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Purpose: Thyroid eye disease (TED) is an autoimmune condition with an array of clinical manifestations, which can be complicated by compressive optic neuropathy. It is important to identify patients with TED early to ensure close monitoring and treatment to prevent potential permanent disability or vision loss. Deep learning artificial intelligence (AI) algorithms have been utilized in ophthalmology and in other fields of medicine to detect disease. This study aims to introduce a deep learning model to evaluate orbital computed tomography (CT) images for the presence of TED and potential compressive optic neuropathy. Design: Retrospective review and deep learning algorithm modeling. Subjects: Patients with TED with dedicated orbital CT scans and with an examination by an oculoplastic surgeon over a 10-year period at a single academic institution. Patients with no TED and normal CTs were used as normal controls. Those with other diagnoses, such as tumors or other inflammatory processes, were excluded. Methods: Orbital CTs were preprocessed and adopted for the Visual Geometry Group-16 network to distinguish patients with no TED, mild TED, and severe TED with compressive optic neuropathy. The primary model included training and testing of all 3 conditions. Binary model performance was also evaluated. An oculoplastic surgeon was also similarly tested with single and serial images for comparison. Main Outcome Measures: Accuracy of deep learning model discernment of region of interest for CT scans to distinguish TED versus normal control, as well as TED with clinical signs of optic neuropathy. Results: A total of 1187 photos from 141 patients were used to develop the AI model. The primary model trained on patients with no TED, mild TED, and severe TED had 89.5% accuracy (area under the curve: range, 0.96–0.99) in distinguishing patients with these clinical categories. In comparison, testing of an oculoplastic surgeon in these 3 categories showed decreased accuracy (70.0% accuracy in serial image testing). Conclusions: The deep learning model developed in the study can accurately detect TED and further detect TED with clinical signs of optic neuropathy based on orbital CT. The model proved superior compared with human expert grading. With further optimization and validation, this TED deep learning model could help guide frontline health care providers in the detection of TED and help stratify the urgency of a referral to an oculoplastic surgeon and endocrinologist. Financial Disclosure(s): The authors have no proprietary or commercial interest in any materials discussed in this article.

HIV-1 infection of target cells can occur through either cell-free virions or cell-cell transmission in a virological synapse, with the latter mechanism of infection reported to be 100- to 1,000-fold more efficient. Neutralizing antibodies and entry inhibitors effectively block cell-free HIV-1, but with few exceptions, they display much less inhibitory activity against cell-mediated HIV-1 transmission. Previously, we showed that engineering HIV-1 target cells by genetically linking single-chain variable fragments (scFvs) of antibodies to glycosyl phosphatidylinositol (GPI) potently blocks infection by cell-free virions and cell-mediated infection by immature dendritic cell (iDC)-captured HIV-1. Expression of scFvs on CD4+ cell lines by transduction with X5 derived anti-HIV-1 Env antibody linked to a GPI attachment signal directs GPI-anchored scFvs into lipid rafts of the plasma membrane. In this study, we further characterize the effect of GPI-scFv X5 on cell-cell HIV-1 transmission from DCs to target cells. We report that expression of GPI-scFv X5 in transduced CD4+ cell lines and human primary CD4+ T cells potently restricts viral replication in iDC- or mDC-captured HIV-1 in trans. Using live-cell imaging, we observed that when GPI-GFP or GPI-scFv X5 transduced T cells are co-cultured with iDCs, GPI-anchored proteins enrich in contact zones and subsequently migrate from T cells into DCs, suggesting that transferred GPI-scFv X5 interferes with HIV-1 infection of iDCs. We conclude that GPI-scFv X5 on the surface of transduced CD4+ T cells not only potently blocks cell-mediated infection by DCs, but it transfers from transduced cells to the surface of iDCs and neutralizes HIV-1 replication in iDCs. Our findings have important implications for HIV-1 antibody-based immunotherapies as they demonstrate a viral inhibitory effect that extends beyond the transduced CD4+ T cells to iDCs which can enhance HIV-1 replication.

Human immunodeficiency virus (HIV) induced AIDS causes a large number of infections and deaths worldwide every year, still no vaccines are available to prevent infection. Recombinant herpes simplex virus type 1 (HSV-1) vector-based vaccines coding the target proteins of other pathogens have been widely used for disease control. Here, a recombinant virus with HIV-1 gp160 gene integration into the internal reverse (IR) region-deleted HSV-1 vector (HSV-BAC), was obtained by bacterial artificial chromosome (BAC) technology, and its immunogenicity investigated in BALB/c mice. The result showed similar replication ability of the HSV-BAC-based recombinant virus and wild type. Furthermore, humoral and cellular immune response showed superiority of intraperitoneal (IP) administration, compared to intranasally (IN), subcutaneous (SC) and intramuscularly (IM), that evidenced by production of significant antibody and T cell responses. More importantly, in a prime-boost combination study murine model, the recombinant viruses prime followed by HIV-1 VLP boost induced stronger and broader immune responses than single virus or protein vaccination in a similar vaccination regimen. Antibody production was sufficient with huge potential for viral clearance, along with efficient T-cell activation, which were evaluated by the enzyme-linked immunosorbent assay (ELISA) and flow cytometry (FC). Overall, these findings expose the value of combining different vaccine vectors and modalities to improve immunogenicity and breadth against different HIV-1 antigens.