This is an example of a negative feedback loop. In a positive feedback loop , changes in one direction tend to produce even more change in that same direction, such as the stages of labor that lead to childbirth. In the case of normal cell proliferation,when the appropriate number of cells has been produced and cells begin to crowd each other growth inhibitory factors trigger a negative feedback mechanism to reduce the rate of cell growth.
While positive feedback can occur normally, the production of excess growth factors by cells drives an abnormal positive feedback loop. Not all abnormally growing cells are cancerous. For example, the term hyperplasia refers to a type of noncancerous growth consisting of rapidly dividing cells, which leads to a larger than usual number of structurally normal cells. Hyperplasia may be a normal tissue response to an irritating stimulus. Calluses that form on your hand when you first learn to swing a tennis racket or a golf club is an example of hyperplastic skin cells.
Although hyperplasia is considered reversible, it some cases it indicates an increased risk of cancer. An example is hyperplasia of the lining of the uterus endometrium. Dysplasia is another noncancerous type of abnormal cell growth characterized by the loss of normal tissue arrangement and cell structure. Dysplastic cells lose the normal architecture that characterizes normal tissues, and may show physical and chemical changes that distinguish them from their normal counterparts.
They may have changes in their DNA, or they may have visible changes in their cell structures especially the cell nucleus that can be seen under the microscope.
These visible changes are often useful in detecting dysplasia early, before it progresses, as it sometimes but not always does lead to cancer. An example is cervical dysplasia , which may become cervical cancer if left untreated over a long period of time. The most severe form of dysplasia, carcinoma in situ , can actually be considered a form of cancer. In Latin, the term "in situ" means "in place".
Carcinoma in situ refers to an uncontrolled growth of cells that remains in the original location in place without invading surrounding tissue, as cancer cells eventually do. Carcinoma in situ however, is considered more serious than moderate dysplasia because the risk of local invasion is much higher.
This is why, when discovered, carcinoma in situ is usually removed surgically. Image 6 illustrates the different types of abnormal cell growth. Image 6: Types of abnormal cell growth.
Cancer basics American Cancer Society website. Updated December 8, Accessed January 30, Cellular and molecular basis of cancer. Merck Manual Professional Version website.
Updated July American Cancer Society website. Updated August 24, Introduction to overview of cancer. Marieb EN. Human Anatomy and Physiology. Understanding cancer. National Cancer Institute website.
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Home Health Library. Population Growth The two simplest models of population growth use deterministic equations equations that do not account for random events to describe the rate of change in the size of a population over time. Exponential Growth Charles Darwin, in developing his theory of natural selection, was influenced by the English clergyman Thomas Malthus.
Logistic Growth Extended exponential growth is possible only when infinite natural resources are available; this is not the case in the real world. Carrying Capacity and the Logistic Model Figure 1. When resources are unlimited, populations exhibit a exponential growth, shown in a J-shaped curve. When resources are limited, populations exhibit b logistic growth. In logistic growth, population expansion decreases as resources become scarce, and it levels off when the carrying capacity of the environment is reached.
The logistic growth curve is S-shaped. Examples of Logistic Growth Yeast, a unicellular fungus used to make bread and alcoholic beverages, exhibits the classical S-shaped curve when grown in a test tube Figure 2a.
Its growth levels off as the population depletes the nutrients that are necessary for its growth. In the real world, however, there are variations to this idealized curve. Examples in wild populations include sheep and harbor seals Figure 2b. In both examples, the population size exceeds the carrying capacity for short periods of time and then falls below the carrying capacity afterwards. This fluctuation in population size continues to occur as the population oscillates around its carrying capacity.
Still, even with this oscillation the logistic model is confirmed. Figure 2. The yeast is visualized using differential interference contrast light micrography. Previous: 4. Next: 4. License Environmental Biology by Matthew R. The current percentage increase in the human population as of is about 1. If we multiply this, as shown above, by the current population, we get the increase in humans per year: 0. That is the equivalent of 2.
It is 1. An astounding growth rate, even though the net reproductive rate is actually quite small. But growth is not evenly distributed around the world. Certain countries are growing faster than others, while some are actually losing growth deaths and emigration exceed births plus immigration - Albania is an example. Remember, only two things affect population growth: births and deaths.
So, have these changed? Liberia 3. Canada 0. Mexico 1. England 0. For example, if the world growth rate is 1. When I was born, the population was about 2 Billion in It is now 50 years later, and the population is 6. That is nearly a tripling!! The world population growth rate was much higher in the past 50 years than it currently is. If you have more young people, then you have more opportunity to make babies!! Developing countries have more young people than developed ones.
In developed countries, couples wait longer to have babies, and, they tend to have fewer per couple. In undeveloped countries, children are produced sooner, and couples have larger families than in developed countries. Mathematical models of transmission have become invaluable management tools in planning for the control of emerging infectious diseases. A key variable in such models is the reproductive number R.
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