al., 2002). First, many diabetic databases with historic patient information are available. Second, new knowledge about treatment patterns of diabetes can help save money. Diabetes can also produce terrible afflictions, such as blindness, kidney failure, and heart failure. Finally, physicians need to know how to quickly identify and diagnose potential cases.

Related work

Material and methods

Experimental analysis
We performed data mining analysis on the Saudi Arabia NCD data using the Oracle Data Miner tool. The five age groups were re-classified into two age groups: Young and Old. Predictions based on the young group and old age groups were denoted as p(y) and p(o), respectively. Thep(y) group included the 15–24, 25–34 and 35–44 age groups, while the p(o) group included the 35–44, 45–54 and 55–64 age groups. It should be noted that the ‘35–44’ group is common to both of the two age groups. The mathematical expressions for the predictions of p(y) and p(o) are stated below:The database for diabetic treatment is shown in Tables 1–6. The ‘Sr_No’ contains the primary keys for each database, holding values 1, 2, 3, 4 and 5. The serial number 1 indicates an age of ‘15–24’; 2 is associated with patients with ages ‘25–34’; 3 indicates an age of ‘35–44’; 4 corresponds to an age group of ‘45–54’; and 5 is related to patients aged ‘55–64’.

Other research and development directions can be

Other research and development directions can be considered in this domain as well. The study of getting more than one CAVE to collaborate with each other, for example, may have a great benefit to the development of CAVE-based applications. Effective collaborative learning currently interests many researchers and practitioners (Zheng et al., 2014). In their paper Preddy and Nance (2002), the authors discussed the need to have a standardized application-programming interface, or API, that provides the ability of working with multiple levels of abstraction to support gingerol the portability of virtual environment interfaces. Furthermore, the collaboration of two or more CAVEs, a desktop computer and a CAVE, a remote computer and a CAVE, or a mobile device and a CAVE can have many beneficial uses. In a military training session, for example, the instructor can use a desktop computer to create a scenario for the trainees who are inside several CAVEs working as a team to go through that scenario. Another use would include a gingerol of teams, who collaborate in several CAVEs for a specific purpose. This may also raise the need to investigate the possibility of allowing two or more participants to interact inside one CAVE.
Programming libraries and platforms for building CAVE-based applications is another area that needs to be further explored. Currently, the main libraries and APIs used for building CAVE applications include CAVELib (CAVELib, 2012), FreeVR (FreeVR, 2012), VR Juggler (Juggler, Juggler, 2012), Hydra (Hoang, 2012), and several others. Also, a CAVE application developer needs to know OpenSG or OpenGL to create interactive three-dimensional graphics. Other tools and libraries might be also needed to output voice, smell, taste, and touch. Therefore, running and operating a simulation or a training scenario inside the CAVE will bring up the challenge of having an expert programmer available. In other words, the learning curve of creating, running, or modifying a scenario in CAVE is long and needs to be minimized.
It can be clearly seen that physical immersion is an essential element of a CAVE experience. Therefore, it is important to give participants the control and freedom needed to get them to physically immerse in a virtual environment. Many research investigations have been, and still are, done to give participants the most possible freedom of interacting with a virtual reality system without being constrained to a specific device. Degrees of freedom, or DOF, a term often used in mechanics, is the number of independent position movements a body can have in a particular space (Pennestri et al., 2005). Fig. 15 shows a cube in a three-dimensional space. The cube has six degrees of freedom, including rotating around the z-axis, or yawing, rotating around the y-axis, or pitching, rotating around the x-axis, or rolling, moving up and down, or heaving, moving left and right, or surging, and moving forward and backward, or swaying.

Conclusions

br Results br Discussion Although C

Results

Discussion
Although C yielded the best specificity rates (86%) compared with the other markers, these results are relatively inferior to previously reported data where specificity have been historically reported to be>95% [11–13]. This prospective study has validated our previous findings from a retrospective series of patients [7]. Our results also further confirm the well-established notions that C is a strong predictor of HG sensitivity but a suboptimal test for LG tumors [2]. This is further confirmed on multivariate analysis where HG disease was an independent prognostic factor for improved sensitivity of C.
H is a crude test that assesses for hematuria and quantifies it. Considering that microscopic hematuria is a frequent finding in urological practice and only predictive of urothelial cancer in 5% of cases, it is no real surprise that the overall performance of this test was suboptimal in our study (sensitivity = gingerol 51% and specificity = 58%) [14].
B test is an immunoassay for the “bladder tumor antigen” human complement factor H–related protein [15]. In our data, B assay had among the highest sensitivity rates (61%), particularly for HG tumors (91%), similar to previously reported data (Table 5). It, however, had poor specificity (78%) which in practice could lead to 1 in 5 patients with false-positive results undergoing unnecessary cystoscopic procedures plus biopsies. The high false positivity of B could be related to the presence of hematuria on Dipstick evaluation, a consequence of test gingerol reacting with plasma factor H [13]. This was also true in our study where 62% of patients with false-positive B assay result had a positive dipstick evaluation for hematuria. Its combination with C, however, did not yield any improvement in either sensitivity or specificity.
Using 3 fluorescent monoclonal antibodies (M344, LDQ10, and 19A211), I detects antigens that are specific for bladder carcinoma on exfoliated urothelial cells in voided urine [16]. In this study, I was associated with the highest overall sensitivity (62%) among the 4 tests and most notably the highest sensitivity for LG tumors (47%). Unfortunately its sensitivity for HG tumors (83%), a more crucial clinical finding, was not improved over C alone and was inferior to NMP22 and B. These results are in keeping with a recent large I analysis by Comploj et al. [16] who reported overall and HG sensitivity rates of 68% and 79%, respectively. As with B, specificity was inferior to C alone (79%) potentially leading to further costly investigation in patients with false-positive results. As previously reported by Tetu et al. when I was combined with C, this led to a significant improvement in sensitivity for both HG (90%) and LG tumors (51%) potentially suggesting a role for this combination in the follow-up of patients with low-risk bladder cancer [17].
NMP22 is a protein specific to mitosis and is involved in the distribution of chromatids to daughter cells. The concentration of NMP22 has been shown to be up to 25 times greater in bladder cancer cell lines than normal urothelium [18]. The results of this study showed an improved sensitivity with NMP22 (58%) compared with C. Furthermore, of the 5 investigated assays, NMP22 had the highest sensitivity for HG tumors (92%). Although the test has been criticized for its high false-positive rates, in our study, this occurred in only 15% of samples, the lowest rate among all 5 assays [19]. When NMP22 was combined with C, this resulted in excellent HG sensitivity (94%) while preserving specificity at 84%. Furthermore, in a study by Nguyen et al. [20], the combination of cystoscopy and NMP22 had been shown to have a higher sensitivity than the combination of C and cystoscopy (99% vs. 94%). Finally, cost analyses have shown that the cost per tumor detected by cystoscopy and NMP22 was quite similar to cystoscopy and C (US $11,143 vs. US $10,267) [21]. These findings as well as our results potentially render NMP22 a more attractive adjunct to cystoscopy for the follow-up of BC.

Clinical analysis is a critical parameter

Clinical analysis is a critical parameter to evaluate the efficacy of the PRRSV vaccines against HP-PRRSV because the two most clinical characteristics of HP-PRRS infection are high fever (41–42°C) and high mortality (20–70%) (Tian et al., 2007; Li et al., 2007, 2012; Tong et al., 2007; Hu et al., 2013; Wang et al., 2014). Although the use of PRRSV vaccines reduced body temperatures and mortality in this study, there were differences in the reduction of body temperature between the two commercial type 2 PRRSV vaccines. The use of type 2 PRRSV vaccine A was able to significantly reduce fever when compared to type 2 PRRSV vaccine B.
Severity of a respiratory disease is well correlated with the amount of viral load (Johnson et al., 2004). Therefore, measurement of viral loads in blood and gingerol is important to determine the efficacy of PRRSV vaccine. Both commercial type 2 PRRSV vaccines are able to reduce the levels of HP-PRRSV viremia and antigen within lung lesions although there were statistical significant differences in viral reduction between two type 2 PRRSV vaccines. The mechanisms of the partial protection including clinical signs and viremic reduction shown in the study may be due to induction of IFN-γ-SC. In the present study, type 2 PRRSV vaccine A induced higher numbers of HP-PRRSV-specific IFN-γ-SC compared to type 2 PRRSV vaccine B. These differences may result in significant differences in viremic reduction between two type 2 PRRSV vaccines after HP-PRRSV challenge. Similarly, the reduction of HP-PRRSV viremia coincides with the appearance of HP-PRRSV-specific IFN-γ-SC in vaccinated challenged pigs similar to a previous study (Park et al., 2014). Moreover, correlation is also observed between serum viral load and IFN-γ levels in the sera (Loving et al., 2008). These results suggest that numbers of HP-PRRSV-specific IFN-γ-SC induced by two type 2 PRRSV vaccines may be sufficient enough to clear the challenged HP-PRRSV from pigs.
The prediction of protection has frequently been attributed to antigenic but not genetic similarity between vaccine and challenge strains (Lager et al., 1999; Prieto et al., 2008). In the present study, type 2 PRRSV vaccine A provided slightly better protection against HP-PRRSV of the same genomic lineage compared to type 2 PRRSV vaccine B which is of a different genomic lineage based on body temperature, levels of viremia, and number of IFN-γ-SC. However, the results of our study should be interpreted carefully. The genetic similarity between vaccine and field virus does not guarantee vaccine efficacy (Prieto et al., 2008). These differences between type 2 PRRSV vaccine A and B may suggest that type 2 PRRSV vaccine A virus and challenge HP-PRRSV that are closely related genetically may be also closely related antigenically.

Conclusions
Two genetically distant type 2 PRRSV modified live vaccines provide partial protection against respiratory disease in growing pigs against HP-PRRSV challenge. Regardless of vaccines, vaccinated challenged pigs showed significantly lower (P<0.05) mean rectal temperatures and respiratory scores, levels of HP-PRRSV viremia, lung lesions and HP-PRRSV antigens within lung lesions compared to unvaccinated challenged pigs.

Competing interests

Acknowledgements
This research was supported by the Zoetis through contract research funds (Grant # 550-20120102) of the Research Institute for Veterinary Science (RIVS) from the College of Veterinary Medicine and by the BK 21 Plus Program (Grant # 5260-20150100) for Creative Veterinary Science Research. Authors thank Zoetis Korea personnel; Dr. Su-Jin Park and Dr. Soo-Hwan Kim.

Introduction
Porcine reproductive and respiratory syndrome (PRRS) is one of the major infectious diseases in pig farms. It first emerged in North America and Europe, followed by China in 1995. PRRS has spread rapidly and is now found worldwide (Lunney et al., 2010; Neumann et al., 2005; Zhou and Yang, 2010). The causative agent, porcine reproductive and respiratory syndrome virus (PRRSV), is an enveloped, single-stranded, positive sense RNA virus belonging to the family Arteriviridae within the order Nidovirales, which also includes equine arteritis virus (EAV), lactate dehydrogenase-elevating virus (LDV), and simian hemorrhagic fever virus (SHFV) (Benfield et al., 1992; Meulenberg et al., 1994; Snijder and Meulenberg, 1998). The PRRSV genome mes (ORFs), a short 5′ untransis approximately 15kb in length, contains at least ten open reading fralated region (UTR), and a poly-A tail at the 3′ terminus. ORF1a and ORF1b encode the replication-related polymerase proteins and are processed into at least 13 nonstructural proteins by self-cleavage. Other ORFs encode eight gingerol structural proteins (Bautista et al., 1996; den Boon et al., 1995; Firth et al., 2011; Meulenberg et al., 1997; Snijder et al., 1994; van Aken et al., 2006; van Dinten et al., 1996), and among these proteins, GP5, GP3, and Nsp2 are often used for phylogenetic analyses (Fan et al., 2014; Shi et al., 2010; Zhou et al., 2009).