Search

A nefrolitíase possui prevalência mundial situada entre 5% e 15%, além de recorrência em adultos de até 75% em 20 anos entre os pacientes que sofrem recidivas. Já em crianças, a nefrolitíase é relativamente infrequente, com prevalência que varia entre 2 a 2,7%, sendo o tratamento dividido entre medidas farmacológicas e medidas não farmacológicas. O presente estudo de revisão buscou avaliar novas evidências na abordagem terapêutica da nefrolitíase em pacientes pediátricos, documentados por meio de estudos clínicos e randomizados. Trata-se de uma pesquisa de revisão integrativa realizada por meio da base de dados PubMed, que levou em consideração os seguintes critérios de inclusão: ensaios clínicos e testes controlados e aleatórios; artigos publicados nos últimos quatro anos; que possuíam texto completo disponível e que abordassem acerca da terapêutica da nefrolitíase na população pediátrica. Ficou constatado que no âmbito do tratamento farmacológico, o lumasiran se mostrou útil na diminuição da taxa de eventos provocados por cálculos em crianças com hiperoxalúria tipo 1. No que se refere à terapêutica não invasiva, estudos apontaram as frequências intermediárias como as melhores a serem utilizadas durante a LECO em crianças, além do aumento da eficácia deste procedimento quando feito após hidronefrose artificial. Quanto à terapêutica invasiva, foi atestado o aumento da eficácia da NLPC quando feito sob a técnica de dilatação de passo único em crianças com nefrolitíase. Ainda sobre a NLPC, foi observado não haver diferença significativa de complicações ou dor pós-operatória da cirurgia em relação à posição da criança.

Background Most cases of malaria in Brazil are concentrated in the Amazon region. One of the vector control alternatives recommended by the WHO is the long-lasting insecticidal net (LLIN). This tool is used in the nine federal states of the Brazilian Legal Amazon, where LLINs are essential for reducing vector density and disease transmission as they prevent contact between the mosquito and the individual. The objective of this study was to evaluate the residuality and use of LLIN insecticides in different health regions in a city located in the Brazilian Amazon. Methods A total of 17,027 LLINs were installed in the third, fifth and ninth health regions of the municipality of Porto Velho, Rondonia State, Brazil. The LLINs were of two types: Olyset (permethrin), for around the bed, and Interceptor (alphacypermethrin), for around hammocks. The residuality of 172 LLINs was evaluated using cone bioassays to verify the mortality rate of the mosquito Nyssorhynchus darlingi, over a period of 2 years. Structured questionnaires on the acceptance and use of LLINs were distributed to the participating population (n = 391), covering a total sample of 1147 mosquito nets. The mortality rate was evaluated both in terms of days after LLIN installation and the type of insecticide used. Statistical analyses were based on analysis of variance (ANOVA) and Chi-square and were performed using the SPSS statistical program. Results For the Ny. darlingi mosquito, Interceptor-type LLINs showed residual efficacy, with mortality rates ≥ 80% during the 2-year study period, as determined by the WHO. In contrast, Olyset-type LLINs were associated with a reduction in mortality rates, with 76% and 45% mortality rates in the last two assessments, which occurred during the last 6 months of the study period. Based on the structured questionnaires, the acceptance rate, i.e. percentage of individuals accepting the permanence of the 1147 LLINs sampled, in the three health regions of Porto Velho was 93.8% (of 1076 LLINs). Conclusion The alphacypermethrin-impregnated LLIN was more effective than the LLIN impregnated with permethrin. The results indicate that the correct use of mosquito nets—and consequently the protection of the population—needs to be supported by health promotion actions. These initiatives are considered to be essential for the success of this vector control strategy. New studies that consider the monitoring of the placement of mosquito nets are necessary to provide effective support in the correct use of this methodology. Graphical Abstract

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.

Objetivo: Expor a experiência dos residentes de Ginecologia e Obstetrícia acerca do treinamento, aos moldes de ensino semipresencial, do procedimento de Exérese da Zona de Transformação (EZT) em um modelo anatômico de colo uterino. Métodos: Trata-se de um trabalho do tipo transversal, observacional, qualitativo e unicêntrico, tendo a coleta sido realizada no mês de fevereiro de 2023, no Centro de Especialidades Médicas, tendo um total de 26 participantes, que estavam cursando residência de Ginecologia e Obstetrícia. Resultados: A pesquisa mostrou que a ferramenta tecnológica como auxílio no processo de aprendizagem dos residentes é um método válido e eficaz. A avaliação feita pelo Google Forms acerca do nível de experiência dos residentes no procedimento de EZT com o modelo utilizado apresentou um resultado maioritariamente positivo, tendo eles se autoavaliado como Muito bom: 17 (65,38%), Bom: 1 (3,84%), Mediano: 4 (15,38%), Ruim: 2 (7,69%) e Muito ruim: 2 (7,69%). Conclusão: Os participantes da pesquisa relataram mais segurança, habilidade e experiência ao realizar o procedimento de EZT após o treinamento proposto, evidenciando os benefícios deste modelo de ensino.

Background Machine learning (ML) has made a significant impact in medicine and cancer research; however, its impact in these areas has been undeniably slower and more limited than in other application domains. A major reason for this has been the lack of availability of patient data to the broader ML research community, in large part due to patient privacy protection concerns. High-quality, realistic, synthetic datasets can be leveraged to accelerate methodological developments in medicine. By and large, medical data is high dimensional and often categorical. These characteristics pose multiple modeling challenges. Methods In this paper, we evaluate three classes of synthetic data generation approaches; probabilistic models, classification-based imputation models, and generative adversarial neural networks. Metrics for evaluating the quality of the generated synthetic datasets are presented and discussed. Results While the results and discussions are broadly applicable to medical data, for demonstration purposes we generate synthetic datasets for cancer based on the publicly available cancer registry data from the Surveillance Epidemiology and End Results (SEER) program. Specifically, our cohort consists of breast, respiratory, and non-solid cancer cases diagnosed between 2010 and 2015, which includes over 360,000 individual cases. Conclusions We discuss the trade-offs of the different methods and metrics, providing guidance on considerations for the generation and usage of medical synthetic data.

Three-dimensional (3D) in vitro models have become increasingly popular as systems to study cell-cell and cell-ECM interactions dependent on the spatial, mechanical, and chemical cues within the environment of the tissue, which is limited in traditional two-dimensional (2D) models. Although electrophysiological recordings of neuronal action potentials through 2D microelectrode arrays (MEAs) are a common and trusted method of evaluating neuronal function, network communication, and response to chemicals and biologicals, there are currently limited options for measuring electrophysiological activity from many locations simultaneously throughout a 3D network of neurons in vitro. Here, we have developed a thin-film, 3D flexible microelectrode array (3DMEA) that non-invasively interrogates a 3D culture of neurons and can accommodate 256 channels of recording or stimulation. Importantly, the 3DMEA is straightforward to fabricate and integrates with standard commercially available electrophysiology hardware. Polyimide probe arrays were microfabricated on glass substrates and mechanically actuated to collectively lift the arrays into a vertical position, relying solely on plastic deformation of their base hinge regions to maintain vertical alignment. Human induced pluripotent stem cell (hiPSC)-derived neurons and astrocytes were entrapped in a collagen-based hydrogel and seeded onto the 3DMEA, enabling growth of suspended cells in the matrix and the formation and maturation of a neural network around the 3DMEA probes. The 3DMEA supported the growth of functional neurons in 3D with action potential spike and burst activity recorded over 45 days in vitro. This platform is an important step in facilitating noninvasive electrophysiological characterization of 3D networks of electroactive cells in vitro.

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.

Este estudio1 tuvo como objetivo identificar la relación entre la consciencia ambiental y el comportamiento de practicantes de actividades de ocio en la naturaleza, concretamente de turistas y habitantes locales de las islas del Rio Araguaia durante las vacaciones del mes de julio. Se utilizó la Escala de Comportamiento Responsable, en una muestra de 235 personas. Se partió de la premisa de que la consciencia y el comportamiento no se relacionan de manera lineal. La investigación indicó que el 90.87% de los sujetos investigados presentan un nivel de consciencia ambiental adecuado, un número muy cercano de los 90.09% que conectan este nivel de consciencia con un comportamiento responsable. Los datos indican que existe la necesidad de profundizar en las prácticas pedagógicas, en las acciones y en las políticas públicas que puedan ayudar a las personas a reflexionar sobre su consciencia en sus actitudes.1 Esta investigación ha tenido apoyo de la UFG – Universidade Federal de Goiás y de la FAPEG - Fundação de Apoio à Pesquisa de Goiás, para su realización.

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

While machine learning (ML) has shown some promise in medical research, its actual impact has been limited relative to other application domains. One reason for this disparity is the lack of high-quality, patient-level data available to the broader ML research community. Such datasets are often not made available due to protections around patient privacy. To overcome these obstacles, high-quality, synthetic datasets could be leveraged to accelerate methodological developments in the application of ML to biomedical research. Clinical data in the form of electronic health records present a rich data source to be used for synthetic data generation. Such data can be high dimensional and predominantly categorical, which poses multiple challenges from a modeling perspective. In this paper, we evaluate four classes of synthetic data generation techniques, as well as several metrics for evaluating the quality of the synthetic data. While the results and discussions are broadly applicable to medical data, for demonstration purposes we generate synthetic datasets from the publicly available Surveillance Epidemiology and End Results (SEER) program. Specifically, our cohort consists of breast cancer cases diagnosed in the year of 2010, which includes over 26000 individual cases. Finally, we discuss the trade-offs of the different methods and metrics, providing guidance on considerations for the generation and usage of synthetic medical data. Citation Format: Andre R. Goncalves, Priyadip Ray, Braden Soper, Madhumita Myneni, Jennifer L. Stevens, Linda M. Coyle, Ana Paula Sales. Generation and evaluation of medical synthetic data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1641.

O presente trabalho teve como objetivo central estudar o comportamento reprodutivo do Aedes aegypti L. em condicoes adversas para a especie e verificar novas opcoes de desovas em criadouros alternativos como as fossas, alem de relacionar aos diferentes aspectos socioeconomicos, ambientais e climaticos onde este mosquito encontra-se incriminado na transmissao da dengue. Realizou-se mapeamento dos imoveis inspecionados, destacando-se aqueles com criadouros positivos para formas imaturas de Ae. aegypti , alem de entrevistas com os residentes locais. No bairro do Trem os depositos moveis, detiveram 21,90% das larvas coletadas no periodo seco e 24,60% no periodo chuvoso; depositos fixos 22,38% no periodo seco e 20,59% no periodo chuvoso; e depositos removiveis 49,05% no periodo seco e 48,93% no periodo chuvoso. No Bairro Cidade Nova as fossas e depositos de agua para consumo, detiveram 26,79% e 18,66% no periodo seco respectivamente. No periodo chuvoso, os depositos de agua para consumo subiram para 34,16%, as fossas se mantiveram inalteradas, sugerindo que esse ultimo seja considerado um recipiente preferencial para a desova do mosquito. Conclui-se que o criadouro do tipo “fossa”, mostrou ser uma nova opcao de oviposicao para o Ae. aegypti e que e imprescindivel a implementacao de medidas de controle e solucoes definitivas para o fator fossa, considerando-se a adaptacao do vetor neste ambiente e o fato do municipio de Macapa apresentar cerca de 8% apenas, de rede coletora de esgoto sanitario, o que reforca a possibilidade da oviposicao do Ae. aegypti nas fossas de outros bairros com caracteristicas de infraestrutura urbana semelhantes. http://dx.doi.org/10.22292/mas.v9i4.378

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.

Emergence of drug-resistant strains of the pathogen Mycobacterium tuberculosis (Mtb) and the ineffectiveness of BCG in curtailing Mtb infection makes vaccine development for tuberculosis an important objective. Identifying immunogenic CD8+ T cell peptide epitopes is necessary for peptide-based vaccine strategies. We present a three-tiered strategy for identifying and validating immunogenic peptides: first, identify peptides that form stable complexes with class I MHC molecules; second, determine whether cytotoxic T lymphocytes (CTLs) raised against the whole protein antigen recognize and lyse target cells pulsed with peptides that passed step 1; third, determine whether peptides that passed step 2, when administered in vivo as a vaccine in HLA-A2 transgenic mice, elicit CTLs that lyse target cells expressing the whole protein antigen. Our innovative approach uses dendritic cells transfected with Mtb antigen-encoding mRNA to drive antigen expression. Using this strategy, we have identified five novel peptide epitopes from the Mtb proteins Apa, Mtb8.4 and Mtb19.

Alterations in tooth shape and size are related to impaired esthetic appearance and facial harmony and can have a negative effect on a patient's self-esteem. Many alternatives for treating peg-shaped teeth are described in the literature. This article presents two clinical cases involving peg-shaped lateral incisors that were transformed with composite resins, addressing aspects related to treatment alternatives and facial esthetics. At a seven-year recall visit, the composite resins were problem-free and the patients remained very happy with the treatment outcome. Composite resin appears to be an adequate alternative to direct treatment options for restoring peg-shaped incisors.

Motivation: Immunoassays are primary diagnostic and research tools throughout the medical and life sciences. The common approach to the processing of immunoassay data involves estimation of the calibration curve followed by inversion of the calibration function to read off the concentration estimates. This approach, however, does not lend itself easily to acceptable estimation of confidence limits on the estimated concentrations. Such estimates must account for uncertainty in the calibration curve as well as uncertainty in the target measurement. Even point estimates can be problematic: because of the non-linearity of calibration curves and error heteroscedasticity, the neglect of components of measurement error can produce significant bias. Methods: We have developed a Bayesian approach for the estimation of concentrations from immunoassay data that treats the propagation of measurement error appropriately. The method uses Markov Chain Monte Carlo (MCMC) to approximate the posterior distribution of the target concentrations and numerically compute the relevant summary statistics. Software implementing the method is freely available for public use. Results: The new method was tested on both simulated and experimental datasets with different measurement error models. The method outperformed the common inverse method on samples with large measurement errors. Even in cases with extreme measurements where the common inverse method failed, our approach always generated reasonable estimates for the target concentrations. Availability: Project name: Baecs; Project home page: www.computationalimmunology.org/utilities/; Operating systems: Linux, MacOS X and Windows; Programming language: C++; License: Free for Academic Use. Contact: feng.feng@duke.edu Supplementary information: Supplementary data are available at Bioinformatics online.