266 research outputs found
Publisher Correction: Deep coverage whole genome sequences and plasma lipoprotein(a) in individuals of European and African ancestries
No full text© 2018, The Author(s). The original version of this article contained an error in the name of the author Ramachandran S. Vasan, which was incorrectly given as Vasan S. Ramachandran. This has now been corrected in both the PDF and HTML versions of the article
Corrigendum to ‘Decision making for invasive and non-invasive optional procedures within an acute HIV research cohort in Bangkok,’ [Contemporary Clinical Trials Communication (2023)101054](S2451865422001715)(10.1016/j.conctc.2022.101054)
No full textThe authors regret that Dr. Sandhya Vasan was missed in the author list for this article. The revised author order is as follows: Sinéad Isaacson1,2, Kristine Kuczynski1, Nuchanart Ormsby1, Holly L. Peay3, Stuart Rennie1,4, R. Jean Cadigan1,4, Eugène Kroon5, Nittaya Phanuphak 5, Jintanat Ananworanich6, Sandhya Vasan7,8, Thidarat Jupimai9, Peeriya Prueksakaew5, Gail E. Henderson1§ 1 Social Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA. 2 Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA. 3 RTI International, Research Triangle Park, Durham, North Carolina, USA. 4 Center for Bioethics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA. 5 SEARCH, Institute of HIV Research and Innovation, Bangkok, Thailand. 6 Department of Global Health, Amsterdam University Medical Centers, University of Amsterdam, and Amsterdam Institute for Global Health and Development, Amsterdam, The Netherlands. 7 US Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring, MD, USA. 8 The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, USA. 9 Center of Excellence in Pediatric Infectious Diseases and Vaccines Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand. The authors regret that Acknowledgements omitted reference to cooperative agreements that funded this work, and the Disclaimer was omitted. The corrected versions are as follows
Comparison of the antigen sensitivity of Gag-specific CD4+ T cell responses in controlled HIV infection and HIV vaccination
No full textSpontaneous control of HIV infection is characterized by a highly efficient cellular immune response. We showed in particular that HIV Controllers from the ANRS CO21 CODEX cohort harbor a population of specific CD4+ T cells that detect the immunodominant CD4 epitope Gag293 with high antigen sensitivity. To determine whether candidate vaccines can induce the high sensitivity responses seen in Controllers, we analyzed Gag293-specific responses in healthy volunteers who received the ADVAX DNA vaccine administered by electroporation. Comparison of Gag293-specific responses in primary CD4+ T cell lines via IFN-γ ELISpot revealed that the median antigen sensitivity in vaccinees was similar to that observed for Controllers (5x10-8 M) but higher than that in treated patients (5x10-7 M). However, antigen sensitivity remained higher in a subset of Controllers compared to vaccinees. TCR repertoire analysis of Gag293-specific CD4+ T cells from vaccinees revealed a preferential amplification of TCRβ family chain TRBV2, which also predominates in Controllers. However, TRAV family gene usage appeared more diverse in vaccinees compared to Controllers. Sequence analysis of the TCR chains amplified in 4 vaccinees revealed a biased TCR repertoire with the presence of public clonotypes (3 TRAV24 and 2 TRBV2) shared with HIV Controllers. In conclusion, DNA vaccination administered by electroporation has the potential to induce Gag-specific CD4+ T cells responses with a high antigen sensitivity and partial TCR repertoire overlap with that of Controllers. Monitoring the amplification of public TCR clonotypes could provide a novel approach to evaluate the quality of HIV vaccine responses
DNA vaccination by electroporation amplifies broadly cross-restricted public TCR clonotypes shared with HIV Controllers
No full textRare patients who spontaneously control HIV replication provide a useful model to inform HIV vaccine development. HIV controllers develop particularly efficient antiviral CD4+T cell responses mediated by shared high-Affinity TCRs. To determine whether the candidate DNA vaccine ADVAX could induce similar responses, we analyzed Gag-specific primary CD4+T cells from healthy volunteers who received ADVAX DNA by electroporation. Vaccinated volunteers had an immunodominant response to the Gag293 epitope with a functional avidity intermediate between that of controllers and treated patients. The TCR repertoire of Gag293-specific CD4+T cells proved highly biased, with a predominant usage of the TCRÎ2 variable gene 2 (TRBV2) in vaccinees as well as controllers. TCRα variable gene (TRAV) gene usage was more diverse, with the dominance of TRAV29 over TRAV24 genes in vaccinees, whereas TRAV24 predominated in controllers. Sequence analysis revealed an unexpected degree of overlap between the specific repertoires of vaccinees and controllers, with the sharing of TRAV24 and TRBV2 public motifs (>30%) and of public clonotypes characteristic of high-Affinity TCRs. MHC class II tetramer binding revealed a broad HLA-DR cross-restriction, explaining how Gag293-specific public clonotypes could be selected in individuals with diverse genetic backgrounds. TRAV29 clonotypes also proved cross-restricted, but conferred responses of lower functional avidity upon TCR transfer. In conclusion, DNAvaccination by electroporation primed for TCR clonotypes that were associated with HIV control, highlighting the potential of this vaccine delivery method. To our knowledge, this study provides the first proof-of-concept that clonotypic analysis may be used as a tool to monitor the quality of vaccine-induced responses and modulate these toward "controller-like" responses
[[alternative]]Erratum to: Deep coverage whole genome sequences and plasma lipoprotein(a) in individuals of European and African ancestries (Nature Communications, (2018), 9, 1, (2606), 10.1038/s41467-018-04668-w)
No full text[[abstract]]The original version of this article contained an error in the name of the author Ramachandran S. Vasan, which was incorrectly given as Vasan S. Ramachandran. This has now been corrected in both the PDF and HTML versions of the article
Endoscopic surgery--an assessment of laparoscopic and arthroscopic techniques and instrumentation
No full textThesis: B.S., Massachusetts Institute of Technology, Department of Mechanical Engineering, 1992Includes bibliographical references (leaves 83-85).by Sandya Vasan.B.S.B.S. Massachusetts Institute of Technology, Department of Mechanical Engineerin
Developing a model for improving trust in artificial intelligence
No full textThe increasing availability of data, computing power & advances in the algorithms has really driven the development of Artificial Intelligence (AI) in recent years. However, many industries & societies despite realizing the value of AI are still skeptical in accepting AI, especially when several controversial incidents have come into our spotlights and the challenges that AI have been posing in recent years. This has increasingly raised the concern over the trust in AI and has become a major impediment while adopting AI. Almost every stakeholder, potential users put their concerns upfront to the developers of the technology & management and all these concerns address to one main question – How can I trust AI or Whether AI can be trusted. Addressing the concerns posed by the clients & ensuring that AI solutions developed are trustworthy and responsible has now become one of the top priority and challenges for several technology-based companies. From the stands of scientific literature, there hasn’t been substantial research done on the factors influencing the trust in AI despite the growing attention paid over the importance of trust in AI in recent times. At least, there hasn’t been enough study done on the concepts of trust in the field of AI from the management and socio-technical aspects. This research will focus mainly on improving the trust in AI by identifying the essential trust factors of data in terms of data quality dimensions (DQ) & AI model and the prime objective is to develop a trusted AI model incorporating such trust factors that can help the management & developers to assess the trust factors and improve the trust in AI. The research would mainly be employed with a qualitative study using an inductive approach in order to generate valuable theories as it is mainly supported by literature review, desktop research, interviews, and use of a case study. To be more precise, the research was divided into two phases where the first phase involves the identification of potential factors that influences the trust in data and AI model and they were primarily derived from the extensive study done on the literature review & desktop research, and the second phase involves the identification of important trust factors from the perspective of actors involved in the development of AI. Based on the findings from the interview combined with the initial analysis done on the literature review, an initial version of the model was developed. Since the model was relatively new & comprehensive, it required further evaluation with the experts and based on those reflections combined with the previous analysis (literature review & findings from the initial interviews), a final version of the model was developed. To improve the utility of the proposed model & overall research, the model was compared with some of the core themes laid by AI-based research institutions and leading tech firms to ensure that the model has considered those themes and distinguish the major value of this model. The final version of trusted AI model thus contains nine main phases involved in AI development and in each of the phases, trust factors that were crucial to be considered were tagged along with the detailed indicators for each of the phases. The trusted AI model at the end would mainly help the management and developers ( Technology creators) to establish a robust trust over the AI model or the solutions created & provide a seal of trust to the investors, clients and other stakeholders involved. From this study, identification of essential trust factors of resulting AI model and essential trust factors of data in the form of DQ dimensions were considered to be one of the prime handouts to the scientific research apart from the trusted AI model proposed.Management of Technology (MoT
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