Sample records for dianabol
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Khan, Naik T; Zafar, Salman; Noreen, Shagufta; Al Majid, Abdullah M; Al Othman, Zeid A; Al-Resayes, Saud Ibrahim; Atta-ur-Rahman; Choudhary, M Iqbal
2014-07-01
Biotransformation of the anabolic steroid dianabol (1) by suspended-cell cultures of the filamentous fungi Cunninghamella elegans and Macrophomina phaseolina was studied. Incubation of 1 with C. elegans yielded five hydroxylated metabolites 2-6, while M. phaseolina transformed compound 1 into polar metabolites 7-11. These metabolites were identified as 6β,17β-dihydroxy-17α-methylandrost-1,4-dien-3-one (2), 15α,17β-dihydroxy-17α-methylandrost-1,4-dien-3-one (3), 11α,17β-dihydroxy-17α-methylandrost-1,4-dien-3-one (4), 6β,12β,17β-trihydroxy-17α-methylandrost-1,4-dien-3-one (5), 6β,15α,17β-trihydroxy-17α-methylandrost-1,4-dien-3-one (6), 17β-hydroxy-17α-methylandrost-1,4-dien-3,6-dione (7), 7β,17β,-dihydroxy-17α-methylandrost-1,4-dien-3-one (8), 15β,17β-dihydroxy-17α-methylandrost-1,4-dien-3-one (9), 17β-hydroxy-17α-methylandrost-1,4-dien-3,11-dione (10), and 11β,17β-dihydroxy-17α-methylandrost-1,4-dien-3-one (11). Metabolite 3 was also transformed chemically into diketone 12 and oximes 13, and 14. Compounds 6 and 12-14 were identified as new derivatives of dianabol (1). The structures of all transformed products were deduced on the basis of spectral analyses. Compounds 1-14 were evaluated for β-glucuronidase enzyme inhibitory activity. Compounds 7, 13, and 14 showed a strong inhibition of β-glucuronidase enzyme, with IC50 values between 49.0 and 84.9 μM. Copyright © 2014 Elsevier Inc. All rights reserved.
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Nonprescription Steroids on the Internet
McDonald, Christen L.; Marlowe, Douglas B.; Patapis, Nicholas S.; Festinger, David S.; Forman, Robert F.
2008-01-01
This study evaluated the degree to which anabolic-androgenic steroids are proffered for sale over the Internet and how they are characterized on popular websites. Searches for specific steroid product labels (e.g., Dianabol) between March and June, 2006 revealed that approximately half of the websites advocated their “safe” use, and roughly one-third offered to sell them without prescriptions. The websites frequently presented misinformation about steroids and minimized their dangers. Less than 5% of the websites presented accurate health risk information about steroids or provided information to abusers seeking to discontinue their steroid use. Implications for education, prevention, treatment and policy are discussed. PMID:22080724
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Nonprescription steroids on the Internet.
Clement, Christen L; Marlowe, Douglas B; Patapis, Nicholas S; Festinger, David S; Forman, Robert F
2012-02-01
This study evaluated the degree to which anabolic-androgenic steroids are proffered for sale over the Internet and how they are characterized on popular Web sites. Searches for specific steroid product labels (e.g., Dianabol) between March 2006 and June 2006 revealed that approximately half of the Web sites advocated their "safe" use, and roughly one third offered to sell them without prescriptions. The Web sites frequently presented misinformation about steroids and minimized their dangers. Less than 5% of the Web sites presented accurate health risk information about steroids or provided information to abusers seeking to discontinue their steroid use. Implications for education, prevention, treatment, and policy are discussed.
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NASA Astrophysics Data System (ADS)
Rose, Jake; Martin, Michael; Bourlai, Thirimachos
2014-06-01
In law enforcement and security applications, the acquisition of face images is critical in producing key trace evidence for the successful identification of potential threats. The goal of the study is to demonstrate that steroid usage significantly affects human facial appearance and hence, the performance of commercial and academic face recognition (FR) algorithms. In this work, we evaluate the performance of state-of-the-art FR algorithms on two unique face image datasets of subjects before (gallery set) and after (probe set) steroid (or human growth hormone) usage. For the purpose of this study, datasets of 73 subjects were created from multiple sources found on the Internet, containing images of men and women before and after steroid usage. Next, we geometrically pre-processed all images of both face datasets. Then, we applied image restoration techniques on the same face datasets, and finally, we applied FR algorithms in order to match the pre-processed face images of our probe datasets against the face images of the gallery set. Experimental results demonstrate that only a specific set of FR algorithms obtain the most accurate results (in terms of the rank-1 identification rate). This is because there are several factors that influence the efficiency of face matchers including (i) the time lapse between the before and after image pre-processing and restoration face photos, (ii) the usage of different drugs (e.g. Dianabol, Winstrol, and Decabolan), (iii) the usage of different cameras to capture face images, and finally, (iv) the variability of standoff distance, illumination and other noise factors (e.g. motion noise). All of the previously mentioned complicated scenarios make clear that cross-scenario matching is a very challenging problem and, thus, further investigation is required.