OBJECTIVE: To determine whether the gross tumor volume of resectable gastric adenocarcinoma on multidetector computed tomography could predict the presence of regional lymph node metastasis and could determine N categories. MATERIALS AND METHODS: A total of 202 consecutive patients with gastric adenocarcinoma who had undergone gastrectomy 1 week after contrast-enhanced multidetector computed tomography were retrospectively identified. The gross tumor volume was evaluated on multidetector computed tomography images. Univariate and multivariate analyses were performed to determine whether the gross tumor volume could predict regional lymph node metastasis, and the Mann-Whitney U test was performed to compare the gross tumor volume among N categories. Additionally, a receiver operating characteristic analysis was performed to identify the accuracy of the gross tumor volume in differentiating N categories. RESULTS: The gross tumor volume could predict regional lymph node metastasis (p<0.0001) in the univariate analysis, and the multivariate analyses indicated that the gross tumor volume was an independent risk factor for regional lymph node metastasis (p=0.005, odds ratio=1.364). The Mann-Whitney U test showed that the gross tumor volume could distinguish N0 from the N1-N3 categories, N0-N1 from N2-N3, and N0-N2 from N3 (all p<0.0001). In the T1-T4a categories, the gross tumor volume could differentiate N0 from the N1-N3 categories (cutoff, 12.3 cm3), N0-N1 from N2-N3 (cutoff, 16.6 cm3), and N0-N2 from N3 (cutoff, 24.6 cm3). In the T4a category, the gross tumor volume could differentiate N0 from the N1-N3 categories (cutoff, 15.8 cm3), N0-N1 from N2-N3 (cutoff, 17.8 cm3), and N0-N2 from N3 (cutoff, 24 cm3). CONCLUSION: The gross tumor volume of resectable gastric adenocarcinoma on multidetector computed tomography could predict regional lymph node metastasis and N categories.

The increased incidence of diabetes, coupled with the introduction of alternative insulin delivery methods that rely on higher doses, is expected to result in a substantial escalation in the future demand for affordable insulin. Plant-based systems offer a safe and economical method for producing pharmaceutical proteins. We used peanut (Arachis hypogaea L.) as bio-reactors to express biosafe, stable proinsulin. We designed two proinsulin analogues (FAIA and LAIA) with substitutions in their amino acid sequences. The fast-acting insulin analogue (FAIA) contains a Gly inserted between Cys19 and Gly20, as well as a Pro28Asp substitution, in the B chain. The long-acting insulin analogue (LAIA) contains a Gly inserted between Cys19 and Gly20 and two Arg residues inserted into the terminus of the B chain, as well as an Asn21Gly substitution in the A chain. Four plasmids were constructed: pROKII-Flag-FAIA, pROKII-Flag-LAIA, pCAMBIA2301-Oleosin-FAIA and pCAMBIA2301-Oleosin-LAIA. These plasmids were transferred into peanut to produce recombinant proinsulin. Western blot and GUS staining analysis indicated that some transgenic peanut successfully expressed exogenous proinsulin. Peanut seeds can act as insulin storage sites, which is the foundation for further production of recombinant proinsulin from peanut seeds.