Topology of the C-Terminal Tail of HIV-1 gp41: Differential Exposure of the Kennedy Epitope on Cell and Viral Membranes

The C-terminal tail (CTT) of the HIV-1 gp41 envelope (Env) protein is increasingly recognized as an important determinant of Env structure and functional properties, including fusogenicity and antigenicity. While the CTT has been commonly referred to as the “intracytoplasmic domain” based on the assumption of an exclusive localization inside the membrane lipid bilayer, early antigenicity studies and recent biochemical analyses have produced a credible case for surface exposure of specific CTT sequences, including the classical “Kennedy epitope” (KE) of gp41, leading to an alternative model of gp41 topology with multiple membrane-spanning domains. The current study was designed to test these conflicting models of CTT topology by characterizing the exposure of native CTT sequences and substituted VSV-G epitope tags in cell- and virion-associated Env to reference monoclonal antibodies (MAbs). Surface staining and FACS analysis of intact, Env-expressing cells demonstrated that the KE is accessible to binding by MAbs directed to both an inserted VSV-G epitope tag and the native KE sequence. Importantly, the VSV-G tag was only reactive when inserted into the KE; no reactivity was observed in cells expressing Env with the VSV-G tag inserted into the LLP2 domain. In contrast to cell-surface expressed Env, no binding of KE-directed MAbs was observed to Env on the surface of intact virions using either immune precipitation or surface plasmon resonance spectroscopy. These data indicate apparently distinct CTT topologies for virion- and cell-associated Env species and add to the case for a reconsideration of CTT topology that is more complex than currently envisioned

Risk factors for the development of severe typhoid fever in Vietnam

Background Typhoid fever is a systemic infection caused by the bacterium Salmonella enterica serovar Typhi. Age, sex, prolonged duration of illness, and infection with an antimicrobial resistant organism have been proposed risk factors for the development of severe disease or fatality in typhoid fever. Methods We analysed clinical data from 581 patients consecutively admitted with culture confirmed typhoid fever to two hospitals in Vietnam during two periods in 1993–1995 and 1997–1999. These periods spanned a change in the antimicrobial resistance phenotypes of the infecting organisms i.e. fully susceptible to standard antimicrobials, resistance to chloramphenicol, ampicillin and trimethoprim-sulphamethoxazole (multidrug resistant, MDR), and intermediate susceptibility to ciprofloxacin (nalidixic acid resistant). Age, sex, duration of illness prior to admission, hospital location and the presence of MDR or intermediate ciprofloxacin susceptibility in the infecting organism were examined by logistic regression analysis to identify factors independently associated with severe typhoid at the time of hospital admission. Results The prevalence of severe typhoid was 15.5% (90/581) and included: gastrointestinal bleeding (43; 7.4%); hepatitis (29; 5.0%); encephalopathy (16; 2.8%); myocarditis (12; 2.1%); intestinal perforation (6; 1.0%); haemodynamic shock (5; 0.9%), and death (3; 0.5%). Severe disease was more common with increasing age, in those with a longer duration of illness and in patients infected with an organism exhibiting intermediate susceptibility to ciprofloxacin. Notably an MDR phenotype was not associated with severe disease. Severe disease was independently associated with infection with an organism with an intermediate susceptibility to ciprofloxacin (AOR 1.90; 95% CI 1.18-3.07; p = 0.009) and male sex (AOR 1.61 (1.00-2.57; p = 0.035). Conclusions In this group of patients hospitalised with typhoid fever infection with an organism with intermediate susceptibility to ciprofloxacin was independently associated with disease severity. During this period many patients were being treated with fluoroquinolones prior to hospital admission. Ciprofloxacin and ofloxacin should be used with caution in patients infected with S. Typhi that have intermediate susceptibility to ciprofloxacin

Unusual frequency of conglomerates in a series of 5-aryl-5-alkyl hydantoins

International audienc

The Diagnosis of Dengue in Patients Presenting With Acute Febrile Illness Using Supervised Machine Learning and Impact of Seasonality

BackgroundSymptomatic dengue infection can result in a life-threatening shock syndrome and timely diagnosis is essential. Point-of-care tests for non-structural protein 1 and IgM are used widely but performance can be limited. We developed a supervised machine learning model to predict whether patients with acute febrile illnesses had a diagnosis of dengue or other febrile illnesses (OFI). The impact of seasonality on model performance over time was examined.MethodsWe analysed data from a prospective observational clinical study in Vietnam. Enrolled patients presented with an acute febrile illness of &amp;lt;72 h duration. A gradient boosting model (XGBoost) was used to predict final diagnosis using age, sex, haematocrit, platelet, white cell, and lymphocyte count collected on enrolment. Data was randomly split 80/20% into a training and hold-out set, respectively, with the latter not used in model development. Cross-validation and hold out set testing was used, with performance over time evaluated through a rolling window approach.ResultsWe included 8,100 patients recruited between 16th October 2010 and 10th December 2014. In total 2,240 (27.7%) patients were diagnosed with dengue infection. The optimised model from training data had an overall median area under the receiver operator curve (AUROC) of 0.86 (interquartile range 0.84–0.86), specificity of 0.92, sensitivity of 0.56, positive predictive value of 0.73, negative predictive value (NPV) of 0.84, and Brier score of 0.13 in predicting the final diagnosis, with similar performances in hold-out set testing (AUROC of 0.86). Model performances varied significantly over time as a function of seasonality and other factors. Incorporation of a dynamic threshold which continuously learns from recent cases resulted in a more consistent performance throughout the year (NPV &amp;gt;90%).ConclusionSupervised machine learning models are able to discriminate between dengue and OFI diagnoses in patients presenting with an early undifferentiated febrile illness. These models could be of clinical utility in supporting healthcare decision-making and provide passive surveillance across dengue endemic regions. Effects of seasonality and changing disease prevalence must however be taken into account—this is of significant importance given unpredictable effects of human-induced climate change and the impact on health.</jats:sec