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Breast cancer is the most common cancer in women, with approximately 300,000 diagnosed with breast cancer in 2023. It ranks second in cancer-related deaths for women, after lung cancer with nearly 50,000 deaths. Scientists have identified important genetic mutations in genes like BRCA1 and BRCA2 that lead to the development of breast cancer, but previous studies were limited as they focused on specific populations. To overcome limitations, diverse populations and powerful statistical methods like genome-wide association studies and whole-genome sequencing are needed. Explainable artificial intelligence (XAI) can be used in oncology and breast cancer research to overcome these limitations of specificity as it can analyze datasets of diagnosed patients by providing interpretable explanations for identified patterns and predictions. This project aims to achieve technological and medicinal goals by using advanced algorithms to identify breast cancer subtypes for faster diagnoses. Multiple methods were utilized to develop an efficient algorithm. We hypothesized that an XAI approach would be best as it can assign scores to genes, specifically with a 90% success rate. To test that, we ran multiple trials utilizing XAI methods through the identification of class-specific and patient-specific key genes. We found that the study demonstrated a pipeline that combines multiple XAI techniques to identify potential biomarker genes for breast cancer with a 95% success rate.

Here, recognizing that the immune response to cancer results in biomarkers that can be used to assess the immune status of cancer patients, the authors investigated the concentrations of key cytokines (TH1 and TH2 cytokines) in healthy controls and cancer patients. They identified significant changes in resting and activated cytokine profiles, suggesting that data of biomarkers such as these could serve as a starting point for further treatment with regard to a patient's specific immune profile.

Wang and Gong developed a novel dynamic gene-searching algorithm called Dynamic Gene Search (DyGS) to create a gene panel for each of the 12 cancers with the highest annual incidence and death rate. The 12 gene panels the DyGS algorithm selected used only 3.5% of the original gene mutation pool, while covering every patient sample. About 40% of each gene panel is druggable, which indicates that the DyGS-generated gene panels can be used for early cancer detection as well as therapeutic targets in treatment methods.

The authors looked at biomarkers in glioblastoma patients they hypothesized to be correlated with survival rate. Ultimately they did not find hMSH2 or hMSH6, genes involved in mismatch repair, to be significantly associated with outcomes related to increased survival.

This study analyzed patient demographics in the ophthalmology department at Indira Gandhi Medical College and Hospital (IGMCH) to assess relationships between age, sex, and eye conditions. While the overall sex distribution was equal, individual conditions varied, with cataracts and retinal disorders more common in males and conjunctival conditions slightly more prevalent in females, though none were statistically significant (p > 0.05) except for cataract patients aged 50–89 (p < 0.001). Understanding these trends can help medical facilities allocate resources more effectively for improved patient care.

In this study, the authors investigate differences in psychological outcomes from patients who undergo different surgical procedures.

The authors found that treatment with AS20 suppressed phorbol 12-myristate 13-acetate (PMA) and 5-flurouracil (5-FU) induction of COX2 expression. We also observed AS20 treated cells showed DNA fragmentation in HeLa cells.

The authors develop and test a machine learning algorithm for predicting diabetes diagnoses.

Here, seeking to better understand the effects of gadolinium-based contrast agents, dyes typically used for MRI scans, the authors evaluated the activity of catalase found in beef liver both with and without gadodiamide when exposed to hydrogen peroxide. They found that gadioamide did not significantly inhibit catalase's activity, attributing this lack of effects to the chelating agent found in gadodiamide.

The authors describe the design and testing of new guide RNAs targeting the DMPK gene, which is responsible for myotonic dystrophy.