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Modulation of VEGF and TGF beta in 5-FU induced inflammation in MCF-7 using an herbal formulation

Vinay Nair et al. | Jun 03, 2022

Modulation of VEGF and TGF beta in 5-FU induced inflammation in MCF-7 using an herbal formulation

Acquired drug resistance is an increasing challenge in treating cancer with chemotherapy. One mechanism
behind this resistance is the increased inflammation that supports the progression and development of
cancer that arises because of the drug’s presence. Integrative oncology is the field that focuses on including natural products alongside traditional therapy to create a treatment that focuses on holistic patient well-being.
In this study, the authors demonstrate that the use of an herbal formulation, consisting of turmeric and green tea, alongside a traditional chemotherapeutic drug, 5-fluorouracil (FU) significantly decreases the level of cytokines produced in breast cancer cells when compared to the levels produced when exposed solely to the chemo drug. The authors conclude that this combination of treatment, based on the principle of integrative oncology, shows potential for reducing the resistance against treatment conferred through increased inflammation. Consequently, this suggests a prospective way forward in improving the efficacy of cancer treatment.

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Luteolin's positive inhibition of melanoma cell lines.

Su et al. | Nov 17, 2020

Luteolin's positive inhibition of melanoma cell lines.

Luteolin (3′,4′,5,7-tetrahydroxyflavone) is a flavonoid that occurs in fruits, vegetables, and herbs. Research suggests that luteolin is effective against various forms of cancer by triggering apoptosis pathways. This experiment analyzes the effects of luteolin on the cell viability of malignant melanoma cells using an in vitro experiment to research alternative melanoma treatments and hopefully to help further cancer research as a whole.

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Using explainable artificial intelligence to identify patient-specific breast cancer subtypes

Suresh et al. | Jan 12, 2024

Using explainable artificial intelligence to identify patient-specific breast cancer subtypes

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.

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Upregulation of the Ribosomal Pathway as a Potential Blood-Based Genetic Biomarker for Comorbid Major Depressive Disorder (MDD) and PTSD

Ravi et al. | Aug 22, 2018

Upregulation of the Ribosomal Pathway as a Potential  Blood-Based Genetic Biomarker for Comorbid Major Depressive Disorder (MDD) and PTSD

Major Depressive Disorder (MDD), and Post-Traumatic Stress Disorder (PTSD) are two of the fastest growing comorbid diseases in the world. Using publicly available datasets from the National Institute for Biotechnology Information (NCBI), Ravi and Lee conducted a differential gene expression analysis using 184 blood samples from either control individuals or individuals with comorbid MDD and PTSD. As a result, the authors identified 253 highly differentially-expressed genes, with enrichment for proteins in the gene ontology group 'Ribosomal Pathway'. These genes may be used as blood-based biomarkers for susceptibility to MDD or PTSD, and to tailor treatments within a personalized medicine regime.

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Automated classification of nebulae using deep learning & machine learning for enhanced discovery

Nair et al. | Feb 01, 2024

Automated classification of nebulae using deep learning & machine learning for enhanced discovery

There are believed to be ~20,000 nebulae in the Milky Way Galaxy. However, humans have only cataloged ~1,800 of them even though we have gathered 1.3 million nebula images. Classification of nebulae is important as it helps scientists understand the chemical composition of a nebula which in turn helps them understand the material of the original star. Our research on nebulae classification aims to make the process of classifying new nebulae faster and more accurate using a hybrid of deep learning and machine learning techniques.

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