Modeling the temporal network dynamics of neuronal cultures.

Neurons form complex networks that evolve over multiple time scales. In order to thoroughly characterize these networks, time dependencies must be explicitly modeled. Here, we present a statistical model that captures both the underlying structural and temporal dynamics of neuronal networks. Our model combines the class of Stochastic Block Models for community formation with Gaussian processes to model changes in the community structure as a smooth function of time. We validate our model on synthetic data and demonstrate its utility on three different studies using in vitro cultures of dissociated neurons.

Generation and evaluation of synthetic patient data.

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.

Bayesian multitask learning regression for heterogeneous patient cohorts.

Multitask learning (MTL) leverages commonalities across related tasks with the aim of improving individual task performance. A key modeling choice in designing MTL models is the structure of the tasks' relatedness, which may not be known. Here we propose a Bayesian multitask learning model that is able to infer the task relationship structure directly from the data. We present two variations of the model in terms of a priori information of task relatedness. First, a diffuse Wishart prior is placed on a task precision matrix so that all tasks are assumed to be equally related a priori. Second, a Bayesian graphical LASSO prior is used on the task precision matrix to impose sparsity in the task relatedness. Motivated by machine learning applications in the biomedical domain, we emphasize interpretability and uncertainty quantification in our models. To encourage model interpretability, linear mappings from the shared input spaces to task-dependent output spaces are used. To encourage uncertainty quantification, conjugate priors are used so that full posterior inference is possible. Using synthetic data, we show that our model is able to recover the underlying task relationships as well as features jointly relevant for all tasks. We demonstrate the utility of our model on three distinct biomedical applications: Alzheimer's disease progression, Parkinson's disease assessment, and cervical cancer screening compliance. We show that our model outperforms Single Task (STL) models in terms of predictive performance, and performs better than existing MTL methods for the majority of the scenarios.

Flexible, cluster-based analysis of the electronic medical record of sepsis with composite mixture models.

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.

Spirituality, Religiosity, Quality of Life and Mental Health Among Pantaneiros: A Study Involving a Vulnerable Population in Pantanal Wetlands, Brazil.

This study aims to investigate the relationship between spirituality, religiosity (S/R), mental health and quality of life in a vulnerable population in the Pantanal wetlands of Brazil. A total of 129 individuals were interviewed. We examined mental health (Hospital Anxiety and Depression Scale), quality of life (SF-12), spirituality (self-spirituality rating scale), religiosity (DUREL) and R/S opinions. Individuals had high levels of spirituality, non-organizational and intrinsic religiosity, but low levels of religious attendance. Most participants said they would like to have their faith addressed by a health professional and that this approach would strengthen their trust on the doctors. Higher levels of spirituality were associated with less anxiety (ß = - 0.236, p < 0.01) and depressive symptoms (ß = - 0.398, p < 0.001); higher levels of non-organizational religiosity were associated with less anxiety (ß = - 0.250, p < 0.01) and depressive symptoms (ß = - 0.351, p < 0.001); and higher levels of intrinsic religiosity were associated with less depressive symptoms (ß = - 0.315, p < 0.001). Quality of life was not associated with any religious/spiritual measures.

[Choice of type of delivery: factors reported by puerperal woman]. / Escolha do tipo de parto: fatores relatados por puérperas.

OBJECTIVE: To know the factors listed by puerperal woman that influenced the choice in type of delivery. METHODS: Qualitative research, developed with 25 puerperal women in a University Hospital of Mato Grosso do Sul, between September and November 2014. A semi-structured interview was used for data collection and the Collective Subject Discourse was used to organize and tabulate the data collected. RESULTS: The Collective Subject Discourses resulted in the following categories: Desire for the type of delivery; Respect for the chosen type of delivery and Factors that influenced the choice. Most of the women interviewed (76%) expressed a preference for normal birth due to rapid recovery, less pain and suffering. CONCLUSIONS: The influence of family members, previous experience with childbirth, professional-customer interaction and internet information influenced the type of delivery chosen, which reinforces the importance of health education since the pre-natal stage, highlighting the need to equip women to perform a conscious choice.

A Bayesian approach for estimating calibration curves and unknown concentrations in immunoassays.

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.

Improving five-year survival prediction via multitask learning across HPV-related cancers.

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.

A flexible 3-dimensional microelectrode array for in vitro brain models.

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.

Functional and transcriptional characterization of complex neuronal co-cultures.

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.

Tissue-specific extracellular matrix accelerates the formation of neural networks and communities in a neuron-glia co-culture on a multi-electrode array.

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.

Improving peptide-MHC class I binding prediction for unbalanced datasets.

BACKGROUND: Establishment of peptide binding to Major Histocompatibility Complex class I (MHCI) is a crucial step in the development of subunit vaccines and prediction of such binding could greatly reduce costs and accelerate the experimental process of identifying immunogenic peptides. Many methods have been applied to the prediction of peptide-MHCI binding, with some achieving outstanding performance. Because of the experimental methods used to measure binding or affinity between peptides and MHCI molecules, however, available datasets are enriched for nonbinders, and thus highly unbalanced. Although there is no consensus on the ideal class distribution for training sets, extremely unbalanced datasets can be detrimental to the performance of prediction algorithms. RESULTS: We have developed a decision-theoretic framework to construct cost-sensitive trees to predict peptide-MHCI binding and have used them to 1) Assess the impact of the training data's class distribution on classifier accuracy, and 2) Compare resampling and cost-sensitive methods as approaches to compensate for training data imbalance. Our results confirm that highly unbalanced training sets can reduce the accuracy of classifier predictions and show that, in the peptide-MHCI binding context, resampling methods do not improve the classifier performance. In contrast, cost-sensitive methods significantly improve accuracy of decision trees. Finally, we propose the use of a training scheme that, when the training set is enriched for nonbinders, consistently improves the overall classifier accuracy compared to cost-insensitive classifiers and, in particular, increases the sensitivity of the classifiers. This method minimizes the expected classification cost for large datasets. CONCLUSION: Our method consistently improves the performance of decision trees in predicting peptide-MHC class I binding by using cost-balancing techniques to compensate for the imbalance in the training dataset.

Evaluation of in vitro neuronal platforms as surrogates for in vivo whole brain systems.

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.

Insulin autoimmune syndrome in an occidental woman: a case report and literature review.

Insulin autoimmune syndrome (IAS, Hirata's disease) is a rare hypoglycemic disorder characterized by spontaneous hypoglycemia associated with extremely high circulating insulin levels and positive anti-insulin antibody results. Thus far, most cases have been reported in Asian countries, notably Japan, with few cases reported in western countries. As a possible cause, it is associated with the use of drugs containing sulfhydryl radicals, such as captopril. This report refers to a 63-year-old female Brazilian patient with a history of postprandial hypoglycemia. After extensive investigation and exclusion of other causes, her hyperinsulinemic hypoglycemia was considered to have likely been induced by captopril. Most cases of IAS are self-limiting. However, dietary management, corticosteroids, plasmapheresis, and rituximab have already been used to treat patients with IAS. In our case, after discontinuation of captopril, an initial decrease in insulin autoantibody levels was observed followed by improvement in episodes of hypoglycemia. Although it is a rare disease, IAS should be considered in the differential diagnosis of endogenous hyperinsulinemic hypoglycemia. Patients with suspected IAS must be screened for autoimmunity-related drugs for insulin. Initial clinical suspicion of IAS can avoid unnecessary costs associated with imaging examinations and/or invasive surgical procedures.

Fatores de risco para near miss materno: revisão integrativa / Risk factors for maternal near miss: an integrative review / Factores de riesgo para near miss materno: revisión integrativa

Objetivo: sumarizar os principais fatores de risco relacionados ao near miss materno. Método: revisão integrativa da literatura. A busca foi efetuada em 21 de março de 2021, nas bases de dados: NationalLibrary of Medicine - Medline via PubMed; Current Index to Nursing and Allied Health Literature; Science Direct,Elservier's Scopus, Web of Science e no portal da Biblioteca Virtual de Saúde. Os estudos foram avaliados com a Hierarchy of Evidence for Intervention Studies. Resultados: 12 artigos compuseram a revisão, todos de método quantitativo e idioma inglês. As evidências destacaram como risco para near miss materno: distúrbios hipertensivos, complicações hemorrágicas e a sepse puerperal. Demais achados relacionam-se à distância da moradia e dificuldade de acesso aos serviços de saúde além da baixa escolaridade. Conclusões: os fatores de risco para near miss materno se relacionam com pré-natal inadequado, decorrente de questões geográficas e falta de acesso aos serviços, questões econômicas, educacionais e sociais.

Maternal exposure to an environmentally relevant dose of triclocarban results in perinatal exposure and potential alterations in offspring development in the mouse model.

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.

Mapa conceitual: representação social na graduação de enfermagem / Conceptual map: social representation in nursing graduation / Mapa conceptual: representación social en graduación de enfermería

Objetivo: Compreender as representações sociais do uso de mapas conceituais em grupo tutorial de estudantes de enfermagem. Método: Pesquisa qualitativa, feita a partir da análise do Discurso do Sujeito Coletivo de 26 estudantes de uma universidade pública no Centro-Oeste do Brasil. Resultados: Emergiram duas categorias provenientes da análise do discurso: 1) Mapa Conceitual: ferramenta facilitadora de organização da aprendizagem e 2) Mapa Conceitual: ferramenta que provoca tensão e desfavorece o aprendizado. Conclusões: O mapa conceitual facilita o auto estudo e a aprendizagem, pois possibilita a sintetização do conhecimento e a organização do raciocínio. Quanto à sua utilização coletiva, observou-se falhas em relação ao tempo gasto na elaboração e na necessidade de encontrar palavras de ligação. (AU)

Objective: To understand the social representations of the use of concept maps in a tutorial group of nursing students. Methods: Qualitative research, based on the Collective Subject Discourse analysis of 26 students from a public university in the Midwest of Brazil. Results: Two categories emerged from the discourse analysis: 1) Concept Map: a facilitating tool for learning organization and 2) Concept Map: a tool that causes tension and disadvantages learning. Conclusion: The concept map facilitates self-study and learning, as it enables the synthesis of knowledge and the organization of reasoning. Regarding their collective use, there were flaws in the time spent in elaboration and in the need to find linking words. (AU)

Objetivo: Comprender las representaciones sociales del uso de mapas conceptuales en un grupo tutorial de estudiantes de enfermería. Métodos: Investigación cualitativa, basada en el análisis del discurso del sujeto colectivo de 26 estudiantes de una universidad pública en el medio oeste de Brasil. Resultados: Del análisis del discurso surgieron dos categorías: 1) Mapa conceptual: una herramienta facilitadora para la organización del aprendizaje y 2) Mapa conceptual: una herramienta que causa tensión y desventajas del aprendizaje. Conclusion: El mapa conceptual facilita el autoaprendizaje y el aprendizaje, ya que permite la síntesis del conocimiento y la organización del razonamiento. Con respecto a su uso colectivo, hubo fallas en el tiempo dedicado a la elaboración y en la necesidad de encontrar palabras de enlace. (AU)

High-throughput identification and dendritic cell-based functional validation of MHC class I-restricted Mycobacterium tuberculosis epitopes.

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.

Insulin autoimmune syndrome in an occidental woman: a case report and literature review

SUMMARY Insulin autoimmune syndrome (IAS, Hirata's disease) is a rare hypoglycemic disorder characterized by spontaneous hypoglycemia associated with extremely high circulating insulin levels and positive anti-insulin antibody results. Thus far, most cases have been reported in Asian countries, notably Japan, with few cases reported in western countries. As a possible cause, it is associated with the use of drugs containing sulfhydryl radicals, such as captopril. This report refers to a 63-year-old female Brazilian patient with a history of postprandial hypoglycemia. After extensive investigation and exclusion of other causes, her hyperinsulinemic hypoglycemia was considered to have likely been induced by captopril. Most cases of IAS are self-limiting. However, dietary management, corticosteroids, plasmapheresis, and rituximab have already been used to treat patients with IAS. In our case, after discontinuation of captopril, an initial decrease in insulin autoantibody levels was observed followed by improvement in episodes of hypoglycemia. Although it is a rare disease, IAS should be considered in the differential diagnosis of endogenous hyperinsulinemic hypoglycemia. Patients with suspected IAS must be screened for autoimmunity-related drugs for insulin. Initial clinical suspicion of IAS can avoid unnecessary costs associated with imaging examinations and/or invasive surgical procedures.

Prevalência do tabagismo e terapêutica da dependência de nicotina: uma revisão integrativa / Smoking prevalence and of nicotine dependence’s therapeutics: anintegrative review / Prevalencia de tabaquismo y tratamiento de la dependencia a la nicotina: una revisión integradora

Objective: To identify available evidence in the literature on the epidemiological aspects, the approach to the smoker, and therapeutic of nicotine addiction in the population. Method: This is an integrative review carried out in the Health Virtual Library using the following combinations of keywords: “prevalence and smoking,” “dependence and nicotine”, “depression and smoking,” and “smoking cessation.” Results: The results from the sixteen selected articles pointed out: incomplete primary education, low income, early teenage smoking, and higher prevalence and recurrence among women than men. Conclusion: Smoking is more prevalent in women,and women have a higher incidence of relapse after treatment than men. The most widely used therapy was the useof drugs in conjunction with a psychosocial approach and/or cognitive behavioral method. It is necessary to train health professionals to understand the smoking habit as a public health problem to refer individuals to treatment.

Objetivo: Identificar evidências disponíveis na literatura sobre os aspectos epidemiológicos, abordagem do tabagista e terapêutica da dependência da nicotina na população. Método: Trata-se de revisão integrativa realizada na Biblioteca Virtual de Saúde, com as seguintes associações de palavras chave:“prevalência e tabagismo”, “dependência e nicotina”, “depressão e tabagismo” e “abandono do hábito de fumar”. Resultados: Os resultados dos dezesseis artigos selecionados apontaram: ensino fundamental incompleto, baixa renda, início do tabagismo na adolescência, maior prevalência e recidiva entre mulheres. Conclusão: O tabagismo é mais prevalente em mulheres e estas apresentam maior recidiva após o tratamento. A terapêutica mais utilizada foi o uso conjunto de fármacos com a abordagem psicossocial e/ou método cognitivo comportamental. É necessário capacitar os profissionais de saúde para compreender o tabagismo como problema de saúde pública e encaminhar o usuário para tratamento.

Objetivo: Identificar evidencias disponibles en la literatura sobre los aspectos epidemiológicos, abordaje del tabaquista y terapéutica de la dependencia de la nicotina en la población. Método: Se trata de una revisión integradora realizada en la Biblioteca Virtual de Salud, con las siguientes asociaciones de palabras-clave: “prevalencia y tabaquismo”, “dependencia y nicotina”, “depresión y tabaquismo”, y “abandono del hábito de fumar”. Resultados: Los resultados de los dieciséis artículos seleccionados señalaron: enseñanza primaria incompleta, baja renta, inicio del tabaquismo en la adolescencia, mayor prevalencia y recurrencia entre mujeres. Conclusión: El tabaquismo es más frecuente entre mujeres y éstas presentan mayor recurrencia después del tratamiento. La terapéutica más utilizada fue el uso conjunto de fármacos con el abordaje psicosocial y/o método cognitivo comportamental. Es necesario capacitar los profesionales de salud para comprender el tabaquismo como problema de salud pública y encaminar el usuario para tratamiento.