Increased Glucose Availability Sensitizes Pancreatic Cancer to Chemotherapy

Pancreatic Ductal Adenocarcinoma (PDAC) is highly resistant to chemotherapy. Effective alternative therapies have yet to emerge, as chemotherapy remains the best available systemic treatment. However, the discovery of safe and available adjuncts to enhance chemotherapeutic efficacy can still improve survival outcomes. We show that a hyperglycemic state substantially enhances the efficacy of conventional single- and multi-agent chemotherapy regimens against PDAC. Molecular analyses of tumors exposed to high glucose levels reveal that the expression of GCLC (glutamate-cysteine ligase catalytic subunit), a key component of glutathione biosynthesis, is diminished, which in turn augments oxidative anti-tumor damage by chemotherapy. Inhibition of GCLC phenocopies the suppressive effect of forced hyperglycemia in mouse models of PDAC, while rescuing this pathway mitigates anti-tumor effects observed with chemotherapy and high glucose

The Effect of Self-Differentiation Therapy on Assertiveness of Female Students of Allameh Tabataba’i University

The present study aims to study the effectiveness of selfdifferentiation therapy on assertiveness of female students of Allameh Tabataba’i University. The research employed a semi-experimental design with pre-test and post-test. Using convenience sampling method, 20 subjects were selected from among first-year undergraduate female students of the Faculty of Psychology and Educational Sciences, Allameh Tabataba’i University. The sample populated was randomly assigned into two groups, namely experiment and control groups, each consisting of 10 people. The experiment group received 8 sessions of self-differentiation therapy. The control group received no intervention. The data was analyzed using Covariance analysis. The results showed that self-differentiation therapy was effective on improvement of assertiveness (P<0.01). Generally, it can be concluded that self-differentiation therapy helped to increase the level of assertiveness of the participants

Limited nutrient availability in the tumor microenvironment renders pancreatic tumors sensitive to allosteric IDH1 inhibitors

Nutrient-deprived conditions in the tumor microenvironment (TME) restrain cancer cell viability due to increased free radicals and reduced energy production. In pancreatic cancer cells a cytosolic metabolic enzyme, wild-type isocitrate dehydrogenase 1 (wtIDH1), enables adaptation to these conditions. Under nutrient starvation, wtIDH1 oxidizes isocitrate to generate α-ketoglutarate (αKG) for anaplerosis and NADPH to support antioxidant defense. In this study, we show that allosteric inhibitors of mutant IDH1 (mIDH1) are potent wtIDH1 inhibitors under conditions present in the TME. We demonstrate that low magnesium levels facilitate allosteric inhibition of wtIDH1, which is lethal to cancer cells when nutrients are limited. Furthermore, the Food &amp; Drug Administration (FDA)-approved mIDH1 inhibitor ivosidenib (AG-120) dramatically inhibited tumor growth in preclinical models of pancreatic cancer, highlighting this approach as a potential therapeutic strategy against wild-type IDH1 cancers

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