**Synthesis and Characterization of Crystalline Zirconium Titanate Obtained by Sol-Gel**

Venina dos Santos1 and C.P. Bergmann2

*1Center of Exact Science and Technology, University of Caxias do Sul, Caxias do Sul, RS, 2Laboratory of Ceramic Materials (LACER), Departament of Materials, Engineering School, Federal University of Rio Grande do Sul, Osvaldo Aranha, Porto Alegre, RS, Brazil*


**Cationic Fluorescent Nanogel Thermometers based on Thermoresponsive Poly(***N***-isopropylacrylamide) and Environment-Sensitive Benzofurazan**

#### **Teruyuki Hayashi 1, Kyoko Kawamoto 2, Noriko Inada 3 and Seiichi Uchiyama 2,\***

Received: 21 June 2019; Accepted: 29 July 2019; Published: 4 August 2019

**Abstract:** Cationic nanogels of *N*-isopropylacrylamide (NIPAM), including NIPAM-based cationic fluorescent nanogel thermometers, were synthesized with a cationic radical initiator previously developed in our laboratory. These cationic nanogels were characterized by transmission electron microscopy (TEM), dynamic light scattering (DLS), zeta potential measurements and fluorescence spectroscopy, as summarized in the temperature-dependent fluorescence response based on the structural change in polyNIPAM units in aqueous solutions. Cellular experiments using HeLa (human epithelial carcinoma) cells demonstrated that NIPAM-based cationic fluorescent nanogel thermometers can spontaneously enter the cells under mild conditions (at 25 ◦C for 20 min) and can show significant fluorescence enhancement without cytotoxicity with increasing culture medium temperature. The combination of the ability to enter cells and non-cytotoxicity is the most important advantage of cationic fluorescent nanogel thermometers compared with other types of fluorescent polymeric thermometers, i.e., anionic nanogel thermometers and cationic/anionic linear polymeric thermometers.

**Keywords:** fluorescence; imaging; nanogel; temperature; thermometer; thermometry


Socialization

A Multidimensional Perspective

*Edited by Rosalba Morese, Sara Palermo and Juri Nervo*

**SOCIALIZATION - A**

**PERSPECTIVE**

and **Juri Nervo**

**MULTIDIMENSIONAL**

Edited by **Rosalba Morese, Sara Palermo**


**4. Discussion**

In summary, this review, limited by the bias against publication of null findings, highlights the complexities inherent in epidemiological research and, particularly, in molecular epidemiological research on colorectal cancer. Studies on possible associations between SNPs in genes involved in defence of oxidative DNA damages and in nucleotide excision repair and risk of colorectal cancer have not obtained consistent results, why the issue of whether the SNPs are possible biomarkers of susceptibility for colorectal cancer is not satisfactorily clarified at present.

Polymorphisms in Nucleotide Excision Repair

Cancer Society (Grant number R2-A84-09-S2).

**6. Acknowledgement**

**7. References**

1037)

Genes and Risk of Colorectal Cancer: A Systematic Review 553

Overall, the role of genetic variants as SNPs in genes involved in NER is not satisfactorily clarified at present. It is possible that some of the SNPs may contribute to development of adenomas or colorectal cancer only in concomitance with certain dietary and life style factors. Furthermore, it may be only the joint effect of multiple polymorphisms that will provide us with information about genetic susceptibility for colorectal cancer. Larger carefully designed studies with stratified/adjusted analyses of gene-gene and geneenvironment interactions may be required in the future to achieve convincing statistically

Our special thanks go to Anne Tjønneland and Kim Overvad for giving us the opportunity to study the polymorphisms in the Danish prospective cohort Diet, Cancer and Health. We thank Anne-Karin Jensen and Lourdes M. Pedersen for excellent technical assistance for the genotyping in our studies on the Danish cohort. And a thank goes to our co-authors of the manuscripts on the Danish studies for the valuable feedback when writing the manuscripts. The work of the present book chapter was supported by a research grant from the Danish

Andrew, A.S., Nelson, H.H., Kelsey, K.T., Moore, J.H., Meng, A.C., Casella, D.P., Tosteson,

Baccarelli, A., Calista, D., Minghetti, P., Marinelli, B., Albetti, B., Tseng, T., Hedayati, M.,

Balbi, J.C., Larrinaga, M.T., De Stefani, E., Mendilaharsu, M., Ronco, A.L., Boffetta, P. &

Batty, D., Rapic'-Otrin, V., Levine, A.S. & Wood, R.D. (2000). Stable binding of human XPC

Benhamou, S., Laplanche, A., Guillonneau, B., Mejean, A., Desgrandchamps, F., Schrameck,

Benhamou, S. & Sarasin, A. (2005). ERCC2 /XPD gene polymorphisms and lung cancer: a

Berndt, S.I., Platz, E.A., Fallin, M.D., Thuita, L.W., Hoffman, S.C. & Helzlsouer, K.J. (2006).

risk. *Cancer Epidemiol.Biomarkers Prev.,* Vol. 15, No. 11, pp. (2263-2269) Bigler, J., Ulrich, C.M., Kawashima, T., Whitton, J. & Potter, J.D. (2005). DNA repair

melanoma risk. *Br.J.Cancer,* Vol. 90, No. 2, pp. (497-502)

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T.D., Schned, A.R. & Karagas, M.R. (2006). Concordance of multiple analytical approaches demonstrates a complex relationship between DNA repair gene SNPs, smoking and bladder cancer susceptibility. *Carcinogenesis,* Vol. 27, No. 5, pp. (1030-

Grossman, L., Landi, G., Struewing, J.P. & Landi, M.T. (2004). XPD gene polymorphism and host characteristics in the association with cutaneous malignant

Brennan, P. (2001). Foods and risk of bladder cancer: a case-control study in

complex to irradiated DNA confers strong discrimination for damaged sites.

C., Degieux, V. & Perin, F. (2003). DNA adducts in normal bladder tissue and

Genetic variation in the nucleotide excision repair pathway and colorectal cancer

polymorphisms and risk of colorectal adenomatous or hyperplastic polyps. *Cancer*

significant results on factors involved in colorectal carcinogenesis.

Sample size coupled with allele frequency may have influenced the validity of the results. Differences in the study design, like distribution of gender, age, topology, ethnicity and criteria for recruitment of comparison individuals may have contributed to the dissimilar findings. The application of large, well-designed association studies of the polymorphisms will make it statistically reasonable to make stratified analyses for obtaining information on risk factors in sub-groups and will generally decrease the risk of chance findings. Furthermore, studies including both cases with pre-stages of colorectal cancer and cancer cases will contribute with valuable information of the processes during colorectal carcinogenesis.

Most of the studies analyze individual polymorphisms in genes with modest effect in relation to risk of cancer. Cancer is a complex multigenic and multistage disease involving the interplay of many genetic and environmental factors. Hence, it is unlikely that a single genetic polymorphism in low-penetrance genes would have a dramatic effect on cancer risk. More information may be obtained from haplotyping multiple polymorphisms within genes or from combining multiple polymorphisms within pathways. The continued advances in SNP maps and in high-throughput genotyping methods will facilitate these analyses. Defining haplotypes and whole genome association studies may yield information on unexplored regions of the genome that has impact on colorectal cancer risk and development.

Colorectal cancer is probably caused by a complex interaction between many genetic and environmental factors over time. More and large studies with information on life style factors are required to assess these very possible gene-environment interactions. Identification of gene-environment interactions in cohorts with large relevant exposures has proven to be a useful approach.

Most environmental carcinogens require metabolic activation before they are able to form DNA damages. These activated forms may be detoxified or induce DNA repair or apoptosis. Thus, genetically determined susceptibility to colorectal cancer may depend on the balance among enzymes involved in metabolism and detoxification of carcinogens and on the balance between induction of DNA repair or apoptosis. Further investigations of the combined effects of polymorphisms between genes involved in these four mechanisms may help to clarify the influence of genetic variation in the carcinogenic process and may shed light on the complexities of the many pathways involved in colorectal cancer development, providing hypotheses for future functional studies.


**4. Empirical research**

To assess the factors of influence of cluster elements and management organizations on innovative development and the level of competitiveness of national innovation systems in Russia and world countries, an experimental decision was made to implement statistical analysis using multiple linear regression models [15].

The empirical objectives of the study favored the determination of the following hypotheses, which reflect the meaning of the updated research questions:

For the implementation of statistical analysis, 29 linear regression models were created with the participation of 119 variables, which were constructed from 9 databases containing 6656 values. Data on Russian innovation clusters were obtained from the Internet portals of the Russian Cluster Observatory [6], the values of innovation subindices and the Global Innovation Index (GII) were found on the World Intellectual Property Organization (WIPO) page, and the competitiveness ratings of states were found in the data from IMD World Competitiveness Center [12]. All data came out in 2021 and represented the most recent information provided by the publishers. This decision was made due to the need for alternative options found in the free Internet access.

To avoid the adverse effects of autocorrelation, multicollinearity, heteroscedasticity, and the size of outliers on the objectivity of the obtained statistical results, tests are carried out on the assumptions of the quality of the used models [16]. The Durbin-Watson test was used, the coefficients were studied by the test on variance inflation factor, and the graphical analysis was produced. For this reason, 23 models were assessed and established, which leveled out the conflicting interactions of variables in the implementation of regression analysis [17]. The workflow of the operations performed in the context of regression analysis can be seen in **Figure 3**.

The threat of unreliability of the obtained results is potentially present due to the limited number of observations, which eliminates the possibility of removing every cause of the statistical outliers when improving the quality of regression models. Another source of doubt about the validity of the obtained results is the indices from the Russian Cluster Observatory, WIPO, and IMD, on which databases the analysis was executed [13]. Also, the adequacy of using the WIPO's data regarding the countries' innovation achievements to represent the success of the countries' clusters can come under scrutiny. However, after conducting the qualitative analysis, the author believes this approach can be justified since the innovation clusters significantly impact the countries' innovation development.

In the first section of the regression analysis, 20 regions were selected to uncover the impact of variables on the innovation index of clusters in Russia. These regions were initiated according to pilot projects of the country's innovative cluster development [3].

To confirm or reject the first hypothesis, the relationship among the leading macroeconomic indicators, the educational potential of the population, the financing of research and development, the effectiveness of research and development, the export of goods and services, the regulatory and legal framework for innovation policy, the organizational support for innovation policy, budget expenditures on science, and innovations on the dependent variable—innovation index of cluster regions of Russia —is explored. This procedure is presented in **Table 8**.

### **Figure 3.**

*Methodical thinking behind the regression analysis. Source: The figure and research were designed and carried out by the authors.*

### **Table 8.**

*First variables' influence on the innovation index of clusters in Russia.*

According to the analysis of all variables, the effectiveness of research and development has a high statistical significance (0.00388) with the regional innovation index. On average, each change in research and development performance is accompanied by an increase in the innovation index by 0.364625, while other variables remain the same.

The export of goods and services (0.07029) and budget expenditures on science and innovations (0.08543) have the minimum significance. The first variable provides a positive effect of 0.217058, and the second is 0.155944 on the performance indicator of the statistical model.

For these variables, p-values allow us to move away from the first null hypothesis (H0-1) to accept that the variables are related to the innovation index.

Based on the adjusted R-square value (0.7058), the fitted model explains 70.58% of the statistical relationships of the linear regression variables. Also, the model's p-value (0.002566 < 0.05) demonstrates the rejection of the null hypothesis about the absence of effects of variables on the result, which confirms the statistical reliability of this regression analysis.

Like the last time, a new group of variables was compiled to accept or refute the first hypothesis. It aims to explore the relationship between the leading macroeconomic indicators, the potential of digitalization, scientific personnel, the innovative activity of organizations, small innovative businesses, the cost of technological innovation, the effectiveness of the innovative activity, export of knowledge, and

participation in federal scientific, technical, and innovation policy with the same dependent variable. This procedure is presented in **Table 9**.

According to the analysis of all predictors, the moderate statistical significance (0.0477) with the innovation index of the region is observed when looking at the cost of technological innovation. On average, each change in the cost of technological innovation is accompanied by an increase in the innovation index by 0.08538 when other variables remain unchanged. Also, the p-value allows us to move away from the first null hypothesis (H0–1) to accept that the predictor has a relationship with the output indicator.

Based on the adjusted R-square value (0.8744), the fitted model explains 87.44% of the statistical relationships of the linear regression variables. Also, the model's pvalue (0.0000884 < 0.05) demonstrates the rejection of the null hypothesis about the absence of effects of variables on the result, which confirms the statistical reliability of this regression analysis.

A review of the subindices of the new group of variables is carried out to accept or reject the first hypothesis. It is done to see if there is any impact on the dependent variable from the share of organizations implementing technological innovations, the share of organizations implementing nontechnological innovations, the share of organizations developing technological innovations on their own, the share of

### **Table 9.**

*Second group's variables' influence on Russia's innovation index.*

### *Policies for Improving the Efficiency of Innovative Clustering in an Emerging Market DOI: http://dx.doi.org/10.5772/intechopen.112150*

organizations participating in scientific cooperation, the share of small enterprises implementing technological innovations, the intensity of costs for technological innovations, the share of innovative products, the share of new innovative products, and the share of organizations that have reduced material and energy costs as a result of innovation. This procedure is presented in **Table 10**.

Looking at the analysis results of all variables, the moderate statistical significance (0.042635) and (0.023314) can be observed in the share of organizations participating in scientific cooperation and the intensity of costs for technological innovation. These variables increase the innovation index of the region by 0.119038 and 0.126316 when other variables remain unchanged. For these variables, the p-value allows us to move away from the first null hypothesis (H0-1) to accept that the variables are related to the output variable.

The minimum significance (0.098626) with an impact (0.092058) on the innovation index also has the share of small enterprises that carried out technological innovations.

Based on the adjusted R-square value (0.8306), the fitted model explains 83.06% of the statistical relationships of the linear regression variables. Also, the model's pvalue (0.0003683 < 0.05) demonstrates the rejection of the null hypothesis about the

### **Table 10.**

*Third group's variables' influence on Russia's innovation index.*

absence of effects of variables on the result, which confirms the statistical reliability of this regression analysis.

Going forward, the detailed group of variables is reviewed to see if they influence the innovation index of Russian cluster regions. The group includes the export of goods, nonresource exports of goods, exports of services, the share of exports in the volume of innovative products, patent activity abroad, the export of technologies, and the share of foreign students of higher education programs. This procedure is visualized in **Table 11**.

There is visible a moderate statistical significance (0.04952) of the export of services. This variable increases the dependent variable by 0.12507 when other variables remain unchanged. Also, patent activity abroad has a minimum significance (0.08445) and influence (0.10925) on the output variable.

For these variables, the p-value allows us to accept that the variables are related to the innovation index.

The fitted model explains 50.46% of the statistical relationships of the linear regression variables. Also, the model's p-value (0.02154 < 0.05) demonstrates the rejection of the null hypothesis about the absence of predictor effects on the final result, confirming the moderate statistical reliability of this regression analysis.

Further, the review of the variables from the new group of subindices is carried out. The model tests if there is an impact on the innovation index of clusters in Russia

### **Table 11.**

*Fourth group's variables' influence on Russia's innovation index.*