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当前对线性无阈假设的错误解读。

Current misinterpretations of the linear no-threshold hypothesis.

作者信息

Bond V P, Wielopolski L, Shani G

机构信息

Medical Department, Brookhaven National Laboratory, Upton, NY 11973, USA.

出版信息

Health Phys. 1996 Jun;70(6):877-82. doi: 10.1097/00004032-199606000-00014.

Abstract

Contrary to the "linear no-threshold hypothesis," which implies that "any amount, however small" of radiation energy is a serious cancer threat, it is shown here that only relatively quite large amounts of such energy can pose such a threat to a person or population. Key to doing this is to make a sharp distinction between the actual amount of the radiation agent imparted energy, epsilon, which must be expressed in units of joules, and the average concentration or density of energy, epsilon/m (i.e., absorbed dose), which is expressed in units of Gy. With any cellular system, e.g., in tissue culture, one can easily adjust the numbers of cells used at each dose point so that a clearly significant number of radiation-induced quantal responses (e.g., mutations, chromosome aberrations, malignant transformations, cell death), in the absorbed dose range of about 0.7 to 3 or more Gy, can be observed. However, if the number of cells is held constant as the absorbed dose is progressively reduced, a point is reached at which no significant excess is observable. This situation is frequently "remedied" by including more cells at that point, which, of course, can increase the number of malignant transformations sufficiently to render the excess statistically valid. However, because both axes are expressed in relative terms, the data point, despite having gained statistical significance, remains at the same location on the graph. This gives the false impression that no more of the agent energy was added or needed to achieve significance. However, if both coordinates are put in absolute terms, i.e., the actual number of quantal responses vs. imparted energy, and the same exercise of "improving the statistics" at low exposures is attempted, it then becomes evident that any point thus rendered significant must be relocated at a substantially higher energy point on the graph. This demonstrates unequivocally the fallacy in the proof of the "linear hypothesis" which is based on agent concentration response curves and not agent amount. It shows that the smaller the agent concentration (absorbed dose; epsilon/m), the larger the amount of radiation energy that must be added to the system in order to demonstrate a radiation-induced response. This suggests a minimum average energy requirement for production of a radiation-attributable cancer. It Ls concluded that the "linear hypothesis" should be abandoned as the cornerstone of radiation protection and practice.

摘要

与“线性无阈假设”相反,该假设意味着“任何数量,无论多么微小”的辐射能量都是严重的癌症威胁,但本文表明,只有相对大量的此类能量才会对个人或人群构成此类威胁。关键在于要明确区分辐射剂传递能量的实际数量ε(必须以焦耳为单位表示)和能量的平均浓度或密度ε/m(即吸收剂量),其以戈瑞为单位表示。对于任何细胞系统,例如在组织培养中,可以轻松调整每个剂量点使用的细胞数量,以便在约0.7至3或更高戈瑞的吸收剂量范围内,能够观察到明显数量的辐射诱导的定量反应(例如突变、染色体畸变、恶性转化、细胞死亡)。然而,如果随着吸收剂量逐渐降低而保持细胞数量不变,会达到一个无法观察到显著过量的点。这种情况通常通过在该点加入更多细胞来“补救”,当然,这可以充分增加恶性转化的数量,以使过量在统计学上有效。然而,由于两个轴都以相对术语表示,数据点尽管获得了统计学意义,但在图表上仍处于相同位置。这给人一种错误的印象,即没有添加或不需要更多的剂能量来达到显著性。然而,如果将两个坐标都用绝对术语表示,即定量反应的实际数量与传递的能量,并尝试在低暴露水平下进行相同的“改进统计”操作,那么很明显,任何因此变得显著的点都必须重新定位在图表上更高的能量点。这明确证明了基于剂浓度反应曲线而非剂量的“线性假设”证明中的谬误。它表明剂浓度(吸收剂量;ε/m)越小,为证明辐射诱导反应必须添加到系统中的辐射能量就越大。这表明产生可归因于辐射的癌症存在最低平均能量要求。结论是,“线性假设”应被摒弃,不再作为辐射防护和实践的基石。

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