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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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Enhancing activity of antibiotics against Staphylococcus aureus with Shuang-Huang-Lian

Liu et al. | Sep 29, 2022

Enhancing activity of antibiotics against Staphylococcus aureus with Shuang-Huang-Lian

Staphylococcus aureus is a major pathogen in both hospitals and the community and can cause systemic infections such as pneumonia. Multi-drug resistant strains, such as Methicillin-resistant S. aureus (MRSA) are particularly worrisome. In order to reduce the development of bacterial resistance, we hypothesized that two selected traditional Chinese medicines, Shuang-Huang-Lian (SHL) and Lan-Qin, would be effective against S. aureus. The results showed that SHL had a synergistic effect with gentamicin as well as additive effects with penicillin and cefazolin against S. aureus compared with using antibiotics alone.

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RNAi-based Gene Therapy Targeting ZGPAT Promotes EGF-dependent Wound Healing

Lee et al. | Nov 15, 2021

RNAi-based Gene Therapy Targeting ZGPAT Promotes EGF-dependent Wound Healing

Wound-healing involves a sequence of events, such as inflammation, proliferation, and migration of different cell types like fibroblasts. Zinc Finger CCCH-type with G-Patch Domain Containing Protein (ZGPAT), encodes a protein that has its main role as a transcription repressor by binding to a specific DNA sequence. The aim of the study was to find out whether inhibiting ZGPAT will expedite the wound healing process by accelerating cell migration. This treatment strategy can provide a key to the development of wound healing strategies in medicine and cellular biology.

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Culturally Adapted Assessment Tool for Autism Spectrum Disorder and its Clinical Significance

Das et al. | Apr 19, 2021

Culturally Adapted Assessment Tool for Autism Spectrum Disorder and its Clinical Significance

Diagnosing of Autism Spectrum Disorder (ASD) using tools developed in the West is challenging in the Indian setting due to a huge diversity in sociocultural and economic backgrounds. Here, the authors developed a home-based, audiovisual game app (Autest) suitable for ASD risk assessment in Indian children under 10 years of age. Ratings suggested that the tool is effective and can reduce social inhibition and facilitate assessment. Further usage and development of Autest can improve risk assessment and early intervention measures for children with ASD in India.

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A Novel Approach to Prevent and Restrict Early Stages of Cancer Cell Growth Using a Combination of Moringa and Sesame in a Drosophila Model

Ganesh et al. | Sep 28, 2020

A Novel Approach to Prevent and Restrict Early Stages of Cancer Cell Growth Using a Combination of Moringa and Sesame in a <em>Drosophila</em> Model

Sesame (Sesamum indicum) and moringa (Moringa oleifera) have natural antioxidants that could prevent cancer growth. Previously, this group found that sesame and moringa individually suppress eye tumor grown in the Drosophila melanogaster model. In the present study, combinations of sesame and moringa at different concentrations were included in the D. melanogaster diet. The impact on eye tumor development was assessed at different stages of growth.

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Behavioral Longevity: The Impact of Smoking, Alcohol Consumption, and Obesity on Life Expectancy

Han et al. | Oct 03, 2019

Behavioral Longevity: The Impact of Smoking, Alcohol Consumption, and Obesity on Life Expectancy

In this article, the authors look into what is already known about the factor affecting longevity and determine the importance of behavioral factors including alcohol consumption, smoking, and obesity on longevity. The authors quantify data from over 150 countries and, interestingly, find that the impact each factor has on longevity is at least in part dependent on the country's economic development status. Overall, they conclude that an average person’s life expectancy can increase by more than 3 years if smoking and alcohol consumption is reduced by a half and weight is decreased by 10%.

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The role of CYP46A1 and its metabolic product, 24S-hydroxycholesterol, in Neuro 2A cell death

Ni et al. | May 11, 2021

The role of CYP46A1 and its metabolic product, 24S-hydroxycholesterol, in Neuro 2A cell death

Cholesterol is a major component of neuronal cell membrane and myelin sheath. In this study, the authors either transfected Neuro 2A cells with CYP46A1 cDNA or treated the cells with 24SHC. Cells expressing CYP46A1 had significantly less viability compared to the negative control. Up to 55% reduction in cell viability was also observed in 24S-HC-treated cells. This work supports that CYP46A1 and 24S-HC could directly trigger cell death. The direct involvement of 24S-HC in cell death provides further evidence that 24S-HC can be a promising biomarker for diagnosing brain damage severity.

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Risk assessment modeling for childhood stunting using automated machine learning and demographic analysis

Sirohi et al. | Sep 25, 2022

Risk assessment modeling for childhood stunting using automated machine learning and demographic analysis

Over the last few decades, childhood stunting has persisted as a major global challenge. This study hypothesized that TPTO (Tree-based Pipeline Optimization Tool), an AutoML (automated machine learning) tool, would outperform all pre-existing machine learning models and reveal the positive impact of economic prosperity, strong familial traits, and resource attainability on reducing stunting risk. Feature correlation plots revealed that maternal height, wealth indicators, and parental education were universally important features for determining stunting outcomes approximately two years after birth. These results help inform future research by highlighting how demographic, familial, and socio-economic conditions influence stunting and providing medical professionals with a deployable risk assessment tool for predicting childhood stunting.

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