Thiago Mazzu-Nascimento

Objectives: To develop a microfluidic paper-based analytical device (μPAD) that identifies myeloperoxidase (MPO) levels in the saliva of healthy patients and those with periodontal disease. Materials and Methods: A platform similar to a 96-well plate was printed on Watman^® chromatography paper to run the experimental analysis with unstimulated saliva samples were collected from two groups of patients: those with periodontal health (H, n = 15) and established periodontitis (PD, n = 15). Then, three types of chromophore substrates were pipetted into the wells of the prototype: (1) Guaiacol; (2) Guaiacol, 4,4 ′-diaminodifenilsulfon (DAB) and hydrogen peroxide in Tris-HCl buffer; and (3) 3,3′,5,5′-Tetramethylbenzidine (TMB), followed by saliva samples. The reaction images were analyzed by numbering according to the intensity scale. Results: The comparative results of the reactions using μPAD demonstrated that both the H and PD groups were compatible with each other without differences among the chromophore substrates (p > 0.05). However, the protocol with TMB showed a faster reaction and better color difference when comparing 15.62 ng/mL and 7.81 ng/mL of MPO in the plate embedded with Guaiacol; 1000 ng/mL and 62.5 ng/mL on the Guaiacol and DAB plate; and 62.5 ng/mL of TMB. The average detectable concentrations of MPO in saliva using TMB were H = 21.2 ± 10.4 ng/mL and PD = 28.9 ± 12.8 ng/mL (p = 0.08). Conclusions: The developed microfluidic paper-based analytical device has been tested for identifying the myeloperoxidase saliva levels of healthy patients and those with periodontal disease. This rapid test demonstrated its possible applicability mainly when associated with the TMB chromophore, but further studies are required with different biomarkers to explore this promising diagnostic platform.

Violence against women is a public health and safety problem. This article is the result of a study of the profile of female victims of domestic violence in the state of São Paulo using data from the São Paulo State Department of Public Security (SSP/SP), and from the Informatics Department of the Unified Health System (DataSUS), between March 2020 to February 2021. The analysis of the data collected reveals the need for public actions that anticipate violence and that could help to prevent cases, especially in a period when social weaknesses are even more exposed. For this, it is important that the victims are encouraged to report and find adequate spaces and services for reception, listening and protection. In this sense, digital media and technological tools, such as smartphone applications, represent important reporting channels that can help to contain these cases of violence.

Cardiovascular diseases are the leading cause of death in the world. People living in vulnerable and poor places such as slums, rural areas and remote locations have difficulty in accessing medical care and diagnostic tests. In addition, given the COVID-19 pandemic, we are witnessing an increase in the use of telemedicine and non-invasive tools for monitoring vital signs. These questions motivate us to write this point of view and to describe some of the main innovations used for non-invasive [...] Abstract Cardiovascular diseases are the leading cause of death in the world. People living in vulnerable and poor places such as slums, rural areas and remote locations have difficulty in accessing medical care and diagnostic tests. In addition, given the COVID-19 pandemic, we are witnessing an increase in the use of telemedicine and non-invasive tools for monitoring vital signs. These questions motivate us to write this point of view and to describe some of the main innovations used for non-invasive

Anemia and jaundice are common health conditions that affect millions of children, adults, and the elderly worldwide. Recently, the pandemic caused by severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2), the virus that leads to COVID-19, has generated an extreme worldwide concern and a huge impact on public health, education, and economy, reaching all spheres of society. The development of techniques for non-invasive diagnosis and the use of mobile health (mHealth) is reaching more and more space. The analysis of a simple photograph by smartphone can allow an assessment of a person's health status. Image analysis techniques have advanced a lot in a short time. Analyses that were previously done manually, can now be done automatically by methods involving artificial intelligence. The use of smartphones, combined with machine learning techniques for image analysis (preprocessing, extraction of characteristics, classification, or regression), capable of providing predictions with high sensitivity and specificity, seems to be a trend. We presented in this review some highlights of the evaluation of anemia, jaundice, and COVID-19 by photo analysis, emphasizing the importance of using the smartphone, machine learning algorithms, and applications that are emerging rapidly. Soon, this will certainly be a reality. Also, these innovative methods will encourage the incorporation of mHealth technologies in telemedicine and the expansion of people's access to health services and early diagnosis.

The pandemic caused by the new coronavirus (SARS-COV-2) has led to more than two million deaths in the world by March 2021. The worldwide call to reduce transmission is enormous. Recently, there has been a rapid growth of telemedicine and the use of mobile health (mHealth) in the context of the COVID-19 pandemic. Smartphone accessories such as a flashlight, camera, microphone, and microprocessor can measure different clinical parameters such as oxygen saturation, blood pressure, heart rate, breathing rate, fever, pulmonary auscultation, and even voice analysis. All these parameters are of great clinical importance when evaluating suspected patients of COVID-19 or monitoring infected patients admitted in various hospitals or in-home isolation. In remote medical care, the results of these parameters can be sent to a call center or a health unit for interpretation by a qualified health professional. Thus, the patient can receive orientations or be immediately referred for in-patient care. The application of machine learning and other artificial intelligence strategies assume a central role in signal processing and are gaining much space in the medical field. In this work, we present different approaches for evaluating clinical parameters that are valuable in the case of COVID-19 and we hope that soon all these parameters can be measured by a single smartphone application, facilitating remote clinical assessments.

Anemia is a public health problem that can have different causes, such as iron deficiency, vitamin deficiency, inflammation, hemolytic anemias, and anemias associated with bone marrow disease. Anemia shows a decrease in the concentration of hemoglobin, a pigmented molecule in the erythrocytes. The objectives of this review were to highlight the impact of nutritional factors on morbidity and mortality caused by anemia and to present different non-invasive approaches that use a smartphone to analyze hemoglobin levels to detect anemia. According to the records of the Brazilian Unified Health System (SUS, in the Portuguese acronym), ∼ 440,000 people checked in hospitals due to anemia between January 2015 and April 2020, with 215,000 deaths. The government spent ∼ 294 million Brazilian Reais (more than 50 million US dollars) on anemia hospitalization cases during this period. There is a worldwide search to provide noninvasive diagnostics and mobile health (mHealth) tools to help diagnosing anemia. The smartphone appears to be a viable device to detect anemia by a camera with colorimetric analysis of images providing a quantitative, instantaneous, and noninvasive result. These images can be obtained as a photograph or extracted from video frames. The review presents three different methods of detecting anemia using a smartphone: i) photoplethysmograph from video obtained from the tip of the index finger, ii) photo of the palpebral conjunctiva, and iii) fingernail photo app. Therefore, it seems urgent that these approaches may be applied in routine clinical diagnosis to allow remote, needy, low-tech locations to have access to anemia screening.

Diabetes is a chronic disease and one of the major public health problems worldwide. It is a multifactorial disease, caused by genetic factors and lifestyle habits. Brazil had ∼ 16.8 million individuals living with diabetes in 2019 and is expected to reach 26 million people by 2045. There are global increasing needs for the development of noninvasive diagnostic methods and use of mobile health, mainly in face of the pandemic caused by the coronavirus disease 2019 (COVID-19). For daily glycemic control, diabetic patients use a portable glucometer for glycemic self-monitoring and need to prick their fingertips three or more times a day, generating a huge discomfort throughout their lives. Our goal here is to present a review with very recent emerging studies in the field of noninvasive diagnosis and to emphasize that smartphone-based photoplethysmography (spPPG), powered by artificial intelligence, might be a trend to self-monitor blood glucose levels. In photoplethysmography, a light source travels through the tissue, interacts with the interstitium and with cells and molecules present in the blood. Reflection of light occurs as it passes through the biological tissues and a photodetector can capture these interactions. When using a smartphone, the built-in flashlight is a white light-emitting LED and the camera works as a photodetector. The higher the concentration of circulating glucose, the greater the absorbance and, consequently, the lesser the reflected light intensity will be. Due to these optical phenomena, the signal intensity captured will be inversely proportional to the blood glucose level. Furthermore, we highlight the microvascular changes in the progression of diabetes that can interfere in the signals captured by the photodetector using spPPG, due to the decrease of peripheral blood perfusion, which can be confused with high blood glucose levels. It is necessary to create strategies to filter or reduce the impact of these vascular changes in the blood glucose level analysis. Deep learning strategies can help the machine to solve these challenges, allowing an accurate blood glucose level and interstitial glucose prediction.

RESUMO Objetivos Identificar como é a formação de profissionais da saúde quanto à Língua Brasileira de Sinais (Libras). Métodos Trata-se de estudo descritivo e transversal, desenvolvido com dados secundários, coletados no banco de dados eletrônico do Ministério da Educação. Foram analisados a grade curricular e o projeto pedagógico de todos os cursos de graduação na área da saúde em Instituições de Ensino Superior (IES) brasileiras, procurando-se identificar e caracterizar a disciplina de Libras. Resultados Foram localizados 5317 cursos e, destes, 2293 (43,1%) ofereciam disciplina de Libras, sendo 16,7% como disciplina obrigatória e a maioria (83,3%) como optativa. Em relação ao período ofertado, não houve um padrão, variando desde o primeiro até o décimo. Quanto à carga horária destinada à disciplina, dentre os 2077 cursos que disponibilizavam essa informação, 11,1% ofertavam a disciplina com carga horária de até 20 horas, 49,4% com carga horária entre 21 e 40 horas, 29,9% entre 41 e 60 horas, 9,1% entre 61 e 80 horas. Apenas 0,5% dos cursos destinavam mais que 80 horas para o ensino de Libras. Em média, os cursos de graduação em IES públicas (N=217) dedicavam 53,1 horas ao ensino de Libras, enquanto os cursos de IES privadas (N=1860) dedicavam 45,8 horas. Conclusão Há evidências de fragilidade na formação dos profissionais de saúde quanto ao ensino da Libras, o que reflete diretamente no atendimento integral dos surdos.