To address the need for more meaningful biomarkers of tauopathies, we have developed an ultrasensitive tau seed amplification assay (4R RT-QuIC) for the 4-repeat (4R) tau aggregates of progressive supranuclear palsy (PSP), corticobasal degeneration (CBD), and other diseases with 4R tauopathy. The assay detected seeds in 10⁶-10⁹-fold dilutions of 4R tauopathy brain tissue but was orders of magnitude less responsive to brain with other types of tauopathy, such as from Alzheimer's disease cases. The analytical sensitivity for synthetic 4R tau fibrils was ~ 50 fM or 2 fg/sample. A novel dimension of this tau RT-QuIC testing was the identification of three disease-associated classes of 4R tau seeds; these classes were revealed by conformational variations in the in vitro amplified tau fibrils as detected by thioflavin T fluorescence amplitudes and FTIR spectroscopy. Tau seeds were detected in postmortem cerebrospinal fluid (CSF) from all neuropathologically confirmed PSP and CBD cases but not in controls. CSF from living subjects had weaker seeding activities; however, mean assay responses for cases clinically diagnosed as PSP and CBD/corticobasal syndrome were significantly higher than those from control cases. Altogether, 4R RT-QuIC provides a practical cell-free method of detecting and subtyping pathologic 4R tau aggregates as biomarkers.

The original version of this article unfortunately contained a mistake. The Panel A in the published figure 5 is incorrect. The corrected Figure 5 is placed in the following page.

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.