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An Improved Tat/Rev Induced Limiting Dilution Assay With Enhanced Sensitivity and Breadth of Detection.

ABSTRACT: Tat/Rev Induced Limiting Dilution Assay (TILDA) is instrumental in estimating the size of latent reservoirs of HIV-1. Here, we report an optimized TILDA containing a broader detection range compared to the reported methods and high sensitivity. Giving priority to sequence conservation, we positioned the two forward primers and the probe in exon-1 of HIV-1. The reverse primers are positioned in highly conserved regions of exon-7. The optimized TILDA detected eight molecular clones belonging to five major genetic subtypes of HIV-1 with a comparable detection sensitivity. Using the optimized assay, we show that only a minor proportion of CD4⁺ T cells of primary clinical samples can spontaneously generate multiply spliced viral transcripts. A significantly larger proportion of the cells produced viral transcripts following activation. The optimized TILDA is suitable to characterize HIV-1 latent reservoirs and the therapeutic strategies intended to target the reservoir size.

SUBMITTER: Mehta K

PROVIDER: S-EPMC8375296 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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An Improved Tat/Rev Induced Limiting Dilution Assay With Enhanced Sensitivity and Breadth of Detection.

Mehta Kavita K Gohil Yuvrajsinh Y Mishra Swarnima S D'silva Anish A Amanullah Afzal A Selvam Deepak D Pargain Neelam N Nala Narendra N Sanjeeva G N GN Ranga Udaykumar U

Frontiers in immunology 20210805

Tat/Rev Induced Limiting Dilution Assay (TILDA) is instrumental in estimating the size of latent reservoirs of HIV-1. Here, we report an optimized TILDA containing a broader detection range compared to the reported methods and high sensitivity. Giving priority to sequence conservation, we positioned the two forward primers and the probe in exon-1 of HIV-1. The reverse primers are positioned in highly conserved regions of exon-7. The optimized TILDA detected eight molecular clones belonging to fi ...[more]

PMID: 34421920

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Project description:PurposeTo compare the segmentation and detection performance of a deep learning model trained on a database of human-labeled clinical stroke lesions on diffusion-weighted (DW) images to a model trained on the same database enhanced with synthetic stroke lesions.Materials and methodsIn this institutional review board-approved study, a stroke database of 962 cases (mean patient age ± standard deviation, 65 years ± 17; 255 male patients; 449 scans with DW positive stroke lesions) and a normal database of 2027 patients (mean age, 38 years ± 24; 1088 female patients) were used. Brain volumes with synthetic stroke lesions on DW images were produced by warping the relative signal increase of real strokes to normal brain volumes. A generic three-dimensional (3D) U-Net was trained on four different databases to generate four different models: (a) 375 neuroradiologist-labeled clinical DW positive stroke cases (CDB); (b) 2000 synthetic cases (S2DB); (c) CDB plus 2000 synthetic cases (CS2DB); and (d) CDB plus 40 000 synthetic cases (CS40DB). The models were tested on 20% (n = 192) of the cases of the stroke database, which were excluded from the training set. Segmentation accuracy was characterized using Dice score and lesion volume of the stroke segmentation, and statistical significance was tested using a paired two-tailed Student t test. Detection sensitivity and specificity were compared with labeling done by three neuroradiologists.ResultsThe performance of the 3D U-Net model trained on the CS40DB (mean Dice score, 0.72) was better than models trained on the CS2DB (Dice score, 0.70; P < .001) or the CDB (Dice score, 0.65; P < .001). The deep learning model (CS40DB) was also more sensitive (91% [95% confidence interval {CI}: 89%, 93%]) than each of the three human readers (human reader 3, 84% [95% CI: 81%, 87%]; human reader 1, 78% [95% CI: 75%, 81%]; human reader 2, 79% [95% CI: 76%, 82%]), but was less specific (75% [95% CI: 72%, 78%]) than each of the three human readers (human reader 3, 96% [95% CI: 94%, 98%]; human reader 1, 92% [95% CI: 90%, 94%]; human reader 2, 89% [95% CI: 86%, 91%]).ConclusionDeep learning training for segmentation and detection of stroke lesions on DW images was significantly improved by enhancing the training set with synthetic lesions.Supplemental material is available for this article.© RSNA, 2020.

Dataset Information

An Improved Tat/Rev Induced Limiting Dilution Assay With Enhanced Sensitivity and Breadth of Detection.

Publications

An Improved Tat/Rev Induced Limiting Dilution Assay With Enhanced Sensitivity and Breadth of Detection.

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