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Machine learning and deep learning techniques can be used to predict the early onset of breast cancer. The main objective of this analysis was to determine whether machine learning algorithms can be used to predict the onset of breast cancer with more than 90% accuracy. Based on research with supervised machine learning algorithms, Gaussian Naïve Bayes, K Nearest Algorithm, Random Forest, and Logistic Regression were considered because they offer a wide variety of classification methods and also provide high accuracy and performance. We hypothesized that all these algorithms would provide accurate results, and Random Forest and Logistic Regression would provide better accuracy and performance than Naïve Bayes and K Nearest Neighbor.

Pulmonary diseases like lung cancer and valley fever pose serious health challenges, making accurate and rapid diagnostics essential. This study developed a MATLAB-based software tool that uses computer vision techniques to differentiate between these diseases by analyzing features of lung nodules in CT scans, achieving higher precision than traditional methods.

Osteosarcoma is a type of bone cancer that affects young adults and children. Early diagnosis of osteosarcoma is crucial to successful treatment. The current methods of diagnosis, which include imaging tests and biopsy, are time consuming and prone to human error. Hence, we used deep learning to extract patterns and detect osteosarcoma from histological images. We hypothesized that the combination of two different technologies (transfer learning and data augmentation) would improve the efficacy of osteosarcoma detection in histological images. The dataset used for the study consisted of histological images for osteosarcoma and was quite imbalanced as it contained very few images with tumors. Since transfer learning uses existing knowledge for the purpose of classification and detection, we hypothesized it would be proficient on such an imbalanced dataset. To further improve our learning, we used data augmentation to include variations in the dataset. We further evaluated the efficacy of different convolutional neural network models on this task. We obtained an accuracy of 91.18% using the transfer learning model MobileNetV2 as the base model with various geometric transformations, outperforming the state-of-the-art convolutional neural network based approach.

Treatments inhibiting Notch signaling pathways have been explored by researchers as a new approach for the treatment of glioblastoma tumors, which is a fast-growing and aggressive brain tumor. Recently, retinoic acid (RA) therapy, which inhibits Notch signaling, has shown a promising effect on inhibiting glioblastoma progression. RA, which is a metabolite of vitamin A, is very important in embryonic cellular development, which includes the regulation of multiple developmental processes, such as brain neurogenesis. However, high doses of RA treatment caused many side effects such as headaches, nausea, redness around the injection site, or allergic reactions. Therefore, we hypothesized that a combination treatment of RA and siRNA targeting NOTCH1 (siNOTCH1), the essential gene that activates Notch signaling, would effectively inhibit brain cancer cell proliferation. The aim of the study was to determine whether inhibiting NOTCH1 would inhibit the growth of brain cancer cells by cell viability assay. We found that the combination treatment of siNOTCH1 and RA in low concentration effectively decreased the NOTCH1 expression level compared to the individual treatments. However, the combination treatment condition significantly decreased the number of live brain cancer cells only at a low concentration of RA. We anticipate that this novel combination treatment can provide a solution to the side effects of chemotherapy.

Inefficient penetration of cancer drugs into the interior of the three-dimensional (3D) tumor tissue limits drugs' delivery. The authors hypothesized that the addition of phospholipase A2 (PLA2) would increase the permeability of the drug doxorubicin for efficient drug penetration. They found that 1 mM PLA2 had the highest permeability. Increased efficiency in drug delivery would allow lower concentrations of drugs to be used, minimizing damage to normal cells.

In this study, the authors investigate whether a new compound has anti-cancer properties. Using the crude extract from the Amaranthus spinosus plant, HeLa cancer cells were assessed for cell death. Findings reveal that the extract (AS20) has cytotoxic effects on HeLa cells. Their findings introduce a new compound to potentially pursue in the hunt for novel cancer treatments.

A significant percentage of cancer survivors develop a second primary cancer. Using data of deceased patients provided by the Peninsula Regional Medical Center, Li and Holdai conducted a retrospective statistical analysis to investigate whether the type of the first cancer affects the occurrence time and type of the second primary cancer.

The authors investigate whether human blood cancers carrying mutations in DNA repair genes possess increased sensitivity to common chemotherapy drugs cisplatin or gemcitabine.

The current five-year survival rate of metastasized prostate cancer is only 30% and occurs in every one in nine men. Researchers have shown that people with a type of dwarfism called Laron’s Syndrome are immune to cancer due to their low levels of insulin-like growth factor-1 (IGF-1). For this reason, experimentally modifying the level of IGF-1 could provide better insight into whether lowering the levels of IGF-1 in prostate cancer cell lines (e.g. PC-3) could be an effective treatment to reduce their rates of proliferation and migration and increase apoptosis. We selected three compounds, which researchers have shown decrease IGF-1 levels, to test and combine to determine which is the most promising.

Although the 5-year survival rate for colorectal cancer is below 10%, it increases to greater than 90% if it is diagnosed early. We hypothesized from our research that analyzing non-synonymous single nucleotide variants (SNVs) in a patient's exome sequence would be an indicator for high genetic risk of developing colorectal cancer.