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induction of gynecomastia, infertility, and overstimulation of erythropoiesis On the other hand, use of SARMs for selected indications provides the rationale for developing molecules with distinct tissue speci city For example, if the target is bone growth in elderly men with osteopenia or osteoporosis, but with no overt signs of hypogonadism, a more anabolic SARM with clear effects on bone and muscle, but lesser activity on prostate or other sex accessory tissues, would be more desirable The compound 7 -methyl-19-nortestosterone (MENT) has a biopotency per molecule 10 times greater than T [224] and it does not undergo 5 reduction, which may or may not be an advantage for its effects on the prostate It retains, however, its capacity to be aromatized to estradiol [225] It cannot be regarded as a SARM since there is no tissue selectivity based on receptor mechanisms, but it is potentially prostate-sparing The effects of MENT on the prostate are two to three times as potent as those of T, but studies with MENT given in a dose suf cient to sustain gonadotropin suppression and anabolism (1/10 of the amount of T) showed a 50% loss in

Table 185 Desired pro le of activity of new selective androgen receptor modulators (SARMs): male applications Indications Tissue/parameter Prostate/sex accessory tissues Libido Inhibition of gonadotropins Hair growth Bone growth Muscle mass/strength Fat-free mass Lipids/cardiovascular risk factors Blood pressure/ uid retention Erythropoiesis Liver function (enzyme elevation) Breast (gynecomastia) Hypogonadism Stimulatory, but less than DHT Stimulatory Present Stimulatory Stimulatory Stimulatory Increase Neutral Neutral Weakly stimulatory Neutral Neutral

.

Selected indications* Weak or neutral Stimulatory/neutral Absent/reduced Neutral Stimulatory Stimulatory Increase Neutral/bene cial Neutral Stimulatory Neutral Neutral

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A third less widely used technique is called speci cation-based detection We now discuss each of these techniques in detail The misuse detection technique involves analyzing the collected data for speci c behavior patterns known to be consistent with speci c attacks These behavior patterns are called signatures For example, a UDP packet destined to port 0 can crash some machines [79] The signature of a ping-of-death attack is a very large ping packet; the signature of an RPC locator attack is a packet intended for port 135 that contains a command that the system is not expecting; the signature of a Bubonic attack is made from various values, such as a TTL of 255, a TOS eld value of 0 C9, exactly 20 byte payload in the IP datagram, and the fragment ID value with consistent increments of 256 [79] The misuse detection technique is being used widely today by commercial systems because it allows for very precise detection of attacks, thereby resulting in low false alarm rates When a signature for an attack has been identi ed (typically a bit-by-bit precise speci cation of an attack packet), it is straightforward to detect the attack by comparing a packet against the precise attack signature The problem with misuse detection intrusion detection techniques is that these techniques can only detect previously known attacks with wellde ned patterns of malicious behavior Novel attacks that have not been seen before cannot be detected until a new signature for the attack is created The anomaly detection technique involves looking for behavior that is outside the normal expected behavior This is usually done by utilizing statistical techniques that compare the observed behavior against the statistics of the expected normal behavior These techniques often utilize thresholds (such as amount of excessive loading of CPU, counts of traf c of speci c type, etc) that if exceeded indicate an attack Therefore, detectors utilizing this technique require prior training so that the thresholds used for detecting abnormal behavior are set at the appropriate values The advantage of anomaly-based detection is that this technique does not require the existence of precise signatures and can therefore be used for detecting attacks not previously seen The disadvantage of anomaly-based detection is that, due to its statistical nature, it is more prone to false positives depending on how the thresholds are set There is always a trade-off between setting very tight thresholds (close to normal behavior), thus triggering a lot of false positives when users deviate even slightly from expected behavior, and setting very loose thresholds that minimize false positives but allow.

* Selected indications may include glucocorticoid-induced osteoporosis, androgen replacement in eldery men, HIV-wasting, cancer cachexia, certain anemias, muscular dystrophies, and male contraception DHT, dihydrotestosterone With permission of Negro-Villar A J Clin Endocrin Metab 1999;84:3459 3462 [224]

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