New genotypes of Helicobacter Pylori VacA d-region identified from global strains.

BACKGROUND: Pathogenesis of Helicobacter Pylori (HP) vacuolating toxin A (vacA) depends on polymorphic diversity within the signal (s), middle (m), intermediate (i), deletion (d) and c-regions. These regions show distinct allelic diversity. The s-region, m-region and the c-region (a 15 bp deletion at the 3'-end region of the p55 domain of the vacA gene) exist as 2 types (s1, s2, m1, m2, c1 and c2), while the i-region has 3 allelic types (i1, i2 and i3). The locus of d-region of the vacA gene has also been classified into 2 genotypes, namely d1 and d2. We investigated the "d-region"/"loop region" through bioinformatics, to predict its properties and relation to disease. One thousand two hundred fifty-nine strains from the NCBI nucleotide database and the dryad database with complete vacA sequences were included in the study. The sequences were aligned using BioEdit and analyzed using Lasergene and BLAST. The secondary structure and physicochemical properties of the region were predicted using PredictProtein. RESULTS: We identified 31 highly polymorphic genotypes in the "d-region", with a mean length of 34 amino acids (9 ~ 55 amino acids). We further classified the 31 genotypes into 3 main types, namely K-type (strains starting with the KDKP motif in the "d-region"), Q-type (strains starting with the KNQT motif), and E-type (strains starting with the ESKT motif) respectively. The most common type, K-type, is more prevalent in cancer patients (80.87%) and is associated with the s1i1m1c1 genotypes (P < .01). Incidentally, a new region expressing sequence diversity (2 aa deletion) at the C-terminus of the p55 domain of vacA was identified during bioinformatics analysis. CONCLUSIONS: Prediction of secondary structures shows that the "d-region" adopts a loop conformation and is a disordered region.

A Fuzzy Expert System to Predict the Risk of Postpartum Hemorrhage.

INTRODUCTION: The American College of Obstetricians and Gynecologists (ACOG) defines postpartum hemorrhage (PPH) as a blood loss of >500mL following vaginal delivery or >1000mL following cesarean section. PPH is widely recognized as a common cause of maternal death. However, there is currently no effective method to predict its risk of occurrence. AIM: To develop a fuzzy expert system to predict the risk of developing PPH and to evaluate its performance in the clinical setting. METHODS: This system was developed using MATLAB software. Mamdani inference was used to simulate reasoning of experts in the field. To evaluate the performance of the system, a dataset of 1705 patients admitted at the Labor and Delivery ward of The Second Affiliated Hospital of Nanjing Medical University from 2017-10 to 2018-04, was considered. RESULTS: The Negative Predictive value (NPV), Positive Predictive value PPV), Specificity and Sensitivity were calculated and were 99.72%, 18.50%, 87.48% and 92.16% respectively. CONCLUSIONS: Our findings suggest that the fuzzy expert system can be used to predict PPH in clinical settings and thus decrease maternal mortality rate due to hemorrhage.