Cannabis and Δ-tetrahydrocannabinol (THC) for weight loss?

Obesity is one of the highest preventable causes of morbidity and mortality in the developed world [1]. It has been well known for a long time that exposure to cannabis produces an increase of appetite (a phenomenon referred to as the ‘munchies’). This phenomenon led to an exploration of the role of the endocannabinoid system in the regulation of obesity and associated metabolic syndrome. This effort subsequently led to the development of a successful therapeutic approach for obesity that consisted of blocking the cannabinoid CB₁ receptors using ligands such as Rimonabant in order to produce weight loss and improve metabolic profile [2]. Despite being efficacious, Rimonabant was associated with increased rates of depression and anxiety and therefore removed from the market. We recently discovered that the prevalence of obesity is paradoxically much lower in cannabis users as compared to non-users and that this difference is not accounted for by tobacco smoking status and is still present after adjusting for variables such as sex and age. Here, we propose that this effect is directly related to exposure to the Δ⁹-tetrahydrocannabinol (THC) present in cannabis smoke. We therefore propose the seemingly paradoxical hypothesis that THC or a THC/cannabidiol combination drug may produce weight loss and may be a useful therapeutic for the treatment of obesity and its complications.     

Reflectin genes and development of iridophore patterns in Sepia officinalis embryos (Mollusca,Cephalopoda)

Background: In the cuttlefish Sepia officinalis, iridescence is known to play a role in patterning and communication. In iridophores, iridosomes are composed of reflectins, a protein family, which show great diversity in all cephalopod species. Iridosomes are established before hatching, but very little is known about how these cells are established, their distribution in embryos, or the contribution of each reflectin gene to iridosome structures. Results: Six reflectin genes are expressed during the development of iridosomes in Sepia officinalis. We show that they are expressed in numerous parts of the body before hatching. Evidence of the colocalization of two different genes of reflectin was found. Curiously, reflectin mRNA expression was no longer detectable at the time of hatchling, while reflectin proteins were present and gave rise to visible iridescence. Conclusion: These data suggest that several different forms of reflectins are simultaneously used to produce iridescence in S. officinalis and that mRNA production and translation are decoupled in time during iridosome development. Developmental Dynamics 242:550–561, 2013. © 2013 Wiley Periodicals, Inc.     

缬沙坦氨氯地平片在中国健康人体内的生物等效性研究

目的 评价缬沙坦氨氯地平片受试制剂与参比制剂在中国健康人体内的生物等效性和观察两制剂的安全性。方法 采用单中心、随机、开放、3周期、部分重复交叉设计。受试者经高脂高热餐后/空腹单剂量口服缬沙坦氨氯地平片受试制剂或参比制剂,采血至给药后72 h,周期间的清洗期为14 d。采用经方法学验证的LC-MS/MS检测血浆中缬沙坦和氨氯地平的浓度。选择Phoenix WinNonlin 软件(8.0版本),以非房室模型(non-compartmental analysis,NCA)计算缬沙坦、氨氯地平的药动学参数。采用参比制剂校正的平均生物等效性(reference-scaled average bioequivalence,RSABE)和平均生物等效性(average bioequivalence,ABE)方法评价缬沙坦的生物等效性,ABE方法评价氨氯地平的生物等效性。结果 缬沙坦在空腹条件下的Cmax、AUC0-t和AUC0-∞和在餐后条件下的Cmax的个体内变异系数均>0.294,采用参比制剂标度的平均生物等效性方法。计算得到单侧95%置信上限<0和几何均值比估计值为80.00%~125.00%。缬沙坦餐后条件下的AUC0-t和AUC0-∞的个体内变异系数<0.294,采用平均生物等效性方法,受试制剂与参比制剂的AUC0-t、AUC0-∞经对数转换后的几何均值比值的90%置信区间均在80.00%~125.00%内。综上,两制剂中的缬沙坦具有生物等效性。氨氯地平的Cmax、AUC0-t和AUC0-∞的个体内变异系数<0.294,采用平均生物等效性方法,受试制剂与参比制剂的Cmax、AUC0-t和AUC0-∞经对数转换后的几何均值比值的90%置信区间均在80.00%~125.00%内,表明两制剂中的氨氯地平具有生物等效性。结论 缬沙坦氨氯地平片受试制剂和参比制剂在中国健康人体内具有生物等效性。     

Advances in the management of diabetic macular oedema based on evidence from the Diabetic Retinopathy Clinical Research Network

The Diabetic Retinopathy Clinical Research Network (DRCR.net) performs studies on new treatments for diabetic retinopathy. This review aims to summarise recent findings from DRCR.net studies on the treatment of diabetic macular oedema. We performed a PubMed search of articles from the DRCR.net, which included all studies pertaining to the treatment of diabetic maculopathy. The main outcome measures were retinal thickening as assessed by central subfield thickness on optical coherence tomography and improvement of visual acuity on the Early Treatment Diabetic Retinopathy Study (ETDRS) chart. Findings from each study were divided into modalities of treatment, namely photocoagulation, bevacizumab, triamcinolone, ranibizumab and vitrectomy. While modified ETDRS focal/grid laser remains the standard of care, intravitreal corticosteroids or anti-vascular endothelial growth factor agents have also proven to be effective, although they come with associated side effects. The choice of treatment modality for diabetic macular oedema is a clinical judgement call, and depends on the patient’s clinical history and assessment.     

Life insurance: genomic stratification and risk classification

With the development and increasing accessibility of new genomic tools such as next-generation sequencing, genome-wide association studies, and genomic stratification models, the debate on genetic discrimination in the context of life insurance became even more complex, requiring a review of current practices and the exploration of new scenarios. In this perspective, a multidisciplinary group of international experts representing different interests revisited the genetics and life insurance debate during a 2-day symposium ‘Life insurance: breast cancer research and genetic risk prediction seminar'' held in Quebec City, Canada on 24 and 25 September 2012. Having reviewed the current legal, social, and ethical issues on the use of genomic information in the context of life insurance, the Expert Group identified four main questions: (1) Have recent developments in genomics and related sciences changed the contours of the genetics and life insurance debate? (2) Are genomic results obtained in a research context relevant for life insurance underwriting? (3) Should predictive risk assessment and risk stratification models based on genomic data also be used for life insurance underwriting? (4) What positive actions could stakeholders in the debate take to alleviate concerns over the use of genomic information by life insurance underwriters? This paper presents a summary of the discussions and the specific action items recommended by the Expert Group.Access to genetic information by life insurers has been a topic of discussion for many years.¹ The possibility of using genetic data to underwrite an applicant''s insurance policy has given rise to concerns about the emergence of ‘genetic discrimination''. Genetic discrimination in the field of life insurance is not necessarily illegal in that in insurance underwriting questions about health, family history of disease, or genetic information may constitute legal exceptions to antidiscrimination legislation.^{2, 3} Nevertheless, the expression ‘genetic discrimination'' has acquired public notoriety⁴ and we will use more neutral language in this paper.Countries including Canada, the United States, Russia, and Japan⁵ have chosen not to adopt laws specifically prohibiting access to genetic data for underwriting by life insurers.⁶ In these countries, life insurance underwriters treat genetic data like other types of medical or lifestyle data. However, a growing number of countries such as Belgium, France, and Norway⁵ have chosen to adopt laws to prevent or limit insurers'' access to genetic data for life insurance underwriting. Other countries including Finland and the United Kingdom have developed voluntary arrangements with the industry (ie moratoria) with similar objectives.⁷Life insurance is a private contract between the policy-holder and the insurer. Its principal role is to provide financial security to the beneficiaries in the event of the insured''s death.⁸ Because of this important role, life insurance is often required, or strongly recommended for those seeking loans to acquire primary social goods, like housing or cars.⁹ In Europe, a consequence of the advent of the welfare state is that private insurance has increasingly played a complementary and supplementary role to social insurance by offering additional security and protection to the population. Thus, in this region, insurance is often considered as a social good that allows individuals to live a comfortable life and as a tool to promote social integration.¹⁰ In other regions of the world, this social role of life insurance is also recognized to a lesser extent. Given this social role, equitable access to life insurance is perceived as a sensitive issue and cases of denial looked upon negatively in popular media. Although documented incidents of denial or of increased premiums on the basis of genetic information have remained limited to the context of a few relatively well known, highly penetrant, familial, adult-onset, genetic conditions,¹¹ they have nevertheless generated significant public concern. Fear that insurers will have access to genetic information generated in a clinical or research setting for use in underwriting has been reported by several studies as a reason for non-participation in genetic research or recommended clinical genetic testing.^{12, 13, 14}The clinical utility of genetic testing for monogenic disorders such as Huntington disease, and hereditary forms of cancer are well established.¹⁵ However, genomic risk profiles based on the known common susceptibility variants have limited utility in risk prediction at the individual level, although they could be used for risk stratification in prevention programmes in populations.¹⁶ Today, a new era of genomic research has made it increasingly affordable to scan the entire genome of an individual. Researchers and physicians can interpret these data together with medical and lifestyle information in the form of sophisticated risk prediction models.¹⁷ Moreover, improvement in computing technologies coupled with the Internet make predictive information increasingly available, whether through direct-to-consumer marketing of genetic tests, genetic data sharing online communities, or international research database projects. Given these important technological and scientific changes, and their impact on various stakeholders. The term ‘stakeholders'' is used in this text to refer to the following groups of individuals: actuaries (person who computes insurance risk and premium rates based on statistical data), academic researchers, community representatives, ethics committees, genetic counsellors, genomic researchers, human rights experts, insurers, governmental representatives, non-governmental organisations, patient representatives, physicians, policy makers, popular media, reinsurers (company in charge of calculating the risk and premium amount for insuring a particular customer), research participants, and underwriters (company or person in charge of calculating the risk involved in providing insurance for a particular customer and to decide how much should be paid for the premium). This list is not meant to be exhaustive as relevant new groups may emerge as this topic further develops in the coming years. A multidisciplinary group of international experts representing different interests (hereinafter ‘the Expert Group'') revisited the genetics and life insurance debate. The following text presents a summary of the issues discussed and the ‘Action Items'' agreed upon by the Expert Group at the ‘Life Insurance, Risk Stratification, and Personalized Medicine Symposium''.     

Hospitalizations and Costs Incurred at the Facility Level after Scale-up of Malaria Control: Pre-Post Comparisons from Two Hospitals in Zambia

There is little evidence on the impact of malaria control on the health system, particularly at the facility level. Using retrospective, longitudinal facility-level and patient record data from two hospitals in Zambia, we report a pre-post comparison of hospital admissions and outpatient visits for malaria and estimated costs incurred for malaria admissions before and after malaria control scale-up. The results show a substantial reduction in inpatient admissions and outpatient visits for malaria at both hospitals after the scale-up, and malaria cases accounted for a smaller proportion of total hospital visits over time. Hospital spending on malaria admissions also decreased. In one hospital, malaria accounted for 11% of total hospital spending before large-scale malaria control compared with < 1% after malaria control. The findings demonstrate that facility-level resources are freed up as malaria is controlled, potentially making these resources available for other diseases and conditions.