Characterizing the scent and chemical composition of Panthera leo marking fluid using solid-phase microextraction and multidimensional gas chromatography–mass spectrometry-olfactometry

Lions (Panthera leo) use chemical signaling to indicate health, reproductive status, and territorial ownership. To date, no study has reported on both scent and composition of marking fluid (MF) from P. leo. The objectives of this study were to: 1) develop a novel method for simultaneous chemical and scent identification of lion MF in its totality (urine + MF), 2) identify characteristic odorants responsible for the overall scent of MF as perceived by human panelists, and 3) compare the existing library of known odorous compounds characterized as eliciting behaviors in animals in order to understand potential functionality in lion behavior. Solid-phase microextraction and simultaneous chemical-sensory analyses with multidimensional gas-chromatography-mass spectrometry-olfactometry improved separating, isolating, and identifying mixed (MF, urine) compounds versus solvent-based extraction and chemical analyses. 2,5-Dimethylpyrazine, 4-methylphenol, and 3-methylcyclopentanone were isolated and identified as the compounds responsible for the characteristic odor of lion MF. Twenty-eight volatile organic compounds (VOCs) emitted from MF were identified, adding a new list of compounds previously unidentified in lion urine. New chemicals were identified in nine compound groups: ketones, aldehydes, amines, alcohols, aromatics, sulfur-containing compounds, phenyls, phenols, and volatile fatty acids. Twenty-three VOCs are known semiochemicals that are implicated in attraction, reproduction, and alarm-signaling behaviors in other species

Chronic dietary aflatoxins exposure in Kenya and emerging public health concerns of impaired growth and immune suppression in children

Aflatoxins are toxic secondary metabolites produced by fungi and contaminate various agricultural commodities either before harvest or under post-harvest conditions. Acute aflatoxin poisoning leading to casepatientsand deaths has continued to occur in several parts of Kenya. However, there is emerging evidence implicating chronic aflatoxins exposure as an important factor in infant growth stunting and immune suppression. The consumption of smaller dosages overtime produces no obvious symptoms as would happen with acute dosage. Thus, it has not attracted much attention in Kenya in terms of public health priorities. Aflatoxins have been detected mainly in the staple foods such as cereals and legumes commodities, which form the main gruel ingredients used to compose weaning foods in most rural households. This suggests that children may be more exposed to mycotoxins than the rest of the population and this could be the reason for increased cases of infant malnutrition and mortality in certain areas in Kenya. The extent to which stunted growth and immune suppression contribute to the overall burden of infectious disease merits consideration. Therefore, this paper discusses dietary chronic mycotoxins exposure in Kenya and emerging public health concerns of stunted growth and immune suppression as reported in various related animal and human studies. It also highlights several factors that may enhance the dietary mycotoxinsexposure especially amongst children and further explores various localized control measures and research areas within the context of food scarcity and extreme poverty experienced in rural Kenya. This paper aims at reinforcing that presence of mycotoxins within the food system should be addressed as an urgent food safety issue as they place a significant hindrance towards the attainment of the Millennium Development Goals (MDGs) 4 and 6 on reduction of child mortality and combating of diseases, respectively

Impact of metric and sample size on determining malaria hotspot boundaries

The spatial heterogeneity of malaria suggests that interventions may be targeted for maximum impact. It is unclear to what extent different metrics lead to consistent delineation of hotspot boundaries. Using data from a large community-based malaria survey in the western Kenyan highlands, we assessed the agreement between a model-based geostatistical (MBG) approach to detect hotspots using Plasmodium falciparum parasite prevalence and serological evidence for exposure. Malaria transmission was widespread and highly heterogeneous with one third of the total population living in hotspots regardless of metric tested. Moderate agreement (Kappa = 0.424) was observed between hotspots defined based on parasite prevalence by polymerase chain reaction (PCR)- and the prevalence of antibodies to two P. falciparum antigens (MSP-1, AMA-1). While numerous biologically plausible hotspots were identified, their detection strongly relied on the proportion of the population sampled. When only 3% of the population was sampled, no PCR derived hotspots were reliably detected and at least 21% of the population was needed for reliable results. Similar results were observed for hotspots of seroprevalence. Hotspot boundaries are driven by the malaria diagnostic and sample size used to inform the model. These findings warn against the simplistic use of spatial analysis on available data to target malaria interventions in areas where hotspot boundaries are uncertain

Impact of metric and sample size on determining malaria hotspot boundaries

The spatial heterogeneity of malaria suggests that interventions may be targeted for maximum impact. It is unclear to what extent different metrics lead to consistent delineation of hotspot boundaries. Using data from a large community-based malaria survey in the western Kenyan highlands, we assessed the agreement between a model-based geostatistical (MBG) approach to detect hotspots using Plasmodium falciparum parasite prevalence and serological evidence for exposure. Malaria transmission was widespread and highly heterogeneous with one third of the total population living in hotspots regardless of metric tested. Moderate agreement (Kappa = 0.424) was observed between hotspots defined based on parasite prevalence by polymerase chain reaction (PCR)- and the prevalence of antibodies to two P. falciparum antigens (MSP-1, AMA-1). While numerous biologically plausible hotspots were identified, their detection strongly relied on the proportion of the population sampled. When only 3% of the population was sampled, no PCR derived hotspots were reliably detected and at least 21% of the population was needed for reliable results. Similar results were observed for hotspots of seroprevalence. Hotspot boundaries are driven by the malaria diagnostic and sample size used to inform the model. These findings warn against the simplistic use of spatial analysis on available data to target malaria interventions in areas where hotspot boundaries are uncertain