Genetic and epigenetic alteration in thyroid cancer: review article

Thyroid cancer is one of the most common endocrine malignancies and in the last two decades the number of involved people in the world has been increased. Thyroid cancer in Iran is the seventh most common cancer in women and 14th in men. In recent years many achievements regarding to molecular pathogenic factors such as the substantial role of signaling pathways and molecular abnormalities have been made. Nowadays there is no efficient treatment for progressed thyroid cancer that does not respond to radioiodine therapy which are included poorly differentiated, anaplastic and metastatic or recurrent differentiated thyroid cancer. Although the results of some clinical trials in phase II for treatment of progressed thyroid cancer are rewarding but none of the treated patients responded to treatment and only a few of them responded partially to the treatment which indicates that the treatment can only control the condition of patients with advanced disease, therefore it is needed to consider other alternative solutions which would be helpful in controlling the disease. Epigenetic is referred to study of heritable changes in gene expression without changes in primary DNA sequence. The main mechanisms of genetic and epigenetic alterations are including mutations, increasing the gene copy number and aberrant gene methylation. Epigenetic defects are prevalent in different types of cancers. Aberrant methylation of genes that control cell proliferation and invasion (p16INK4A, RASSF1A, PTEN, Rap1GAP, TIMP3, DAPK, RARβ2, E-cadherin, and CITED1), as well as specific genes involved in differentiation of thyroid cancer (Na+/I- symport, TSH receptor, pendrin, SL5A8, and TTF-1) in association with genetic alterations, leads to tumor progression. Growing evidence shows that acquired epigenetic abnormalities participate with genetic alterations to cause altered patterns of gene expression or function. Many of these molecular changes can be used as molecular markers for prognosis, diagnosis and new therapeutic targets for thyroid cancer. This article is about the most common genetic and epigenetic alterations in thyroid cancer which can be complementary together in recognition of new treatments for the disease

Modulation of toxicity effects of CuSO4 by sulfated polysaccharides extracted from brown algae (Sargassum tenerrimum) in Danio rerio as a model

Abstract Copper is widely used in agriculture and aquaculture due to its high disinfection properties and relatively low cost. However, the increase in copper concentration due to evaporation can lead to water reservoir pollution, which can harm the health of consumers. The present study aimed to determine the role of sulfated polysaccharides (SPs) extracted from Sargassum tenerimum algae in reducing lesions caused by the heavy metal copper. Zebrafish (Danio rerio) were used as a human model in five treatments. The negative and positive control groups were fed a diet containing zero percent of SPs, while the experimental groups were fed 0.5%, 1%, and 1.5% of SPs in three treatments for 56 days, finally CuSO4 was exposed only to the positive control group and the groups fed with SPs. Results showed a significant decrease in the activity level of ALT enzymes (39–16 U/mL), AST (67–46 U/mL), and ALP (485–237 U/mL), confirming the results obtained from histopathological studies in CuSO4 exposed groups. The addition of SPs to the diet resulted in a significant reduction (sig < 0.05) of mortalities due to the decrease of tissue damage. Additionally, due to the anti-inflammatory properties and the protective effect of SPs, a significant decrease (sig < 0.05) was observed in the relative expression of Il-1β and Tnf-α genes

Artificial Intelligence in Cancer Care: From Diagnosis to Prevention and Beyond

&lt;p&gt;Artificial Intelligence (AI) has made significant strides in revolutionizing cancer care, encompassing various aspects from diagnosis to prevention and beyond. With its ability to analyze vast amounts of data, recognize patterns, and make accurate predictions, AI has emerged as a powerful tool in the fight against cancer. This article explores the applications of AI in cancer care, highlighting its role in diagnosis, treatment decision-making, prevention, and ongoing management. In the realm of cancer diagnosis, AI has demonstrated remarkable potential. By processing patient data, including medical imaging, pathology reports, and genetic profiles, AI algorithms can assist in early detection and accurate diagnosis. Image recognition algorithms can analyze radiological images, such as mammograms or CT scans, to detect subtle abnormalities and assist radiologists in identifying potential tumors. AI can also aid pathologists in analyzing tissue samples, leading to more precise and efficient cancer diagnoses. AI's impact extends beyond diagnosis into treatment decision-making. The integration of AI algorithms with clinical data allows for personalized treatment approaches. By analyzing patient characteristics, disease stage, genetic markers, and treatment outcomes, AI can provide valuable insights to oncologists, aiding in treatment planning and predicting response to specific therapies. This can lead to more targeted and effective treatment strategies, improving patient outcomes and reducing unnecessary treatments and side effects. Furthermore, AI plays a crucial role in cancer prevention. By analyzing genetic and environmental risk factors, AI algorithms can identify individuals at higher risk of developing certain cancers. This enables targeted screening programs and early interventions, allowing for timely detection and prevention of cancer. Additionally, AI can analyze population-level data to identify trends and patterns, contributing to the development of public health strategies for cancer prevention and control. AI's involvement in cancer care goes beyond diagnosis and treatment, encompassing ongoing management and survivorship. AI-powered systems can monitor treatment response, track disease progression, and detect recurrence at an early stage. By continuously analyzing patient data, including imaging, laboratory results, and clinical assessments, AI algorithms can provide real-time insights, facilitating timely interventions and adjustments to treatment plans. This proactive approach to disease management improves patient outcomes and enhances quality of life.&lt;/p&gt