Colorectal cancer represents the third most common and the second deadliest type of cancer for both men and women in the United States claiming over 50000 lives in 2014. The 5-year survival rate for patients diagnosed...Colorectal cancer represents the third most common and the second deadliest type of cancer for both men and women in the United States claiming over 50000 lives in 2014. The 5-year survival rate for patients diagnosed with metastatic colon and rectal cancer is < 15%. Early detection and more effective treatments are urgently needed to reduce morbidity and mortality of patients afflicted with this disease. Here we will review the risk factors and current treatment paradigms for colorectal cancer, with an emphasis on the role of chemoprevention as they relate to epidermal growth factor receptor(EGFR) blockade. We will discuss how various EGFR ligands are upregulated in the presence of Western diets high in saturated and N-6 polyunsaturated fats. We will also outline the various mechanisms of EGFR inhibition that are induced by naturally occurring chemopreventative agents such as ginseng, green tea, and curcumin. Finally, we will discuss the current role of targeted chemotherapy in colon cancer and outline the limitations of our current treatment options, describing mechanisms of resistance and escape.展开更多
Synthetic and naturally occurring particles,such as nanoparticles(NPs)and exosomes;a type of extracellular vesicles(EVs),have garnered widespread attention across various fields,including biomaterials,oncology,and del...Synthetic and naturally occurring particles,such as nanoparticles(NPs)and exosomes;a type of extracellular vesicles(EVs),have garnered widespread attention across various fields,including biomaterials,oncology,and delivery systems for drugs and vaccines.Traditional methods for identifying NPs and EVs,such as transmission electron microscopy,are often prohibitively expensive and labor-intensive.As an alternative,the assessment of electrokinetic attributes such as zeta potential or electrophoretic mobility,conductance,and mean count rate,offers a more cost-effective,rapid,and reliable means of characterizing these particles.In this context,we introduce the first application of a quantum machine learning(QML)-based electrokinetic mining for the identification of green-synthesized iron-and cobalt-based NPs,as well as exosomes derived from human embryonic stem cells(hESC),human lung cancer(A549)cells,and colorectal cancer(CRC)cells,based solely on their electrokinetic attributes.Comparative analyses involving cross-validation,train-test splits,confusion matrices,and Receiver Operating Characteristic(ROC)curves revealed that classical ML techniques could accurately identify the types of NPs and EVs.Notably,QML demonstrated proficiency in differentiating between various NPs and EVs,including the distinction of EVs in the plasma of CRC patients versus those of healthy individuals.Furthermore,QML’s application has been extended to the identification of NPs along with EVs in the plasma of CRC patients and experimental mice,achieving higher prediction performance even with a minimal training dataset,demonstrating that QML based electrokinetic mining could identify NPs or EVs with minimal training data,thereby facilitating novel clinical development in the realm of liquid biopsies.展开更多
文摘Colorectal cancer represents the third most common and the second deadliest type of cancer for both men and women in the United States claiming over 50000 lives in 2014. The 5-year survival rate for patients diagnosed with metastatic colon and rectal cancer is < 15%. Early detection and more effective treatments are urgently needed to reduce morbidity and mortality of patients afflicted with this disease. Here we will review the risk factors and current treatment paradigms for colorectal cancer, with an emphasis on the role of chemoprevention as they relate to epidermal growth factor receptor(EGFR) blockade. We will discuss how various EGFR ligands are upregulated in the presence of Western diets high in saturated and N-6 polyunsaturated fats. We will also outline the various mechanisms of EGFR inhibition that are induced by naturally occurring chemopreventative agents such as ginseng, green tea, and curcumin. Finally, we will discuss the current role of targeted chemotherapy in colon cancer and outline the limitations of our current treatment options, describing mechanisms of resistance and escape.
基金supported by the National Cancer Institute(NCI)R00 CA226353-01A1,Cancer Research Foundation Young Investigator Award and a Lung Cancer Research Foundation(LCRF)Pilot Project Award to HJC.
文摘Synthetic and naturally occurring particles,such as nanoparticles(NPs)and exosomes;a type of extracellular vesicles(EVs),have garnered widespread attention across various fields,including biomaterials,oncology,and delivery systems for drugs and vaccines.Traditional methods for identifying NPs and EVs,such as transmission electron microscopy,are often prohibitively expensive and labor-intensive.As an alternative,the assessment of electrokinetic attributes such as zeta potential or electrophoretic mobility,conductance,and mean count rate,offers a more cost-effective,rapid,and reliable means of characterizing these particles.In this context,we introduce the first application of a quantum machine learning(QML)-based electrokinetic mining for the identification of green-synthesized iron-and cobalt-based NPs,as well as exosomes derived from human embryonic stem cells(hESC),human lung cancer(A549)cells,and colorectal cancer(CRC)cells,based solely on their electrokinetic attributes.Comparative analyses involving cross-validation,train-test splits,confusion matrices,and Receiver Operating Characteristic(ROC)curves revealed that classical ML techniques could accurately identify the types of NPs and EVs.Notably,QML demonstrated proficiency in differentiating between various NPs and EVs,including the distinction of EVs in the plasma of CRC patients versus those of healthy individuals.Furthermore,QML’s application has been extended to the identification of NPs along with EVs in the plasma of CRC patients and experimental mice,achieving higher prediction performance even with a minimal training dataset,demonstrating that QML based electrokinetic mining could identify NPs or EVs with minimal training data,thereby facilitating novel clinical development in the realm of liquid biopsies.
