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By: Y. Candela, M.B. B.CH. B.A.O., M.B.B.Ch., Ph.D.

Assistant Professor, Campbell University School of Osteopathic Medicine

The genetic theory of equilibrium between mutation and selection that underlies the use of the doubling dose method predicts that when a population sustains radiation exposure in every generation arrhythmia on ultrasound order cheapest indapamide and indapamide, a new equilibrium between mutation and selection will eventually be reached arrhythmia breathing generic indapamide 2.5mg line, albeit after tens or hundreds of generations into the distant future blood pressure cuff purchase indapamide 1.5mg without a prescription. The various papers published over the past four decades on this research program have been compiled by Neel and Schull (1991) heart attack pulse buy indapamide 2.5 mg cheap. Since the beginning of these studies, their focus has always been on a direct assessment of adverse hereditary effects in the first-generation progeny of survivors, using indicators of genetic damage that were practicable at the time the studies were initiated in the early 1950s. As the research progressed, it became clear that no statistically significant adverse effects could be demonstrated in the children of survivors, and this conclusion was found to hold when all of the available data until 1990 were analyzed (Neel and others 1990). Strengths and Weaknesses of the Risk Estimates Presented in this Report For the first time in genetic risk estimation, it has been possible to present risk estimates for all classes of genetic disease. In part, this is due to the incorporation of advances in human molecular biology within the conceptual framework of risk estimation. It is important to realize however that human data that bear on hereditary effects of radiation remain limited, and estimates of risk still have to be obtained indirectly using several assumptions. While the risk estimates presented in this document represent what is achievable at the present state of knowledge, it is instructive to examine the assumptions (and consequent uncertainties) and, more importantly, the overlap of the estimates made. If indeed human immature oocytes turn out to be less sensitive than stem cell spermatogonia, then the sexaveraged rate of induced mutations would be lower. It seems unlikely that radiation-induced deletions would share these specificities, certainly not in all genomic regions. Although statistically such a calculation can be defended, the implicit biological assumption that at low doses of radiation, two independent mutations underlying a chronic disease may be induced simultaneously and recovered seems unrealistic. Although it would have been ideal to use the average rate based on all of the genes contributing to diseases included under P (the baseline frequency of diseases), this was not possible because of lack of data. When full annotations of all of the genes in the human genome and knowledge of their disease potential and mutation rates become available, it is likely that the estimate of average rate of mutations will change. The estimate for s has been obtained from an analysis of only a subset of naturally occurring autosomal dominant diseases for which such information was available and is therefore not applicable to all autosomal dominant and X-linked diseases included under P. Further, it may be necessary to revisit the assumption that the s value for induced mutations that cause disease is similar to those of spontaneous disease-causing mutations. The important point is that since all of these represent dominant effects (and spontaneous mutations in many developmental genes are known to cause Mendelian diseases), there must be overlap between the classes of risk grouped under the headings of autosomal dominant + X-linked diseases and of congenital abnormalities, although at present it is difficult to assess its magnitude. The consequence is that the sum may overestimate the actual risk of dominant effects. Conceptualized this way, it is a problem of quantitative genetics, the theoretical foundations for which were laid by Fisher (1918). Multifactorial diseases per se, however, are not quantitative traits, but qualitative ones. Consequently, methods originally developed for studies of quantitative traits and their inheritance were adapted to deal with these diseases. Carter (1961) proposed the concepts of a hypothetical variable called disease liability that underlies multifactorial diseases and of threshold. The concept of disease liability enables one to envisage a graded scale of the degree of being affected or being normal. Likewise, the concept of threshold enables one to envision a certain value in the liability scale that, when exceeded, will cause the disease. When the population frequency of the disease is low, only relatives have a significant risk. For example, in Hungary, congenital pyloric stenosis is about three times more common in males than in females (0. The risk to brothers of affected females is about 20%, which is much higher than the value of 4% for the brothers of affected males (Czeizel and Tusnady 1984). On the assumption that the threshold is farther from the mean in females than in males. Relatives of female patients would therefore receive more of these (thus being at correspondingly higher risk) than relatives of male patients (see Figure 4A-2).

Evidence for a close relationship between gene mutations and chromosome aberrations is that several induced gene mutations are associated with macroscopic region-specific chromosomal deletions or rearrangements (Cox and Masson 1978; Thacker and Cox 1983; Morris and Thacker 1993) blood pressure chart based on height and weight purchase genuine indapamide online. For technical reasons heart attack types proven 1.5mg indapamide, dose-response relationships for gene mutations are far less precise than those for chromosome aberrations heart attack effects indapamide 2.5 mg discount. In general heart attack jack 1 life 2 live cheap indapamide online mastercard, however, a linear or linear-quadratic relationship provides a satisfactory descrip- tion of the dose-response down to ~200 mGy (Thacker 1992) and, from limited data, at lower doses. The exceptions to this are the data from a particularly sensitive in vivo system that scores reversion mutations (as hair color changes) at the pink-eyed unstable (Bonassi and others 1995) locus in the mouse. The following sections consider specific aspects of cellular response relating to cell cycle effects, adaptive responses to radiation, the transfer of damage signals between cells (bystander effects), induced and persistent genomic instability, low-dose hyper-radiation sensitivity, and other aspects of dose-response. This persistent instability is expressed as chromosomal rearrangements, chromosomal bridge formation, chromatid breaks and gaps, and micronuclei (Grosovsky and others 1996; Murnane 1996; Poupon and others 1996; Limoli and others 1997a; Suzuki and others 1998) in the progeny of cells that survive irradiation. Reduction in cell cloning efficiency several generations after irradiation is called delayed lethality; it is supposedly a manifestation of genomic instability associated with an increase in lethal mutations (Seymour and Mothersill 1997). The spectrum of these de novo mutations resembles that of spontaneous mutations. There is controversy, however, as to whether all of these different end points represent the same fundamental chromosomal alterations that result in genomic instability (Chang and Little 1992; Morgan and others 1996; Limoli and others 1997a; Little 1998; Mothersill and others 2000a). However, the similarity in the frequencies of genomic instability induced in X-irradiated cells, (3 to 19) Ч 10­5 per cell/mGy, Copyright National Academy of Sciences. There is controversy concerning the fundamental radiation target and lesions that result in genomic instability. There are also data indicating that reactive oxygen species (Limoli and others 2001; Little 2003), potentially persistent over several generations, may play an important role in ongoing genomic instability. In addition, alterations in signal transduction pathways may be involved (Morgan and others 1996), and alterations in nucleotide pools have been shown to lead to genomic instability (Poupon and others 1996). Another possibility is that damage to centrosomes might be an important target because centrosome defects are thought to result in genomic instability through missegregation of chromosomes (Pihan and others 1998; Duensing and others 2001) that would result in aneuploidy (Duensing and Munger 2001). Chromosome instability can be associated with prolonged B/F/B cycles; these cycles arise as a consequence of breakage of fused sister chromatids when their centromeres are pulled in opposite directions during anaphase, with subsequent re-fusion in the next cell cycle. However, because the nonreciprocal translocations provide telomeres that stabilize the marker chromosome, those chromosomes that donate the nonreciprocal translocations can become unstable due to the loss of their telomeres. Then, a subsequent nonreciprocal translocation can serve to transfer instability to another chromosome (Murnane and Sabatier 2004; Sabatier and others 2005). Thus, the loss of a single telomere can result in transfer of instability from one chromosome to another, leading to extensive genomic instability. The importance of telomere loss as a mechanism for chromosome instability through B/F/B cycles in cancer has been emphasized by the demonstration that telomerase-deficient mice that are also deficient in p53 have a high cancer incidence (Artandi and others 2000; Chang and others 2001; Rudolph and others 2001). The analysis of the tumor cells from these mice demonstrated the presence of chromosome rearrangements typical of B/F/B cycles, including gene amplification and nonreciprocal translocations commonly seen in human cancer. A question that has to be addressed is the relevance of radiation-induced genomic instability for radiation-induced cancer, and a corollary of this question is the relationship among expression of p53, radiation-induced apoptosis, and radiation-induced genomic instability. Evidence has been presented that radiation-induced apoptosis can occur via p53-dependent and p53-independent mechanisms (Strasser and others 1994) initiated by damage in the nucleus (Guo and others 1997) or cytoplasm-membrane (Haimovitz-Friedman 1998). This damage results in cells undergoing apoptosis either during interphase without attempting division (Endlich and others 2000), several hours after they have divided a few times (Forrester and others 1999), or during an aberrant mitosis (Endlich and others 2000). In accord with the guardian-of-the-genome hypothesis, mouse tumors undergoing apoptosis in a p53-independent manner contained abnormally amplified centrosomes, aneuploidy, and gene amplification (Fukasawa and others 1997). Also, a decrease in radiation-induced apoptosis associated with nonfunctional p53 or expression of Bcl2 correlated with an increase in mutagenesis (Xia and others 1995; Cherbonnel-Lasserre and others 1996; Yu and others 1997). However, there is evidence that radiation-induced genomic instability is independent of p53 expression (Kadhim and others 1996). Furthermore, when the guardian-of-the-genome hypothesis was tested in lymphocyte cultures that were irradiated under different doserate and mitogen-treatment conditions, postradiation incubation allowing apoptotic processes to remove damaged cells did not prevent the development of chromosomal instability during long-term cell proliferation over 51­57 days (Holmberg and others 1998). Thus, the relationship between radiation-induced genomic instability, radiation-induced apoptosis, and radiation-induced cancer is uncertain (discussed at length in Chapter 3). Furthermore, radiation-induced genomic instability could not be induced in normal diploid human fibroblasts (Dugan and Bedford 2003) and may be related to confounding in vitro stress factors (Bouffler and others 2001) or to the cells being partially transformed. Finally, as discussed in Chapter 3, it may be that genomic instability plays a more important role in tumor progression than in tumor initiation.

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The problem of estimating risk equation parameters from data with estimated doses is a little more complicated blood pressure medication patch generic indapamide 1.5mg without prescription. Errors in estimated doses can arise in a number of different ways hypertension what is it discount indapamide 1.5mg otc, not all of which have the same impact on risk parameter estimation arrhythmia heart purchase indapamide 2.5 mg overnight delivery. For example blood pressure chart good and bad order 1.5mg indapamide visa, flaws in a dosimetry system have the potential to affect all (or many) dose estimates in the same manner, leading to systematic errors for which all (or many) dose estimates are too high or too low. Errors or incomplete records in data from which dose estimates are constructed. For example, risk equations derived from data with doses that are overestimated by a constant factor (>1) will result in an underestimation of risk at a particular given dose d; doses that are underestimated by a constant factor (<1) will result in an overestimation of risk. Random errors in dose estimates also have the potential to bias estimated risk equations. That is, random errors tend to have the same qualitative effect as systematic overestimation of doses. To the extent that it is based on correct assumptions about the forms and sizes of dose uncertainties, it removes the bias due to random dose measurement errors. Data from Select Populations Ideally, risk models would be developed from data gathered on individuals selected at random from the population for which risk estimates are desired. For example, in estimating risks for medical workers exposed to radiation on the job, the ideal data set would consist of exposure and health information from a random sample of the population of such workers. However, data on specific populations of interest are generally not available in sufficient quantity or with exposures over a wide enough range to support meaningful statistical modeling. Radiation epidemiology is by necessity opportunistic with regard to the availability of data capable of supporting risk modeling, as indicated by the intense study of A-bomb survivors and victims of the Chernobyl accident. A consequence of much significance and concern is the fact that risk models are often estimated using data from one population (often not even a random sample) for the purpose of estimating risks in some other population(s). Cross-population extrapolation of this type is referred to as "transporting" the model from one population to another. The potential problem it creates is the obvious one-namely, that a risk equation valid for one population need not be appropriate for another. Just as there are differences in the risk of cancer among males and females and among different age groups, there are differences in cancer risks among different populations. Transporting models is generally regarded as a necessity, and much thought and effort are expended to ensure that problems of model transportation are minimized. Problems of transporting models from one population to another can never be eliminated completely. However, to avoid doing so would mean that risk estimates would have to be based on data so sparse as to render estimated risks statistically unreliable. Models are developed for estimating lifetime risks of cancer incidence and mortality and take account of sex, age at exposure, dose rate, and other factors. Estimates are given for all solid cancers, leukemia, and cancers of several specific sites. However, the vast literature on both medically exposed persons and nuclear workers exposed at relatively low doses has been reviewed to evaluate whether findings from these studies are compatible with A-bomb survivor-based models. In many cases, results of fitting models similar to those in this chapter have been published. Risk estimates are subject to several sources of uncertainty due to inherent limitations in epidemiologic data and in our understanding of exactly how radiation exposure increases the risk of cancer. In addition to statistical uncertainty, the populations and exposures for which risk estimates are needed nearly always differ from those for whom epidemiologic data are available. This means that assumptions are required, many of which involve considerable uncertainty. Risk may depend on the type of cancer, the magnitude of the dose, the quality of the radiation, the dose-rate, the age and sex of the person exposed, exposure to other carcinogens such as tobacco, and other characteristics of the exposed individual. Despite the abundance of epidemiologic and experimental data on the health effects of exposure to radiation, data are not adequate to quantify these dependencies precisely. These include its large size, the inclusion of both sexes and all ages, a wide range of doses that have been estimated for individual subjects, and high-quality mortality and cancer incidence data. Another consideration in the choice of data was that it was considered essential that the data used by the committee eventually be available to other investigators.

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An echocardiographic study of diagnostic accuracy hypertension pulmonary cheap indapamide 1.5mg on line, prediction of surgical approach arteria iliaca communis buy indapamide with mastercard, and outcome for fetuses diagnosed with discordant ventriculo-arterial connections excel blood pressure chart indapamide 1.5mg without a prescription. Fetal pulmonary venous Doppler patterns in hypoplastic left heart syndrome: relationship to atrial septal restriction pulse pressure in athletes purchase generic indapamide pills. Doppler color flow mapping: a new technique for the diagnosis of congenital heart disease. Evaluation of the right and left ventricles: an integrated approach measuring the area, length, and width of the chambers in normal fetuses. Z-scores of the fetal aortic isthmus and duct: an aid to assessing arch hypoplasia. Cardiac dimensions determined by cross-sectional echocardiography in the normal human fetus from 18 weeks to term. Quantitative evaluation of the fetal right and left ventricular fractional area change using speckle-tracking technology. Mmode assessment of ventricular size and contractility during the second and third trimesters of pregnancy in the normal fetus. Quantification of regional left and right ventricular longitudinal function in 75 normal fetuses using ultrasound-based strain rate and strain imaging. Fetal and neonatal diastolic myocardial strain rate: normal reference ranges and reproducibility in a prospective, longitudinal cohort of pregnancies. A modified myocardial performance (Tei) index based on the use of valve clicks improves reproducibility of fetal left cardiac function assessment. Heart stroke volume and cardiac output by four-dimensional ultrasound in normal fetuses. Feasibility and reproducibility of a standard protocol for 2D speckle tracking and tissue Doppler-based strain and strain rate analysis of the fetal heart. A new and simple Doppler method for measurement of fetal cardiac isovolumetric contraction time. Speckle tracking of the basal lateral and septal wall annular plane systolic excursion of the right and left ventricles of the fetal heart. Changes in fetal cardiac geometry with gestation: implications for 3- and 4-dimensional fetal echocardiography. An aortic aneurysm is a bulge in the aorta that develops in areas where the aorta wall is weak. The pressure of the blood pumping through it causes the weakened section to bulge out like a balloon. The location of the aneurysm determines its type: Abdominal aortic aneurysms occur in the section of the aorta that passes through the abdomen. A thoracic aortic aneurysm can develop in the aortic root, the ascending aorta, aortic arch (the section of the aorta in the chest that bends) or descending aorta. The aorta is the main blood vessel that carries oxygen-rich blood from the heart to all parts of the body. When an aneurysm gets too large, it can rupture and cause life-threatening bleeding or instant death - without any prior warning. If a fraction of a clot gets stuck in a brain or heart blood vessel, it can cause stroke or heart attack. In other vital organs, like the kidneys or liver, a piece of blood clot can disrupt normal function. At the least, a clot fragment that blocks blood flow in the legs, feet or arm can cause numbness, weakness, tingling, or coldness, light-headedness or localized pain. The aneurysm is usually discovered by X-ray during a routine health exam for some other, unrelated condition. Many aortic aneurysms will grow slowly for years before they are large enough to cause symptoms. Some people describe a pulsing sensation in the abdomen as a symptom of an abdominal aortic aneurysm. A thoracic aortic aneurysm may cause back pain, shortness of breath or difficulty swallowing.

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