https://journals.bilpubgroup.com/index.php/jor <p>ISSN: 2630-5267(Online)</p> <p>Email: jor@bilpublishing.com</p> <p><a href="https://journals.bilpubgroup.com/index.php/jor/about/submissions#onlineSubmissions" target="_black"><button class="cmp_button">Online Submissions</button></a></p> BILINGUAL PUBLISHING GROUP en-US Journal of Oncology Research 2630-5267 <p><strong>Copyright and Licensing</strong></p><p>The authors shall retain the copyright of their work but allow the Publisher to publish, copy, distribute, and convey the work.</p><p><em>Journal of Oncology Research</em> publishes accepted manuscripts under <span><a href="https://creativecommons.org/licenses/by-nc/4.0/" target="_blank">Creative Commons Attribution-NonCommercial 4.0 International License</a></span> (CC BY-NC 4.0). Authors who submit their papers for publication by <em>Journal of Oncology Research</em> agree to have the CC BY-NC 4.0 license applied to their work, and that anyone is allowed to reuse the article or part of it free of charge for non-commercial use. As long as you follow the license terms and original source is properly cited, anyone may copy, redistribute the material in any medium or format, remix, transform, and build upon the material.</p><p><strong>License Policy for Reuse of Third-Party Materials</strong></p><p>If a manuscript submitted to the journal contains the materials which are held in copyright by a third-party, authors are responsible for obtaining permissions from the copyright holder to reuse or republish any previously published figures, illustrations, charts, tables, photographs, and text excerpts, etc. When submitting a manuscript, official written proof of permission must be provided and clearly stated in the cover letter.<br />The editorial office of the journal has the right to reject/retract articles that reuse third-party materials without permission.</p><p><strong>Journal Policies on Data Sharing</strong></p><p>We encourage authors to share articles published in our journal to other data platforms, but only if it is noted that it has been published in this journal.</p> https://journals.bilpubgroup.com/index.php/jor/article/view/5115 <p>Aim: To investigate the in vitro structure-activity relationship (SAR) of a range of tetrahydrocannabinolic (THCA) and cannabidiolic (CBDA) derivatives using the PANC-1 tumor cell line (pancreas, ductal carcinoma). Materials and methods: The in vitro effects of a range of THCA and CBDA derivatives with different carbonyl group substituents were tested on the PANC-1 cells cell line using the CellTiter Glo Viability Assay (72 hours) and the XTT assay (48 hours). Results: A study of a series of THCA and CBDA derivatives containing different functional groups at the carbonyl nitrogen atom demonstrated that THCA amides have better inhibitory activity, on the PANC-1 tumor cell line, than CBDA derivatives. Conclusions: THCA derivatives have better inhibitory activity than CBDA analogs with the same substituents. It is noteworthy that even a slight change in the structure of the substituent of the amide or hydrazone moiety of the molecule has a dramatic effect on the activity of these compounds.</p> Alexander Aizikovich Copyright © 2022 Author(s) 2022-10-31 2022-10-31 5 1 10.30564/jor.v5i1.5115 https://journals.bilpubgroup.com/index.php/jor/article/view/4977 <p align="justify">Early diagnosis of breast cancer does not only increase the chances of survival but also control the diffusion of cancerous cells in the body. Previously, researchers have developed machine learning algorithms in breast cancer diagnosis such as Support Vector Machine, K-Nearest Neighbor, Convolutional Neural Network, K-means, Fuzzy C-means, Neural Network, Principle Component Analysis (PCA) and Naive Bayes. Unfortunately these algorithms fall short in one way or another due to high levels of computational complexities. For instance, support vector machine employs feature elimination scheme for eradicating data ambiguity and detecting tumors at initial stage. However this scheme is expensive in terms of execution time. On its part, k-means algorithm employs Euclidean distance to determine the distance between cluster centers and data points. However this scheme does not guarantee high accuracy when executed in different iterations. Although the K-nearest Neighbor algorithm employs feature reduction, principle component analysis and 10 fold cross validation methods for enhancing classification accuracy, it is not efficient in terms of processing time. On the other hand, fuzzy c-means algorithm employs fuzziness value and termination criteria to determine the execution time on datasets. However, it proves to be extensive in terms of computational time due to several iterations and fuzzy measure calculations involved. Similarly, convolutional neural network employed back propagation and classification method but the scheme proves to be slow due to frequent retraining. In addition, the neural network achieves low accuracy in its predictions. Since all these algorithms seem to be expensive and time consuming, it necessary to integrate quantum computing principles with conventional machine learning algorithms. This is because quantum computing has the potential to accelerate computations by simultaneously carrying out calculation on many inputs. In this paper, a review of the current machine learning algorithms for breast cancer prediction is provided. Based on the observed shortcomings, a quantum machine learning based classifier is recommended. The proposed working mechanisms of this classifier are elaborated towards the end of this paper.</p> Rebecca Nyasuguta Arika Agnes Mindila W. Cheruiyo Copyright © 2022 Author(s) 2022-11-02 2022-11-02 5 1 10.30564/jor.v5i1.4977