Objective: To determine the additional relationship between factors discovered by searching for sociodemographic and metastasis factors, as well as treatment outcomes, which could help improve the prediction of the survival rate in cancer patients. Material and Methods. A total of 56 patients were recruited from the ambulatory clinic at the Hospital Universiti Sains Malaysia (USM). In this retrospective study, advanced computational statistical modeling techniques were used to evaluate data descriptions of several variables such as treatment, age, and distant metastasis. The R-Studio software and syntax were used to implement and test the hazard ratio. The statistics for each sample were calculated using a combination model that included methods such as bootstrap and multiple linear regression (MLR).
Results: The statistical strategy showed R demonstrates that regression modeling outperforms an R-squared. It demonstrated that when data is partitioned into a training and testing dataset, the hybrid model technique performs better at predicting the outcome. The variable validation was determined using the well-established bootstrap-integrated MLR technique. In this case, three variables are considered: age, treatment, and distant metastases. It is important to note that three things affect the hazard ratio: age (β 1: -0.006423; p < 2e - 16), treatment (β 2: -0.355389; p < 2e - 16), and distant metastasis (β 3: -0.355389; p < 2e - 16). There is a 0.003469102 MSE for the linear model in this scenario.
Conclusion: In this study, a hybrid approach combining bootstrapping and multiple linear regression will be developed and extensively tested. The R syntax for this methodology was designed to ensure that the researcher completely understood the illustration. In this case, a hybrid model demonstrates how this critical conclusion enables us to better understand the utility and relative contribution of the hybrid method to the outcome. The statistical technique used in this study, R, demonstrates that regression modeling outperforms R-squared values of 0.9014 and 0.00882 for the predicted mean squared error, respectively. The conclusion of the study establishes the superiority of the hybrid model technique used in the study.
CONCLUSION: The progress of developing a cancer treatment that may successfully and efficiently target mutant p53 is on the verge of development. Mutant p53 proteins not only initiate oncogenesis but also cause resistance in cancer cells to certain chemo or radiotherapies, further endorse cancer cell survival and promote migration as well as metastasis of cancerous cells. With this regard, many mutant p53 inhibitors have been developed, some of which are currently being evaluated at the pre-clinical level and have been identified and discussed. To date, APR-246 is the most prominent one that has progressed to the Phase III clinical trial.
OBJECTIVE: To study the antiamoebic effects of four novel synthetic dihydropyridine (DHP) compounds against Acanthamoeba castellanii belonging to the T4 genotype. Furthermore, to evaluate their activity against amoeba-mediated host cells cytopathogenicity as well as their cytotoxicity against human cells.
METHODS: Dihydropyridines were synthesized by cyclic dimerization of alkylidene malononitrile derivatives. Four analogues of functionally diverse DHPs were tested against Acanthamoeba castellanii by using amoebicidal, encystation and excystation assays. Moreover, Lactate dehydrogenase assays were carried out to study cytopathogenicity and cytotoxicity against human cells.
RESULTS: These compounds showed significant amoebicidal and cysticidal effects at 50 μM concentration, whereas, two of the DHP derivatives also significantly reduced Acanthamoebamediated host cell cytotoxicity. Moreover, these DHPs were found to have low cytotoxicity against human cells suggesting a good safety profile.
CONCLUSION: The results suggest that DHPs have potential against Acanthamoeba especially against the more resistant cyst stage and can be assessed further for drug development.