Awareness about Precision Medicine among Dental Students: A Survey

Sabaritha A1, Kavitha S*2, Sridevi G3, Vishnupriya2, Gayathri2 1Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai -77, Tamil Nadu, India 2Department of Biochemistry, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai -77, Tamil Nadu, India 3Department of Physiology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai -77, Tamil Nadu, India


INTRODUCTION
Precision medicine trials aim to provide evidence that targeting speci ic molecular abnormalities with speci ic treatment will result in greater effectiveness than current methods (Conley, 2016). While we know of some instances where the tumour type in luences the response to these agents, there are other instances where reactions can be seen in different malignancies if they have the same molecular abnormality (Conley, 2016). The Precision Medicine Initiative (PMI), announced on 30 January 2015 by President Obama, aims to advance our understanding of genetic variations within diseases and develop treatments for them, starting with cancer. The principle of precision medicine is the prevention and care approaches taking into account patient variation is not new; for example, blood typing has been used to direct blood transfusions for more than a century. Yet the possibility of broad implementation of this principle has been signi icantly improved by the recent emergence of large-scale biological databases such as the sequence of the human genome (Collins and Varmus, 2015).
The new plan has two key components: a near-term emphasis on cancers and a longer-term aim of creating information that extends to the entire health and disease spectrum. Because of developments in basic science, both components are now within our scope, including molecular biology, genomics and bioinformatics. More than one million people have contributed their data for precision medicine in an amazing fact (Ashley, 2016).
In the previous study application of precision medicine has been studied in detail (Ashley, 2016). Promising applications of precision medicine as it currently exists then continue to address the challenges faced by our community in the ields of sequencing technology, algorithm creation and data sharing in order to bring genomics to a clinical level. Genomics applications for genetic disorders such as cystic ibrosis and cancer and pharmacogenomics are promising for the application of genomics to medicine in a broader sense. Genomic and precision medicine encompasses areas as varied as intellectual property, regulation of the Food and Drug Administration (FDA), and coverage of insurance (Rai, 2017). Precision medicine is used by a large number of individuals for epidemiological and other follow up studies for treating a wide number of diseases (Verma, 2017).
Consequences, epigenetic, socioeconomic, and behaviour diversity in different racial and ethnic groups will be critical in developing pro iles for personalized, and precision medicine approaches for patients. Pattern recognition which is very useful in the classi ication of big data, extraction of signals, Biomarkers play an important role in precision medicine (Faintuch and Faintuch, 2020). Accurate, reliable statistical and machine learning tools for diagnosing the treatment in precision medicine (Deigner and Kohl, 2018). The term predictive medicine is replaced as precision medicine (Verma, 2017). Precision medicine has created a potential impact on environmental health (Martin-Sanchez et al., 2020). Furthermore, research has to be done to prove that precision medicine will reduce the rate of mortality.
The researchers also wanted to prove that precision medicine doesn't affect the cost of living. The aim of this study is to create awareness about precision medicine among dental students.

MATERIALS AND METHODS
A descriptive cross-sectional study was done to analyse knowledge, attitude and practice of precision medicine among dental students based. Approval was obtained from the institutional review board to conduct an online survey. The survey was conducted among 150 dental students. A self-administered questionnaire of 15 closed-ended and open questions was prepared and distributed among dental students through online based survey forms "google forms" The questionnaire contained questions on demographic details also. A self-administrated questionnaire was prepared. The method of sampling that is done is simple random sampling. The responses were collected, tabulated in the excel sheet and analysed. Data entered in SPSS, and the results were represented in a Bar graph. Chi square test was used to analyse and compare the educational level of students and their knowledge and awareness of precision medicine. The list of independent variables is age, sex and locality. The list of dependent variables is awareness and knowledge. 13.9 % of the population says that Precision medicine is otherwise known as personalised medicine, 7.8% of the population say that Precision medicine is otherwise known as genomic medicine, 77.7% of the people feel that precision medicine is otherwise known as personalised and genomic medicine 6% of the people disagree to it ( Figure 5). 77.1% of people feel that the treatment of precision medicine can be done with the help of DNA. It's treatment, 6% of the population feel that the treatment of precision medicine is based on signs and symptoms, 13.9% of the people say that the treatment of precision medicine is based on both DNA and signs and symptoms and 3% of the people disagree to it. 89.8% of the people were aware that father of precision medicine was Archibald E Garrod, 7.8% of the people feel that the father of  precision medicine is Robert L Perlman, 6% of the population say that the father of precision medicine is Govindaraju and 1.8% of the population disagree to all the above options ( Figure 6). 96.4% of the people feel that precision medicine is advantageous to human health and 3.6 % of the people say no to it (Figure 7). 24.7% of the people feel that precision medicine is affordable by everyone, and 75.3% of the people feel that precision medicine is not affordable by everyone (Figure 8). 78.1% of the people feel that precision medicine has no side effects and 21.1% of the people feel that precision medicine has side effects ( Figure 9). 87.3% of the people feel that precision medicine is the same as  personalised medicine and 12.7% of the people disagree with it ( Figure 10). 75. 3% of feel that allopathy is a better method of treatment, 21% of the people feel that precision medicine is a better method of treatment, 1.2% of the people feel that Siddha is a better method for treatment and 2.4% of the people feel that the best methods of treatment are Unani ( Figure 11). 85.5 % of the people say that precision medicine varies according to age and sex, and 14.5 % of the people disagree to it. 92.2% of the people agree that precision medicine will enhance their Practice in future and 7.8% of the people disagree with it ( Figure 12). 34.4% of the people say  that precision medicine will not lead to chronic disease and 65.7% of the people disagree with it (Figure 13). 92.8% of the people feel that teaching precision medicine is worthy among dental students, and 7.2% of the people disagree with it ( Figure 14).

RESULTS AND DISCUSSION
This survey was done based on awareness of precision medicine where the P value is 0.329 > 0.05, Which is statistically insigni icant (Figure 15). There is an article with similar indings published by J. Bousquet et al. where he has told 69% of the people were aware of it 31% of people are not aware of it There is an opposing article by Agustin Elbe where he says that only 24% of people are aware and 76% of people are unaware the precision medicine will lead to chronic disease. In this survey, the survey was done about awareness of teaching precision medicine is worth among dental students  where P value will be 0.656 > 0.05, Which is statistically insigni icant (Figure 23). There are two articles which have similar indings were 79% of people were aware and 19% of were unaware of this was published by Caroline Eden (Eden et al., 2016) and 75% of the people were aware of, and 25% of people were unaware this was published by Jieu palvika et al. There is an opposing article for this which is published by Andrej Jopolian where only 43% of the people are aware that teaching Precision medicine is worth among students and 57% of the people were  unaware of it. This present study has a question in the survey based on are precision medicine and personalised medicine same where the P value is 0.145 > 0.05, Which is statistically insigni icant (Figure 20). There are two articles which have similar indings, and these are published by Fernando A.L Marson (Marson et al., 2017) where 75% of people knew that precision and personalised medicine were the same and 25% of the people were unaware of it. The other article with a similar was published by Jose D, where 63% were aware of it, and 37% were not aware of it. There is an opposing article  published by Carmen, where 56.3% are aware of it, and 44% of people are unaware of it. Figure 1, Blue color represents the students of 17 years of age, red color represents students of 18 years of age, green color a present student of 19 years of age, orange color represent students of 20 years of age, yellow color represents students of 21years of age, the greenish-blue color represents students of 22 years of age, pink color a present students of 23 years of age. Majority of the students (22.89%) participated in the survey were 21 years of age.               personalised medicine and genomic medicine. Figure 6, The color blue represents Archibald E Garrod, red represents Robert L Perlman, green represents Govindaraju, orange represents none. Majority of the population (89.8%) were aware that the father of precision medicine is Archibald E Garrod. Figure 7, The color blue represents yes and the colour red represents no. Majority of dental students (96.4%) responded that precision medicine is an advantage to human health. Figure 8, The color red represents no, and the blue represents yes. Majority of the dental students (75.3%) responded that precision medicine is not affordable by everyone. Figure 9, Red color represents no and blue color represents yes. Majority of the population (78.1%) responded that precision medicine would not lead to any side effects. Figure 10, The color blue represents yes and red represents no. Majority of the students(87.3%) felt that precision medicine and personalised medicine are the same. Figure 11, The blue color represents allopathy, red color represents Precision medicine, green color represents Siddha, and orange color represents Unani. Majority of the students (75.3%) responded as Allopathy. Figure 12, The color blue represents yes and red represents no. The majority of the dental students (92.2%) felt that precision medicine would enhance the dental practice in the future. Figure 13, The color red represents no and the blue represents yes. The majority of dental students (65.4%) responded that precision medicine will not lead to any side effect. Figure 14, The color blue represents yes and the color red represents no. The majority of dental students (92.8%) felt that teaching precision medicine is worth it. Figure 15, X-axis represents the year of study and Y-axis represents the number of responses (Yesblue, No-green). Majority of the second year and third-year students (46 students in each) were more aware of precision medicine. However, the difference is not statistically signi icant (Chi-square value-4.617, p-value-0.329 (>0.05) hence not significant).
Figure 16, X-axis represents the year of study and Y-axis represents the number of responses (personalised medicine(blue), genomic medicine(green), both(Sandal), none(purple)). Thirty-ive students of the second year were more aware that precision medicine is otherwise known as personalised medicine and genomic medicine. However, the difference is not statistically signi icant(Chi-square value-11.536, p-value-0.484 (>0.05) hence not signi icant). Figure 17, X-axis represents the year of study, and Yaxis represents the number of responses Yes(blue), No(green). The second-year and third-year students (46 participants from each group) were more aware that precision medicine is advantageous to human health. However, the difference is not statistically signi icant (Chi-square value-3.667, p-value-0.453 (>0.05) hence not signi icant). Figure 18, X-axis represents the year of study, and Yaxis represents the number of responses Yes(blue), No(green). Majority of the second-year students (38 participants) were more aware that precision medicine is not affordable by everyone. However, the difference is not statistically signi icant(Chisquare value-7.726, p-value-0.102 (>0.05) hence not signi icant). Figure 19, X-axis represents the year of study and Yaxis represents the number of responses (Yes(blue), No(green)). Majority of the second-year students (41 participants) have the opinion that precision medicine will not lead to any side-effects. However, the difference is not statistically signi icant(Chisquare value-3.236, p-value-0.519 (>0.05) hence not signi icant).
Figure 20, X-axis represents the year of study and Yaxis represents the number of responses. Yes(blue), No(green). Majority of the second-year students (42 participants) were more aware that precision medicine is the same as personalised medicine. However, the difference is not statistically signi icant (Chi-square value-6.838, p-value-0.145 (>0.05) hence not signi icant).
Figure 21, X-axis represents the year of study and Yaxis represents the number of responses Yes(blue), No(green). Majority of the third-year students (44 participants) were more aware that precision medicine will enhance future practice in dentistry. However, the difference is not statistically signi icant (Chi-square value-2.782, p-value-0.595 (>0.05) hence not signi icant). Figure 22, X-axis represents the year of study and Y-axis represents the number of responses to precision medicine leading to chronic disease. Majority of the second year (35 participants) students felt that precision medicine would not lead to chronic disease. However, the difference is not statistically signi icant (Chi-square value-4.402, p-value-0.345 (>0.05) hence not signi icant).
Figure 23, X-axis represents the year of study and Yaxis represents the number of responses Yes(blue), No(green). 28 males from the second year and 44 second-year students followed by 44 third-year students were more aware that teaching precision medicine is worth. However, the difference is not statistically signi icant (Chi-square value-1.526, pvalue-0.656 (>0.05) hence not signi icant).
The limitations of this study was minimum sample size where results may vary with other researchers and may not be accurate. This study has only a selected population inhomogenous. Furthermore, study can be done, and population size can be increased among dental students, and more aware awareness can be created about precision medicine. The limitation can be explored and sorted out.

CONCLUSIONS
This present study showed that the awareness of precision medicine among dental students was quite good. The chi square analysis showed that awareness among second year and third-year students is more compared to other students.