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1.  Communicating Genetics and Smoking Through Social Media: Are We There Yet? 
Background
Social media is a recent source of health information that could disseminate new scientific research, such as the genetics of smoking.
Objective
The objectives were (1) to evaluate the availability of genetic information about smoking on different social media platforms (ie, YouTube, Facebook, and Twitter) and (2) to assess the type and the content of the information displayed on the social media as well as the profile of people publishing this information.
Methods
We screened posts on YouTube, Facebook, and Twitter with the terms “smoking” and “genetic” at two time points (September 18, 2012, and May 7, 2013). The first 100 posts were reviewed for each media for the time points. Google was searched during Time 2 as an indicator of available information on the Web and the other social media that discussed genetics and smoking. The source of information, the country of the publisher, characteristics of the posts, and content of the posts were extracted.
Results
On YouTube, Facebook, and Twitter, 31, 0, and 84 posts, respectively, were included. Posts were mostly based on smoking-related diseases, referred to scientific publications, and were largely from the United States. From the Google search, most results were scientific databases. Six scientific publications referred to within the Google search were also retrieved on either YouTube or Twitter.
Conclusions
Despite the importance of public understanding of smoking and genetics, and the high use of social media, little information on this topic is actually present on social media. Therefore, there is a need to monitor the information that is there and to evaluate the population’s understanding of the information related to genetics and smoking that is displayed on social media.
doi:10.2196/jmir.2653
PMCID: PMC3785980  PMID: 24018012
genetics; Internet; public health genomics; smoking; social media; Web 2.0
2.  Translational Potential into Health Care of Basic Genomic and Genetic Findings for Human Immunodeficiency Virus, Chlamydia trachomatis, and Human Papilloma Virus 
BioMed Research International  2013;2013:892106.
Individual variations in susceptibility to an infection as well as in the clinical course of the infection can be explained by pathogen related factors, environmental factors, and host genetic differences. In this paper we review the state-of-the-art basic host genomic and genetic findings' translational potential of human immunodeficiency virus (HIV), Chlamydia trachomatis (CT), and Human Papilloma Virus (HPV) into applications in public health, especially in diagnosis, treatment, and prevention of complications of these infectious diseases. There is a significant amount of knowledge about genetic variants having a positive or negative influence on the course and outcome of HIV infection. In the field of Chlamydia trachomatis, genomic advances hold the promise of a more accurate subfertility prediction test based on single nucleotide polymorphisms (SNPs). In HPV research, recent developments in early diagnosis of infection-induced cervical cancer are based on methylation tests. Indeed, triage based on methylation markers might be a step forward in a more effective stratification of women at risk for cervical cancer. Our review found an imbalance between the number of host genetic variants with a role in modulating the immune response and the number of practical genomic applications developed thanks to this knowledge.
doi:10.1155/2013/892106
PMCID: PMC3676999  PMID: 23781508
3.  Impact of Genetic Notification on Smoking Cessation: Systematic Review and Pooled-Analysis 
PLoS ONE  2012;7(7):e40230.
Objectives
This study aimed to evaluate the impact of genetic notification of smoking-related disease risk on smoking cessation in the general population. Secondary objectives were to assess the impact of genetic notification on intention-to-quit smoking and on emotional outcomes as well as the understanding and the recall of this notification.
Methods
A systematic review of articles from inception to August 2011 without language restriction was realized using PubMed, Embase, Scopus, Web of Science, PsycINFO and Toxnet. Other publications were identified using hand search. The pooled-analysis included only randomized trials. Comparison groups were (i) high and low genetic risk versus control, and (ii) high versus low genetic risk. For the pooled-analysis random effect models were applied and sensitivity analyses were conducted.
Results
Eight papers from seven different studies met the inclusion criteria of the review. High genetic risk notification was associated with short-term increased depression and anxiety. Four randomized studies were included in the pooled-analysis, which revealed a significant impact of genetic notification on smoking cessation in comparison to controls (clinical risk notification or no intervention) in short term follow-up less than 6 months (RR = 1.55, 95% CI 1.09–2.21).
Conclusions
In short term follow-up, genetic notification increased smoking cessation in comparison to control interventions. However, there is no evidence of long term effect (up to 12 month) on smoking cessation. Further research is needed to assess more in depth how genetic notification of smoking-related disease could contribute to smoking cessation.
doi:10.1371/journal.pone.0040230
PMCID: PMC3394798  PMID: 22808123
4.  Public health and valorization of genome-based technologies: a new model 
Background
The success rate of timely translation of genome-based technologies to commercially feasible products/services with applicability in health care systems is significantly low. We identified both industry and scientists neglect health policy aspects when commercializing their technology, more specifically, Public Health Assessment Tools (PHAT) and early on involvement of decision makers through which market authorization and reimbursements are dependent. While Technology Transfer (TT) aims to facilitate translation of ideas into products, Health Technology Assessment, one component of PHAT, for example, facilitates translation of products/processes into healthcare services and eventually comes up with recommendations for decision makers. We aim to propose a new model of valorization to optimize integration of genome-based technologies into the healthcare system.
Methods
The method used to develop our model is an adapted version of the Fish Trap Model and the Basic Design Cycle.
Results
We found although different, similarities exist between TT and PHAT. Realizing the potential of being mutually beneficial justified our proposal of their relative parallel initiation. We observed that the Public Health Genomics Wheel should be included in this relative parallel activity to ensure all societal/policy aspects are dealt with preemptively by both stakeholders. On further analysis, we found out this whole process is dependent on the Value of Information. As a result, we present our LAL (Learning Adapting Leveling) model which proposes, based on market demand; TT and PHAT by consultation/bi-lateral communication should advocate for relevant technologies. This can be achieved by public-private partnerships (PPPs). These widely defined PPPs create the innovation network which is a developing, consultative/collaborative-networking platform between TT and PHAT. This network has iterations and requires learning, assimilating and using knowledge developed and is called absorption capacity. We hypothesize that the higher absorption capacity, higher success possibility. Our model however does not address the phasing out of technology although we believe the same model can be used to simultaneously phase out a technology.
Conclusions
This model proposes to facilitate optimization/decrease the timeframe of integration in healthcare. It also helps industry and researchers to come to a strategic decision at an early stage, about technology being developed thus, saving on resources, hence minimizing failures.
doi:10.1186/1479-5876-9-207
PMCID: PMC3296632  PMID: 22142533
Technology Transfer; Health Technology Assessment; Public Health Genomics; Health Needs Assessment; Health Impact Assessment; Valorization; Translational Research; Healthcare; Health Policy; Genomics
5.  Public health in the genomic era: will Public Health Genomics contribute to major changes in the prevention of common diseases? 
The completion of the Human Genome Project triggered a whole new field of genomic research which is likely to lead to new opportunities for the promotion of population health. As a result, the distinction between genetic and environmental diseases has faded. Presently, genomics and knowledge deriving from systems biology, epigenomics, integrative genomics or genome-environmental interactions give a better insight on the pathophysiology of common diseases. However, it is barely used in the prevention and management of diseases. Together with the boost in the amount of genetic association studies, this demands for appropriate public health actions. The field of Public Health Genomics analyses how genome-based knowledge and technologies can responsibly and effectively be integrated into health services and public policy for the benefit of population health. Environmental exposures interact with the genome to produce health information which may help explain inter-individual differences in health, or disease risk. However today, prospects for concrete applications remain distant. In addition, this information has not been translated into health practice yet. Therefore, evidence-based recommendations are few. The lack of population-based research hampers the evaluation of the impact of genomic applications. Public Health Genomics also evaluates the benefits and risks on a larger scale, including normative, legal, economic and social issues. These new developments are likely to affect all domains of public health and require rethinking the role of genomics in every condition of public health interest. This article aims at providing an introduction to the field of and the ideas behind Public Health Genomics.
doi:10.1186/0778-7367-69-8
PMCID: PMC3436652  PMID: 22958637
Epidemiology; Genomics; Epigenomics; Prevention; Public Health; Public Health Genomics; Translational Research; Policymaking; Personalised Healthcare

Results 1-6 (6)