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Developing a Portable, Reusable, and Inexpensive Magnesium-Air Fuel Cell

Tota et al. | Mar 28, 2019

Developing a Portable, Reusable, and Inexpensive Magnesium-Air Fuel Cell

One of the greatest challenges we face today is the sustainable production, storage, and distribution of electrical power. One emerging technology with great promise in this area is that of metal-air fuel cells—a long-term and reusable electricity storage system made from a reactive metal anode and a saline solution. In this study the authors tested several different types of metal to determine which was the most suitable for this application. They found that a fuel cell with a magnesium anode was superior to fuel cells made from aluminum or zinc, producing a voltage and current sufficient for real-world applications such as charging a mobile phone.

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Effects of Paan Extracts on Periodontal Ligament and Osteosarcoma Cells

Venkatachalam et al. | Sep 20, 2018

Effects of Paan Extracts on Periodontal Ligament and Osteosarcoma Cells

In South Asian countries, the major cause of oral cancer is reported to be chewing paan, which is comprised of betel leaf daubed with slaked lime paste and areca nut. To investigate how paan may contribute to the onset of cancer, the authors treated two immortalized cell lines with extracts of betel leaf, areca nut, and lime and evaluated how these treatments affected cell proliferation and cell death. Initial results indicate that while betel leaf alone may inhibit cell growth, areca nut promoted cancer cell survival and proliferation, even when co-treated with betel leaf. These data suggest that areca nut could exacerbate the progression of oral cancer in humans.

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Antimicrobial properties of common household spices on microbes cultured from two kitchen locations

Carroll et al. | May 12, 2022

Antimicrobial properties of common household spices on microbes cultured from two kitchen locations

The number of bacterial infections in humans is rising, and a major contributor is foodborne illnesses, which affect a large portion of the population and result in many hospitalizations and deaths. Common household cleaners are an effective strategy to combat foodborne illness, but they are often costly and contain harmful chemicals. Thus, the authors sought to test the antimicrobial effectiveness of spices (clove, nutmeg, astragalus, cinnamon, turmeric, and garlic) on microbes cultured from refrigerator handles and cutting boards. Results from this study demonstrate long-lasting, antimicrobial effects of multiple spices that support their use as alternatives to common household cleaners.

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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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