SARS-CoV-2 is the cause of an ongoing pandemic that has infected over 36 million and killed over 1 million people. Informed implementation of government public health policies depends on accurate data on SARS-CoV-2 immunity at population scale. We hypothesized that detection of SARS-CoV-2 salivary antibodies could serve as a non-invasive alternative to serological testing for monitoring of SARS-CoV-2 infection and seropositivity at population scale. We developed a multiplex SARS-CoV-2 antibody immunoassay based on Luminex technology that comprised 12 CoV antigens, mostly derived from SARS-CoV-2 nucleocapsid (N) and spike (S). Saliva and sera collected from confirmed COVID-19 cases and from the pre-COVID-19 era were tested for IgG, IgA and IgM to the antigen panel. Matched saliva and serum IgG responses (n=28) were significantly correlated. The salivary anti-N IgG response resulted in highest sensitivity (100%), exhibiting a positive response in 24/24 RT-PCR-confirmed COVID-19 cases sampled at >14 days post-symptom onset (DPSO), whereas the salivary anti-receptor binding domain (RBD) IgG response yielded 100% specificity. Temporal kinetics of IgG in saliva were consistent with those observed in blood and indicated that most individuals seroconvert around 10 DPSO. Algorithms employing a combination of the IgG response to N and S antigens result in high diagnostic accuracy (100%) as early as 10 DPSO. These results support the use of saliva-based antibody testing as a non-invasive and scalable alternative to blood-based antibody testing.

Understanding the immune response during acute Zika in humans will aid vaccine design and testing. In 5 acute patients, including 2 pregnant women, viral levels and innate, T-, and B-cell responses against Zika or dengue viruses are described.

Non-invasive SARS-CoV-2 antibody testing is urgently needed to estimate the incidence and prevalence of SARS-CoV-2 infection at the general population level. Precise knowledge of population immunity could allow government bodies to make informed decisions about how and when to relax stay-at-home directives and to reopen the economy. We hypothesized that salivary antibodies to SARS-CoV-2 could serve as a non-invasive alternative to serological testing for widespread monitoring of SARS-CoV-2 infection throughout the population. We developed a multiplex SARS-CoV-2 antibody immunoassay based on Luminex technology and tested 167 saliva and 324 serum samples, including 134 and 118 negative saliva and serum samples, respectively, collected before the COVID-19 pandemic, and 33 saliva and 206 serum samples from participants with RT-PCR-confirmed SARS-CoV-2 infection. We evaluated the correlation of results obtained in saliva vs. serum and determined the sensitivity and specificity for each diagnostic media, stratified by antibody isotype, for detection of SARS-CoV-2 infection based on COVID-19 case designation for all specimens. Matched serum and saliva SARS-CoV-2 antigen-specific IgG responses were significantly correlated. Within the 10-plex SARS-CoV-2 panel, the salivary anti-nucleocapsid (N) protein IgG response resulted in the highest sensitivity for detecting prior SARS-CoV-2 infection (100% sensitivity at ≥10 days post-SARS-CoV-2 symptom onset). The salivary anti-receptor binding domain (RBD) IgG response resulted in 100% specificity. Among individuals with SARS-CoV-2 infection confirmed with RT-PCR, the temporal kinetics of IgG, IgA, and IgM in saliva were consistent with those observed in serum. SARS-CoV-2 appears to trigger a humoral immune response resulting in the almost simultaneous rise of IgG, IgM and IgA levels both in serum and in saliva, mirroring responses consistent with the stimulation of existing, cross-reactive B cells. SARS-CoV-2 antibody testing in saliva can play a critically important role in large-scale 'sero'-surveillance to address key public health priorities and guide policy and decision-making for COVID-19.

Background: International travellers are at risk of travel-related, vaccine-preventable diseases. More data are needed on the proportion of travellers who refuse vaccines during a pre-travel health consultation and their reasons for refusing vaccines.Methods: We analyzed data on travellers seen for a pre-travel health consultation from July 2012 through June 2014 in the Global TravEpiNet (GTEN) consortium. Providers were required to indicate one of three reasons for a traveller refusing a recommended vaccine: (1) cost concerns, (2) safety concerns or (3) not concerned with the illness. We calculated refusal rates among travellers eligible for each vaccine based on CDC recommendations current at the time of travel. We used multivariable logistic regression models to examine the effect of individual variables on the likelihood of accepting all recommended vaccines.Results: Of 24 478 travellers, 23 768 (97%) were eligible for at least one vaccine. Travellers were most frequently eligible for typhoid (N = 20 092), hepatitis A (N = 12 990) and influenza vaccines (N = 10 539). Of 23 768 eligible travellers, 6573 (25%) refused one or more recommended vaccine(s). Of those eligible, more than one-third refused the following vaccines: meningococcal: 2232 (44%) of 5029; rabies: 1155 (44%) of 2650; Japanese encephalitis: 761 (41%) of 1846; and influenza: 3527 (33%) of 10 539. The most common reason for declining vaccines was that the traveller was not concerned about the illness. In multivariable analysis, travellers visiting friends and relatives (VFR) in low or medium human development countries were less likely to accept all recommended vaccines, compared with non-VFR travellers (OR = 0.74 (0.59–0.95)).Conclusions: Travellers who sought pre-travel health care refused recommended vaccines at varying rates. A lack of concern about the associated illness was the most commonly cited reason for all refused vaccines. Our data suggest more effective education about disease risk is needed for international travellers, even those who seek pre-travel advice.

BackgroundClinical, virologic, and immunologic characteristics of Zika virus (ZIKV) infections in US patients are poorly defined.MethodsUS subjects with suspected ZIKV infection were enrolled. Clinical data and specimens were prospectively collected for ZIKV RNA detection and serologic and cellular assays. Confirmed ZIKV infection (cases) and ZIKV-negative (controls) subjects were compared. Dengue-experienced and dengue-naïve cases were also compared.ResultsWe enrolled 45 cases and 14 controls. Commonly reported symptoms among cases and controls were maculopapular rash (97.8% and 81.8%), fatigue (86.7% and 81.8%), and arthralgia (82.2% and 54.5%), respectively. The sensitivity (94%) and duration of infection detection (80% positivity at 65–79 days after disease onset) by polymerase chain reaction were highest in whole-blood specimens. ZIKV-neutralizing antibodies had a half-life of 105 days and were significantly higher in dengue virus–experienced cases than naïve ones (P = .046). In intracellular cytokine staining assays, the ZIKV proteins targeted most often by peripheral blood mononuclear cells from cases were structural proteins C and E for CD4+ T cells and nonstructural proteins NS3, NS5, and NS4B for CD8+ T cells.ConclusionsZIKV RNA detection was more frequent and prolonged in whole-blood specimens. Immunoglobulin G (IgG) and neutralizing antibodies, but not IgM, were influenced by prior dengue infection. Robust cellular responses to E and nonstructural proteins have potential vaccine development implications.

This case series of patients from a Hansen's disease clinic focuses on the frequency of delays in diagnosis and of HD reactions, both known risk factors for nerve damage.

Abstract. In a study of children having polyparasitic infections in a Schistosoma haematobium-endemic area, we examined the hypothesis that S. haematobium-positive children, compared with S. haematobium-negative children (antisoluble worm antigen preparation [SWAP] negative and egg negative) have increased systemic production of proinflammatory cytokines (interleukin [IL]-6, tumor necrosis factor [TNF]-α) and decreased down-regulatory IL-10. A total of 804 children, 2-19 years of age, were surveyed between July and December 2009 and tested for S. haematobium, Plasmodium falciparum, filariasis, and soil-transmitted helminth infections. Plasma levels of IL-6, TNF-α, and IL-10 were compared for S. haematobium-positive and S. haematobium-negative children, adjusting for malaria, filaria, and hookworm co-infections, and for nutritional status, age group, sex, and geographic location. IL-10 was significantly elevated among children infected with S. haematobium, showing bimodal peaks in 7-8 and 13-14 years age groups. IL-10 was also higher among children who were acutely malnourished, whereas IL-10 levels were lower in the presence of S. haematobium-filaria co-infection. After adjustment for co-factors, IL-6 was significantly elevated among children of 5-6 years and among those with P. falciparum infection. Lower levels of IL-6 were found in malaria-hookworm co-infection. High levels of TNF-α were found in children aged 11-12 years regardless of infection status. In addition, village of residence was a strong predictor of IL-6 and IL-10 plasma levels. In adolescent children infected with S. haematobium, there is an associated elevation in circulating IL-10 that may reduce the risk of later morbidity. Although we did not find a direct link between S. haematobium infection and circulating pro-inflammatory IL-6 and TNF-α levels, future T-cell stimulation studies may provide more conclusive linkages between infection and cytokine responses in settings that are endemic for multiple parasites and multiple co-infections.

Background According to the World Health Organization, achieving targets for control of leprosy by 2030 will require disease elimination and interruption of transmission at the national or regional level. India and Brazil have reported the highest leprosy burden in the last few decades, revealing the need for strategies and tools to help health professionals correctly manage and control the disease. Objective The main objective of this study was to develop a cross-platform app for leprosy screening based on artificial intelligence (AI) with the goal of increasing accessibility of an accurate method of classifying leprosy treatment for health professionals, especially for communities further away from major diagnostic centers. Toward this end, we analyzed the quality of leprosy data in Brazil on the National Notifiable Diseases Information System (SINAN). Methods Leprosy data were extracted from the SINAN database, carefully cleaned, and used to build AI decision models based on the random forest algorithm to predict operational classification in paucibacillary or multibacillary leprosy. We used Python programming language to extract and clean the data, and R programming language to train and test the AI model via cross-validation. To allow broad access, we deployed the final random forest classification model in a web app via shinyApp using data available from the Brazilian Institute of Geography and Statistics and the Department of Informatics of the Unified Health System. Results We mapped the dispersion of leprosy incidence in Brazil from 2014 to 2018, and found a particularly high number of cases in central Brazil in 2014 that further increased in 2018 in the state of Mato Grosso. For some municipalities, up to 80% of cases showed some data discrepancy. Of a total of 21,047 discrepancies detected, the most common was “operational classification does not match the clinical form.” After data processing, we identified a total of 77,628 cases with missing data. The sensitivity and specificity of the AI model applied for the operational classification of leprosy was 93.97% and 87.09%, respectively. Conclusions The proposed app was able to recognize patterns in leprosy cases registered in the SINAN database and to classify new patients with paucibacillary or multibacillary leprosy, thereby reducing the probability of incorrect assignment by health centers. The collection and notification of data on leprosy in Brazil seem to lack specific validation to increase the quality of the data for implementations via AI. The AI models implemented in this work had satisfactory accuracy across Brazilian states and could be a complementary diagnosis tool, especially in remote areas with few specialist physicians.