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Informative Speech On Cancer

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Informative Speech On Cancer
Everyone has their own idea of what cancer is like, whether it’s images of the struggle these victims endure full of hospital beds, IVs, and surgeries or images of survivors complete with smiles, cute head wraps, and remission stories. It all depends on what each person’s encounter with cancer was like. Well, I’ve seen both sides of cancer. I’ve seen the smiles as well as the hospital beds and have heard the remission stories as well as the beeps of heart rate monitors. Things can be going great one day, then everything can come crashing down the next.
September 9, 2017. That was the day my grandma was diagnosed with acute myeloid leukemia (AML), a cancer in which the rapid production of abnormal white blood cells builds up in the bone marrow
…show more content…
Alfred-Armand-Louis-Marie Velpeau, a French anatomist and surgeon, recorded the first case of AML when he described a 63-year-old with fever, weakness, urinary stones, and substantial enlargement of the liver and spleen. Nothing else came of it until 1845 when J.H. Bennett, an English physician, physiologist, and pathologist, reported multiple deaths of patients with “enlarged spleens and changes in the ‘colors and consistencies of their blood.’” He used the term “leucocythemia” to depict the condition. In 1856, Rudolf Virchow, a German physician, anthropologist, pathologist, and biologist, coined the term “leukemia,” which is Greek for white blood, and was the first to illustrate the abnormal abundance of white blood cells in patients described by Velpeau and Bennett (News Medical). To help describe normal and abnormal white blood cells in detail, Paul Ehrlich, an American biologist, developed a technique of staining blood films in 1877 (The Hematologist). His contribution to the world of leukemia allowed doctors to more easily and accurately analyze the white blood cells of …show more content…
Indiana University- Purdue University Indianapolis School of Science has developed a computer machine-learning model that can accurately predict relapse or remission of AML. The machine utilizes blood data from healthy people and compares it to bone marrow data and past medical histories of AML patients to predict remission rates at 100 percent accuracy and relapse rates at 90 percent accuracy (UPI News Current “Predict

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