Incorporation of tetanus-epitope into virus-like particles achieves vaccine responses even in older recipients in models of psoriasis, Alzheimer’s and cat allergy

Monoclonal antibodies are widely used to treat non-infectious conditions but are costly. Vaccines could offer a cost-effective alternative but have been limited by sub-optimal T-cell stimulation and/or weak vaccine responses in recipients, for example, in elderly patients. We have previously shown that the repetitive structure of virus-like-particles (VLPs) can effectively bypass self-tolerance in therapeutic vaccines. Their efficacy could be increased even further by the incorporation of an epitope stimulating T cell help. However, the self-assembly and stability of VLPs from envelope monomer proteins is sensitive to geometry, rendering the incorporation of foreign epitopes difficult. We here show that it is possible to engineer VLPs derived from a non human-pathogenic plant virus to incorporate a powerful T-cell-stimulatory epitope derived from Tetanus toxoid. These VLPs (termed CMVTT) retain self-assembly as well as long-term stability. Since Th cell memory to Tetanus is near universal in humans, CMVTT-based vaccines can deliver robust antibody-responses even under limiting conditions. By way of proof of concept, we tested a range of such vaccines against chronic inflammatory conditions (model: psoriasis, antigen: interleukin-17), neurodegenerative (Alzheimer’s, β-amyloid), and allergic disease (cat allergy, Fel-d1), respectively. Vaccine responses were uniformly strong, selective, efficient in vivo, observed even in old mice, and employing low vaccine doses. In addition, randomly ascertained human blood cells were reactive to CMVTT-VLPs, confirming recognition of the incorporated Tetanus epitope. The CMVTT-VLP platform is adaptable to almost any antigen and its features and performance are ideally suited for the design of vaccines delivering enhanced responsiveness in aging populations

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency–Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research

Formulation, Delivery and Stability of Bone Morphogenetic Proteins for Effective Bone Regeneration

Drug Delivery Technolog