Ethical Issues in Nanomedicine

Graur, Florin; Elisei, Rossella; Szasz, Andrew; Neagos, H. C.; Muresan, A.; Furcea, Luminiţa; Berindan‐Neagoe, Ioana; Braicu, Cornelia; Katona, Gabriel; Diudea, Mircea V.

doi:10.1007/978-3-642-22586-4_3

Cited by 13 publications

(11 citation statements)

References 9 publications

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“…Now, in order to find the true number (N Ia in Equation 5) of SNe Ia and II at each redshift, we need to estimate the contamination of our SNe Ia sample by SNe II. There are many studies that derive core collapse supernova rates, and these results are consistent each other Melinder et al 2012;Graur et al 2011;Bazin et al 2009;Botticella et al 2008;Mattila et al 2012;Li et al 2011;Smart et al 2009;Cappellaro et al 1999;Horiuchi et al 2011;Magnelli et al 2009). Since higher redshift SN II rates have larger uncertainty, we use the measured nearby SN II rate and assume that the SN II rate is increasing in proportion to the cosmic star for- Figure 11.…”

Section: Estimating the Misclassification Ratiosupporting

confidence: 71%

“…We also compare our measurements to those at similar or slightly higher redshifts. Graur et al (2011;hereafter Gr11) obtained highredshift SN Ia rates in the Subaru Deep Field (SDF) using the multi-color SED fitting classification method (Poznanski et al 2007a). Gr11 carried out very deep photometric observations and detected very high-redshift SNe Ia up to z ∼ 2.0.…”

Section: Comparison With Previous Sn Rate Studiesmentioning

confidence: 99%

“…This delay time is one of the primary methods for understanding the progenitor scenario of SNe Ia. Recent comparisons of the SN Ia rate and the cosmic star formation history (e.g., Madau et al 1998;Hopkins & Beacon 2006) have resulted in a wide range of derived delay times (e.g., Cappellaro et al 1999;Pain et al 2002;Tonry et al 2003; Barris & Tonry 2006;Neill et al 2006;Poznanski et al 2007;Botticella et al 2008;Dahlen et al 2008;Dilday et al 2008;Kuznetsova et al 2008Graur et al 2011, Maoz et al 2012, Barbary et al 2012, Perrett et al 2012). The delay time can be predicted theoretically from the SD scenario or the DD scenario (see Wang & Han 2012, for a review).…”

Section: Introductionmentioning

confidence: 99%

“…Other high-z rate measurements have been reported in the literature. Graur et al 2011 derived the SN Ia rate up to z ∼ 2.0 using 150 SNe from a SN survey in the Subaru Deep Field (SDF), and found that the SN Ia rate levels off at 1.0 < z < 2.0. Their SN classification method is based on a single epoch in the R,i ′ ,and z ′ bands, provided in Poznanski et al 2007.…”

Section: Introductionmentioning

confidence: 99%

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The Type Ia supernovae rate with Subaru/XMM-Newton Deep Survey

Okumura¹,

Ihara²,

Doi³

et al. 2014

Publications of the Astronomical Society of Japan

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We present measurements of the rates of high-redshift Type Ia supernovae derived from the Subaru/XMM-Newton Deep Survey (SXDS). We carried out repeat deep imaging observations with Suprime-Cam on the Subaru Telescope, and detected 1040 variable objects over 0.918 deg 2 in the Subaru/XMM-Newton Deep Field. From the imaging observations, light curves in the observed i ′ -band are constructed for all objects, and we fit the observed light curves with template light curves. Out of the 1040 variable objects detected by the SXDS, 39 objects over the redshift range 0.2 < z < 1.4 are classified as Type Ia supernovae using the light curves. These are among the most distant SN Ia rate measurements to date. We find that the Type Ia supernova rate increase up to z ∼ 0.8 and may then flatten at higher redshift. The rates can be fitted by a simple power law, r V (z) = r 0 (1 + z) α with r 0 = 0.20 +0.52 −0.16 (stat.) +0.26 −0.07 (syst.)×10 −4 yr −1 Mpc −3 , and α = 2.04 +1.84 −1.96 (stat.) +2.11 −0.86 (syst.).

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Section: Estimating the Misclassification Ratiosupporting

confidence: 71%

Section: Comparison With Previous Sn Rate Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Type Ia supernovae rate with Subaru/XMM-Newton Deep Survey

Okumura¹,

Ihara²,

Doi³

et al. 2014

Publications of the Astronomical Society of Japan

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“…93 In addition, and perhaps not surprising given the nascence of the field, there is almost no literature on the long-term effects of nanomaterials in humans. 94 All of these problems make it hard for an IRB to evaluate whether risks to subjects are reasonable and minimized. If IRB members use their knowledge of risk assessment at larger scales, they may miss the special challenges involved in making nano risk evaluations.…”

Section: Methodsmentioning

confidence: 99%

Recommendations for Nanomedicine Human Subjects Research Oversight: An Evolutionary Approach for an Emerging Field

Fatehi

Wolf

McCullough

et al. 2012

J. Law. Med. Ethics

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The nanomedicine field is fast evolving toward complex, “active,” and interactive formulations. Like many emerging technologies, nanomedicine raises questions of how human subjects research (HSR) should be conducted and the adequacy of current oversight, as well as how to integrate concerns over occupational, bystander, and environmental exposures. The history of oversight for HSR investigating emerging technologies is a patchwork quilt without systematic justification of when ordinary oversight for HSR is enough versus when added oversight is warranted. Nanomedicine HSR provides an occasion to think systematically about appropriate oversight, especially early in the evolution of a technology, when hazard and risk information may remain incomplete. This paper presents the consensus recommendations of a multidisciplinary, NIH-funded project group, to ensure a science-based and ethically informed approach to HSR issues in nanomedicine, and integrate HSR analysis with analysis of occupational, bystander, and environmental concerns. We recommend creating two bodies, an interagency Human Subjects Research in Nanomedicine (HSR/N) Working Group and a Secretary’s Advisory Committee on Nanomedicine (SAC/N). HSR/N and SAC/N should perform 3 primary functions: (1) analysis of the attributes and subsets of nanomedicine interventions that raise HSR challenges and current gaps in oversight; (2) providing advice to relevant agencies and institutional bodies on the HSR issues, as well as federal and federal-institutional coordination; and (3) gathering and analyzing information on HSR issues as they emerge in nanomedicine. HSR/N and SAC/N will create a home for HSR analysis and coordination in DHHS (the key agency for relevant HSR oversight), optimize federal and institutional approaches, and allow HSR review to evolve with greater knowledge about nanomedicine interventions and greater clarity about attributes of concern.

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Ethics of Nanomedicine

Sheng

Ding

et al. 2022

Nanomedicine

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Ethical Issues in Nanomedicine

Cited by 13 publications

References 9 publications

The Type Ia supernovae rate with Subaru/XMM-Newton Deep Survey

The Type Ia supernovae rate with Subaru/XMM-Newton Deep Survey

Recommendations for Nanomedicine Human Subjects Research Oversight: An Evolutionary Approach for an Emerging Field

Ethics of Nanomedicine

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