A new species of Euphranta Loew (Diptera: Tephritidae: Trypetinae: Adramini) from India.

A new species of Euphranta Loew, E. siruvani David, Hancock Sankararaman, sp. n. is described from India. Postabdominal structures of E. hyalipennis David Freidberg and E. haldwanica Hancock Goodger are illustrated and described. A revised key to Euphranta from India is also provided.

Comparative evaluation of H&H and WFNS grading scales with modified H&H (sans systemic disease): A study on 1000 patients with subarachnoid hemorrhage.

The comparative studies on grading in subarachnoid hemorrhage (SAH) had several limitations such as the unclear grading of Glasgow Coma Scale 15 with neurological deficits in World Federation of Neurosurgical Societies (WFNS), and the inclusion of systemic disease in Hunt and Hess (H&H) scales. Their differential incremental impacts and optimum cut-off values for unfavourable outcome are unsettled. This is a prospective comparison of prognostic impacts of grading schemes to address these issues. SAH patients were assessed using WFNS, H&H (including systemic disease), modified H&H (sans systemic disease) and followed up with Glasgow Outcome Score (GOS) at 3 months. Their performance characteristics were analysed as incremental ordinal variables and different grading scale dichotomies using rank-order correlation, sensitivity, specificity, positive predictive value, negative predictive value, Youden's J and multivariate analyses. A total of 1016 patients were studied. As univariate incremental variable, H&H sans systemic disease had the best negative rank-order correlation coefficient (-0.453) with respect to lower GOS (p < 0.001). As univariate dichotomized category, WFNS grades 3-5 had the best performance index of 0.39 to suggest unfavourable GOS with a specificity of 89% and sensitivity of 51%. In multivariate incremental analysis, H&H sans systemic disease had the greatest adjusted incremental impact of 0.72 (95% confidence interval (CI) 0.54-0.91) against a lower GOS as compared to 0.6 (95% CI 0.45-0.74) and 0.55 (95% CI 0.42-0.68) for H&H and WFNS grades, respectively. In multivariate categorical analysis, H&H grades 4-5 sans systemic disease had the greatest impact on unfavourable GOS with an adjusted odds ratio of 6.06 (95% CI 3.94-9.32). To conclude, H&H grading sans systemic disease had the greatest impact on unfavourable GOS. Though systemic disease is an important prognostic factor, it should be considered distinctly from grading. Appropriate cut-off values suggesting unfavourable outcome for H&H and WFNS were 4-5 and 3-5, respectively, indicating the importance of neurological deficits in addition to level of consciousness.

Impact of medication and psychological behaviour assessment by community pharmacists in type 2 diabetes mellitus patients after hospital stay.

BACKGROUND: Drug related problem (DRPs) is a key factor which will affect the outcome of therapy and safety. OBJECTIVE: To assess the DRPs in type 2 diabetes mellitus (T2DM) patients and psychological aspects of patients by community pharmacists to observe the rate of DRP. METHODS: Prospective randomized controlled intervention study involved T2DM patients and conducted in two community pharmacies at Kanpur from January 2012 to December 2012. The assessment of DRPs was based on the PCNE. Changes in glycosylated hemoglobin (HBA1c), low density lipoproteins (LDL), blood pressure(BP), foot examinations, changes medical and medication utilization were studied. Using as control group, received usual care, and interventional group provided, intervened with use of the standard treatment guidelines (STG). Researcher provided the knowledge to community pharmacists and patients. Baseline and interventional data were collected at 0,3,6,9 and 12 months. RESULTS: Over the 12 month study, participants' average HBA1C reduced from 8.9% at initial visit to 7.5%. During this time, the eye examination rate was raised from 31% to 48%, and the foot examination rate was raised from 35% to 50%. CONCLUSION: The intervention of pharmacists showed little influence on any of the intermediate health outcomes in T2DM.

Primary Retroperitoneal Inter-aorto-Caval Paraganglioma of the Organ of Zuckerkandl.

Tumour that arise from chromaffin cells at extra-adrenal locations are termed paragangliomas which are rare tumour. The organ of Zuckerkandl located along aortic bifurcation is the most common site of their occurrence. Herein, we report a case of 20-year-old male with large abdomino-pelvic paraganglioma of the organ of zuckerkandl with multiple pedicles to abdominal aorta. On exploratory laparotomy it revealed a large retroperitoneal mass with variable consistency with surfaces covered with tortuous vessels. This mass was adhered to the retroperitoneum with multiple arterial pedicles to abdominal aorta. Histopathologic evaluation revealed features of extra-adrenal paraganglioma with characteristic Zellballen appearance. Postoperative course was uneventful.

Monitoring corrosion of steel bars in reinforced concrete structures.

Corrosion of steel bars embedded in reinforced concrete (RC) structures reduces the service life and durability of structures causing early failure of structure, which costs significantly for inspection and maintenance of deteriorating structures. Hence, monitoring of reinforcement corrosion is of significant importance for preventing premature failure of structures. This paper attempts to present the importance of monitoring reinforcement corrosion and describes the different methods for evaluating the corrosion state of RC structures, especially hal-cell potential (HCP) method. This paper also presents few techniques to protect concrete from corrosion.

Improved prediction of MHC class I binders/non-binders peptides through artificial neural network using variable learning rate: SARS corona virus, a case study.

Fundamental step of an adaptive immune response to pathogen or vaccine is the binding of short peptides (also called epitopes) to major histocompatibility complex (MHC) molecules. The various prediction algorithms are being used to capture the MHC peptide binding preference, allowing the rapid scan of entire pathogen proteomes for peptide likely to bind MHC, saving the cost, effort, and time. However, the number of known binders/non-binders (BNB) to a specific MHC molecule is limited in many cases, which still poses a computational challenge for prediction. The training data should be adequate to predict BNB using any machine learning approach. In this study, variable learning rate has been demonstrated for training artificial neural network and predicting BNB for small datasets. The approach can be used for large datasets as well. The dataset for different MHC class I alleles for SARS Corona virus (Tor2 Replicase polyprotein 1ab) has been used for training and prediction of BNB. A total of 90 datasets (nine different MHC class I alleles with tenfold cross validation) have been retrieved from IEDB database for BNB. For fixed learning rate approach, the best value of AROC is 0.65, and in most of the cases it is 0.5, which shows the poor predictions. In case of variable learning rate, of the 90 datasets the value of AROC for 76 datasets is between 0.806 and 1.0 and for 7 datasets the value is between 0.7 and 0.8 and for rest of 7 datasets it is between 0.5 and 0.7, which indicates very good performance in most of the cases.

Prediction of MHC class I binding peptides using probability distribution functions.

Binding of peptides to specific Major Histo-compatibility Complex (MHC) molecule is important for understanding immunity and has applications to vaccine discovery and design of immunotherapy. Artificial neural networks (ANN) are widely used by predictions tools to classify the peptides as binders or non-binders (BNB). However, the number of known binders to a specific MHC molecule is limited in many cases, which poses a computational challenge for prediction of BNB and hence, needs improvement in learning of ANN. Here, we describe, the application of probability distribution functions to initialize the weights and biases of the artificial neural network in order to predict HLA-A*0201 binders and non-binders. The 10-fold cross validation has been used to validate the results. It is evident from the results that the A(ROC) for 90% of test cases for Weibull, Uniform and Rayleigh distributions is in the range 0.90-1.0. Further, the standard deviation for AROC was minimum for Weibull distribution, and may be used to train the artificial neural network for HLA-A*0201 MHC Class-I binders and non-binders prediction.