Feline immunodeficiency virus (FIV) proviral DNA was detected by the polymerase chain reaction method (PCR). PCR products were detected by gel electrophoresis and ethidium bromide staining. The P-10, P-15 and P-24 regions of the gag gene of FIV were chosen as the target sequences for amplification, and three primer pairs were prepared. The PCR products subjected to amplification with each primer pair were found to possess sites of digestion by a restriction enzyme, as hypothesized. They did not react with feline leukemia virus (FeLV)-infected or feline syncytium-forming virus (FeSFV)-infected cell-derived DNA, and specifically amplified FIV-infected cell-derived DNA. FIV proviral DNA was detected by the PCR method with either primer pair (one-step amplification: single PCR) in DNA derived from peripheral blood lymphocytes (PBL) from 7 of 12 FIV antibody-positive cats. When PCR products in each of the 12 cats were subjected to a second amplification using the same primer pair (two-step amplification: double PCR), FIV proviral DNA was detected in all of the cats. When PBL samples collected from three cats that were negative and three that were positive in the single PCR were cultured for a few weeks in the presence of interleukin 2, FIV proviral DNA was detected in all six cats by the single PCR method. The results suggest that either the use of cultured PBL as the sample or the performance of the double PCR method enables simple and specific detection of FIV proviral DNA in PBL.

The Kitasato Institute, 5-9-1 Shirokane, Minato-ku, Tokyo 108-8642, Japan 2 Kitasato Institute for Life Sciences, Kitasato University, 5-9-1 Shirokane, Minato-ku, Tokyo 108-8641, JapanStrain KV-677 T , a Gram-positive, aerobic, motile, rod-shaped bacterium, was isolated from park soil in Tokyo, Japan, and characterized. It grew well at 15-30 6C on nutrient agar and colonies were pale yellow. The cell-wall peptidoglycan contained diaminobutyric acid, glycine, alanine and glutamic acid and the muramic acid acyl type was acetyl. The predominant menaquinone was MK-12. Mycolic acids were not detected. The DNA G+C content was 70 mol%. 16S rRNA gene sequence analysis revealed that strain KV-677 T fell within the cluster of the family Microbacteriaceae and formed a separate lineage joining the genera Salinibacterium, Rhodoglobus, Subtercola and Agreia, showing 95.5-96.9 % sequence similarities with the type strains of the type species of the above genera. However, strain KV-677 T clearly differed from these and other genera with relatively high sequence similarity in its chemotaxonomic characteristics. Therefore, it is proposed that strain KV-677 T represents a novel species in a new genus, Microterricola viridarii gen. nov., sp. nov., in the family Microbacteriaceae. The type strain of Microterricola viridarii is KV-677 T (5NRRL B-24538 T 5NBRC 102123 T ).A novel strain, KV-677 T , was isolated from park soil in Tokyo. The strain contained diaminobutyric acid (DAB) as the diamino acid in the cell wall and other chemotaxonomic characteristics and phylogenetic analysis showed that the strain belonged to the family Microbacteriaceae (Park et al., 1993;Stackebrandt et al., 1997). Currently, the family Microbacteriaceae consists of 21 genera, including Agreia ( Strain KV-677 T was isolated using GPM agar (1 % Dglucose, 0.5 % peptone, 0.5 % meat extract, 0.3 % NaCl, 1.2 % agar, pH 7) containing 0.002 % Benlate (Dupont) and 0.0025 % nalidixic acid (Sigma) from soil collected at Arisugawa Park in the Tokyo metropolitan area, Japan, after incubation for 1 week at 27 u C. Morphological characteristics of the strain were observed by scanning electron microscopy (SEM) (JSM-5600; JEOL) and transmission electron microscopy (TEM) (JEM-1200EXII; JEOL). For SEM, cells grown in TSB (Difco) for 3 days at 27 u C were rinsed with 0.05 M Tris/HCl buffer (pH 7.6) and filtered onto a membrane. The sample was fixed using 4 % osmium tetroxide vapour following lyophilization with liquid nitrogen. For TEM, cells grown on nutrient agar for 2 days at 27 u C were stained with 1.5 % (w/v) uranyl acetate after cell motility was observed with a light microscope. Gram-staining was performed using a Gram's stain reagent kit (Nacalai Tesque). Cultural and physiological characteristics were observed after incubation for 2 days at 27 u C. The NaCl tolerance and pH range for growth were determined on YD agar (1.0 % yeast extract, Abbreviations: DAB, diaminobutyric acid; SEM, scanning electron microscopy; TEM, transmission electron microscopy.The GenBank/EMBL...

Aim To compare the long‐term efficacy of sodium‐glucose co‐transporter‐2 inhibitors and dipeptidyl peptidase‐4 inhibitors as second‐line drugs after metformin for patients not at high risk of atherosclerotic cardiovascular disease (ASCVD). Materials and methods In a 52‐week randomized open‐label trial, we compared ipragliflozin and sitagliptin in Japanese patients diagnosed with type 2 diabetes, without prior ASCVD and treated with metformin. The primary endpoint was a glycated haemoglobin (HbA1c) reduction of ≥0.5% (5.5 mmol/mol) without weight gain at 52 weeks. Results Of a total of 111 patients (mean age 59.2 years, mean body mass index [BMI] 26.6 kg/m2, 61.3% men), 54 patients received ipragliflozin and 57 received sitagliptin. After 52 weeks, achievement of the primary endpoint was not significantly different (37.0% and 40.3%; P = 0.72). HbA1c reduction rate at 24 weeks was greater for sitagliptin (56.1%) than for ipragliflozin (31.5%; P = 0.01). From 24 to 52 weeks, the HbA1c reduction with sitagliptin was attenuated, with no significant difference in HbA1c reduction after 52 weeks between sitagliptin (54.4%) and ipragliflozin (38.9%; P = 0.10). Improvements in BMI, C‐peptide and high‐density lipoprotein cholesterol were greater with ipragliflozin than with sitagliptin. Adverse events occurred in 17 patients with ipragliflozin and in 10 patients with sitagliptin (P = 0.11). Conclusion The HbA1c‐lowering effect at 24 weeks was greater with sitagliptin than with ipragliflozin, but with no difference in efficacy related to HbA1c and body weight at 52 weeks. However, some ASCVD risk factors improved with ipragliflozin.

Background Applications of machine learning for the early detection of diseases for which a clear-cut diagnostic gold standard exists have been evaluated. However, little is known about the usefulness of machine learning approaches in the decision-making process for decisions such as insulin initiation by diabetes specialists for which no absolute standards exist in clinical settings. Objective The objectives of this study were to examine the ability of machine learning models to predict insulin initiation by specialists and whether the machine learning approach could support decision making by general physicians for insulin initiation in patients with type 2 diabetes. Methods Data from patients prescribed hypoglycemic agents from December 2009 to March 2015 were extracted from diabetes specialists’ registries, resulting in a sample size of 4860 patients who had received initial monotherapy with either insulin (n=293) or noninsulin (n=4567). Neural network output was insulin initiation ranging from 0 to 1 with a cutoff of >0.5 for the dichotomous classification. Accuracy, recall, and area under the receiver operating characteristic curve (AUC) were calculated to compare the ability of machine learning models to make decisions regarding insulin initiation to the decision-making ability of logistic regression and general physicians. By comparing the decision-making ability of machine learning and logistic regression to that of general physicians, 7 cases were chosen based on patient information as the gold standard based on the agreement of 8 of the 9 specialists. Results The AUCs, accuracy, and recall of logistic regression were higher than those of machine learning (AUCs of 0.89-0.90 for logistic regression versus 0.67-0.74 for machine learning). When the examination was limited to cases receiving insulin, discrimination by machine learning was similar to that of logistic regression analysis (recall of 0.05-0.68 for logistic regression versus 0.11-0.52 for machine learning). Accuracies of logistic regression, a machine learning model (downsampling ratio of 1:8), and general physicians were 0.80, 0.70, and 0.66, respectively, for 43 randomly selected cases. For the 7 gold standard cases, the accuracies of logistic regression and the machine learning model were 1.00 and 0.86, respectively, with a downsampling ratio of 1:8, which were higher than the accuracy of general physicians (ie, 0.43). Conclusions Although we found no superior performance of machine learning over logistic regression, machine learning had higher accuracy in prediction of insulin initiation than general physicians, defined by diabetes specialists’ choice of the gold standard. Further study is needed before the use of machine learning–based decision support systems for insulin initiation can be incorporated into clinical practice.