Primary Reflection

Longevity Industry mainly focussed in making extensions in lifetime through AgeTech, Geroscience, P4 Medicine and “Novel Financial System”. In this paper, I would provide ideas through research and thereby collect data based on the implementation of Artificial Intelligence for developing medicines (P4 medicines) that will help in providing cure of different types of chronic diseases by 2025. I would suggest that this industry is being involved in the determination of the future with the implementation of artificial intelligence for the production of medicines and thereby detecting the problems in ageing that will happen in future.

I suggest that based on the goals and objectives that are being targeted for increasing the productivity of the different types of research that are performed by Longevity will help in getting productive outcomes through the development of medicines in future that will help in curing different diseases in future.

Business simulation is done for getting the idea through training developed in business by Longevity which will be helpful in the development of strategic decisions through proper thinking, thereby solving problems through financial analysis and efficient leadership and teamwork (Lemonniera and Sagnerh, 2017). In this section, I will provide the data that is collected through research and thereby provide the strategies that should be developed for the achievement of different goals.

P4 medicine states personalized, participatory, preventive and precision medicine. Based on this issue, data is collected from different steps.

•       In the first step, the team members and I are involved in the collection of data of different persons based on health status, age and gender. Decisions were taken for making such collections will be helpful in getting the personal traits of different patients and hence will be able to make differences in performing research. Team members with the help of web-portal made entries of different people’ details in which teenagers, adults and elder people ages, health status and gender are recorded.
•       Based on the collection of such data in the initial stage, efforts were made by me and my team members in the collection of blood samples for making records of chromosomes or genes of different persons and thereby getting an idea about each individual (Vellas and Gourinat, 2017). Samples are recorded as per the data collected from the initial stage.
•       In the third step, I performed the research with my team with the implementation of artificial intelligence which involves different types of data mining techniques for making predictions of the curability of different diseases in future.
•       In the fourth step, based on the suitable prediction with the implementation of artificial intelligence by using the chromosomes, age details, gender and health status, micro-dosages are developed through strategic decisions taken for the development of drugs for different persons (Gaitskell, 2017).
•       In the fifth stage, decisions were taken by my team members for making effective diagnostics through analysis of different data from different persons and hence generating predictive data for risk analysis.
•       Finally, effective trials are tried through “clinical implementation” by my team members for getting ideas about the suitability of the developed drugs in different persons.

All these stages are performed by my team members and me in getting ideas about the sources of data that is collected for performing effective research through the implementation of artificial intelligence and hence developing drugs with proper dosages. [Referred to Appendix 1]

Secondary reflection

Development of P4 medicines is developed by performing suitable research with the implementation of artificial intelligence for developing drugs for curing chronic diseases of different individuals in future (Lavie, 2019). This is basically done by taking data from personality traits of different individuals and thereby performing suitable research for getting suitable results in future.

Figure 1: Health predictions

(Source:https://www.linkedin.com/pulse/longevity-industry-biggest-most-complex-human-history-colangelo)

I have been in the development of this project and have played a role of a researcher in making suitable analysis of different personal traits especially the chromosomes of different persons before the implementation of artificial intelligence for making suitable predictions (Blake, 2016). With the involvement of web-portal, which has been designed by a software developer in my team, I have collected all the samples of genes and hence figuring out the age and other details from the software developer. I did a research based on the characteristics that are possessed by major and minor chromosomes of a particular individual and thereby segregating the paternal and maternal traits in those genes. After this step, with the involvement of a Data Analyst, I made use of artificial intelligence in making suitable predictions through data mining and hence developing the probability range of different individuals. Data that is collected by a data analyst was provided to me, and hence I processed those data for the development of charts for making suitable analysis. With the involvement of the Principal investigator, the dosages of the medicines are involved for developing the drugs with the utilization of personal traits and predictions through AI. After this, clinical trials are performed with the help of medical experts for understanding the affectability of these developed drugs and hence experimenting those drugs on humans for understanding the range of suitability (Ciccone and Maier, 2020). I recommended risk investigators for making suitable analysis of all possible risks developed in the entire process and hence making risk assessment for the aforesaid purpose.

Figure 2: P4 Medicine

(Source:https://www.linkedin.com/pulse/longevity-industry-biggest-most-complex-human-history-colangelo)

The implementation of Driscoll’s model for reflection is implemented by my team members and me in providing a suitable reflection on different contributions that are done with the help of my team in developing P4 medicines for future analysis of chronic diseases. As per the initial stage of this model, data is collected for doing research based on the personal trait of every individual for getting acknowledged with personal details (Butt and Jahan, 2020). Also, efforts are made for segregation of different types of genes for proceeding in research. The application second stage of this model is implemented by using artificial intelligence for making predictions for developing data and hence making drugs as per the dosages required by different individuals. This stage has proved to be beneficial in the development of efficient teamwork and hence making utmost benefit through team performance. Finally, the final stage is implemented for performing clinical trials by different team members for understanding the suitability that is developed with the invention and hereby introduction of those medicines for curing of diseases for individuals in future.

Figure 3: Preventive treatment

(Source:https://www.linkedin.com/pulse/longevity-industry-biggest-most-complex-human-history-colangelo)

I have gained enough knowledge about the implementation of artificial intelligence in biomedical fields and got ideas about the future analysis of those data. I am grateful for getting such an efficient team who has helped me efficiently in performing this suitable research for the development of P4 medicines through predictions. This will be helpful for mankind for curing diseases which are chronic and hence will be able to take precautions by the usage of these medicines which will help them in getting cured by 2025. The implementation of Driscoll’s model will be helpful in providing a suitable analysis of different data that is gathered with the help of team members and hence helps in providing efficient teamwork for getting the desired product in future.

Table 1: Strategic simulation table

Future Intentions

I suggest that the intentions are to develop medicines for the future through the implementation of artificial intelligence by getting personal traits from different individuals. I suggest that the project has been successful in getting its desired objective, but there is the presence of some limitations that are involved in this project while its generation (Hernayanti and Simanjuntak, 2019). I suggest that these limitations must be focussed for increasing the rate of productivity of this project and thereby resulting in an increase in the accuracy in the development of P4 medicines. The limitations are as follows:

•       First of all, the mortality of a particular individual cannot be judged, hence taking genes from a particular individual will not be fruitful in making suitable solutions.
•       Secondly, there are many drawbacks that are present in the utilization of artificial intelligence which needs to be focussed immediately for making suitable results through predictions.
•       Third, the drugs that are produced might not be suitable for every individual and hence might turn out to be fatal through the wrong implementation of those drugs.
•       Finally, more team effort to be developed for getting better output from every member of the team.

Based on the above limitations that are noticed and mentioned by me, I suggest some recommendations that will help me in increasing the efficiency of the production of P4 medicines in future. The recommendations are as follows:

•       Efforts to be made for performing more research for making suitable classification of genes which are considered to be one of the major features in making P4 medicines.
•       Efforts to be made for the implementation of suitable data mining methods that will help in increasing the performance of Artificial intelligence in the field of biomedical engineering for the development of P4 medicines.
•       Dosages that are being decided while performing the research should be done with utmost care before the utilization of those medicines for clinical trials. This will help in providing protection.
•       Suitable team management to be done for efficient coverage of all responsibilities and duties that are given to team members and thereby providing timely completion of this project.
•       Other “reflective” models should be consulted in future for developing P4 medicines with the help of a suitable team (www.researchgate.net, 2016).

Reference list

Journals

Blake, D.P., 2016. Helping savers to manage longevity risk. We Need a National Narrative: Building a Consensus around Retirement Income”, Report of the Independent Review of Retirement Income.

Butt, M.A., Ullah, A., Kiyani, M.M. and Jahan, S., 2020. Ameliorative effects of selenium nanoparticles on letrozole induced polycystic ovarian syndrome in adult rats. International Journal of Biomedical Nanoscience and Nanotechnology, 4(1-2), pp.49-69.

Ciccone, N., Patou, F., Komashie, A., Lamé, G., Clarkson, P.J. and Maier, A.M., 2020, May. Healthcare systems design: a sandbox of current research themes presented at an international meeting. In Proceedings of the Design Society: DESIGN Conference (Vol. 1, pp. 1873-1882). Cambridge University Press.

Gaitskell, K., 2017. Personalised medicine approaches to screening and prevention. The New Bioethics, 23(1), pp.21-29.

Hernayanti, H. and Simanjuntak, S.B.I., 2019. Antioxidant Effect of Chlorella vulgaris on Physiological Response of Rat Induced by Carbon Tetrachloride. Biosaintifika: Journal of Biology & Biology Education, 11(1), pp.84-90.

Lavie, C.J., 2019. Continuing with good statistics at progress in cardiovascular diseases. Progress in cardiovascular diseases, 62(4), pp.370-372.

Lemonniera, N., Zhoub, G.B., Prasherc, B., Mukerjic, M., Chene, Z., Brahmacharid, S.K., Nobleg, D., Auffraya, C. and Sagnerh, M., 2017. PROGRESS IN PREVENTIVE MEDICINE.

Vellas, M., Fualdes, C., Morley, J.E., Dray, C., Rodriguez-Manas, L., Meyer, A., Michel, L., Rolland, Y. and Gourinat, Y., 2017. Aeroaging—A new collaboration between life sciences experts and aerospace engineers. The journal of nutrition, health & aging, 21(9), pp.1024-1030.

Online Articles

www.researchgate.net, 2016, Aging, inflammation, immunity and periodontal disease, Available at: https://www.researchgate.net/profile/James_Hartsfield/publication/306005044_Aging_inflammation_immunity_and_periodontal_disease/links/5cd2a9d6299bf14d957ea7db/Aging-inflammation-immunity-and-periodontal-disease.pdf, [Accessed on: 29.07.2020]

Appendices

Appendix 1

(Source:https://www.linkedin.com/pulse/longevity-industry-biggest-most-complex-human-history-colangelo)