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Artificial Intelligence is Revolutionizing Cancer Research
Published in K. Gayathri Devi, Kishore Balasubramanian, Le Anh Ngoc, Machine Learning and Deep Learning Techniques for Medical Science, 2022
B. Sudha, K. Suganya, K. Swathi, S. Sumathi
Machine learning technologies can provide a vital predictive and therapeutic prediction for patients with Epithelial Ovarian cancer before initial care. Predictive analytics can promote customized care choices by patient pre-treatment stratified (Kawakami et al. 2019). A comprehensive and entirely automatic system for the diagnosis and location of PTEN (Phosphatase and tensin homolog) loss in prostate cancer tissue samples has been demonstrated. AI-based algorithms can streamline sample evaluations in scientific and clinical laboratories (Harmon et al. 2021). ML-based collection of image retrieval approaches accompanied by survival analysis to predict particular miRNA biomarkers for breast cancer (Sarkar et al. 2021). A transformative interdisciplinary approach was proposed to identify early lung cancer diagnostic biomarkers with the association of integrating metabolomics and machine learning approaches. Metabolic biomarkers show significant diagnostic power for the early diagnosis of lung tumors (Xie et al. 2021).
The impact of electric fields on cell processes, membrane proteins, and intracellular signaling cascades
Published in Ze Zhang, Mahmoud Rouabhia, Simon E. Moulton, Conductive Polymers, 2018
Another exciting area of study in bioelectricity and electrophysiology attempts to answer the question, what happens to cells internally after they sense electrical cues? Intuitively, the answer to this question involves carefully controlled regulation of a multitude of intracellular signaling events downstream of the changes taking place at the cell membrane (Hronik-Tupaj and Kaplan 2012)—with a strong role of various phosphorylation events. In 2006, a groundbreaking study by Zhao et al. identified, for the first time, genes that modulate EF-guided keratinocyte movements during wound healing. Investigators found that EFs guide cell migration through phosphatidylinositol-3-OH kinase-g (PI(3)Kg) and phosphatase and tensin homolog (PTEN). Specifically, they found that loss of PTEN expression enhanced EF-induced Akt and ERK expression in keratinocytes (Figure 8.5a and b), which leads to increased cell migration (Figure 8.5c and d). Furthermore, they observed the effects of PTEN deficiency during EF-guided monolayer wound healing and found it significantly increased the migration of keratinocytes into the wound (Figure 8.5e and g) and away from the wound (Figure 8.5f and g) when fields were directed into the wound center and away from the wound center, respectively.
SaaS Clouds Supporting Biology and Medicine
Published in Olivier Terzo, Lorenzo Mossucca, Cloud Computing with e-Science Applications, 2017
Philip Church, Andrzej Goscinski, Adam Wong, Zahir Tari
Using the work of the Cancer Genome Atlas Network (2012), a list of genes was defined for each subtype of cancer: luminal A, luminal B, basal-like, and human epidermal growth factor receptor 2 enriched (HER2E) (see Table 11.2). The luminal A and B signatures overlap, both involving the mutations of tumor protein 53 (TP53), Phosphatidylinositol 3-kinase (PIK3CA), and mitogen-activated protein kinase kinase kinase 1 (MAP3K1). However, luminal A can be identified through the mutation of GATA binding factor 3 (GATA3) and Forkhead Box protein (FOXA1), which are unique to this cancer subtype. Basal-like tumors have high levels of mutation in the TP53, retinoblastoma 1 (RB1), and breast cancer 1 early onset (BRACA1) genes. HER2E differs from other subtypes by having a high level of PIK3CA mutations and lower frequency of phosphatase and tensin homolog (PTEN) mutations.
Characterization of pulmonary responses in mice to asbestos/asbestiform fibers using gene expression profiles
Published in Journal of Toxicology and Environmental Health, Part A, 2018
Naveena Yanamala, Elena R. Kisin, Dmitriy W. Gutkin, Michael R. Shurin, Martin Harper, Anna A. Shvedova
IPA core analysis of DEG at 7 days exposure to crocidolite, tremolite asbestos, erionite or wollastonite in mouse lungs predicted the involvement of a total of 51, 55, 28, and 32 enriched canonical pathways, respectively (Figure 6B) and are presented in File S3. The top canonical pathways associated with DEG on day 7 post-treatment with erionite, crocidolite, tremolite asbestos and wollastonite included adipogenesis pathway, epithelial adherens junction signaling, LXR/RXR activation and circadian rhythm signaling, respectively. While altered inflammatory pathways such as acute phase response signaling, clathrin-mediated endocytosis signaling, LXR/RXR and FXR/RXR signaling were observed to be commonly enriched at day 7 post exposure to tremolite asbestos or wollastonite fibers, B-cell receptor signaling and apoptosis signaling were commonly perturbed upon administration of crocidolite or erionite. The circadian rhythm signaling pathway was predicted to be commonly perturbed among tremolite asbestos, erionite and wollastonite exposures. In addition, phosphatase and tensin homolog (PTEN), nuclear factor kappa-B (NF-kB) activation and apoptosis signaling pathways were predicted to be activated upon exposure to crocidolite. Several pathways related to cellular processes such as transforming growth factor-beta (TGFb-), VEGF-, platelet derived growth factor (PDGF-), interleukin 8 (IL8-) and chemokine signaling were predicted to be down-regulated in the lungs treated with crocidolite. A predicted activation of mostly canonical pathways related to inflammation including acute phase response, toll-like receptor, p38 mitogen activated protein kinase (p38 MAPK), NF-kB and Interleukin 6 (IL6) signaling was found upon administration of tremolite asbestos (File S3). Similarly, exposure to wollastonite resulted in the predicted activation of pathways related to inflammation including acute phase response signaling and oxidative stress such as production of nitric oxide and reactive oxygen species in macrophages. Importantly, the DEG that participate in signaling pathways, including apoptosis, circadian rhythm/entrainment and disease pathways related to cancer/systemic responses, were previously reported (Nymark et al. 2007; Roe et al. 2009) to be activated upon exposure to asbestos materials (File S2 & S3).