Radioimmunoassay of serum creatine kinase BB as index of brain damage after head injury.

Brain-type creatine kinase isoenzymes (CK-BB) was measured by radioimmunoassay in the serum of 54 patients with head injuries. CK-BB was not detectable in 476 out of 1006 controls, the remaining 530 normal samples containing a mean of 1.5 +/- SD0.75 microgram/l. The mean CK-BB concentrations in patients with mild, moderate, and fatal head injuries were all significantly higher than the control value (p < 0.01 in each instance). Patients with serious head injury had serum concentrations many times the normal value, in two cases within 30 minutes after impact. Fatally injured patients continued to have high serum concentrations several days after injury. In less serious cases values approached normal within two or three days. Every patient with evidence of cerebral laceration, bruising, or swelling had a serum CK-BB concentration above normal. Raised concentrations were found in 14 out of 22 patients with concussion only. The serum CK-BB concentration appears to be a sensitive index of brain damage and may prove useful in the management and follow-up of head-injured patients.     

Chemical Variability of Cleistopholis patens (Benth.) Engl. et Diels Leaf Oil from Ivory Coast

The chemical composition of 48 leaf oil samples isolated from individual plants of Cleistopholis patens (Benth .) Engl. et Diels harvested in four Ivoirian forests was investigated by GC‐FID (determination of retention indices), GC/MS, and ¹³C‐NMR analyses. The main components identified were β‐pinene (traces–59.1%), sabinene (traces–54.2%), (E)‐β‐caryophyllene (0.3–39.3%), linalool (0.1–38.5%), (E)‐β‐ocimene (0.1–33.2%), germacrene D (0.0–33.1%), α‐pinene (0.1–32.3%), and germacrene B (0–21.2%). The 48 oil compositions were submitted to hierarchical clustering and principal components analyses, which allowed the distinction of three groups within the oil samples. The oil composition of the major group (Group I, 33 samples) was dominated by (E)‐β‐caryophyllene and linalool. The oils of Group II (eight samples) contained mainly β‐pinene and α‐pinene, while those of Group III (seven samples) were dominated by sabinene, limonene, and β‐phellandrene. Moreover, the compositions of the Ivoirian C. patens leaf oils differed from those of Nigerian and Cameroonian origins.     

Differential pathogenesis of closely related 2018 Nigerian outbreak clade III Lassa virus isolates

Nigeria continues to experience ever increasing annual outbreaks of Lassa fever (LF). The World Health Organization has recently declared Lassa virus (LASV) as a priority pathogen for accelerated research leading to a renewed international effort to develop relevant animal models of disease and effective countermeasures to reduce LF morbidity and mortality in endemic West African countries. A limiting factor in evaluating medical countermeasures against LF is a lack of well characterized animal models outside of those based on infection with LASV strain Josiah originating form Sierra Leone, circa 1976. Here we genetically characterize five recent LASV isolates collected from the 2018 outbreak in Nigeria. Three isolates were further evaluated in vivo and despite being closely related and from the same spatial / geographic region of Nigeria, only one of the three isolates proved lethal in strain 13 guinea pigs and non-human primates (NHP). Additionally, this isolate exhibited atypical pathogenesis characteristics in the NHP model, most notably respiratory failure, not commonly described in hemorrhagic cases of LF. These results suggest that there is considerable phenotypic heterogeneity in LASV infections in Nigeria, which leads to a multitude of pathogenesis characteristics that could account for differences between subclinical and lethal LF infections. Most importantly, the development of disease models using currently circulating LASV strains in West Africa are critical for the evaluation of potential vaccines and medical countermeasures.     

Two-stage lot quality assurance sampling framework for monitoring and evaluation of neglected tropical diseases,allowing for imperfect diagnostics and spatial heterogeneity

BackgroundMonitoring and evaluation (M&E) is a key component of large-scale neglected tropical diseases (NTD) control programs. Diagnostic tests deployed in these M&E surveys are often imperfect, and it remains unclear how this affects the population-based program decision-making.MethodologyWe developed a 2-stage lot quality assurance sampling (LQAS) framework for decision-making that allows for both imperfect diagnostics and spatial heterogeneity of infections. We applied the framework to M&E of soil-transmitted helminth control programs as a case study. For this, we explored the impact of the diagnostic performance (sensitivity and specificity), spatial heterogeneity (intra-cluster correlation), and survey design on program decision-making around the prevalence decisions thresholds recommended by WHO (2%, 10%, 20% and 50%) and the associated total survey costs.Principal findingsThe survey design currently recommended by WHO (5 clusters and 50 subjects per cluster) may lead to incorrect program decisions around the 2% and 10% prevalence thresholds, even when perfect diagnostic tests are deployed. To reduce the risk of incorrect decisions around the 2% prevalence threshold, including more clusters (≥10) and deploying highly specific diagnostic methods (≥98%) are the most-cost saving strategies when spatial heterogeneity is moderate-to-high (intra-cluster correlation >0.017). The higher cost and lower throughput of improved diagnostic tests are compensated by lower required sample sizes, though only when the cost per test is <6.50 US$ and sample throughput is ≥3 per hour.Conclusion/SignificanceOur framework provides a means to assess and update M&E guidelines and guide product development choices for NTD. Using soil-transmitted helminths as a case study, we show that current M&E guidelines may severely fall short, particularly in low-endemic and post-control settings. Furthermore, specificity rather than sensitivity is a critical parameter to consider. When the geographical distribution of an NTD within a district is highly heterogeneous, sampling more clusters (≥10) may be required.     

Differential response of seven duiker species to human activities in Taï National Park,Côte d'Ivoire

African rainforest is severely impacted by human activities, ranging from resource collection, selective logging to fragmentation and scale deforestation. Consequently, large mammal communities occurring therein are strongly modified. Here, we present a study conducted in Taï National Park (TNP), which characterises the spatial distribution and differential response of seven duiker species to human activities. Based on extensive survey data recorded between 2005 and 2015, we used a maximum entropy modelling approach for predicting duiker species distribution and a hierarchical clustering approach to identify potential subgroups in the duiker community. The seven duiker species clearly differed in their spatial distribution, with Cephalophus dorsalis and Philantomba maxwellii being the most common and widely distributed with no clear response towards gradients of impact from human activities. In contrast, Cephalophus ogilbyi, Cephalophus jentinki, Cephalophus sylvicultor and Cephalophus zebra showed increasing responses towards anthropogenic impact gradients, with the latter two being particularly sensitive. These duikers are not found in areas of illegal human activities. The restricted distribution of Cephalophus niger seems artificial and may indicate species misidentification. The strong spatial signature of human activities in the duiker community of TNP is of concern. Effective park management, including extensive ranger patrols, is vital for ensuring the persistence of this unique duiker community in West Africa.     

A fully online sensor-equipped,disposable multiphase microbioreactor as a screening platform for biotechnological applications

A new disposable, multiphase, microbioreactor (MBR; with a working volume of 550 μl) equipped with online sensors is presented for biotechnological screening research purposes owing to its high-throughput potential. Its design and fabrication, online sensor integration, and operation are described. During aerobic cultivation, sufficient oxygen supply is the most important factor that influences growth and product formation. The MBR is a microbubble column bioreactor (μBC), and the oxygen supply was realized by active pneumatic bubble aeration, ensuring sufficient volumetric liquid-phase mass transfer (k _L a) and proper homogenization of the cultivation broth. The μBC was equipped with miniaturized sensors for the pH, dissolved oxygen, optical density and glucose concentration that allowed real-time online monitoring of these process variables during cultivation. The challenge addressed here was the integration of sensors in the limited available space. The MBR was shown to be a suitable screening platform for the cultivation of biological systems. Batch cultivations of Saccharomyces cerevisiae were performed to observe the variation in the process variables over time and to show the robustness and operability of all the online sensors in the MBR.     

Improvement of the antibody-dependent respiratory burst assay for assessing protective immune responses to malaria

The antibody-dependent respiratory burst (ADRB) assay is a sensitive isoluminol-based chemiluminescence (CL) functional assay designed to assess the capacity of opsonizing antibodies against merozoites to induce neutrophil respiratory burst. ADRB was shown to measure protective immunity against malaria in endemic areas, but the assay needed further improvement to ensure better sensitivity and reproducibility. Here, we adjusted parameters such as the freezing–thawing procedure of merozoites, merozoites''s concentration and the buffer solution''s pH, and we used the improved assay to measure ADRB activity of 207 sera from 97 and 110 individuals living, respectively, in Dielmo and Ndiop villages with differing malaria endemicity. The improvement led to increased CL intensity and assay sensitivity, and a higher reproducibility. In both areas, ADRB activity correlated with malaria endemicity and individual''s age discriminated groups with and without clinical malaria episodes, and significantly correlated with in vivo clinical protection from Plasmodium falciparum malaria. Our results demonstrate that the improved ADRB assay can be valuably used to assess acquired immunity during monitoring by control programmes and/or clinical trials.     

An exhaustive, non-euclidean, non-parametric data mining tool for unraveling the complexity of biological systems--novel insights into malaria

Complex, high-dimensional data sets pose significant analytical challenges in the post-genomic era. Such data sets are not exclusive to genetic analyses and are also pertinent to epidemiology. There has been considerable effort to develop hypothesis-free data mining and machine learning methodologies. However, current methodologies lack exhaustivity and general applicability. Here we use a novel non-parametric, non-euclidean data mining tool, HyperCube®, to explore exhaustively a complex epidemiological malaria data set by searching for over density of events in m-dimensional space. Hotspots of over density correspond to strings of variables, rules, that determine, in this case, the occurrence of Plasmodium falciparum clinical malaria episodes. The data set contained 46,837 outcome events from 1,653 individuals and 34 explanatory variables. The best predictive rule contained 1,689 events from 148 individuals and was defined as: individuals present during 1992–2003, aged 1–5 years old, having hemoglobin AA, and having had previous Plasmodium malariae malaria parasite infection ≤10 times. These individuals had 3.71 times more P. falciparum clinical malaria episodes than the general population. We validated the rule in two different cohorts. We compared and contrasted the HyperCube® rule with the rules using variables identified by both traditional statistical methods and non-parametric regression tree methods. In addition, we tried all possible sub-stratified quantitative variables. No other model with equal or greater representativity gave a higher Relative Risk. Although three of the four variables in the rule were intuitive, the effect of number of P. malariae episodes was not. HyperCube® efficiently sub-stratified quantitative variables to optimize the rule and was able to identify interactions among the variables, tasks not easy to perform using standard data mining methods. Search of local over density in m-dimensional space, explained by easily interpretable rules, is thus seemingly ideal for generating hypotheses for large datasets to unravel the complexity inherent in biological systems.