Pooled nucleic acid testing strategy for monitoring HIV-1 treatment in resource limited settings.

BACKGROUND: Virological monitoring (VM) and drug resistance (DR) analysis are crucial for effective HIV management. Due to the high cost of commercial assays, VM and DR analysis is not performed in resource-limited-settings. OBJECTIVE: The objective of this study is to develop a pooling based algorithm for the combined identification of virologic treatment failure (VTF) by nucleic acid testing (NAT) and DR by sequencing - NAT+DR assay. STUDY DESIGN: We enrolled 559 participants on first-line therapy and analyzed for VTF. The virologically suppressed participants were followed-up to see the VTF prevalence (>1000 copies/mL) and DR by the NAT+DR pooling. Each pool comprising 5 plasma samples were amplified by targeting reverse transcriptase gene, if found positive, the pool was deconvoluted and samples were individually tested for HIV RNA and DR. Assay characteristics of NAT+DR assay were calculated in comparison with commercial assay. RESULTS: Of 559 participants, 67 had VTF at baseline and were excluded. Of the remaining 478 participants, 325 returned for follow-up and NAT+DR assay was performed for them. Of 65 pools tested, 13 pools were positive. On deconvolution 14 individuals were found to have VTF. Sensitivity, specificity, positive predictive value and negative predictive value was 100%, relative efficiency was 59% and 87% & 85% cost was saved for identifying VTF and combined identification of VTF and DR, respectively. CONCLUSIONS: Pooled NAT+DR assay is likely a good strategy to drastically reduce the cost and sustainability of the VM and can thereby facilitate the scale-up of successful HIV treatment programs, and reduce unnecessary switching to second-line drugs in resource-limited-settings.

Load Balancing in Cloud Computing Environment Using Improved Weighted Round Robin Algorithm for Nonpreemptive Dependent Tasks.

Cloud computing uses the concepts of scheduling and load balancing to migrate tasks to underutilized VMs for effectively sharing the resources. The scheduling of the nonpreemptive tasks in the cloud computing environment is an irrecoverable restraint and hence it has to be assigned to the most appropriate VMs at the initial placement itself. Practically, the arrived jobs consist of multiple interdependent tasks and they may execute the independent tasks in multiple VMs or in the same VM's multiple cores. Also, the jobs arrive during the run time of the server in varying random intervals under various load conditions. The participating heterogeneous resources are managed by allocating the tasks to appropriate resources by static or dynamic scheduling to make the cloud computing more efficient and thus it improves the user satisfaction. Objective of this work is to introduce and evaluate the proposed scheduling and load balancing algorithm by considering the capabilities of each virtual machine (VM), the task length of each requested job, and the interdependency of multiple tasks. Performance of the proposed algorithm is studied by comparing with the existing methods.