18 research outputs found

    Effects of Transient Administration of the NMDA Receptor Antagonist MK-801 in Drosophila melanogaster Activity, Sleep, and Negative Geotaxis

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    MK-801, also called dizocilpine, is an N-methyl-D-aspartate (NMDA) receptor antagonist widely used in animal research to model schizophrenia-like phenotypes. Although its effects in rodents are well characterised, little is known about the outcomes of this drug in other organisms. In this study, we characterise the effects of MK-801 on the locomotion, sleep, and negative geotaxis of the fruit fly Drosophila melanogaster. We observed that acute (24 h) and chronic (7 days) administration of MK-801 enhanced negative geotaxis activity in the forced climbing assay for all tested concentrations (0.15 mM, 0.3 mM, and 0.6 mM). Moreover, acute administration, but not chronic, increased the flies' locomotion in a dose-dependent matter. Finally, average sleep duration was not affected by any concentration or administration protocol. Our results indicate that acute MK-801 could be used to model hyperactivity phenotypes in Drosophila melanogaster. Overall, this study provides further evidence that the NMDA receptor system is functionally conserved in flies, suggesting the usefulness of this model to investigate several phenotypes as a complement and replacement of the rodent models within drug discovery

    Socio-economic and environmental factors associated with high lymphatic filariasis morbidity prevalence distribution in Bangladesh

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    Background Lymphatic filariasis (LF) is a vector-borne parasitic disease which affects 70 million people worldwide and causes life-long disabilities. In Bangladesh, there are an estimated 44,000 people suffering from clinical conditions such as lymphoedema and hydrocoele, with the greatest burden in the northern Rangpur division. To better understand the factors associated with this distribution, this study examined socio-economic and environmental factors at division, district, and sub-district levels. Methodology A retrospective ecological study was conducted using key socio-economic (nutrition, poverty, employment, education, house infrastructure) and environmental (temperature, precipitation, elevation, waterway) factors. Characteristics at division level were summarised. Bivariate analysis using Spearman’s rank correlation coefficient was conducted at district and sub-district levels, and negative binomial regression analyses were conducted across high endemic sub-districts (n = 132). Maps were produced of high endemic sub-districts to visually illustrate the socio-economic and environmental factors found to be significant. Results The highest proportion of rural population (86.8%), poverty (42.0%), tube well water (85.4%), and primary employment in agriculture (67.7%) was found in Rangpur division. Spearman’s rank correlation coefficient at district and sub-district level show that LF morbidity prevalence was significantly (p&lt;0.05) positively correlated with households without electricity (district rs = 0.818; sub-district rs = 0.559), households with tube well water (sub-district rs = 0.291), households without toilet (district rs = 0.504; sub-district rs = 0.40), mean annual precipitation (district rs = 0.695; sub-district rs = 0.503), mean precipitation of wettest quarter (district rs = 0.707; sub-district rs = 0.528), and significantly negatively correlated with severely stunted children (district rs = -0.723; sub-district rs = -0.370), mean annual temperature (district rs = -0.633.; sub-district rs = 0.353) and mean temperature (wettest quarter) ((district rs = -0.598; sub-district rs = 0.316) Negative binomial regression analyses at sub-district level found severely stunted children (p = &lt;0.001), rural population (p = 0.002), poverty headcount (p = 0.001), primary employment in agriculture (p = 0.018), households without toilet (p = &lt;0.001), households without electricity (p = 0.002) and mean temperature (wettest quarter) (p = 0.045) to be significant. Conclusions This study highlights the value of using available data to identify key drivers associated with high LF morbidity prevalence, which may help national LF programmes better identify populations at risk and implement timely and targeted public health messages and intervention strategies. </jats:sec
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