Application of real time polymerase chain reaction targeting kex 1 gene & its comparison with the conventional methods for rapid detection of Pneumocystis jirovecii in clinical specimens.

BACKGROUND & OBJECTIVES: As there are no standard laboratory techniques for the rapid detection of Pneumocystis jirovecii in India, this study was undertaken to evaluate and establish an optimal and rapid technique for the detection of P. jirovecii by comparing three different techniques - staining technique, application of a real time polymerase chain reaction (RT-PCR) targeting kex 1 gene and application of nested PCR targeting mitochondrial large subunit (mtLSU) gene for rapid detection of P. jirovecii in HIV positive patients. METHODS: One hundred and fifty sputum specimens from HIV positive (n = 75) and HIV negative (n = 75) patients were subjected to three different techniques -KOH/Calcoflour and Grocott methanamine silver staining (GMS), RT-PCR targeting kex1 gene, PCR targeting mtLSU region followed by DNA sequencing and BLAST analysis. RESULTS: Among the 75 HIV positive patients, P. jirovecii was detected in 19 (25.33%) patients by the staining techniques, and in 23 (30.65%) patients each by PCR targeting mtLSU region and by RT- PCR targeting kex1 gene of P. jirovecii. PCR based DNA sequencing targeting mtLSU region revealed 97-100 per cent sequence homology with P. jirovecii sequences in GenBank. INTERPRETATION & CONCLUSIONS: Of the three techniques for detection of P. jirovecii evaluated in this study, false negativity was found to be more in staining technique and it also required high technical expertise to interpret the result. Both nested PCR and RT-PCR were reliable and equally sensitive, in rapid detection of P. jirovecii, but RT-PCR technique also generated the copy numbers for knowing the severity of infection.

Initial virologic response and HIV drug resistance among HIV-infected individuals initiating first-line antiretroviral therapy at 2 clinics in Chennai and Mumbai, India.

Human immunodeficiency virus drug resistance (HIVDR) in cohorts of patients initiating antiretroviral therapy (ART) at clinics in Chennai and Mumbai, India, was assessed following World Health Organization (WHO) guidelines. Twelve months after ART initiation, 75% and 64.6% of participants at the Chennai and Mumbai clinics, respectively, achieved viral load suppression of <1000 copies/mL (HIVDR prevention). HIVDR at initiation of ART (P <.05) and 12-month CD4 cell counts <200 cells/µL (P <.05) were associated with HIVDR at 12 months. HIVDR prevention exceeded WHO guidelines (≥ 70%) at the Chennai clinic but was below the target in Mumbai due to high rates of loss to follow-up. Findings highlight the need for defaulter tracing and scale-up of routine viral load testing to identify patients failing first-line ART.

Artificial Neural Network-Based Failure Pressure Prediction of API 5L X80 Pipeline with Circumferentially Aligned Interacting Corrosion Defects Subjected to Combined Loadings.

Conventional pipeline corrosion assessment methods produce conservative failure pressure predictions for pipes under the influence of both internal pressure and longitudinal compressive stress. Numerical approaches, on the other hand, are computationally expensive. This work provides an assessment method (empirical) for the failure pressure prediction of a high toughness corroded pipe subjected to combined loading, which is currently unavailable in the industry. Additionally, a correlation between the corrosion defect geometry, as well as longitudinal compressive stress and the failure pressure of a pipe based on the developed method, is established. An artificial neural network (ANN) trained with failure pressure from FEA of an API 5L X80 pipe for varied defect spacings, depths, defect lengths, and longitudinal compressive loads were used to develop the equation. With a coefficient of determination (R2) of 0.99, the proposed model was proven to be capable of producing accurate predictions when tested against arbitrary finite element models. The effects of defect spacing, length, and depth, and longitudinal compressive stress on the failure pressure of a corroded pipe with circumferentially interacting defects, were then investigated using the suggested model in a parametric analysis.

A Review of Finite Element Analysis and Artificial Neural Networks as Failure Pressure Prediction Tools for Corroded Pipelines.

This paper discusses the capabilities of artificial neural networks (ANNs) when integrated with the finite element method (FEM) and utilized as prediction tools to predict the failure pressure of corroded pipelines. The use of conventional residual strength assessment methods has proven to produce predictions that are conservative, and this, in turn, costs companies by leading to premature maintenance and replacement. ANNs and FEM have proven to be strong failure pressure prediction tools, and they are being utilized to replace the time-consuming methods and conventional codes. FEM is widely used to evaluate the structural integrity of corroded pipelines, and the integration of ANNs into this process greatly reduces the time taken to obtain accurate results.