35 research outputs found

    Skin Bleaching Among African and Afro-Caribbean Women in New York City: Primary Findings from a P30 Pilot Study

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    © 2019 by the authors. Licensee MDPI, Basel, Switzerland. Coinciding with the increase in sequenced bacteria, mining of bacterial genomes for biosynthetic gene clusters (BGCs) has become a critical component of natural product discovery. The order Myxococcales, a reputable source of biologically active secondary metabolites, spans three suborders which all include natural product producing representatives. Utilizing the BiG-SCAPE-CORASON platform to generate a sequence similarity network that contains 994 BGCs from 36 sequenced myxobacteria deposited in the antiSMASH database, a total of 843 BGCs with lower than 75% similarity scores to characterized clusters within the MIBiG database are presented. This survey provides the biosynthetic diversity of these BGCs and an assessment of the predicted chemical space yet to be discovered. Considering the mere snapshot of myxobacteria included in this analysis, these untapped BGCs exemplify the potential for natural product discovery from myxobacteria

    Associations of risk factors of e-cigarette and cigarette use and susceptibility to use among baseline PATH study youth participants (2013–2014)

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    Introduction: Improved understanding of the distribution of traditional risk factors of cigarette smoking among youth who have ever used or are susceptible to e-cigarettes and cigarettes will inform future longitudinal studies examining transitions in use. Methods: Multiple logistic regression analysis was conducted using data from youth (ages 12–17 years) who had ever heard of e-cigarettes at baseline of the PATH Study (n = 12,460) to compare the distribution of risk factors for cigarette smoking among seven mutually exclusive groups based on ever cigarette/e-cigarette use and sus- ceptibility status. Results: Compared to committed never users, youth susceptible to e-cigarettes, cigarettes, or both had increasing odds of risk factors for cigarette smoking, with those susceptible to both products at highest risk, followed by cigarettes and e-cigarettes. Compared to e-cigarette only users, dual users had higher odds of nearly all risk factors (aOR range = 1.6–6.8) and cigarette only smokers had higher odds of other (non-e-cigarette) tobacco use (aOR range=1.5–2.3), marijuana use (aOR=1.9, 95%CI=1.4–2.5), a high GAIN substance use score (aOR = 1.9, 95%CI = 1.1–3.4), low academic achievement (aOR range = 1.6–3.4), and exposure to smoking (aOR range = 1.8–2.1). No differences were observed for externalizing factors (depression, anxiety, etc.), sen- sation seeking, or household use of non-cigarette tobacco. Conclusions: Among ever cigarette and e-cigarette users, dual users had higher odds of reporting traditional risk factors for smoking, followed by single product cigarette smokers and e-cigarette users. Understanding how e- cigarette and cigarette users differ may inform youth tobacco use prevention efforts and advise future studies assessing probability of progression of cigarette and e-cigarette use

    Postinflammatory Hyperpigmentation: Epidemiology, Clinical Presentation, Pathogenesis and Treatment

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    Postinflammatory hyperpigmentation (PIH) is a reactive hypermelanosis that develops following cutaneous inflammation. Common causes of PIH include intrinsic skin conditions (e.g., acne and eczema) as well as external insults to the skin, such as burn injuries and dermatologic procedures. PIH more commonly occurs in individuals with darker skin, for whom it is often a source of significant psychological distress. Several therapeutic modalities are available for the treatment of PIH, including topical agents, chemical peels, and energy-based devices. We review the epidemiology, clinical presentation, pathogenesis, and treatment of PIH

    Associations of risk factors of e-cigarette and cigarette use and susceptibility to use among baseline PATH study youth participants (2013–2014)

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    Introduction: Improved understanding of the distribution of traditional risk factors of cigarette smoking among youth who have ever used or are susceptible to e-cigarettes and cigarettes will inform future longitudinal studies examining transitions in use. Methods: Multiple logistic regression analysis was conducted using data from youth (ages 12–17 years) who had ever heard of e-cigarettes at baseline of the PATH Study (n = 12,460) to compare the distribution of risk factors for cigarette smoking among seven mutually exclusive groups based on ever cigarette/e-cigarette use and sus- ceptibility status. Results: Compared to committed never users, youth susceptible to e-cigarettes, cigarettes, or both had increasing odds of risk factors for cigarette smoking, with those susceptible to both products at highest risk, followed by cigarettes and e-cigarettes. Compared to e-cigarette only users, dual users had higher odds of nearly all risk factors (aOR range = 1.6–6.8) and cigarette only smokers had higher odds of other (non-e-cigarette) tobacco use (aOR range=1.5–2.3), marijuana use (aOR=1.9, 95%CI=1.4–2.5), a high GAIN substance use score (aOR = 1.9, 95%CI = 1.1–3.4), low academic achievement (aOR range = 1.6–3.4), and exposure to smoking (aOR range = 1.8–2.1). No differences were observed for externalizing factors (depression, anxiety, etc.), sen- sation seeking, or household use of non-cigarette tobacco. Conclusions: Among ever cigarette and e-cigarette users, dual users had higher odds of reporting traditional risk factors for smoking, followed by single product cigarette smokers and e-cigarette users. Understanding how e- cigarette and cigarette users differ may inform youth tobacco use prevention efforts and advise future studies assessing probability of progression of cigarette and e-cigarette use
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