A relação entre cabelos ruivos, mutação do gene MC1R, dor e anestesia: Uma revisão integrativa da literatura

Objetivo: Abordar a problemática da sensibilidade em ruivos devido a alteração no gene MC1R. Metodologia: Trata-se de uma revisão integrativa da literatura, realizada no mês de janeiro de 2021, nas bases de dados Biblioteca Virtual em Saúde (BVS), PubMed e Google Scholar, foram utilizados artigos escritos na língua portuguesa ou inglesa, publicados no período entre 2017 e 2021. Resultados: Dos 135 artigos encontrados no total, após rigor metodológico por meio dos critérios de inclusão e exclusão, foram selecionados 10 artigos, sendo 1 artigo na PUBMED, 8 no Google Scholar e 1 na BVS. A análise dos artigos encontrados foi feita de forma descritiva e predispôs a etapa de extração dos dados: base de dados, autores/ano de publicação, origem/idioma da publicação, título e temática. Conclusão: Diante dos resultados encontrados na literatura pode-se observar a deficiência de estudos brasileiros acerca do tema, mesmo sendo relevante para o âmbito da ciência, tendo em vista que é necessário conhecer o mecanismo por trás dessa mutação e através das descobertas serem desenvolvidas estratégias que possam proporcionar a essa população uma melhora na sua qualidade de vida.</jats:p

Frailty does not affect prognostic markers in patients with acute coronary syndrome: results from a Brazilian university hospital

OBJECTIVE: To evaluate frailty and its relationship with prognostic markers in hospitalized patients with acute coronary syndrome. METHODS: This cross-sectional study with a prospective variable analysis (prognostic markers) involved adults of both sexes aged &ge; 50 years with acute coronary syndrome. Patients with &ge; 3 of the following criteria were considered frail: 1) unintentional weight loss; 2) exhaustion (assessed by self-reported fatigue); 3) low handgrip strength; 4) low physical activity level; and 5) low gait speed. The included prognostic markers were: metabolic changes (lipid and glycemic profile), changes in inflammatory status (C-reactive protein), thrombolysis in myocardial infarction risk score, troponin level, angioplasty or surgery, hospitalization in the intensive care unit, length of hospital stay, and hospital outcome. RESULTS: The sample consisted of 125 patients, whose mean age was 65.5 (SD, 8.7) years. The prevalence of frailty was 48.00%, which was higher in women (PR = 1.55; 95%CI 1.08–2.22; p = 0.018) and patients with systemic arterial hypertension (PR = 2.18; 95%CI 1.01–5.24; p = 0.030). Frailty was not associated with age, cardiac diagnosis, or prognostic markers (p &gt; 0.05). CONCLUSIONS: Frailty was highly prevalent in patients with acute coronary syndrome, affecting almost half of the sample, particularly women and patients with hypertension, irrespective of age. However, despite its high prevalence, frailty was not associated with markers of metabolic change or poor prognosis.</p

Screening for Chagas disease from the electrocardiogram using a deep neural network

AbstractBackgroundWorldwide it is estimated that more than 6 million people are infected with Chagas disease (ChD). It is considered one of the most important neglected diseases and, when it reaches its chronic phase, the infected person often develops serious heart conditions. While early treatment can avoid complications, the condition is often not detected during its early stages. We investigate whether a deep neural network can detect ChD from electrocardiogram (ECG) tracings. The ECG is inexpensive and it is often performed during routine visits. Being able to evaluate ChD from this exam can help detect potentially hidden cases in an early stage.MethodsWe use a convolutional neural network model, which takes the 12-lead ECG as input and outputs a scalar number associated with the probability of a Chagas diagnosis. To develop the model, we use two data sets, which jointly consist of over two million entries from Brazilian patients, compiled by the Telehealth Network of Minas Gerais within the SaMi-Trop (São Paulo-Minas Gerais Tropical Medicine Research Center) study focused on ChD patients and enriched with the CODE (Clinical Outcomes in Digital Electrocardiology) study focused on a general population. The performance is evaluated on two external data sets of 631 and 13,739 patients, collected in the scope of the REDS-II (Retrovirus Epidemiology Donor Study-II) study and of the ELSA-Brasil (Brazilian Longitudinal Study of Adult Health) study. The first study focuses on ChD patients and the second data set originates from civil servants from five universities and one research institute.FindingsEvaluating our model, we obtain an AUC-ROC value of 0.80 (CI 95% 0.79-0.82) for the validation data set (with samples from CODE and SaMi-Trop), and in external validation datasets: 0.68 (CI 95% 0.63-0.71) for REDS-II and 0.59 (CI 95% 0.56-0.63) for ELSA-Brasil. In these external validation datasets, we report a sensitivity of 0.52 (CI 95% 0.47-0.57) and 0.36 (CI 95% 0.30-0.42) and a specificity of 0.77 (CI 95% 0.72-0.81) and 0.76 (CI 95% 0.75-0.77), respectively, in REDS-II and ELSA-Brasil. We also evaluated the model for considering only patients with Chagas cardiomyopathy as positive. In this case, the model attains an AUC-ROC of 0.82 (CI 95% 0.77-0.86) for REDS-II and 0.77 (CI 95% 0.68-0.85) for ELSA-Brasil.InterpretationThe results indicate that the neural network can detect patients who developed chronic Chagas cardiomyopathy (CCC) from the ECG and – with weaker performance – detect patients before the CCC stage. Future work should focus on curating large and better datasets for developing such models. The CODE is the largest dataset available to us, and their labels are self-reported and less reliable than our other data sets, i.e. REDS-II and ELSA-Brasil. This, we believe, limits our model performance in the case of non-CCC patients. We are positive that our findings constitute the first step towards building tools for more efficient detection and treatment of ChD, especially in high-prevalent regions.FundingThis research is financially supported by the Swedish Foundation for Strategic Research (SSF) via the projectASSEMBLE(Contract number: RIT 15-0012), by theWallenberg AI, Autonomous Systems and Software Program (WASP)funded by Knut and Alice Wallenberg Foundation, byKjell och Märta Beijer Foundation, by the Brazilian Agencies CNPq, CAPES, and FAPEMIG, and by projects IATS, and CIIA-Saúde. The ELSA-Brasil study was supported by the Brazilian Ministries of Health and of Science and Technology (grants 01060010.00RS, 01060212.00BA, 01060300.00ES, 01060278.00MG, 01060115.00SP, and 01060071.00RJ). The SaMi-Trop and REDS-II cohort studies are supported by the National Institutes of Health (P50 AI098461-02, U19AI098461-06, 1U01AI168383-01). LG, SMB, ECS and ALPR receive unrestricted research scholarships from CNPq; ALPR received a Google Latin America Research Award scholarship. The funders had no role in the study design; collection, analysis, and interpretation of data; writing of the report; or decision to submit the paper for publication.Research in contextEvidence before this studyChagas disease (ChD) is a neglected tropical disease, and the diagnosis relies on blood testing of patients from endemic areas. However, there is no clear recommendation on selecting patients for serological diagnosis in those living in endemic regions. Since most of the patients with Chronic ChD are asymptomatic or oligosymptomatic, the diagnostic rates are low, preventing patients from receiving adequate treatment. The Electro-cardiogram (ECG) is a widely available, low-cost exam, often available in primary care settings in endemic countries. Artificial intelligence (AI) algorithms on ECG tracings have allowed the detection of hidden conditions, such as cardiomyopathies and left ventricular systolic dysfunction.Added value of this studyTo the best of our knowledge, this is the first study that presents an AI model for the automatic detection of ChD from the ECG. As part of the model development, we utilise established large cohorts of patients from the relevant population of all-comers in affected regions in the state of Minas Gerais, Brazil. We evaluate the model on data sets with high-quality ground truth labels obtained from the patients’ serological status. Our model has moderate diagnostic performance in recognition of ChD and better accuracy in detecting Chagas cardiomyopathy.Implications of all the available evidenceOur findings demonstrate a promising AI-ECG-based model capacity for discriminating patients with chronic Chagas cardiomyopathy (CCC). However, detecting ChD patients without CCC is still insufficient, and further developments that lead to higher performance are needed. We believe this can be achieved with the addition of epidemiological questions, and that our model can be a useful tool in helping pre-selecting patients for further testing in order to determine the infection with ChD. The use of AI-ECG-based strategies for recognizing CCC patients deserves to be tested in the clinical setting.</jats:sec

Screening for Chagas disease from the electrocardiogram using a deep neural network

Chagas disease (ChD) is a neglected tropical disease, and the diagnosis relies on blood testing of patients from endemic areas. However, there is no clear recommendation on how to select patients for testing in endemic regions. Since most cases of Chronic ChD are asymptomatic, the diagnostic rates are low, preventing patients from receiving adequate treatment. The Electrocardiogram (ECG) is a widely available, low-cost exam, often available in primary care settings. We present an Artificial intelligence (AI) model for automatically detecting ChD from the ECG. AI algorithms have allowed the detection of hidden conditions on the ECG and, to the best of our knowledge, this is the first study that does it for ChD. We utilize large cohorts of patients from the relevant population of all-comers in affected regions in Brazil to develop a model for ChD detection that is then validated on datasets with ground truth labels obtained directly from the patients’ serological status. Our findings demonstrate a promising AI-ECG-based model for discriminating patients with chronic Chagas cardiomyopathy (CCC). The capacity of detecting ChD patients without CCC is still limited. But we believe this can be improved with the addition of epidemiological questions, and that such models can become useful tools for pre-selecting patients for further testing.Background: Worldwide, it is estimated that over 6 million people are infected with Chagas disease (ChD). It is a neglected disease that can lead to severe heart conditions in its chronic phase. While early treatment can avoid complications, the early-stage detection rate is low. We explore the use of deep neural networks to detect ChD from electrocardiograms (ECGs) to aid in the early detection of the disease. Methods: We employ a convolutional neural network model that uses 12-lead ECG data to compute the probability of a ChD diagnosis. Our model is developed using two datasets which jointly comprise over two million entries from Brazilian patients: The SaMi-Trop study focusing on ChD patients, enriched with data from the CODE study from the general population. The model’s performance is evaluated on two external datasets: the REDS-II, a study focused on ChD with 631 patients, and the ELSA-Brasil study, with 13,739 civil servant patients. Findings: Evaluating our model, we obtain an AUC-ROC of 0.80 (CI 95% 0.79-0.82) for the validation set (samples from CODE and SaMi-Trop), and in external validation datasets: 0.68 (CI 95% 0.63-0.71) for REDS-II and 0.59 (CI 95% 0.56-0.63) for ELSA-Brasil. In the latter, we report a sensitivity of 0.52 (CI 95% 0.47-0.57) and 0.36 (CI 95% 0.30-0.42) and a specificity of 0.77 (CI 95% 0.72-0.81) and 0.76 (CI 95% 0.75-0.77), respectively. Additionally, when considering only patients with Chagas cardiomyopathy as positive, the model achieved an AUC-ROC of 0.82 (CI 95% 0.77-0.86) for REDS-II and 0.77 (CI 95% 0.68-0.85) for ELSA-Brasil. Interpretation: The neural network detects chronic Chagas cardiomyopathy (CCC) from ECG—with weaker performance for early-stage cases. Future work should focus on curating large higher-quality datasets. The CODE dataset, our largest development dataset includes self-reported and therefore less reliable labels, limiting performance for non-CCC patients. Our findings can improve ChD detection and treatment, particularly in high-prevalence areas

Search for narrow resonances using the dijet mass spectrum in pp collisions at s√=8 TeV

Results are presented of a search for the production of new particles decaying to pairs of partons (quarks, antiquarks, or gluons), in the dijet mass spectrum in proton-proton collisions at s√=8  TeV. The data sample corresponds to an integrated luminosity of 4.0  fb−1, collected with the CMS detector at the LHC in 2012. No significant evidence for narrow resonance production is observed. Upper limits are set at the 95% confidence level on the production cross section of hypothetical new particles decaying to quark-quark, quark-gluon, or gluon-gluon final states. These limits are then translated into lower limits on the masses of new resonances in specific scenarios of physics beyond the standard model. The limits reach up to 4.8 TeV, depending on the model, and extend previous exclusions from similar searches performed at lower collision energies. For the first time mass limits are set for the Randall–Sundrum graviton model in the dijet channel

Network architecture.

BackgroundWorldwide, it is estimated that over 6 million people are infected with Chagas disease (ChD). It is a neglected disease that can lead to severe heart conditions in its chronic phase. While early treatment can avoid complications, the early-stage detection rate is low. We explore the use of deep neural networks to detect ChD from electrocardiograms (ECGs) to aid in the early detection of the disease.MethodsWe employ a convolutional neural network model that uses 12-lead ECG data to compute the probability of a ChD diagnosis. Our model is developed using two datasets which jointly comprise over two million entries from Brazilian patients: The SaMi-Trop study focusing on ChD patients, enriched with data from the CODE study from the general population. The model’s performance is evaluated on two external datasets: the REDS-II, a study focused on ChD with 631 patients, and the ELSA-Brasil study, with 13,739 civil servant patients.FindingsEvaluating our model, we obtain an AUC-ROC of 0.80 (CI 95% 0.79-0.82) for the validation set (samples from CODE and SaMi-Trop), and in external validation datasets: 0.68 (CI 95% 0.63-0.71) for REDS-II and 0.59 (CI 95% 0.56-0.63) for ELSA-Brasil. In the latter, we report a sensitivity of 0.52 (CI 95% 0.47-0.57) and 0.36 (CI 95% 0.30-0.42) and a specificity of 0.77 (CI 95% 0.72-0.81) and 0.76 (CI 95% 0.75-0.77), respectively. Additionally, when considering only patients with Chagas cardiomyopathy as positive, the model achieved an AUC-ROC of 0.82 (CI 95% 0.77-0.86) for REDS-II and 0.77 (CI 95% 0.68-0.85) for ELSA-Brasil.InterpretationThe neural network detects chronic Chagas cardiomyopathy (CCC) from ECG—with weaker performance for early-stage cases. Future work should focus on curating large higher-quality datasets. The CODE dataset, our largest development dataset includes self-reported and therefore less reliable labels, limiting performance for non-CCC patients. Our findings can improve ChD detection and treatment, particularly in high-prevalence areas.</div

Results on test data: CCC-specific training.

Equivalent to Table 4 when the training is adapted to specifically target patients with chronic Chagas cardiomyopathy. The classification thresholds are 0.51 for REDS-II and 0.33 for ELSA-Brasil. (XLSX)</p