A Computer-Based Glucose Management System Reduces the Incidence of Forgotten Glucose Measurements: A Retrospective Observational Study

Introduction: Frequent glucose measurements are needed for good blood glucose control in hospitals; however, this requirement means that measurements can be forgotten. We developed a novel glucose management system using an iPod and electronic health records. Methods: A time schedule system for glucose measurement was developed using point-ofcare testing, an iPod, and electronic health records. The system contains the glucose measurement schedule and an alarm sounds if a measurement is forgotten. The number of times measurements were forgotten was analyzed. Results: Approximately 7000 glucose measurements were recorded per month. Before implementation of the system, the average number of times measurements were forgotten was 4.8 times per month. This significantly decreased to 2.6 times per month after the system started. We also analyzed the incidence of forgotten glucose measurements as a proportion of the total number of measurements for each period and found a significant difference between the two 9-month periods (43/64,049–24/65,870, P = 0.014, chi-squared test). Conclusions: This computer-based blood glucose monitoring system is useful for the management of glucose monitoring in hospitals

Prospectively Isolated Cancer-Associated CD10+ Fibroblasts Have Stronger Interactions with CD133+ Colon Cancer Cells than with CD133− Cancer Cells

Although CD133 has been reported to be a promising colon cancer stem cell marker, the biological functions of CD133+ colon cancer cells remain controversial. In the present study, we investigated the biological differences between CD133+ and CD133− colon cancer cells, with a particular focus on their interactions with cancer-associated fibroblasts, especially CD10+ fibroblasts. We used 19 primary colon cancer tissues, 30 primary cultures of fibroblasts derived from colon cancer tissues and 6 colon cancer cell lines. We isolated CD133+ and CD133− subpopulations from the colon cancer tissues and cultured cells. In vitro analyses revealed that the two populations showed similar biological behaviors in their proliferation and chemosensitivity. In vivo analyses revealed that CD133+ cells showed significantly greater tumor growth than CD133− cells (P = 0.007). Moreover, in cocultures with primary fibroblasts derived from colon cancer tissues, CD133+ cells exhibited significantly more invasive behaviors than CD133− cells (P<0.001), especially in cocultures with CD10+ fibroblasts (P<0.0001). Further in vivo analyses revealed that CD10+ fibroblasts enhanced the tumor growth of CD133+ cells significantly more than CD10− fibroblasts (P<0.05). These data demonstrate that the in vitro invasive properties and in vivo tumor growth of CD133+ colon cancer cells are enhanced in the presence of specific cancer-associated fibroblasts, CD10+ fibroblasts, suggesting that the interactions between these specific cell populations have important roles in cancer progression. Therefore, these specific interactions may be promising targets for new colon cancer therapies

Permuted Pattern Matching Algorithms on Multi-Track Strings

A multi-track string is a tuple of strings of the same length. Given the pattern and text of two multi-track strings, the permuted pattern matching problem is to find the occurrence positions of all permutations of the pattern in the text. In this paper, we propose several algorithms for permuted pattern matching. Our first algorithm, which is based on the Knuth&#8211;Morris&#8211;Pratt (KMP) algorithm, has a fast theoretical computing time with O ( m k ) as the preprocessing time and O ( n k log &#963; ) as the matching time, where n, m, k, &#963; , and occ denote the length of the text, the length of the pattern, the number of strings in the multi-track, the alphabet size, and the number of occurrences of the pattern, respectively. We then improve the KMP-based algorithm by using an automaton, which has a better experimental running time. The next proposed algorithms are based on the Boyer&#8211;Moore algorithm and the Horspool algorithm that try to perform pattern matching. These algorithms are the fastest experimental algorithms. Furthermore, we propose an extension of the AC-automaton algorithm that can solve dictionary matching on multi-tracks, which is a task to find multiple multi-track patterns in a multi-track text. Finally, we propose filtering algorithms that can perform permuted pattern matching quickly in practice

Clinical Impact of the KL-6 Concentration of Pancreatic Juice for Diagnosing Pancreatic Masses

Background and Aim. Pancreatic juice cytology (PJC) is considered optimal for differentially diagnosing pancreatic masses, but the accuracy of PJC ranges from 46.7% to 93.0%. The aim of this study was to evaluate the clinical impact of measuring the KL-6 concentration of pancreatic juice for diagnosing pancreatic masses. Methods. PJC and the KL-6 concentration measurements of pancreatic juice were performed for 70 consecutive patients with pancreatic masses (39 malignancies and 31 benign). Results. The average KL-6 concentration of pancreatic juice was significantly higher for pancreatic ductal adenocarcinomas (PDACs) (167.7±396.1 U/mL) and intraductal papillary mucinous carcinomas (IPMCs) (86.9±21.1 U/mL) than for pancreatic inflammatory lesions (17.5±15.7 U/mL, P=0.034) and intraductal papillary mucinous neoplasms (14.4±2.0 U/mL, P=0.026), respectively. When the cut-off level of the KL-6 concentration of pancreatic juice was 16 U/mL, the sensitivity, specificity, and accuracy of the KL-6 concentration of pancreatic juice alone were 79.5%, 64.5%, and 72.9%, respectively. Adding the KL-6 concentration of pancreatic juice to PJC when making a diagnosis caused the values of sensitivity and accuracy of PJC to increase by 15.3% (P=0.025) and 8.5% (P=0.048), respectively. Conclusions. The KL-6 concentration of pancreatic juice may be as useful as PJC for diagnosing PDACs