[Preoperative chemotherapy enabled radical resection of tumors in a patient with multiple liver metastases].

The patient was a 61-year-old man with a prominent epigastric mass and dull pain. Sigmoid colon cancer and multiple hepatic metastases were diagnosed upon examination. The liver metastases were adjacent to the right hepatic artery and the portal vein; therefore, the patient received preoperative bevacizumab+XELOX (capecitabine plus oxaliplatin) chemotherapy. After 6 courses of chemotherapy, a sufficient partial response (PR) was achieved to secure a surgical margin during radical resection of the tumors. The patient is alive, without recurrence, 10 months after surgery. This report highlights the importance of securing a surgical margin during conversion therapy and reviews evidence from previous literature reports.

[Evaluation of gambir quality based on quantitative analysis of polyphenolic constituents].

Gambir (asen'yaku in Japanese), an aqueous extract of leaves and young twigs of Uncaria gambir Roxb., has traditionally been used as a treatment for diarrhea and dysentery as an internal medicine and for sore throat as a gargle. Although it is a pharmacopoeic medicine in Japan, the quantitative evaluation of its constituents has not yet been adopted in the Japanese pharmacopoeia. We analyzed polyphenolic constituents in 31 gambir and related products to establish evaluation methods, since gambir contains large amounts of polyphenolic constituents. The total flavan contents in the samples revealed using the vanillin-HCl estimation method ranged from 24-79%. Reversed-phase high performance liquid chromatography (RP-HPLC) analysis indicated that catechin was the most abundant constituent in each sample, with contents in the range of 7-76%. The catechin contents in the gambir products between the first and third quartiles were 28-54%. Thus, the lower limit of the catechin content in gambir products can be set at around 20% for quality management. Fifteen tested samples were subjected to HPLC analysis to show the presence of epicatechin (1.5% on average) and the dimeric compounds procyanidin B1, procyanidin B3, and gambiriin A1 (ca. 1% each). The molecular weight distributions of polymeric flavans in the gambir products were analyzed by gel permeation chromatography (GPC) and showed that the average degree of polymerization for each sample was 3 to 7. These results indicate that the combination of the vanillin-HCl method, RP-HPLC analysis, and GPC analysis gives valuable information for evaluating the polyphenolic profiles of gambir products.

Revised structures of gambiriins A1, A2, B1, and B2, chalcane-flavan dimers from gambir (Uncaria gambir extract).

Gambir, the aqueous extract from Uncaria gambir (Rubiaceae), has been used as an astringent medicine in Asian countries. Investigation of the constituents in the extract led to the isolation of four chalcane-flavan dimers, gambiriin A1 (6), A2 (7), B1 (8), and B2 (9), in addition to (+)-catechin (1), (+)-epicatechin (2), and dimeric proanthocyanidins, procyanidin B1 (3), procyanidin B3 (4), and gambiriin C (5). The spectroscopic and chemical data obtained in the present study indicated that their previously proposed structures 6a, 7a, 8a, and 9a should be revised to 6, 7, 8, and 9, respectively.

Gene/protein name recognition based on support vector machine using dictionary as features.

BACKGROUND: Automated information extraction from biomedical literature is important because a vast amount of biomedical literature has been published. Recognition of the biomedical named entities is the first step in information extraction. We developed an automated recognition system based on the SVM algorithm and evaluated it in Task 1.A of BioCreAtIvE, a competition for automated gene/protein name recognition. RESULTS: In the work presented here, our recognition system uses the feature set of the word, the part-of-speech (POS), the orthography, the prefix, the suffix, and the preceding class. We call these features "internal resource features", i.e., features that can be found in the training data. Additionally, we consider the features of matching against dictionaries to be external resource features. We investigated and evaluated the effect of these features as well as the effect of tuning the parameters of the SVM algorithm. We found that the dictionary matching features contributed slightly to the improvement in the performance of the f-score. We attribute this to the possibility that the dictionary matching features might overlap with other features in the current multiple feature setting. CONCLUSION: During SVM learning, each feature alone had a marginally positive effect on system performance. This supports the fact that the SVM algorithm is robust on the high dimensionality of the feature vector space and means that feature selection is not required.

Viewing the proteome from oligopeptides and prediction of protein function.

Our research activity of making the lexicon of relatively short oligopeptides has been one of the first steps to view the world of proteome from the perspective of oligopeptides. We propose a new method for the prediction of protein function, especially GeneOntology terms (GO terms), based on statistical characteristics of oligopeptides as an application of the lexicon. In the lexicon, a known function of a protein is inherited to its oligopeptides, and the correspondence between oligopeptides and the function is calculated in the whole proteins. In our method, unknown functions of proteins are predicted by means of the correspondence automatically. We measured the prediction performance using the 28,520 whole human proteins registered in RefSeq for several GO terms by recall-precision graphs. The GO terms include 'membrane', 'nucleus', 'ATP binding', 'hydorolase activity', 'GTP binding', 'intracellular signaling cascade' and 'ubiquitin cycle'. In most cases, it scores 70% recall with 80% precision. The prediction for ATP binding and GTP binding results in quite high performance: it scores 80% recall with 80% precision. Even in the worst case (ubiquitin cycle), it scores 62.6% recall with 80% precision. These results suggest that the proposed method is quite efficient for predicting GO terms.