The new molecular markers DDIT3, STT3A, ARG2 and FAM129A are not useful in diagnosing thyroid follicular tumors

Preoperative characterization of thyroid follicular lesions is challenging. Fine-needle aspiration specimens cannot differentiate follicular carcinomas from benign follicular neoplasias. Recently, promising markers have been detected using modern molecular techniques. We conducted a retrospective study to confirm the usefulness of immunohistochemical staining for the protein markers, DDIT3, STT3A (ITM1), ARG2 and FAM129A (C1orf24) in separating benign and malignant thyroid follicular lesions. Formalin-fixed, paraffin-embedded thyroid tissue from 30 in-house cases (15 follicular carcinomas and 15 follicular adenomas), as well as 8 follicular carcinomas and 21 follicular adenomas on tissue microarray slides were stained immunohistochemically for DDIT3, STT3A, ARG2 and FAM129A expression. Control tissue consisted of thyroid parenchyma adjacent to the tumors and 11 separate cases of normal thyroid parenchyma. All in-house cases of follicular adenomas, follicular carcinomas and adjacent normal thyroid tissue showed positive immunostaining with anti-DDIT3 and anti-STT3A. Anti-ARG2 and anti-FAM129A polyclonal antibodies showed positive staining in 20 and 60% of in-house follicular adenomas, and 40 and 87% of in-house follicular carcinomas, respectively. Monoclonal anti-FAM129A demonstrated positive staining in 13 and 33% of in-house follicular adenomas and follicular carcinomas, respectively. Polyclonal anti-DDIT3, -STT3A and -FAM129A antibodies showed positive staining in all tissue microarray slides of follicular carcinoma and in 76, 85 and 81% of the follicular adenomas, respectively. Monoclonal anti-STT3A stained 81% of the follicular adenoma cores. Anti-ARG2 stained positive in 13% of follicular carcinomas and 10% of follicular adenomas on the tissue microarray slides. In conclusion, DDIT3, STT3A, ARG2 and FAM129A immunohistochemistry does not appear to be useful in the diagnosis of thyroid follicular neoplasias, as they do not reliably distinguish follicular thyroid carcinoma from follicular thyroid adenoma

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

The Seventeenth Data Release of the Sloan Digital Sky Surveys: Complete Release of MaNGA, MaStar, and APOGEE-2 Data

This paper documents the seventeenth data release (DR17) from the Sloan Digital Sky Surveys; the fifth and final release from the fourth phase (SDSS-IV). DR17 contains the complete release of the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, which reached its goal of surveying over 10,000 nearby galaxies. The complete release of the MaNGA Stellar Library accompanies this data, providing observations of almost 30,000 stars through the MaNGA instrument during bright time. DR17 also contains the complete release of the Apache Point Observatory Galactic Evolution Experiment 2 survey that publicly releases infrared spectra of over 650,000 stars. The main sample from the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), as well as the subsurvey Time Domain Spectroscopic Survey data were fully released in DR16. New single-fiber optical spectroscopy released in DR17 is from the SPectroscipic IDentification of ERosita Survey subsurvey and the eBOSS-RM program. Along with the primary data sets, DR17 includes 25 new or updated value-added catalogs. This paper concludes the release of SDSS-IV survey data. SDSS continues into its fifth phase with observations already underway for the Milky Way Mapper, Local Volume Mapper, and Black Hole Mapper surveys