Risk Stratification and Treatment in Smoldering Multiple Myeloma.

Smoldering multiple myeloma is a heterogeneous asymptomatic precursor to multiple myeloma. Since its identification in 1980, risk stratification models have been developed using two main stratification methods: clinical measurement-based and genetics-based. Clinical measurement models can be subdivided in three types: baseline measurements (performed at diagnosis), evolving measurements (performed over time during follow-up appointments), and imaging (for example, magnetic resonance imaging). Genetic approaches include gene expression profiling, DNA/RNA sequencing, and cytogenetics. It is important to accurately distinguish patients with indolent disease from those with aggressive disease, as clinical trials have shown that patients designated as "high-risk of progression" have improved outcomes when treated early. The risk stratification models, and clinical trials are discussed in this review

Keratin 19 as a biochemical marker of skin stem cells in vivo and in vitro: keratin 19 expressing cells are differentially localized in function of anatomic sites, and their number varies with donor age and culture stage

This study was undertaken to evaluate keratin 19 (K19) as a biochemical marker for skin stem cells in order to address some long standing questions concerning these cells in the field of cutaneous biology. We first used the well-established mouse model enabling us to identify skin stem cells as [3H]thymidine-label-retaining cells. A site directed antibody was raised against a synthetic peptide of K19. It reacted specifically with a 40 kDa protein (K19) on immunoblotting. It labelled the bulge area of the outer root sheath on mouse skin by immunohistochemistry. Double-labelling revealed that K19-positive-cells were also [3H]thymidine-label-retaining cells, suggesting that K19 is a marker for skin stem cells of hair follicles. K19-expression was then used to investigate the variation in mouse and human skin stem cells as a function of body site, donor age and culture time. K19 was expressed in the hair follicle and absent from the interfollicular epidermis at hairy sites (except for some K18 coexpressing Merkel cells). In contrast, at glabrous sites, K19-positive-cells were in deep epidermal rete ridges. K19 expressing cells also contained high levels of alpha 3 beta 1 integrin. The proportion of K19-positive-cells was greater in newborn than older foreskins. This correlated with keratinocyte culture lifespan variation with donor age. Moreover, it could explain clinical observations that children heal faster than adults. In conclusion, K19 expression in skin provides an additional tool to allow further characterization of skin stem cells under normal and pathological conditions in situ and in vitro

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.Peer reviewe