The effects of leaflet material properties on the simulated function of regurgitant mitral valves

Advances in three-dimensional imaging provide the ability to construct and analyze finite element (FE) models to evaluate the biomechanical behavior and function of atrioventricular valves. However, while obtaining patient-specific valve geometry is now possible, non-invasive measurement of patient-specific leaflet material properties remains nearly impossible. Both valve geometry and tissue properties play a significant role in governing valve dynamics, leading to the central question of whether clinically relevant insights can be attained from FE analysis of atrioventricular valves without precise knowledge of tissue properties. As such we investigated 1) the influence of tissue extensibility and 2) the effects of constitutive model parameters and leaflet thickness on simulated valve function and mechanics. We compared metrics of valve function (e.g., leaflet coaptation and regurgitant orifice area) and mechanics (e.g., stress and strain) across one normal and three regurgitant mitral valve (MV) models with common mechanisms of regurgitation (annular dilation, leaflet prolapse, leaflet tethering) of both moderate and severe degree. We developed a novel fully-automated approach to accurately quantify regurgitant orifice areas of complex valve geometries. We found that the relative ordering of the mechanical and functional metrics was maintained across a group of valves using material properties up to 15% softer than the representative adult mitral constitutive model. Our findings suggest that FE simulations can be used to qualitatively compare how differences and alterations in valve structure affect relative atrioventricular valve function even in populations where material properties are not precisely known

A Comparison of Two Measures of HIV Diversity in Multi-Assay Algorithms for HIV Incidence Estimation

Background: Multi-assay algorithms (MAAs) can be used to estimate HIV incidence in cross-sectional surveys. We compared the performance of two MAAs that use HIV diversity as one of four biomarkers for analysis of HIV incidence. Methods: Both MAAs included two serologic assays (LAg-Avidity assay and BioRad-Avidity assay), HIV viral load, and an HIV diversity assay. HIV diversity was quantified using either a high resolution melting (HRM) diversity assay that does not require HIV sequencing (HRM score for a 239 base pair env region) or sequence ambiguity (the percentage of ambiguous bases in a 1,302 base pair pol region). Samples were classified as MAA positive (likely from individuals with recent HIV infection) if they met the criteria for all of the assays in the MAA. The following performance characteristics were assessed: (1) the proportion of samples classified as MAA positive as a function of duration of infection, (2) the mean window period, (3) the shadow (the time period before sample collection that is being assessed by the MAA), and (4) the accuracy of cross-sectional incidence estimates for three cohort studies. Results: The proportion of samples classified as MAA positive as a function of duration of infection was nearly identical for the two MAAs. The mean window period was 141 days for the HRM-based MAA and 131 days for the sequence ambiguity-based MAA. The shadows for both MAAs were <1 year. Both MAAs provided cross-sectional HIV incidence estimates that were very similar to longitudinal incidence estimates based on HIV seroconversion. Conclusions: MAAs that include the LAg-Avidity assay, the BioRad-Avidity assay, HIV viral load, and HIV diversity can provide accurate HIV incidence estimates. Sequence ambiguity measures obtained using a commercially-available HIV genotyping system can be used as an alternative to HRM scores in MAAs for cross-sectional HIV incidence estimation

Treatment as Prevention: Characterization of Partner Infections in the HIV Prevention Trials Network 052 Trial

HIV Prevention Trials Network (HPTN) 052 demonstrated that antiretroviral therapy (ART) prevents HIV transmission in serodiscordant couples. HIV from index-partner pairs was analyzed to determine the genetic linkage status of partner infections. Forty-six infections were classified as linked, indicating that the index was the likely source of the partner’s infection. Lack of viral suppression and higher index viral load were associated with linked infection. Eight linked infections were diagnosed after the index started ART: four near the time of ART initiation and four after ART failure. Linked infections were not observed when the index participant was stably suppressed on ART

Treatment as Prevention: Characterization of Partner Infections in the HIV Prevention Trials Network 052 Trial

HIV Prevention Trials Network (HPTN) 052 demonstrated that antiretroviral therapy (ART) prevents HIV transmission in serodiscordant couples. HIV from index-partner pairs was analyzed to determine the genetic linkage status of partner infections. Forty-six infections were classified as linked, indicating that the index was the likely source of the partner’s infection. Lack of viral suppression and higher index viral load were associated with linked infection. Eight linked infections were diagnosed after the index started ART: four near the time of ART initiation and four after ART failure. Linked infections were not observed when the index participant was stably suppressed on ART

Brief Report: HIV Drug Resistance in Adults Failing Early Antiretroviral Treatment: Results From the HIV Prevention Trials Network 052 Trial

Early initiation of antiretroviral therapy (ART) reduces HIV transmission and has health benefits. HIV drug resistance can limit treatment options and compromise use of ART for HIV prevention. We evaluated drug resistance in 85 participants in the HPTN 052 trial who started ART at CD4 counts of 350–550 cells/mm(3) and failed ART by May 2011; 8.2% had baseline resistance and 35.3% had resistance at ART failure. High baseline viral load and less education were associated with emergence of resistance at ART failure. Resistance at ART failure was observed in 7/8 (87.5%) participants who started ART at lower CD4 cell counts

Brief Report: HIV Drug Resistance in Adults Failing Early Antiretroviral Treatment: Results From the HIV Prevention Trials Network 052 Trial

Submitted by Sandra Infurna ([email protected]) on 2017-12-21T12:34:31Z No. of bitstreams: 1 mariza_morgado_etal_IOC_2016.pdf: 137842 bytes, checksum: 9c34307109b7b6cd29edde12809a3008 (MD5)Approved for entry into archive by Sandra Infurna ([email protected]) on 2017-12-21T13:02:47Z (GMT) No. of bitstreams: 1 mariza_morgado_etal_IOC_2016.pdf: 137842 bytes, checksum: 9c34307109b7b6cd29edde12809a3008 (MD5)Made available in DSpace on 2017-12-21T13:02:47Z (GMT). No. of bitstreams: 1 mariza_morgado_etal_IOC_2016.pdf: 137842 bytes, checksum: 9c34307109b7b6cd29edde12809a3008 (MD5) Previous issue date: 2016Johns Hopkins Univ. School of Medicine. Dept. of Pathology. Baltimore, Maryland, USA.Johns Hopkins Univ. School of Medicine. Dept. of Pathology. Baltimore, Maryland, USA.Fred Hutchinson Cancer Research Center. Vaccine and Infectious Disease Division. Seattle, VA, USA.Frontier Science & Technology Research Foundation. Amherst, NY, USA.Lancet Laboratories and BARC-SA. Specialty Molecular Division. Johannesburg, South Africa.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de AIDS e Imunologia Molecular. Rio de Janeiro, RJ. Brasil.Y. R. Gaitonade Centre for AIDS Research and Education. Chennai, India.National JALMA Institute for Leprosy and Other Mycobacterial Diseases. Agra, India.Frontier Science & Technology Research Foundation. Amherst, NY, USA.Johns Hopkins Univ. School of Medicine. Dept. of Pathology. Baltimore, MD, USA.Johns Hopkins Univ. School of Medicine. Dept. of Pathology. Baltimore, MD, USA.Science Facilitation Department. FHI 360. Washington, DC, USA.Science Facilitation Department. FHI 360, Durham, NC, USA.Fred Hutchinson Cancer Research Center. Vaccine and Infectious Disease Division. Seattle, WA, USA.Univ. of North Carolina at Chapel Hill. Dept. of Medicine. Chapel Hill, NC, USA.Southwest CARE Center. Santa Fe, NM, USA.College of Medicine. Johns Hopkins Project. Blantyre, Malawi.Botswana Harvard AIDS Institute. Gaborone, Botswana.YRGCARE Medical Centre.VHS. Chennai, India.Chiang Mai University. Research Institute for Health Sciences. Chiang Mai, Thailand.University of Zimbabwe. Dept. of Medicine. Harare, Zimbabwe.Univ. of Witwatersrand. Johannesburg, South Africa.Kenya Medical Research Institute. Kisumu, Kenya. / Center for Disease Control. Kisumu, Kenya.Univ. of North Carolina at Chapel Hill. Division of Infectious Diseases. Chapel Hill, NC, USA / UNC Project-Malawi. Institute for Global Health and Infectious Diseases. Lilongwe, Malawi.Hospital Nossa Senhora da Conceição. Serviço de Infectologia. Porto Alegre, RS, Brasil.National AIDS Research Institute (ICMR). Pune, India.Hospital Geral de Nova Iguacu. Nova Iguaçu, RJ, Brasil / Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de AIDS e Imunologia Molecular. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Rio de Janeiro, RJ, Brasil.University of the Witwatersrand. Perinatal HIV Research Unit. Soweto, South Africa.The Fenway Institute. Fenway Health and Infectious Disease Division. Boston, MA, USA / Harvard Medical School. Beth Israel Deaconess Medical Center/Dept. of Medicine. Boston, MA, USA.Fred Hutchinson Cancer Research Center. Vaccine and Infectious Disease Division and Public Health Sciences Division. Seattle, VA, USA.Univ. of North Carolina at Chapel Hill. Department of Medicine. Chapel Hill, NC, USA.Univ. of North Carolina at Chapel Hill. Dept. of Pathology. Baltimore, MD, USA.Early initiation of antiretroviral treatment (ART) reduces HIV transmission and has health benefits. HIV drug resistance can limit treatment options and compromise use of ART for HIV prevention. We evaluated drug resistance in 85 participants in the HIV Prevention Trials Network 052 trial who started ART at CD4 counts of 350-550 cells per cubic millimeter and failed ART by May 2011; 8.2% had baseline resistance and 35.3% had resistance at ART failure. High baseline viral load and less education were associated with emergence of resistance at ART failure. Resistance at ART failure was observed in 7 of 8 (87.5%) participants who started ART at lower CD4 cell counts

Proportion of samples classified as MAA positive as a function of the duration of HIV infection.

<p>Probability curves are shown for the two MAAs described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0101043#pone-0101043-g001" target="_blank">Figure 1</a>. A probability curve is also shown for the limiting antigen avidity assay (LAg-Avidity assay cutoff<1.5 OD-n) alone <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0101043#pone.0101043-Konikoff1" target="_blank">[10]</a>. Key: blue line, MAA with the high resolution melting (HRM) diversity assay; green line, MAA with sequence ambiguity; dotted line, LAg-Avidity assay alone.</p

Performance characteristics of MAAs and comparison of cross-sectional incidence estimates to longitudinal incidence estimates obtained for three clinical cohorts.

<p>*Includes only LAg-Avidity and BioRad avidity assays; addition of viral load did not impact MAA performance.</p><p>Abbreviations: HRM: high resolution melting; MAA: multi-assay algorithm; HPTN: HIV Prevention Trials Network; HIVNET: HIV Network for Prevention Trials.</p><p>The table compares performance characteristics of the HRM-based MAA (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0101043#pone-0101043-g001" target="_blank">Figure 1</a>), the sequence-ambiguity-based MAA (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0101043#pone-0101043-g001" target="_blank">Figure 1</a>), and a 2-assay MAA described in a previous report <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0101043#pone.0101043-Konikoff1" target="_blank">[10]</a>. The 2-assay MAA includes the LAg-Avidity assay (cutoff<2.8 OD-n) and the BioRad-Avidity assay (cutoff<40%); addition of HIV viral load to this MAA did not improve assay performance <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0101043#pone.0101043-Konikoff1" target="_blank">[10]</a>. For each MAA, the table shows the mean window period, the shadow, and the cross-sectional incidence estimates obtained for each cohort. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0101043#s2" target="_blank">Methods</a> used to calculate cross-sectional incidence estimates and confidence intervals have been described previously <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0101043#pone.0101043-Cousins2" target="_blank">[13]</a>. For each incidence estimate, data presented include the point estimate of incidence (bolded) and the 95% confidence intervals for the incidence estimate (parentheses).</p>a<p>Longitudinal incidence estimates were obtained previously for the three cohorts, where longitudinal HIV incidence = (number of seroconversion events)/(number of person-years of follow-up) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0101043#pone.0101043-Koblin1" target="_blank">[28]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0101043#pone.0101043-SeageIII1" target="_blank">[32]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0101043#pone.0101043-Eshleman1" target="_blank">[33]</a>. For HPTN 064 (low incidence cohort), longitudinal incidence was assessed over 6–12 months of follow-up (1,639 person/years); four seroconverters were identified. For HIVNET 001 (medium incidence cohort), longitudinal incidence was assessed between the 12- and 18-month follow-up visits (2,304 person years); 24 seroconverters were identified. For HPTN 061 (high incidence cohort), longitudinal incidence was assessed over 12 months of follow-up (926 person years); 28 seroconverters were identified.</p>b<p>The cross-sectional incidence estimates obtained for each MAA were compared to the longitudinal incidence estimates. The percent difference was calculated by the following equation: [(absolute value of the cross-sectional incidence estimate minus the longitudinal incidence estimate)×(100)]/(the longitudinal incidence estimate).</p>c<p>The relative survey size shows the size of a cross-sectional survey that would be needed for each of the two new MAAs to obtain the same precision that would be achieved using the previously optimized 2-assay MAA. Because both numbers are <1, a smaller survey would be needed using either of the two new MAAs.</p

Sample sizes used in calculating HIV incidence estimates for three clinical cohorts in the United States with two 4-assay MAAs.

<p>Abbreviations: HPTN: HIV Prevention Trials Network; HIVNET: HIV Network for Prevention Trials; MAA: multi-assay algorithm; LAg-Avidity: limited antigen avidity assay; BioRad-Avidity: avidity assay based on the BioRad 1/2+O EIA; HRM: high resolution melting.</p>a<p>Cross-sectional HIV incidence estimates were obtained by testing samples collected at the end of follow-up in three clinical cohorts: HPTN 064, HIVNET 001, and HPTN 061. The number of HIV-infected vs. HIV-uninfected individuals included in the cross-sectional survey is shown.</p>b<p>Participants in HPTN 064 were followed for either 6 or 12 months.</p>c<p>For HPTN 064, 33 study participants had samples available for analysis; 28 were seropositive at enrollment, one had acute HIV infection at enrollment, and four acquired HIV infection during the study. For HIVNET 001, 79 of 90 HIV-infected study participants had samples available for analysis; all 79 participants were HIV-uninfected at study enrollment. For HPTN 061, 246 participants had samples available for analysis; 218 were seropositive at study enrollment, three had acute HIV infection at enrollment, and 25 acquired HIV infection during the study.</p>d<p>73 of these 79 samples were among the 808 samples from HIVNET 001 that were used to determine the window periods and shadows for the MAAs (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0101043#pone-0101043-g001" target="_blank">Figures 1</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0101043#pone-0101043-g002" target="_blank">2</a>).</p>e<p>One specimen classed as MAA positive by the HRM-based MAA was classified as MAA negative by the ambiguity-based MAA.</p>f<p>One specimen that was classified as MAA negative by the HRM-based MAA was classified as MAA positive by the ambiguity-based MAA.</p>g<p>One specimen failed analysis with sequence ambiguity. Because the MAA could not be completed, this specimen was excluded from incidence calculations.</p

Multi-assay algorithms (MAAs) for cross-sectional HIV incidence estimation.

<p>Two MAAs are shown. The mean window period and shadow for each MAA are shown; 95% confidence intervals are shown in parentheses. Results from the component assays were expressed as follows: BioRad-Avidity assay: percentage (avidity index); limiting antigen avidity enzyme immunoassay (LAg-Avidity): normalized optical density units (OD-n); viral load: copies/mL; high resolution melting (HRM) diversity assay: single number (HRM score); sequence ambiguity: percentage.</p