Search

New investigations using ovitraps showed its presence the Aedes albopictus (Skuse, 1894) in four neighborhoods in the metropolitan region of Macapa, capital of Amapa, Brazil. An Aedes aegypti (Linnaeus, 1762) Infestation Index Rapid Survey (LIRAa) held in January 2020 indicated the presence of Ae. albopictus in 10 different neighborhoods, demonstrating increase dispersion in the municipality. Monitoring the distribution of Ae. albopictus is necessary due to related arboviruses transmission, such as Dengue, Zika and Chikungunya. Therefore, this research is relevant to public health in Brazil.

Brain-on-a-chip systems are designed to simulate brain activity using traditional in vitro cell culture on an engineered platform. It is a noninvasive tool to screen new drugs, evaluate toxicants, and elucidate disease mechanisms. However, successful recapitulation of brain function on these systems is dependent on the complexity of the cell culture. In this study, we increased cellular complexity of traditional (simple) neuronal cultures by co-culturing with astrocytes and oligodendrocyte precursor cells (complex culture). We evaluated and compared neuronal activity (e.g., network formation and maturation), cellular composition in long-term culture, and the transcriptome of the two cultures. Compared to simple cultures, neurons from complex co-cultures exhibited earlier synapse and network development and maturation, which was supported by localized synaptophysin expression, up-regulation of genes involved in mature neuronal processes, and synchronized neural network activity. Also, mature oligodendrocytes and reactive astrocytes were only detected in complex cultures upon transcriptomic analysis of age-matched cultures. Functionally, the GABA antagonist bicuculline had a greater influence on bursting activity in complex versus simple cultures. Collectively, the cellular complexity of brain-on-a-chip systems intrinsically develops cell type-specific phenotypes relevant to the brain while accelerating the maturation of neuronal networks, important features underdeveloped in traditional cultures.

Quantitatively benchmarking similarities and differences between the in vivo central nervous system and in vitro neuronal cultures can qualify discrepancies in functional responses and establish the utility of in vitro platforms. In this work, extracellular electrophysiology responses of cortical neurons in awake, freely-moving animals were compared to in vitro cultures of dissociated cortical neurons. After exposure to two well-characterized drugs, atropine and ketamine, a number of key points were observed: (1) significant differences in spontaneous firing activity for in vivo and in vitro systems, (2) similar response trends in single-unit spiking activity after exposure to atropine, and (3) greater sensitivity to the effects of ketamine in vitro. While in vitro cultures of dissociated cortical neurons may be appropriate for many types of pharmacological studies, we demonstrate that for some drugs, such as ketamine, this system may not fully capture the responses observed in vivo. Understanding the functionality associated with neuronal cultures will enhance the relevance of electrophysiology data sets and more accurately frame their conclusions. Comparing in vivo and in vitro rodent systems will provide the critical framework necessary for developing and interpreting in vitro systems using human cells that strive to more closely recapitulate human in vivo function and response.

Background Malaria is a public health problem in the Brazilian Amazon region. In integrated vector management for malaria (anopheline) control, indoor residual spraying (IRS) represents one of the main tools in the basic strategy applied in the Amazonian states. It is essential to understand the residual efficacy of insecticides on different surfaces to determine spray cycles, ensure their rational use, and prevent wastage. This study aimed to evaluate the residual efficacy of six insecticide formulations used in the National Malaria Control Programme on four different types of walls in a field simulation at a “test house”. Methods The tests were performed as a field-simulating evaluation at a “test house” built in the municipality of Macapá. Six insecticide formulations comprising four pyrethroids, a carbamate, and an organophosphate were used, and evaluated when applied on different wall surfaces: painted wood, unpainted wood, plastered cement, and unplastered cement. The insecticides were applied to the interior walls of the “test house” by a trained technician. Results In the bioassays performed with pyrethroids, deltamethrin water-dispersible granules (WG) performed particularly well, presenting residual bioefficacy of 8 months on both wood surfaces after the IRS, whereas alpha-cypermethrin suspension concentrate (SC) and etofenprox wettable powder (WP) demonstrated residual bioefficacy of 4 months on at least one of the wood surfaces; however, the pyrethroid lambda-cyhalothrin WP showed a low residual bioefficacy ( Conclusion Insecticide efficiency varies among different types of surface; therefore, a “test house” is a valuable evaluation tool. This work highlights the usefulness of associating the residual efficacy of insecticides on the surfaces commonly found in houses in endemic areas, together with knowledge about the transmission cycle duration of the transmission cycle and the insecticide susceptibility of the vector. This association helps in the decision-making for the malaria control intervention regarding.

The brain’s extracellular matrix (ECM) is a macromolecular network composed of glycosaminoglycans, proteoglycans, glycoproteins, and fibrous proteins. In vitro studies often use purified ECM proteins for cell culture coatings, however these may not represent the molecular complexity and heterogeneity of the brain’s ECM. To address this, we compared neural network activity (over 30 days in vitro) from primary neurons co-cultured with glia grown on ECM coatings from decellularized brain tissue (bECM) or MaxGel, a non-tissue-specific ECM. Cells were grown on a multi-electrode array (MEA) to enable noninvasive long-term interrogation of neuronal networks. In general, the presence of ECM accelerated the formation of networks without affecting the inherent network properties. However, specific features of network activity were dependent on the type of ECM: bECM enhanced network activity over a greater region of the MEA whereas MaxGel increased network burst rate associated with robust synaptophysin expression. These differences in network activity were not attributable to cellular composition, glial proliferation, or astrocyte phenotypes, which remained constant across experimental conditions. Collectively, the addition of ECM to neuronal cultures represents a reliable method to accelerate the development of mature neuronal networks, providing a means to enhance throughput for routine evaluation of neurotoxins and novel therapeutics.

Oncology is a highly siloed field of research in which sub-disciplinary specialization has limited the amount of information shared between researchers of distinct cancer types. This can be attributed to legitimate differences in the physiology and carcinogenesis of cancers affecting distinct anatomical sites. However, underlying processes that are shared across seemingly disparate cancers probably affect prognosis. The objective of the current study is to investigate whether multitask learning improves 5-year survival cancer patient survival prediction by leveraging information across anatomically distinct HPV related cancers. Data were obtained from the Surveillance, Epidemiology, and End Results (SEER) program database. The study cohort consisted of 29,768 primary cancer cases diagnosed in the United States between 2004 and 2015. Ten different cancer diagnoses were selected, all with a known association with HPV risk. In the analysis, the cancer diagnoses were categorized into three distinct topography groups of varying specificity. The most specific topography grouping consisted of 10 original cancer diagnoses differentiated by the first two digits of the ICD-O-3 topography code. The second topography grouping consisted of cancer diagnoses categorized into six distinct organ groups. Finally, the third topography grouping consisted of just two groups, head-neck cancers and ano-genital cancers. The tasks were to predict 5-year survival for patients within the different topography groups using 14 predictive features which were selected among descriptive variables available in the SEER database. The information from the predictive features was shared between tasks in three different ways, resulting in three distinct predictive models: 1) Information was not shared between patients assigned to different tasks (single task learning); 2) Information was shared between all patients, regardless of task (pooled model); 3) Only relevant information was shared between patients grouped to different tasks (multitask learning). Prediction performance was evaluated with Brier scores. All three models were evaluated against one another on each of the three distinct topography-defined tasks. The results showed that multitask classifiers achieved relative improvement for the majority of the scenarios studied compared to single task learning and pooled baseline methods. In this study, we have demonstrated that sharing information among anatomically distinct cancer types can lead to improved predictive survival models.

Triclocarban (TCC) is among the top 10 most commonly detected wastewater contaminants in both concentration and frequency. Its presence in water, as well as its propensity to bioaccumulate, has raised numerous questions about potential endocrine and developmental effects. Here, we investigated whether exposure to an environmentally relevant concentration of TCC could result in transfer from mother to offspring in CD-1 mice during gestation and lactation using accelerator mass spectrometry (AMS). 14C-TCC (100 nM) was administered to dams through drinking water up to gestation day 18, or from birth to post-natal day 10. AMS was used to quantify 14C-concentrations in offspring and dams after exposure. We demonstrated that TCC does effectively transfer from mother to offspring, both trans-placentally and via lactation. TCC-related compounds were detected in the tissues of offspring with significantly higher concentrations in the brain, heart and fat. In addition to transfer from mother to offspring, exposed offspring were heavier in weight than unexposed controls demonstrating an 11% and 8.5% increase in body weight for females and males, respectively. Quantitative real-time polymerase chain reaction (qPCR) was used to examine changes in gene expression in liver and adipose tissue in exposed offspring. qPCR suggested alterations in genes involved in lipid metabolism in exposed female offspring, which was consistent with the observed increased fat pad weights and hepatic triglycerides. This study represents the first report to quantify the transfer of an environmentally relevant concentration of TCC from mother to offspring in the mouse model and evaluate bio-distribution after exposure using AMS. Our findings suggest that early-life exposure to TCC may interfere with lipid metabolism and could have implications for human health.

The widespread adoption of electronic medical records (EMRs) in healthcare has provided vast new amounts of data for statistical machine learning researchers in their efforts to model and predict patient health status, potentially enabling novel advances in treatment. In the case of sepsis, a debilitating, dysregulated host response to infection, extracting subtle, uncataloged clinical phenotypes from the EMR with statistical machine learning methods has the potential to impact patient diagnosis and treatment early in the course of their hospitalization. However, there are significant barriers that must be overcome to extract these insights from EMR data. First, EMR datasets consist of both static and dynamic observations of discrete and continuous-valued variables, many of which may be missing, precluding the application of standard multivariate analysis techniques. Second, clinical populations observed via EMRs and relevant to the study and management of conditions like sepsis are often heterogeneous; properly accounting for this heterogeneity is critical. Here, we describe an unsupervised, probabilistic framework called a composite mixture model that can simultaneously accommodate the wide variety of observations frequently observed in EMR datasets, characterize heterogeneous clinical populations, and handle missing observations. We demonstrate the efficacy of our approach on a large-scale sepsis cohort, developing novel techniques built on our model-based clusters to track patient mortality risk over time and identify physiological trends and distinct subgroups of the dataset associated with elevated risk of mortality during hospitalization.AbbreviationsEMRelectronic medical recordCMMcomposite mixture modelKPNCKaiser Permanente Northern CaliforniaBICBayesian information criterionAICAkaike information criterionPAMpartitioning around medoidsMICEmultivariate imputation using chained equations

Relata-se a primeira ocorrência de Lutzomyia longipalpis, o mais importante vetor biológico do agente causador da Leishmaniose Visceral Americana (LVA), no município de Ferreira Gomes, estado do Amapá, Brasil e por tratar-se de uma espécie com grande importância epidemiológica, os autores alertam as autoridades locais. Palavras-chave: Amapá, Ferreira Gomes, Lutzomyia longipalpis, Leishmaniose Visceral Americana. DOI: http://dx.doi.org/10.18561/2179-5746/biotaamazonia.v3n2p179-183

In vitro brain-on-a-chip platforms hold promise in many areas including: drug discovery, evaluating effects of toxicants and pathogens, and disease modelling. A more accurate recapitulation of the intricate organization of the brain in vivo may require a complex in vitro system including organization of multiple neuronal cell types in an anatomically-relevant manner. Most approaches for compartmentalizing or segregating multiple cell types on microfabricated substrates use either permanent physical surface features or chemical surface functionalization. This study describes a removable insert that successfully deposits neurons from different brain areas onto discrete regions of a microelectrode array (MEA) surface, achieving a separation distance of 100 μm. The regional seeding area on the substrate is significantly smaller than current platforms using comparable placement methods. The non-permanent barrier between cell populations allows the cells to remain localized and attach to the substrate while the insert is in place and interact with neighboring regions after removal. The insert was used to simultaneously seed primary rodent hippocampal and cortical neurons onto MEAs. These cells retained their morphology, viability, and function after seeding through the cell insert through 28 days in vitro (DIV). Co-cultures of the two neuron types developed processes and formed integrated networks between the different MEA regions. Electrophysiological data demonstrated characteristic bursting features and waveform shapes that were consistent for each neuron type in both mono- and co-culture. Additionally, hippocampal cells co-cultured with cortical neurons showed an increase in within-burst firing rate (p = 0.013) and percent spikes in bursts (p = 0.002), changes that imply communication exists between the two cell types in co-culture. The cell seeding insert described in this work is a simple but effective method of separating distinct neuronal populations on microfabricated devices, and offers a unique approach to developing the types of complex in vitro cellular environments required for anatomically-relevant brain-on-a-chip devices.

International audience; The nine countries sharing the Amazon forest accounted for 89% of all malaria cases reported in the Americas in 2008. Remote sensing can help identify the environmental determinants of malaria transmission and their temporo-spatial evolution. Seventeen studies characterizing land cover or land use features, and relating them to malaria in the Amazon subregion, were identified. These were reviewed in order to improve the understanding of the land cover/use class roles in malaria transmission. The indicators affecting the transmission risk were summarized in terms of temporal components, landscape fragmentation and anthropic pressure. This review helps to define a framework for future studies aiming to characterize and monitor malaria.