Recording of hippocampal activity on the effect of convulsant doses of caffeine.

Seizures occur when there is a hyper-excitation of the outer layer of the brain, with subsequent excessive synchrony in a group of neurons. According to the World Health Organization (WHO), an estimated 50 million people are affected by this disease, a third of whom are resistant to the treatments available on the market. Caffeine (1,3,7-trimethylxanthine), which belongs to the purine alkaloid family, is the most widely consumed psychoactive drug in the world. It is ingested by people through drinks containing this substance, such as coffee, and as an adjuvant in analgesic therapy with non-steroidal antiflammatory drugs. The present study evaluated the electrocorticographic changes observed in the hippocampus of Wistar rats subjected to acute doses of caffeine (150 mg/kg i.p), which represents a toxic dose of caffeine corresponding to an estimated acute intake of more than 12 cups of coffee to record its convulsant activity. Our results showed, for the first time, that the administration of high doses of caffeine (150 mg/kg i.p.) in rats caused an increase in the spectral distribution of power in all frequency bands and suggested the appearance of periods of ictal and interictal peaks in the electrocorticogram (ECog). We have also shown that the anticonvulsants phenytoin, diazepam and phenobarbital have a satisfactory response when associated with caffeine.

Towards objective and systematic evaluation of bias in artificial intelligence for medical imaging.

OBJECTIVE: Artificial intelligence (AI) models trained using medical images for clinical tasks often exhibit bias in the form of subgroup performance disparities. However, since not all sources of bias in real-world medical imaging data are easily identifiable, it is challenging to comprehensively assess their impacts. In this article, we introduce an analysis framework for systematically and objectively investigating the impact of biases in medical images on AI models. MATERIALS AND METHODS: Our framework utilizes synthetic neuroimages with known disease effects and sources of bias. We evaluated the impact of bias effects and the efficacy of 3 bias mitigation strategies in counterfactual data scenarios on a convolutional neural network (CNN) classifier. RESULTS: The analysis revealed that training a CNN model on the datasets containing bias effects resulted in expected subgroup performance disparities. Moreover, reweighing was the most successful bias mitigation strategy for this setup. Finally, we demonstrated that explainable AI methods can aid in investigating the manifestation of bias in the model using this framework. DISCUSSION: The value of this framework is showcased in our findings on the impact of bias scenarios and efficacy of bias mitigation in a deep learning model pipeline. This systematic analysis can be easily expanded to conduct further controlled in silico trials in other investigations of bias in medical imaging AI. CONCLUSION: Our novel methodology for objectively studying bias in medical imaging AI can help support the development of clinical decision-support tools that are robust and responsible.

Factors associated with older adults' cognitive decline 6 months after gamma-variant SARS-CoV-2 infection.

Background: Cognitive deficits are commonly reported after COVID-19 recovery, but little is known in the older population. This study aims to investigate possible cognitive damage in older adults 6 months after contracting COVID-19, as well as individual risk factors. Methods: This cross-sectional study involved 70 participants aged 60-78 with COVID-19 6 months prior and 153 healthy controls. Montreal Cognitive Assessment-Basic (MoCA-B) screened for cognitive impairment; Geriatric Depression Scale and Geriatric Anxiety Inventory screened for depression and anxiety. Data were collected on demographics and self-reports of comorbid conditions. Results: The mean age of participants was 66.97 ± 4.64 years. A higher proportion of individuals in the COVID group complained about cognitive deficits (χ2 = 3.574; p = 0.029) and presented with deficient MoCA-B scores (χ2 = 6.098, p = 0.014) compared to controls. After controlling for multiple variables, all the following factors resulted in greater odds of a deficient MoCA-B: COVID-19 6-months prior (OR, 2.44; p = 0.018), age (OR, 1.15; p < 0.001), lower income (OR, 0.36; p = 0.070), and overweight (OR, 2.83; p = 0.013). Further analysis pointed to individual characteristics in COVID-19-affected patients that could explain the severity of the cognitive decline: age (p = 0.015), lower income (p < 0.001), anxiety (p = 0.049), ageusia (p = 0.054), overweight (p < 0.001), and absence of cognitively stimulating activities (p = 0.062). Conclusion: Our study highlights a profile of cognitive risk aggravation over aging after COVID-19 infection, which is likely mitigated by wealth but worsened in the presence of overweight. Ageusia at the time of acute COVID-19, anxiety, being overweight, and absence of routine intellectual activities are risk factors for more prominent cognitive decline among those infected by COVID-19.

A multi-center distributed learning approach for Parkinson's disease classification using the traveling model paradigm.

Distributed learning is a promising alternative to central learning for machine learning (ML) model training, overcoming data-sharing problems in healthcare. Previous studies exploring federated learning (FL) or the traveling model (TM) setup for medical image-based disease classification often relied on large databases with a limited number of centers or simulated artificial centers, raising doubts about real-world applicability. This study develops and evaluates a convolution neural network (CNN) for Parkinson's disease classification using data acquired by 83 diverse real centers around the world, mostly contributing small training samples. Our approach specifically makes use of the TM setup, which has proven effective in scenarios with limited data availability but has never been used for image-based disease classification. Our findings reveal that TM is effective for training CNN models, even in complex real-world scenarios with variable data distributions. After sufficient training cycles, the TM-trained CNN matches or slightly surpasses the performance of the centrally trained counterpart (AUROC of 83% vs. 80%). Our study highlights, for the first time, the effectiveness of TM in 3D medical image classification, especially in scenarios with limited training samples and heterogeneous distributed data. These insights are relevant for situations where ML models are supposed to be trained using data from small or remote medical centers, and rare diseases with sparse cases. The simplicity of this approach enables a broad application to many deep learning tasks, enhancing its clinical utility across various contexts and medical facilities.

The context of COVID-19 affected the long-term sleep quality of older adults more than SARS-CoV-2 infection.

Introduction: Sleep problems are one of the most persistent symptoms of post-COVID syndrome in adults. However, most recent research on sleep quality has relied on the impact of the pandemic, with scarcely any data for older adults on the long-term consequences of COVID infection. This study aims to understand whether older individuals present persistently impaired sleep quality after COVID-19 infection and possible moderators for this outcome. Methods: This is a cross-sectional analysis of a longitudinal cohort study with 70 elders with 6-month-previous SARS-CoV-2 infection and 153 controls. The Pittsburgh Sleep Quality Index (PSQI) was used to assess sleep quality; Geriatric Depression Scale and Geriatric Anxiety Inventory for screening depression and anxiety. Demographics and comorbid conditions were collected. Results: The mean age of participants was 66,97 ± 4,64 years. There were no statistical differences in depression and anxiety between groups. Poor sleep quality was found in 52,9% and 43,8% of the COVID and control groups (p=.208). After controlling for multiple variables, all the following factors resulted in greater chances of poor sleep quality: female gender (OR, 2.12; p=.027), memory complaints (OR, 2.49; p=.074), insomnia (OR, 3.66; p=.032), anxiety (OR, 5.46; p<.001), depression (OR, 7.26; p=.001), joint disease (OR, 1.80; p=.050), glucose intolerance (OR, 2.20; p=.045), psychoactive drugs (OR, 8.36; p<.001), diuretics (OR, 2.46; p=.034), and polypharmacy (OR, 2.84; p=.016). Conclusion: Psychosocial burden in the context of the COVID-19 pandemic and pre-existing conditions seems to influence the sleep quality of older adults more than SARS-CoV-2 infection.

What you have, not who you know: food-enhanced social capital and changes in social behavioural relationships in a non-human primate.

Social network position in non-human primates has far-reaching fitness consequences. Critically, social networks are both heterogeneous and dynamic, meaning an individual's current network position is likely to change due to both intrinsic and extrinsic factors. However, our understanding of the drivers of changes in social network position is largely confined to opportunistic studies. Experimental research on the consequences of in situ, controlled network perturbations is limited. Here we conducted a food-based experiment in rhesus macaques to assess whether allowing an individual the ability to provide high-quality food to her group changed her social behavioural relationships. We considered both her social network position across five behavioural networks, as well as her dominance and kin interactions. We found that gaining control over a preferential food resource had far-reaching social consequences. There was an increase in both submission and aggression centrality and changes in the socio-demographic characteristics of her agonistic interaction partners. Further, we found that her grooming balance shifted in her favour as she received more grooming than she gave. Together, these results provide a novel, preliminary insight into how in situ, experimental manipulations can modify social network position and point to broader network-level shifts in both social capital and social power.

Identifying Biases in a Multicenter MRI Database for Parkinson's Disease Classification: Is the Disease Classifier a Secret Site Classifier?

Sharing multicenter imaging datasets can be advantageous to increase data diversity and size but may lead to spurious correlations between site-related biological and non-biological image features and target labels, which machine learning (ML) models may exploit as shortcuts. To date, studies analyzing how and if deep learning models may use such effects as a shortcut are scarce. Thus, the aim of this work was to investigate if site-related effects are encoded in the feature space of an established deep learning model designed for Parkinson's disease (PD) classification based on T1-weighted MRI datasets. Therefore, all layers of the PD classifier were frozen, except for the last layer of the network, which was replaced by a linear layer that was exclusively re-trained to predict three potential bias types (biological sex, scanner type, and originating site). Our findings based on a large database consisting of 1880 MRI scans collected across 41 centers show that the feature space of the established PD model (74% accuracy) can be used to classify sex (75% accuracy), scanner type (79% accuracy), and site location (71% accuracy) with high accuracies despite this information never being explicitly provided to the PD model during original training. Overall, the results of this study suggest that trained image-based classifiers may use unwanted shortcuts that are not meaningful for the actual clinical task at hand. This finding may explain why many image-based deep learning models do not perform well when applied to data from centers not contributing to the training set.

Simulation-based training in Leprosy: development and validation of a scenario for community health workers.

OBJECTIVES: To build and validate a clinical simulation scenario designed to instruct community health workers (CHWs) in active leprosy case detection. METHODS: Methodological study involving the development of a simulated clinical scenario and content validation by experts. The Content Validity Index (CVI) was used to determine the level of agreement among the judging commitee, and a descriptive analysis of their recommendations was performed. RESULTS: A simulated scenario with a simulated participant was developed - a simulation characterized by low complexity, moderate physical/environmental fidelity, moderate to high psychological fidelity, and high conceptual fidelity, lasting 50 minutes and capable of training up to 10 CHWs simultaneously. The scenario was validated by 14 experts, with a CVI exceeding 80% for all components. CONCLUSIONS: The validated clinical simulation possesses attributes that make it highly reproducible in various national health contexts, thereby contributing to the global "Towards Zero Leprosy" strategy.

Image-encoded biological and non-biological variables may be used as shortcuts in deep learning models trained on multisite neuroimaging data.

OBJECTIVE: This work investigates if deep learning (DL) models can classify originating site locations directly from magnetic resonance imaging (MRI) scans with and without correction for intensity differences. MATERIAL AND METHODS: A large database of 1880 T1-weighted MRI scans collected across 41 sites originally for Parkinson's disease (PD) classification was used to classify sites in this study. Forty-six percent of the datasets are from PD patients, while 54% are from healthy participants. After preprocessing the T1-weighted scans, 2 additional data types were generated: intensity-harmonized T1-weighted scans and log-Jacobian deformation maps resulting from nonlinear atlas registration. Corresponding DL models were trained to classify sites for each data type. Additionally, logistic regression models were used to investigate the contribution of biological (age, sex, disease status) and non-biological (scanner type) variables to the models' decision. RESULTS: A comparison of the 3 different types of data revealed that DL models trained using T1-weighted and intensity-harmonized T1-weighted scans can classify sites with an accuracy of 85%, while the model using log-Jacobian deformation maps achieved a site classification accuracy of 54%. Disease status and scanner type were found to be significant confounders. DISCUSSION: Our results demonstrate that MRI scans encode relevant site-specific information that models could use as shortcuts that cannot be removed using simple intensity harmonization methods. CONCLUSION: The ability of DL models to exploit site-specific biases as shortcuts raises concerns about their reliability, generalization, and deployability in clinical settings.

Explainable classification of Parkinson's disease using deep learning trained on a large multi-center database of T1-weighted MRI datasets.

INTRODUCTION: Parkinson's disease (PD) is a severe neurodegenerative disease that affects millions of people. Early diagnosis is important to facilitate prompt interventions to slow down disease progression. However, accurate PD diagnosis can be challenging, especially in the early disease stages. The aim of this work was to develop and evaluate a robust explainable deep learning model for PD classification trained from one of the largest collections of T1-weighted magnetic resonance imaging datasets. MATERIALS AND METHODS: A total of 2,041 T1-weighted MRI datasets from 13 different studies were collected, including 1,024 datasets from PD patients and 1,017 datasets from age- and sex-matched healthy controls (HC). The datasets were skull stripped, resampled to isotropic resolution, bias field corrected, and non-linearly registered to the MNI PD25 atlas. The Jacobian maps derived from the deformation fields together with basic clinical parameters were used to train a state-of-the-art convolutional neural network (CNN) to classify PD and HC subjects. Saliency maps were generated to display the brain regions contributing the most to the classification task as a means of explainable artificial intelligence. RESULTS: The CNN model was trained using an 85%/5%/10% train/validation/test split stratified by diagnosis, sex, and study. The model achieved an accuracy of 79.3%, precision of 80.2%, specificity of 81.3%, sensitivity of 77.7%, and AUC-ROC of 0.87 on the test set while performing similarly on an independent test set. Saliency maps computed for the test set data highlighted frontotemporal regions, the orbital-frontal cortex, and multiple deep gray matter structures as most important. CONCLUSION: The developed CNN model, trained on a large heterogenous database, was able to differentiate PD patients from HC subjects with high accuracy with clinically feasible classification explanations. Future research should aim to investigate the combination of multiple imaging modalities with deep learning and on validating these results in a prospective trial as a clinical decision support system.

The influence of six polymorphisms of uncoupling protein 3 (UCP3) gene and childhood obesity: a case-control study.

BACKGROUND: Obesity is defined as a multifactorial disease, marked by excessive accumulation of body fat, responsible for compromising the individual's health over the years. The energy balance is essential for the proper functioning of the body, as the individual needs to earn and spend energy in a compensatory way. Mitochondrial Uncoupling Proteins (UCP) help in energy expenditure through heat release and genetic polymorphisms could be responsible for reducing energy consumption to release heat and consequently generate an excessive accumulation of fat in the body. Thus, this study aimed to investigate the potential association between six UCP3 polymorphisms, that have not yet been represented in ClinVar®, and pediatric obesity susceptibility. METHODS: A case-control study was conducted with 225 children from Central Brazil. The groups were subdivided into obese (123) and eutrophic (102) individuals. The polymorphisms rs15763, rs1685354, rs1800849, rs11235972, rs647126, and rs3781907 were determined by real-time Polymerase Chain Reaction (qPCR). RESULTS: Biochemical and anthropometric evaluation of obese group showed higher levels of triglycerides, insulin resistance, and LDL-C and low level of HDL-C. Insulin resistance, age, sex, HDL-C, fasting glucose, triglyceride levels, and parents' BMI explained up to 50% of body mass deposition in the studied population. Additionally, obese mothers contribute 2 × more to the Z-BMI of their children than the fathers. The SNP rs647126 contributed to 20% to the risk of obesity in children and the SNP rs3781907 contribute to 10%. Mutant alleles of UCP3 increase the risk for triglycerides, total cholesterol, and HDL-C levels. The polymorphism rs3781907 is the only one that could not be a biomarker for obesity as the risk allele seem to be protective gains the increase in Z-BMI in our pediatric population. Haplotype analysis demonstrated two SNP blocks (rs15763, rs647126, and rs1685534) and (rs11235972 and rs1800849) that showed linkage disequilibrium, with LOD 76.3% and D' = 0.96 and LOD 57.4% and D' = 0.97, respectively. CONCLUSIONS: The causality between UCP3 polymorphism and obesity were not detected. On the other hand, the studied polymorphism contributes to Z-BMI, HOMA-IR, triglycerides, total cholesterol, and HDL-C levels. Haplotypes are concordant with the obese phenotype and contribute minimally to the risk of obesity.

Main modulators of COVID-19 epidemic in sub-Saharan Africa.

Background: The coronavirus disease 2019 (COVID-19) pandemic is responsible for an important global death toll from which sub-Saharan Africa (SSA) seems mostly protected. The reasons explaining this situation are still poorly understood. Methods: We analyzed the correlation between reported COVID-19 data between February 14, 2020 and May 18, 2021, and demographic, socioeconomic, climatic, diagnostic data, and comorbidities in 47 SSA countries. Different databases including the WHO data center, Our World in Data, and the World Bank were used. Findings: As of May 17, 2021, SSA reported 2% of COVID-19 cases and 2.9% of deaths, with the southern region being the most affected with 56.4% of cases and 75.0% of deaths. COVID-19 mortality was positively correlated with medical variables (national obesity rate, diabetes prevalence, cancer incidence, and cardiovascular disease mortality rate), socioeconomic characteristics (international tourism, per capita health expenditure, human development index, HDI, and years of schooling), and health system variables (nurse density, number of COVID-19 tests per capita), but negatively correlated with the population under 15 years of age and the malaria index. Interpretation: Our study suggests that higher economic status fits with high COVID-19 mortality in SSA. In this regard, it represents primarily a disease of modern and wealthy societies, and can therefore be considered as an exception among infectious diseases that historically affected more severely underserved populations living in low- and middle-income countries. However, it should be made clear that observed correlations do not imply inevitably causation and that additional studies are necessary to confirm our observations.

An analysis of the effects of limited training data in distributed learning scenarios for brain age prediction.

OBJECTIVE: Distributed learning avoids problems associated with central data collection by training models locally at each site. This can be achieved by federated learning (FL) aggregating multiple models that were trained in parallel or training a single model visiting sites sequentially, the traveling model (TM). While both approaches have been applied to medical imaging tasks, their performance in limited local data scenarios remains unknown. In this study, we specifically analyze FL and TM performances when very small sample sizes are available per site. MATERIALS AND METHODS: 2025 T1-weighted magnetic resonance imaging scans were used to investigate the effect of sample sizes on FL and TM for brain age prediction. We evaluated models across 18 scenarios varying the number of samples per site (1, 2, 5, 10, and 20) and the number of training rounds (20, 40, and 200). RESULTS: Our results demonstrate that the TM outperforms FL, for every sample size examined. In the extreme case when each site provided only one sample, FL achieved a mean absolute error (MAE) of 18.9 ± 0.13 years, while the TM achieved a MAE of 6.21 ± 0.50 years, comparable to central learning (MAE = 5.99 years). DISCUSSION: Although FL is more commonly used, our study demonstrates that TM is the best implementation for small sample sizes. CONCLUSION: The TM offers new opportunities to apply machine learning models in rare diseases and pediatric research but also allows even small hospitals to contribute small datasets.

Multi-scale phase separation by explosive percolation with single-chromatin loop resolution.

The 2 m-long human DNA is tightly intertwined into the cell nucleus of the size of 10 µm. The DNA packing is explained by folding of chromatin fiber. This folding leads to the formation of such hierarchical structures as: chromosomal territories, compartments; densely-packed genomic regions known as Topologically Associating Domains (TADs), or Chromatin Contact Domains (CCDs), and loops. We propose models of dynamical human genome folding into hierarchical components in human lymphoblastoid, stem cell, and fibroblast cell lines. Our models are based on explosive percolation theory. The chromosomes are modeled as graphs where CTCF chromatin loops are represented as edges. The folding trajectory is simulated by gradually introducing loops to the graph following various edge addition strategies that are based on topological network properties, chromatin loop frequencies, compartmentalization, or epigenomic features. Finally, we propose the genome folding model - a biophysical pseudo-time process guided by a single scalar order parameter. The parameter is calculated by Linear Discriminant Analysis of chromatin features. We also include dynamics of loop formation by using Loop Extrusion Model (LEM) while adding them to the system. The chromatin phase separation, where fiber folds in 3D space into topological domains and compartments, is observed when the critical number of contacts is reached. We also observe that at least 80% of the loops are needed for chromatin fiber to condense in 3D space, and this is constant through various cell lines. Overall, our in-silico model integrates the high-throughput 3D genome interaction experimental data with the novel theoretical concept of phase separation, which allows us to model event-based time dynamics of chromatin loop formation and folding trajectories.

[Individual and contextual factors associated with smoking among young Brazilian adults]. / Fatores individuais e contextuais associados ao tabagismo em adultos jovens brasileiros.

Young people receive special attention regarding smoking as it is a period of life in which the use of this and other substances generally starts and is consolidated. There are no studies on risk factors associated with young adults with a representative sample from Brazil that take into consideration individual and contextual aspects. The objective was to identify factors associated with smoking among young Brazilian adults aged 18 to 24 years, considering the combined influence of individual and contextual factors assessed through the Municipal Human Development Index (MHDI). It involved a cross-sectional, population-based study that used data from the 2019 National Health Survey. Using the Poisson multilevel model with robust variance for estimating the Prevalence Ratio, individual variables were analyzed, and the MHDI as a contextual variable in each Federative Unit. In addition to individual factors, the MHDI was also associated with smoking among young people, with an increase in the prevalence of tobacco consumption among young people as the state's MHDI increases (p<0.001), indicating that living in a state with better conditions according to the MHDI, socioeconomic status is associated with a higher probability of young people smoking when compared to those residing in other states.

Aos jovens é atribuída especial atenção no que tange ao tabagismo por se tratar de um período da vida em que o uso dessa e outras substâncias geralmente inicia e se consolida. Não há estudos sobre fatores de risco associados aos adultos jovens, com amostra representativa do Brasil e que consideram aspectos individuais e contextuais. O objetivo foi identificar fatores associados ao tabagismo em adultos jovens brasileiros de 18 a 24 anos, considerando a influência conjunta de fatores individuais e contextual avaliado por meio do Índice de Desenvolvimento Humano Municipal (IDHM). Estudo transversal, de base populacional, que utilizou dados da Pesquisa Nacional de Saúde de 2019. Por meio do modelo multinível de Poisson com variâncias robustas para estimação da Razão de Prevalência, foram analisadas variáveis individuais, e como variável contextual, o IDHM em cada Unidade Federativa. Além de fatores individuais, o IDHM também se mostrou associado ao tabagismo dos jovens, com aumento da prevalência de consumo de tabaco entre os jovens à medida que se aumenta o IDHM do estado (p<0,001), indicando que residir em UF com melhores condições socioeconômicas segundo o IDHM está associado a maior probabilidade de o jovem fumar se comparados com aqueles que residem nos demais estados.

Sandpile cascades on oscillator networks: The BTW model meets Kuramoto.

Cascading failures abound in complex systems and the Bak-Tang-Weisenfeld (BTW) sandpile model provides a theoretical underpinning for their analysis. Yet, it does not account for the possibility of nodes having oscillatory dynamics, such as in power grids and brain networks. Here, we consider a network of Kuramoto oscillators upon which the BTW model is unfolding, enabling us to study how the feedback between the oscillatory and cascading dynamics can lead to new emergent behaviors. We assume that the more out-of-sync a node is with its neighbors, the more vulnerable it is and lower its load-carrying capacity accordingly. Also, when a node topples and sheds load, its oscillatory phase is reset at random. This leads to novel cyclic behavior at an emergent, long timescale. The system spends the bulk of its time in a synchronized state where load builds up with minimal cascades. Yet, eventually, the system reaches a tipping point where a large cascade triggers a "cascade of larger cascades," which can be classified as a dragon king event. The system then undergoes a short transient back to the synchronous, buildup phase. The coupling between capacity and synchronization gives rise to endogenous cascade seeds in addition to the standard exogenous ones, and we show their respective roles. We establish the phenomena from numerical studies and develop the accompanying mean-field theory to locate the tipping point, calculate the load in the system, determine the frequency of the long-time oscillations, and find the distribution of cascade sizes during the buildup phase.

Fatores individuais e contextuais associados ao tabagismo em adultos jovens brasileiros / Individual and contextual factors associated with smoking among young Brazilian adults

Resumo Aos jovens é atribuída especial atenção no que tange ao tabagismo por se tratar de um período da vida em que o uso dessa e outras substâncias geralmente inicia e se consolida. Não há estudos sobre fatores de risco associados aos adultos jovens, com amostra representativa do Brasil e que consideram aspectos individuais e contextuais. O objetivo foi identificar fatores associados ao tabagismo em adultos jovens brasileiros de 18 a 24 anos, considerando a influência conjunta de fatores individuais e contextual avaliado por meio do Índice de Desenvolvimento Humano Municipal (IDHM). Estudo transversal, de base populacional, que utilizou dados da Pesquisa Nacional de Saúde de 2019. Por meio do modelo multinível de Poisson com variâncias robustas para estimação da Razão de Prevalência, foram analisadas variáveis individuais, e como variável contextual, o IDHM em cada Unidade Federativa. Além de fatores individuais, o IDHM também se mostrou associado ao tabagismo dos jovens, com aumento da prevalência de consumo de tabaco entre os jovens à medida que se aumenta o IDHM do estado (p<0,001), indicando que residir em UF com melhores condições socioeconômicas segundo o IDHM está associado a maior probabilidade de o jovem fumar se comparados com aqueles que residem nos demais estados.

Abstract Young people receive special attention regarding smoking as it is a period of life in which the use of this and other substances generally starts and is consolidated. There are no studies on risk factors associated with young adults with a representative sample from Brazil that take into consideration individual and contextual aspects. The objective was to identify factors associated with smoking among young Brazilian adults aged 18 to 24 years, considering the combined influence of individual and contextual factors assessed through the Municipal Human Development Index (MHDI). It involved a cross-sectional, population-based study that used data from the 2019 National Health Survey. Using the Poisson multilevel model with robust variance for estimating the Prevalence Ratio, individual variables were analyzed, and the MHDI as a contextual variable in each Federative Unit. In addition to individual factors, the MHDI was also associated with smoking among young people, with an increase in the prevalence of tobacco consumption among young people as the state's MHDI increases (p<0.001), indicating that living in a state with better conditions according to the MHDI, socioeconomic status is associated with a higher probability of young people smoking when compared to those residing in other states.

Effectiveness of Pedagogical Tools for Teaching Medical Gross Anatomy During the COVID-19 Pandemic.

Medical institutions have been forced to modify gross anatomy pedagogy to comply with the health restrictions imposed by the novel coronavirus (COVID-19). Boston University School of Medicine (BUSM) is one such institution that temporarily restructured its course. We replaced cadaveric dissection activities with prosections and placed a greater emphasis on a flipped classroom model. This study investigates the effectiveness of new course materials developed to aid these curriculum changes. Course materials were developed for three purposes: (1) preparation before laboratory sessions (orientation videos and Complete Anatomy (3D4Medical, Elsevier) screens); (2) guidance during laboratory sessions (laboratory guides); and (3) review after laboratory sessions (Zoom recitation sessions). We performed a grounded theory thematic analysis of students' responses (80/160, 50% response) to qualitative survey questions and to focus group questions (16 students who self-selected between 4 different sessions). Data from both the survey and focus groups demonstrated that the vast majority of students agreed that the materials helped them navigate through learning gross anatomy. However, laboratory guides were used mostly for post-lab review as opposed to the intended direction during laboratory sessions. Students within all focus groups overwhelmingly touted the value of Zoom recitation sessions, with many stating that they were imperative to course success. When comparing performance data between 2019 (pre-COVID) and 2020 students, we found that the students who took the anatomy course during the onset of COVID had a slightly higher overall average score in all three modules of the course than compared to the 2019 students. We propose that the utilization of course materials that students perceived as time saving and pertinent to their exam performance, when combined with cadaveric prosection, emphasized the benefits of flipped-classroom learning to help students learn gross anatomy effectively and efficiently during the pandemic and beyond. Supplementary Information: The online version contains supplementary material available at 10.1007/s40670-022-01524-x.

Validação de dois cenários de simulação clínica para ensino de prevenção e controle de infecções relacionadas à assistência à saúde / Validation of two clinical scenarios for simulation-based learning for the prevention and control of healthcare-associated infections

Objetivo: validar cenários clínicos para o ensino baseado em simulação sobre prevenção e controle de infecções relacionadas à assistência à saúde. Métodos: estudo metodológico de elaboração, validação de conteúdo de dois cenários clínicos simulados e avaliação do design da simulação. Especialistas (n=10) analisaram abrangência, clareza e pertinência dos cenários, e 44 graduandos de enfermagem avaliaram o design, utilizando a Escala do Design da Simulação. Para análise utilizou-se procedimentos de estatística descritiva, Índice de Validade de Conteúdo e Razão de Validade de Conteúdo. Resultados: os itens do cenário apresentaram índice de validade de conteúdo ≥ 0,8 e razão de validade de conteúdo predominantemente ≥ 0,8. A escala apresentou média de 4,7±0,2, indicando adequação dos cenários pelos participantes da simulação. Conclusão: a validação permitiu alcance de adequada qualidade dos cenários propostos, os quais podem ser amplamente utilizados para o ensino de medidas de prevenção e controle de infecção.

Objective: to validate clinical scenarios for simulation-based learning on the prevention and control of healthcare-associated infections. Methods: a methodological study of elaboration, content validation of two simulated clinical scenarios, and evaluation of the simulation design. Specialists (n=10) analyzed the scope, clarity, and relevance of the scenarios, and 44 undergraduate nurses evaluated the design using the Simulation Design Scale. Descriptive statistics, Content Validity Index, and Content Validity Ratio were used for analysis. Results: the items in the scenario presented a content validity index ≥ 0.8 and a content validity ratio predominantly ≥ 0.8. The scale presented an average of 4.7±0.2, indicating the adequacy of the scenarios by the participants of the simulation. Conclusion: the validation allowed the achievement of adequate quality of the proposed scenarios, which can be widely used for teaching infection prevention and control.