Activation of influenza virus-specific CD4+ and CD8+ T cells: a new role for plasmacytoid dendritic cells in adaptive immunity

Plasmacytoid dendritic cells (pDCs) contribute to innate antiviral immune responses by producing type I interferons (IFNs) upon exposure to enveloped viruses. However, their role in adaptive immune responses, such as the initiation of antiviral T-cell responses, is not known. In this study, we examined interactions between blood pDCs and influenza virus with special attention to the capacity of pDCs to activate influenza-specific T cells. pDCs were compared with CD11c(+) DCs, the most potent antigen-presenting cells (APCs), for their capacity to activate T-cell responses. We found that like CD11c(+) DCs, pDCs mature following exposure to influenza virus, express CCR7, and produce proinflammatory chemokines, but differ in that they produce type I IFN and are resistant to the cytopathic effect of the infection. After influenza virus exposure, both DC types exhibited an equivalent efficiency to expand anti-influenza virus cytotoxic T lymphocytes (CTLs) and T helper 1 (TH1) CD4(+) T cells. Our results pinpoint a new role of pDCs in the induction of antiviral T-cell responses and suggest that these DCs play a prominent role in the adaptive immune response against viruses.     

Risk of Recurrent Venous Thromboembolism: A Danish Nationwide Cohort Study

Purpose

In this study, we aimed to estimate recurrence risk after incident venous thromboembolism, stratified according to unprovoked, provoked, and cancer-related venous thromboembolism in a prospective cohort of inpatients and outpatients receiving routine care.

Anesthesia and the developing brain: a way forward for clinical research

It is now well established that many general anesthetics have a variety of effects on the developing brain in animal models. In contrast, human cohort studies show mixed evidence for any association between neurobehavioural outcome and anesthesia exposure in early childhood. In spite of large volumes of research, it remains very unclear if the animal studies have any clinical relevance; or indeed how, or if, clinical practice needs to be altered. Answering these questions is of great importance given the huge numbers of young children exposed to general anesthetics. A recent meeting in Genoa brought together researchers and clinicians to map a path forward for future clinical studies. This paper describes these discussions and conclusions. It was agreed that there is a need for large, detailed, prospective, observational studies, and for carefully designed trials. It may be impossible to design or conduct a single study to completely exclude the possibility that anesthetics can, under certain circumstances, produce long‐term neurobehavioural changes in humans; however , observational studies will improve our understanding of which children are at greatest risk, and may also suggest potential underlying etiologies, and clinical trials will provide the strongest evidence to test the effectiveness of different strategies or anesthetic regimens with respect to better neurobehavioral outcome.     

The influence of hydration status during prolonged endurance exercise on salivary antimicrobial proteins

Purpose

Antimicrobial proteins (AMPs) in saliva including secretory immunoglobulin A (SIgA), lactoferrin (SLac) and lysozyme (SLys) are important in host defence against oral and respiratory infections. This study investigated the effects of hydration status on saliva AMP responses to endurance exercise.

Methods

Using a randomized design, 10 healthy male participants (age 23 ± 4 years, \(\dot{V}{\text{O}}_{{2{ \hbox{max} }}}\) 56.8 ± 6.5 ml/kg/min) completed 2 h cycling at 60 % \(\dot{V}{\text{O}}_{{2{ \hbox{max} }}}\) in states of euhydration (EH) or dehydration (DH) induced by 24 h fluid restriction. Unstimulated saliva samples were collected before, during, immediately post-exercise and each hour for 3 h recovery.

Results

Fluid restriction resulted in a 1.5 ± 0.5 % loss of body mass from baseline and a 4.3 ± 0.7 % loss immediately post-exercise. Pre-exercise urine osmolality was higher in DH than EH and overall, saliva flow rate was reduced in DH compared with EH (p < 0.05). Baseline SIgA secretion rates were not different between conditions; however, exercise induced a significant increase in SIgA concentration in DH (161 ± 134 to 309 ± 271 mg/L) which remained elevated throughout 3 h recovery. SLac secretion rates increased from pre- to post-exercise in both conditions which remained elevated in DH only. Overall, SLac concentrations were higher in DH than EH. Pre-exercise SLys concentrations were lower in DH compared with EH (1.6 ± 2.0 vs. 5.5 ± 6.7 mg/L). Post-exercise SLys concentrations remained elevated in DH but returned to pre-exercise levels by 1 h post-exercise in EH.

Conclusions

Exercise in DH caused a reduction in saliva flow rate yet induced greater secretion rates of SLac and higher concentrations of SIgA and SLys. Thus, DH does not impair saliva AMP responses to endurance exercise.

     

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''.     

Therapeutic Biomarkers in Lung Neuroendocrine Neoplasia

The well-known classification of neuroendocrine neoplasms of the lung into four major subtypes (including typical and atypical carcinoids and small- and large-cell neuroendocrine carcinomas) has a proven prognostic validity but only partially helps to predict the response to specific therapies. Therapeutic biomarkers are incompletely known and include morphological, immunophenotypic, and molecular markers. Morphology alone has no specific predictive role, nor has any immunophenotypic marker been proven to bear predictive implications. Ki67 is a relevant prognostic marker and can indirectly predict response to chemotherapy, when levels are extremely high in high-grade neuroendocrine (NE) carcinomas. The expression of somatostatin receptors, especially of the type 2A, has been shown to predict response to somatostatin analog treatments, paralleling the information derived from octreotide scintigraphy. mTOR pathway is targeted by specific inhibitors, but the exact cellular molecules predicting response are still to be defined. It seems that high levels of phosphorylated forms of mTOR and of its downstream factor S6K are associated to a better response to rapalogs in experimental models. Data from gene expression profiling and mutational analyses are currently emerging, providing a more detailed map of different molecular activation pathways, potentially leading to a more accurate molecular classification of lung NE tumors as well as to the discovery of new therapeutic targets. The combination of mutational profiles with those of upregulated or downregulated genes also by gene gains or losses may ultimately provide a better characterization of NE tumor histological types in terms of response to specific chemotherapy or biotherapy.