Encapsulated Papillary Thyroid Tumor with Delicate Nuclear Changes and a Mutation as a Possible Novel Subtype of Borderline Tumor

Although papillary thyroid carcinoma (PTC)–type nuclear changes are the most reliable morphological feature in the diagnosis of PTC, the nuclear assessment used to identify these changes is highly subjective. Here, we report a noninvasive encapsulated thyroid tumor with a papillary growth pattern measuring 23 mm at its largest diameter with a nuclear score of 2 in a 26-year-old man. After undergoing left lobectomy, the patient was diagnosed with an encapsulated PTC. However, a second opinion consultation suggested an alternative diagnosis of follicular adenoma with papillary hyperplasia. When providing a third opinion, we identified a low MIB-1 labeling index and a heterozygous point mutation in the KRAS gene but not the BRAF gene. We speculated that this case is an example of a novel borderline tumor with a papillary structure. Introduction of the new terminology “noninvasive encapsulated papillary RAS-like thyroid tumor (NEPRAS)” without the word “cancer” might relieve the psychological burden of patients in a way similar to the phrase “noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP).

OryzaExpress: An Integrated Database of Gene Expression Networks and Omics Annotations in Rice

Similarity of gene expression profiles provides important clues for understanding the biological functions of genes, biological processes and metabolic pathways related to genes. A gene expression network (GEN) is an ideal choice to grasp such expression profile similarities among genes simultaneously. For GEN construction, the Pearson correlation coefficient (PCC) has been widely used as an index to evaluate the similarities of expression profiles for gene pairs. However, calculation of PCCs for all gene pairs requires large amounts of both time and computer resources. Based on correspondence analysis, we developed a new method for GEN construction, which takes minimal time even for large-scale expression data with general computational circumstances. Moreover, our method requires no prior parameters to remove sample redundancies in the data set. Using the new method, we constructed rice GENs from large-scale microarray data stored in a public database. We then collected and integrated various principal rice omics annotations in public and distinct databases. The integrated information contains annotations of genome, transcriptome and metabolic pathways. We thus developed the integrated database OryzaExpress for browsing GENs with an interactive and graphical viewer and principal omics annotations (http://riceball.lab.nig.ac.jp/oryzaexpress/). With integration of Arabidopsis GEN data from ATTED-II, OryzaExpress also allows us to compare GENs between rice and Arabidopsis. Thus, OryzaExpress is a comprehensive rice database that exploits powerful omics approaches from all perspectives in plant science and leads to systems biology

QTL mapping of antixenosis resistance to common cutworm (<i>Spodoptera litura</i> Fabricius) in wild soybean (<i>Glycine soja</i>) - Fig 2

<p><b>Frequency distributions of the antixenosis indices of the recombinant-inbred lines derived from a cross between <i>Glycine soja</i> and ‘Fukuyutaka’ in 2012 (A) and 2013 (B).</b> The antixenosis resistance was evaluated using <i>C</i> values (calculated using Equation 1), which represent the resistance relative to that of a standard cultivar, ‘Akisengoku’. Arrows and vertical lines represent the positions of the standard deviations and mean values of the parents, respectively.</p

Quantitative trait loci (QTLs) associated with the antixenosis index (<i>C</i> value) detected in the recombinant-inbred lines derived from <i>Glycine soja</i> and ‘Fukuyutaka’.

<p>Quantitative trait loci (QTLs) associated with the antixenosis index (<i>C</i> value) detected in the recombinant-inbred lines derived from <i>Glycine soja</i> and ‘Fukuyutaka’.</p

Examples of the leaflet defoliation ratings of soybean.

<p>The numbers below the pictures are the defoliation values (on a scale of 0 to 10) for each leaflet segment.</p

Location of the quantitative trait loci (QTLs) for antixenosis-resistance, <i>qRslx4</i> on chromosome 2 (left) and <i>qRslx3</i> on chromosome 7 (right) in <i>Glycine soja</i> × ‘Fukuyutaka’-derived recombinant-inbred lines.

<p>Labels to the right of the bars show the marker names. The white and gray circles represent the locations of the antixenosis-resistance QTLs observed in 2012 and 2013, respectively.</p