Zika virus (ZIKV) is a flavivirus that is responsible for an unprecedented current epidemic in Brazil and the Americas1,2. ZIKV has been causally associated with fetal microcephaly, intrauterine growth restriction, and other birth defects in both humans3–8 and mice9–11. The rapid development of a safe and effective ZIKV vaccine is a global health priority1,2, but very little is currently known about ZIKV immunology and mechanisms of immune protection. Here we show that a single immunization of a plasmid DNA vaccine or a purified inactivated virus vaccine provides complete protection in susceptible mice against challenge with a ZIKV outbreak strain from northeast Brazil. This ZIKV strain has recently been shown to cross the placenta and to induce fetal microcephaly and other congenital malformations in mice11. We produced DNA vaccines expressing full-length ZIKV pre-membrane and envelope (prM-Env) as well as a series of deletion mutants. The full-length prM-Env DNA vaccine, but not the deletion mutants, afforded complete protection against ZIKV as measured by absence of detectable viremia following challenge, and protective efficacy correlated with Env-specific antibody titers. Adoptive transfer of purified IgG from vaccinated mice conferred passive protection, and CD4 and CD8 T lymphocyte depletion in vaccinated mice did not abrogate protective efficacy. These data demonstrate that protection against ZIKV challenge can be achieved by single-shot subunit and inactivated virus vaccines in mice and that Env-specific antibody titers represent key immunologic correlates of protection. Our findings suggest that the development of a ZIKV vaccine for humans will likely be readily achievable.

Zika virus (ZIKV) is responsible for a major ongoing epidemic in the Americas and has been causally associated with fetal microcephaly. The development of a safe and effective ZIKV vaccine is therefore an urgent global health priority. Here we demonstrate that three different vaccine platforms protect against ZIKV challenge in rhesus monkeys. A purified inactivated virus vaccine induced ZIKV-specific neutralizing antibodies and completely protected monkeys against ZIKV strains from both Brazil and Puerto Rico. Purified immunoglobulin from vaccinated monkeys conferred passive protection in adoptive transfer studies. A plasmid DNA vaccine and a single-shot recombinant rhesus adenovirus serotype 52 vector expressing ZIKV prM-Env also elicited neutralizing antibodies and completely protected monkeys against ZIKV challenge. These data support the rapid clinical development of ZIKV vaccines for humans.

The latent viral reservoir is the critical barrier for the development of an HIV-1 cure. Previous studies have shown that potent HIV-1 Env-specific broadly neutralizing antibodies (bNAbs) administered at the time of antiretroviral therapy (ART) discontinuation can exert direct antiviral effects, but whether bNAbs can target the viral reservoir during ART suppression remains unknown. Here we show that the V3 glycan-dependent bNAb PGT121 together with the TLR7 agonist vesatolimod (GS-9620) administered during ART suppression delayed viral rebound following ART discontinuation in SHIV-SF162P3-infected rhesus monkeys that initiated ART during early acute infection. Moreover, the subset of PGT121+GS-9620 treated monkeys that did not show viral rebound following ART discontinuation also did not reveal virus by highly sensitive adoptive transfer and CD8 depletion studies. These data demonstrate the potential of bNAb administration together with innate immune stimulation as a possible strategy to target the viral reservoir.

SUMMARY Zika virus (ZIKV) is associated with severe neuropathology in neonates as well as Guillain-Barre syndrome and other neurologic disorders in adults. Prolonged viral shedding has been reported in semen, suggesting the presence of anatomic viral reservoirs. Here we show that ZIKV can persist in cerebrospinal fluid (CSF) and lymph nodes (LN) of infected rhesus monkeys for weeks after virus has been cleared from peripheral blood, urine, and mucosal secretions. ZIKV-specific neutralizing antibodies correlated with rapid clearance of virus in peripheral blood but remained undetectable in CSF for the duration of the study. Viral persistence in both CSF and LN correlated with upregulation of mechanistic target of rapamycin (mTOR), proinflammatory, and anti-apoptotic signaling pathways, as well as downregulation of extracellular matrix and cell signaling pathways. These data raise the possibility that persistent or occult neurologic and lymphoid disease may occur following clearance of peripheral virus in ZIKV-infected individuals.

In secondary analysis of electronic health records, a crucial task consists in correctly identifying the patient cohort under investigation. In many cases, the most valuable and relevant information for an accurate classification of medical conditions exist only in clinical narratives. Therefore, it is necessary to use natural language processing (NLP) techniques to extract and evaluate these narratives. The most commonly used approach to this problem relies on extracting a number of clinician-defined medical concepts from text and using machine learning techniques to identify whether a particular patient has a certain condition. However, recent advances in deep learning and NLP enable models to learn a rich representation of (medical) language. Convolutional neural networks (CNN) for text classification can augment the existing techniques by leveraging the representation of language to learn which phrases in a text are relevant for a given medical condition. In this work, we compare concept extraction based methods with CNNs and other commonly used models in NLP in ten phenotyping tasks using 1,610 discharge summaries from the MIMIC-III database. We show that CNNs outperform concept extraction based methods in almost all of the tasks, with an improvement in F1-score of up to 26 and up to 7 percentage points in area under the ROC curve (AUC). We additionally assess the interpretability of both approaches by presenting and evaluating methods that calculate and extract the most salient phrases for a prediction. The results indicate that CNNs are a valid alternative to existing approaches in patient phenotyping and cohort identification, and should be further investigated. Moreover, the deep learning approach presented in this paper can be used to assist clinicians during chart review or support the extraction of billing codes from text by identifying and highlighting relevant phrases for various medical conditions.

Sustained virologic control of human immunodeficiency virus type 1 (HIV-1) infection after discontinuation of antiretroviral therapy (ART) is a major goal of the HIV-1 cure field. A recent study reported that administration of an antibody against α4β7 induced durable virologic control after ART discontinuation in 100% of rhesus macaques infected with an attenuated strain of simian immunodeficiency virus (SIV) containing a stop codon in nef. We performed similar studies in 50 rhesus macaques infected with wild-type, pathogenic SIVmac251. In animals that initiated ART during either acute or chronic infection, anti-α4β7 antibody infusion had no detectable effect on the viral reservoir or viral rebound after ART discontinuation. These data demonstrate that anti-α4β7 antibody administration did not provide therapeutic efficacy in the model of pathogenic SIVmac251 infection of rhesus macaques.

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Background: Timely documentation of care preferences is an endorsed quality indicator for seriously ill patients admitted to intensive care units. Clinicians document their conversations about these preferences as unstructured free text in clinical notes from electronic health records. Aim: To apply deep learning algorithms for automated identification of serious illness conversations documented in physician notes during intensive care unit admissions. Design: Using a retrospective dataset of physician notes, clinicians annotated all text documenting patient care preferences (goals of care or code status limitations), communication with family, and full code status. Clinician-coded text was used to train algorithms to identify documentation and to validate algorithms. The validated algorithms were deployed to assess the percentage of intensive care unit admissions of patients aged ⩾75 that had care preferences documented within the first 48 h. Setting/participants: Patients admitted to one of five intensive care units. Results: Algorithm performance was calculated by comparing machine-identified documentation to clinician-coded documentation. For detecting care preference documentation at the note level, the algorithm had F1-score of 0.92 (95% confidence interval, 0.89 to 0.95), sensitivity of 93.5% (95% confidence interval, 90.0% to 98.0%), and specificity of 91.0% (95% confidence interval, 86.4% to 95.3%). Applied to 1350 admissions of patients aged ⩾75, we found that 64.7% of patient intensive care unit admissions had care preferences documented within the first 48 h. Conclusion: Deep learning algorithms identified patient care preference documentation with sensitivity and specificity approaching that of clinicians and computed in a tiny fraction of time. Future research should determine the generalizability of these methods in multiple healthcare systems.