Seroprevalence of anti-Lassa Virus IgG antibodies in three districts of Sierra Leone: A cross-sectional, population-based study.

BACKGROUND: Lassa virus (LASV), the cause of the acute viral hemorrhagic illness Lassa fever (LF), is endemic in West Africa. Infections in humans occur mainly after exposure to infected excrement or urine of the rodent-host, Mastomys natalensis. The prevalence of exposure to LASV in Sierra Leone is crudely estimated and largely unknown. This cross-sectional study aimed to establish a baseline point seroprevalence of IgG antibodies to LASV in three administrative districts of Sierra Leone and identify potential risk factors for seropositivity and LASV exposure. METHODOLOGY AND PRINCIPAL FINDINGS: Between 2015 and 2018, over 10,642 participants from Kenema, Tonkolili, and Port Loko Districts were enrolled in this cross-sectional study. Previous LASV and LF epidemiological studies support classification of these districts as "endemic," "emerging," and "non-endemic", respectively. Dried blood spot samples were tested for LASV antibodies by ELISA to determine the seropositivity of participants, indicating previous exposure to LASV. Surveys were administered to each participant to assess demographic and environmental factors associated with a higher risk of exposure to LASV. Overall seroprevalence for antibodies to LASV was 16.0%. In Kenema, Port Loko, and Tonkolili Districts, seroprevalences were 20.1%, 14.1%, and 10.6%, respectively. In a multivariate analysis, individuals were more likely to be LASV seropositive if they were living in Kenema District, regardless of sex, age, or occupation. Environmental factors contributed to an increased risk of LASV exposure, including poor housing construction and proximity to bushland, forested areas, and refuse. CONCLUSIONS AND SIGNIFICANCE: In this study we determine a baseline LASV seroprevalence in three districts which will inform future epidemiological, ecological, and clinical studies on LF and the LASV in Sierra Leone. The heterogeneity of the distribution of LASV and LF over both space, and time, can make the design of efficacy trials and intervention programs difficult. Having more studies on the prevalence of LASV and identifying potential hyper-endemic areas will greatly increase the awareness of LF and improve targeted control programs related to LASV.

Structural plasticity in self-assembling transmembrane ß-sheets.

Here we test the hypothesis that membrane-spanning ß-sheets can exhibit structural plasticity in membranes due to their ability to shift hydrogen-bonding patterns. Transmembrane ß-sheet forming peptides of the sequence AcWL(n), where n = 5, 6, or 7, which range from 21 to 27 Å in maximum length, were incorporated into bilayers made of phosphatidylcholine lipids with saturated acyl chains containing 14, 16, or 18 carbons, which are 36-50 Å in thickness. The effect of the peptide ß-sheets on fluid- and gel-phase bilayers were studied with differential scanning calorimetry and circular dichroism spectroscopy. We show that AcWL5 forms a stable, peptide-rich gel phase in all three lipids. The whole family of AcWL(n) peptides appears to form similarly stable, nonmembrane-disrupting ß-sheets in all bilayer phases and thicknesses. Bilayers containing up to 20 mol % peptide, which is the maximum concentration tested, formed gel phases with melting temperatures that were equal to, or slightly higher than, the pure lipid transitions. Given the range of peptide lengths and bilayer thicknesses tested, these experiments show that the AcWL(n) family of membrane-inserted ß-sheets exhibit remarkable structural plasticity in membranes.

Beyond atopy: multiple patterns of sensitization in relation to asthma in a birth cohort study.

RATIONALE: The pattern of IgE response (over time or to specific allergens) may reflect different atopic vulnerabilities which are related to the presence of asthma in a fundamentally different way from current definition of atopy. OBJECTIVES: To redefine the atopic phenotype by identifying latent structure within a complex dataset, taking into account the timing and type of sensitization to specific allergens, and relating these novel phenotypes to asthma. METHODS: In a population-based birth cohort in which multiple skin and IgE tests have been taken throughout childhood, we used a machine learning approach to cluster children into multiple atopic classes in an unsupervised way. We then investigated the relation between these classes and asthma (symptoms, hospitalizations, lung function and airway reactivity). MEASUREMENTS AND MAIN RESULTS: A five-class model indicated a complex latent structure, in which children with atopic vulnerability were clustered into four distinct classes (Multiple Early [112/1053, 10.6%]; Multiple Late [171/1053, 16.2%]; Dust Mite [47/1053, 4.5%]; and Non-dust Mite [100/1053, 9.5%]), with a fifth class describing children with No Latent Vulnerability (623/1053, 59.2%). The association with asthma was considerably stronger for Multiple Early compared with other classes and conventionally defined atopy (odds ratio [95% CI]: 29.3 [11.1-77.2] versus 12.4 [4.8-32.2] versus 11.6 [4.8-27.9] for Multiple Early class versus Ever Atopic versus Atopic age 8). Lung function and airway reactivity were significantly poorer among children in Multiple Early class. Cox regression demonstrated a highly significant increase in risk of hospital admissions for wheeze/asthma after age 3 yr only among children in the Multiple Early class (HR 9.2 [3.5-24.0], P < 0.001). CONCLUSIONS: IgE antibody responses do not reflect a single phenotype of atopy, but several different atopic vulnerabilities which differ in their relation with asthma presence and severity.

Lassa fever in post-conflict sierra leone.

BACKGROUND: Lassa fever (LF), an often-fatal hemorrhagic disease caused by Lassa virus (LASV), is a major public health threat in West Africa. When the violent civil conflict in Sierra Leone (1991 to 2002) ended, an international consortium assisted in restoration of the LF program at Kenema Government Hospital (KGH) in an area with the world's highest incidence of the disease. METHODOLOGY/PRINCIPAL FINDINGS: Clinical and laboratory records of patients presenting to the KGH Lassa Ward in the post-conflict period were organized electronically. Recombinant antigen-based LF immunoassays were used to assess LASV antigenemia and LASV-specific antibodies in patients who met criteria for suspected LF. KGH has been reestablished as a center for LF treatment and research, with over 500 suspected cases now presenting yearly. Higher case fatality rates (CFRs) in LF patients were observed compared to studies conducted prior to the civil conflict. Different criteria for defining LF stages and differences in sensitivity of assays likely account for these differences. The highest incidence of LF in Sierra Leone was observed during the dry season. LF cases were observed in ten of Sierra Leone's thirteen districts, with numerous cases from outside the traditional endemic zone. Deaths in patients presenting with LASV antigenemia were skewed towards individuals less than 29 years of age. Women self-reporting as pregnant were significantly overrepresented among LASV antigenemic patients. The CFR of ribavirin-treated patients presenting early in acute infection was lower than in untreated subjects. CONCLUSIONS/SIGNIFICANCE: Lassa fever remains a major public health threat in Sierra Leone. Outreach activities should expand because LF may be more widespread in Sierra Leone than previously recognized. Enhanced case finding to ensure rapid diagnosis and treatment is imperative to reduce mortality. Even with ribavirin treatment, there was a high rate of fatalities underscoring the need to develop more effective and/or supplemental treatments for LF.

Model-based machine learning.

Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher typically tries to map their problem onto one of these existing methods, often influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations. In this study, we describe an alternative methodology for applying machine learning, in which a bespoke solution is formulated for each new application. The solution is expressed through a compact modelling language, and the corresponding custom machine learning code is then generated automatically. This model-based approach offers several major advantages, including the opportunity to create highly tailored models for specific scenarios, as well as rapid prototyping and comparison of a range of alternative models. Furthermore, newcomers to the field of machine learning do not have to learn about the huge range of traditional methods, but instead can focus their attention on understanding a single modelling environment. In this study, we show how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning, and we outline a large-scale commercial application of this framework involving tens of millions of users. We also describe the concept of probabilistic programming as a powerful software environment for model-based machine learning, and we discuss a specific probabilistic programming language called Infer.NET, which has been widely used in practical applications.

Enzymatic E-colicins bind to their target receptor BtuB by presentation of a small binding epitope on a coiled-coil scaffold.

Toxins and viruses often initiate their attacks by binding to specific proteins on the surfaces of target cells. Bacterial toxins (e.g. bacteriocins) and viruses (bacteriophages) targeting Gram-negative bacteria typically bind to outer membrane proteins. Bacterial E-colicins target Escherichia coli by binding to the outer membrane cobalamin transporter BtuB. Colicins are tripartite molecules possessing receptor-binding, translocation, and toxin domains connected by long coiled-coil alpha-helices. Surprisingly, the crystal structure of colicin E3 does not possess a recognizable globular fold in its receptor-binding domain. We hypothesized that the binding epitope of enzymatic E-colicins is a short loop connecting the two alpha-helices that comprise the coiled-coil region and that this flanking coiled-coil region serves to present the loop in a binding-capable conformation. To test this hypothesis, we designed and synthesized a 34-residue peptide (E-peptide-1) corresponding to residues Ala366-Arg399 of the helix-loop-helix region of colicin E3. Cysteines placed near the ends of the peptide (I372C and A393C) enabled crosslinking for reduction of conformational entropy and formation of a peptide structure that would present the loop epitope. A fluorescent analog was also made for characterization of binding by measurement of fluorescence polarization. Our analysis shows the following. (i). E-peptide-1 is predominantly random coil in aqueous solution, but disulfide bond formation increases its alpha-helical content in both aqueous buffer and solvents that promote helix formation. (ii). Fluorescein-labeled E-peptide-1 binds to purified BtuB in a calcium-dependent manner with a Kd of 43.6 +/- 4.9 nm or 2370 +/- 670 nm in the presence or absence of calcium, respectively. (iii). In the presence of calcium, cyanocobalamin (CN-Cbl) displaces E-peptide-1 with a nanomolar inhibition constant (Ki = 78.9 +/- 5.6 nm). We conclude that the BtuB binding sites for cobalamins and enzymatic E-colicins are overlapping but inequivalent and that the distal loop and (possibly) the short alpha-helical flanking regions are sufficient for high affinity binding.