ABSTRACT: In Brazil, breast cancer, after non-melanoma skin cancer, is the most common neoplasia type among women in all regions, accounting for approximately 30% of cases. Globally, according to the World Health Organization data, over 19 million new cancer cases were recorded in 2020, with approximately 2.26 million cases of breast cancer. Among the therapeutic modalities that enhance the survival of patients with this neoplasia is radiotherapy, capable of combating tumor growth through the use of ionizing radiation. However, its use is associated with cardiac and pulmonary toxicities, as it affects not only the diseased tissue but also adjacent healthy tissues. In this regard, some approaches have been developed to reduce doses, especially to the heart, during radiotherapy. The technique of voluntary deep inspiration has been shown to significantly reduce the average dose to the heart in patients undergoing radiotherapy for left breast cancer. However, commercially available devices for respiratory motion management are costly and not widely accessible in developing countries. This study proposes the development and evaluation of a low-cost respiratory management system based on two components (distance sensor and video camera), which could be used in radiotherapy for left breast cancer with voluntary deep inspiration. The functionality of the distance sensor was developed in the Arduino IDE and then connected to an algorithm developed for the video camera-based component in Python, using the Pyserial library. A user-friendly graphical interface was also developed in Python using the Easygui and OpenCV libraries. To assess the system, tests of accuracy, linearity, reproducibility, constancy, and response time were conducted. The system components were evaluated separately and in combination. Test results indicated that the distance sensor-based system component had accuracy below 0.5 mm, good linearity (R2=0.99), reproducibility less than 1.0 mm, and a response time of approximately 1.16 seconds. Favorable results were also found for the video-based component, with accuracy close to 0.1 mm and a response time of 0.12 seconds. The system's constancy test showed no risk of system locking during use. When both components were evaluated together, the system response time was 0.76 seconds. A comparison of the dose calculated by the planning system and the dose measured with an ionization chamber in a dynamic phantom (end-to-end test) showed a percentage dose variation between the planned and measured of 1.75%. Based on the results obtained, it is inferred that the developed system is capable of accurately mapping the variation in the position of the thoracic wall in breast cancer radiotherapy patients and can now be evaluated in clinical settings.

Visual illusions have been used as tools for psychophysical tests in the investigation of visual processing deficits in many mental disorders. In the present study, we characterized the perception of schizophrenic patients in comparison to neurotypical subjects through subjective interpretations of the Müller-Lyer and Hollow-Mask illusions. The objectives included evaluating susceptibility to different illusions in both groups, highlighting possible cognitive deficits associated with schizophrenia, correlating responses to the Hollow-Mask illusion with medications used to treat schizophrenia, and exploring the potential use of visual illusions in diagnosing the disorder. Statistical analyses have shown a significant difference in susceptibility to the Müller-Lyer illusion between the schizophrenic patient group and the control group, suggesting altered perception in visuospatial judgment among the patients. Susceptibility was also tested across different angles in the Müller-Lyer illusion, revealing an inverse relationship between susceptibility and the widest opening angles. These results suggest that narrower angles may better differentiate schizophrenic patients from healthy subjects. Findings for the Hollow-Mask illusion diverged from those in the literature, as no significant difference was found between the groups. However, it was possible to associate the use of Olanzapine with resistance to the Mask illusion. We conclude that the interpretation of both illusions is influenced by perceptual and cognitive processes, and that schizophrenia may interfere with the integration mechanisms of these high-level processes, thereby supporting the potential diagnostic utility of visual illusions in identifying cognitive dysfunctions associated with this disorder.

Muscle fatigue (MF) is defined as a reduction in the capacity of the neuromuscular system to generate force, and is a common phenomenon in any type of activity that requires resistance. Responsible for limitations in human performance and causing injuries, the fatigue process can involve the ipsilateral limb as well as the contralateral limb, and can even be observed in non homologous limbs. Although the mechanisms of FM are exhaustively researched, few works have investigated the effect of contralateral fatigue (CF) on the lower limbs. In this context, aiming to expand the knowledge of CF, we propose to evaluate the modulation of excitability of descending pathways on the right soleus (SO) muscle after fatigue induction in the contralateral plantar flexors. The sample of the present study consisted of 12 healthy and active volunteers, 27.58 ± 7.34 years old, with a height of 1.67 ±0.08. Electromyographic (EMG) signals were captured by means of surface electrodes positioned on the right OS. A protocol was performed in which data regarding maximum voluntary contraction (MVC) and V-wave (electromyographic signal representing a response of the descending pathways) were collected in four conditions (pre-fatigue, between fatigue, post-fatigue and recovery). V-wave, power spectrum parameters of the contralateral OS electromyogram and in superimposed twitch (ST) force generation were obtained, such as mean power frequency (FMD), median power frequency (FMN) and Root Mean Squared (RMS) between conditions. A one-way repeated measures ANOVA was used to detect differences between the amplitudes of evoked V-waves, in FMD and FMN, in RMS across conditions. The main results of this study were a) that fatigue induced in the left ankle plantar flexors was shown to have a significant effect of CF on V-wave amplitudes; b) a tendency to recover from the effects of fatigue over time; c) modulation of the V-wave by CF was progressive; d) the absence of ipsilateral fatigue; e) that voluntary muscle activation was constant across conditions; f) that V-wave fluctuations were not attributed to different levels of voluntary activity of the target muscle. The present work evidenced that the excitability of descending pathways to the OS muscle can be acutely modulated as a consequence of fatigue induced in the contralateral plantar flexor muscles.

Introduction: Pressure injury is defined as a localized injury to the skin or underlying cutaneous tissue over a bony prominence, resulting from a combination of pressure and local shear. It is caused by the sum of clinical factors, such as nutrition, hydration, and cutaneous-mucous conditions, among others. It occurs in several regions of the body, however, the region that has a greater shear is the sacral region. According to (PATTON, 2018), the use of new technologies decreases the prevalence of pressure injuries associated with mortality rates in a study developed evaluating the technologies used in pressure injuries in hospitalized patients. Based on the results of the clinical trial on the Rapha Equipment research (CAAE 94910718.5.3001.5553) where it prioritized the application of the protocol linked to the LED light-emitting equipment associated with a latex dressing (Rapha Equipment) (FLEURY ROSA, S.S.R, et al, 2017) for the treatment of foot diseases in diabetes, the following proposal was thought: what clinical outcomes would Rapha equipment provide in patients with pressure ulcers? Objectives: The objective was to evaluate the action of the LED light-emitting device together with the latex sheet (Rapha® Equipment) applied to a patient with a pressure injury hospitalized in a hospital institution during a 45-day protocol. Observing at the end of the research if there was attenuation of the total area of the lesion after the protocol. Materials and methods: A multi-strategy typology approach supported by quantitative and qualitative methods for data collection and analysis with an interdisciplinary health perspective, using the approved clinical study research (CAAE 94910718.5.3001.5553). The application methodology was similar to applying a pressure sore dressing, following the predefined protocol for applying the Rapha® equipment daily on the wound for 45 days. In each patient, it was evaluated whether the use of the Rapha® equipment resulted in a reduction in the area of the lesion. Results: Five patients from the experimental group who received the use of the Rapha® equipment and 4 patients from the control group who received the sus protocol were evaluated. Patients in the experimental group had two completely healed lesions and two lesions with a 50% decrease in the total area of the lesion. Compared to the control group, there was no healing and a small percentage of reduction in the area of the lesion. Conclusion: The Rapha® equipment assists in the healing process of pressure injuries in less time than the Unified Health System protocols, however, it favors increased moisture in the injury, which in excess can harm the healing process.

The use of implanted medical devices has been a reality since the 1950s and continues to be considered a necessary solution for monitoring biological functions or for the treatment of situations that require medication application or organ stimulation. However, the limitation of the energy supply, primarily the battery, makes the use of these devices uncomfortable for patients due to the need for surgical procedures for their maintenance. In the presented context, wireless energy transfer technology emerges as a proposal to minimize this inconvenience, providing greater reliability in the use of these devices. The work simulated this procedure, seeking to identify the necessary parameters for its application in implantable devices and confronting them with safety standards proposed by ANVISA and ABNT. To do so, a quantitative approach was used, researching information that corroborates the use of the inductive coupling method in battery charging with the application of the specifications found in the simulation performed in Maxwell HFSS®. The tests conducted demonstrated that the temperatures generated on the skin and the device remained around 20ºC, a value below the maximum acceptable for the human body, and that the specific absorption rate was 0.0851W/kg below the conventionally set minimum. Furthermore, the obtained electric voltage and current were 3.25V and 3.20mA, respectively, and within the desired limits. Based on the adopted parameters and current standards, the results obtained indicate that this technology is a viable option for recharging batteries in implantable medical devices, also considering safety in its application to patients.

Introduction: Information is a fundamental tool nowadays, being essential for control, data rationalization, management quality, resources, among others. A short-term view is noted, with regard to the internal culture of health units, generating high costs in investments in information technology. Although Brazil has a wide experience, there is a lack of monitoring of the advancement of technologies, generating concern in devising strategies for use in the Unified Health System (SUS), especially in the area of immunization, which leads to a lack of motivation by those who use it. The application of these technologies will allow overcoming the technological gap, enabling the creation of a control and monitoring process. The cards proposed with Radio Frequency Identification Technology (RFID) are intended to provide better control of the reception process to the user, increasing the accuracy of information, and people control management, improving the quality of life of both the user and the of health professionals. Objective: To approach the manufacturing project of the Totem and SUS+ Card and ImunaSUS module as a resource to be thought of as a technology that facilitates the lives of patients, analyzing, from the Integrative Literature Review, the scientific evidence, on which tools are being applied to digital immunization control. Methodology: The present research will express two moments to be discussed: the first will present a case study on the proposal of the University of Brasília for the confection of a system that uses a patient data storage card with RFID technology, the Totem SUS+ and ImunaSUS module, describing the adopted methods and necessary materials to produce them. Next, it will show the steps taken to produce an integrative review of textual literature in which literature that talks about similar technologies will be analyzed. Results: The implementation of these equipment, SUS+ and ImunaSUS, which is based on the association of a card with a reader and recorder and a self-service totem, will enable agility in the assistance of the SUS user, ease in the acquisition, organization and retrieval of information in the health databases. With regard to technologies that are being used to control information related to vaccines, little development of technologies can be seen in the consulted literature, not only in Brazil, but in the world. Conclusion: The proposal developed by UnB is relevant, innovative, similar to the articles found, suggesting tests with products in operation in health environments and in the SUS.

The current lifestyle and dietary habits contribute to obesity, type 2 diabetes, and cardiovascular diseases. Various approaches are being used globally to combat these diseases, including drugs, alternative diets, and intermittent fasting (IF). IF has gained popularity and has shown positive outcomes like weight loss and reduced insulin resistance. We conducted a study using a zebrafish model to investigate the impact of IF on mitochondrial function. The fish were divided into three groups: a standard diet (SD) group with unrestricted eating, a group with 24 hours of fasting alternated with 24 hours of eating (IF24), and a group with 48 hours of fasting alternated with 24 hours of eating (IF48). After six weeks, we measured the animals' body weight and performed morphometric measurements and organ dissection for high-resolution respirometry to measure oxygen consumption in cardiac and brain tissues. Our results revealed that IF influenced the animals' morphometric parameters. Mitochondrial dysfunction was not observed in the hearts and brains of animals in the IF24 group, but we observed decreased cardiac mitochondrial metabolism efficiency in the IF48 group. Overall, our findings support the effectiveness of IF in combating obesity, particularly when using a protocol of alternating 24 hours of fasting with 24 hours of eating ad libitum.

Diabetes Mellitus (DM) is a chronic health problem that interferes in the healing process. This interference generates the worsening of ulcers, especially in the lower limbs, causing infections and possibly leading to limb amputation. There are technologies that help in the healing process, such as photobiostimulation. The development of new technologies is important to improve the quality of life of patients. In this work we developed a photobiostimulation equipment with blue LEDs, based on the RAPHA equipment with red LEDs. The blue color was chosen for its bactericidal characteristics. The equipment was developed at the University of Brasilia (UnB) and the constructive parameters were analyzed in the laboratory. The characterization of the equipment and its interaction with the latex blade was done, to determine the wavelength and power. The equipment with blue LED presented a higher power than the red one.

Introduction: FUEL CELLS AS ENERGY SOURCES IN IMPLANTABLE MEDICAL DEVICES: A SYSTEMATIC REVIEW consists of a bibliographic review on fuel cells and their degree of technological development and possible applications in biomedical engineering regarding active implantable medical devices. The fuel cell is an electrochemical device that uses hydrogen gas and oxygen gas to transform chemical energy into electrical energy, silently and without environmental hazards; It is also the objective of this work to highlight the importance of this new technology in the global energy scenario, with emphasis on Brazil, since, as it is a clean energy source, with zero or almost zero environmental impact, it is a strong candidate for replacing the current fossil energy matrix. Objective: This work explains, in general lines, the origin, functioning of this technology, as well as its advantages, limitations and applications with emphasis on biomedical engineering with regard to self-sustaining implantable bioelectronic devices. Methodology: Initially exploratory bibliographic research followed by a systematic review using the PICO strategy, an acronym for Patient, Intervention, Comparison and “Outcomes” (outcome). The research was carried out on a digital journal platform: a) Portal de Periódicos da Capes, b) Pubmed, c) Embase, d) IEEE Xplore, e) Scopus, f) Science Direct. The timespan of the research was between 2021 and 2022. For the inclusion and exclusion criteria of articles, the PRISMA-2015 protocol was used. Conclusion: Research into the development of fuel cells to power Active Implantable Medical Devices is on the rise, but in the development phase. There are already in vitro and in vivo tests. However, many barriers must be overcome for the use of these electrochemical devices in DMIA. The availability of this technology will reduce surgical risks for patients, decrease financial expenses with: medical staff, surgical centers, in addition to eliminating hospitalization time in ICUs.

The research and development of Organ-on-a-chip (OoC) devices have been garnering increasing attention due to their revolutionary potential in the field of medicine and biomedical research. These miniature devices are designed to replicate the structure and function of human organs with greater precision than traditional cell cultures or animal models. They offer highthroughput screening, accelerating the discovery of new drugs and identifying potential side effects or interactions between different organs with greater accuracy. Additionally, they have the potential to reduce the need for animal models in toxicological testing. One of the promising applications for OoCs is the study of Diabetes Mellitus (DM) and its complications, such as diabetic foot ulcers (DFU). DFU is a severe comorbidity associated with high rates of morbidity, mortality, and amputations, posing a major challenge to global healthcare systems. The use of this technology in this context aims to provide crucial insights into the pathophysiology and enable more effective therapeutic approaches, including personalized medicine. This dissertation aims to analyze the ethical and regulatory aspects of implementing OoCs in Translational Health Research (THR) in Brazil, with a focus on the role of Biomedical Engineering in developing this technology. The work is divided into seven chapters, covering various elements related to the devices and their application in the context of diabetic foot ulcers, including two systematic reviews. The first article focuses on the use of microphysiological systems in the DFU scenario, exploring their applicability in understanding functional changes in the body and the underlying disease mechanisms. The second study sought evidence on a potential healing biocompound, urucum, for the treatment of chronic wounds. Additionally, national and international legislations and regulations governing the use of OoCs were analyzed. Furthermore, a scientific marketing plan was developed to disseminate knowledge about these platforms and their significance in scientific research and innovation. This comprehensive methodology allowed exploring different aspects of OoCs and their relevance to DFU studies, as well as promoting the advancement of this technology in national THR. This dissertation represents a significant milestone in the field of legislation and regulation of devices in Brazil. It aims to provide a solid foundation that can be used in shaping public policies and regulatory guidelines in the country. The importance of interdisciplinarity between Biomedical Engineering and the healthcare sector is emphasized, propelling Brazil to the forefront of biomedical research and ensuring that the use of OoCs benefits not only science but society as a whole. This work serves as a call to action for regulatory authorities, researchers, and healthcare professionals to join forces in creating an agile and efficient regulatory environment that encourages the safe and ethical advancement of OoCs, paving the way for a new era of innovative discoveries and treatments with the potential to positively transform the health and quality of life of millions of Brazilians.

Sleep consists of a series of fundamental stages for the quality of life in several species. Its importance is comparable to food and physical activity. However, their understanding still has gaps that need to be studied.

In this sense, two technologies can contribute, in particular, to the understanding of sleep. The first concerns the electroencephalography (EEG), which is relevant in several neuroscience studies, including sleep. The other technology is machine learning, which has been gaining ground as a tool for identifying patterns that aid in diagnosis; but there are still few works that use these techniques to identify pathologies associated with sleep.

Therefore, this work proposes to compare different machine learning approaches from EEG data in order to automate analyzes to aid in the diagnosis of sleep-associated pathologies. For this, the use of support vector machines (SVM) and random forests (Random Forest) with different hyperparameters from characteristics obtained by calculating energies in signal frequency bands were compared.

In experiments with SVM, approaches with grain variation were tested; with Random Forests, maximum depth. Furthermore, these results were compared with those obtained using convolutional neural networks (CNN), defining characteristics dynamically through the layers of this network. In these CNN architectures, distinct convolutional and dense layers were used with changing hyperparameters.

To carry out this work, polysomnography data available on the Physionet portal was used. From these raw data, there was feature selection and, with the exception of experiments that used CNN, feature extraction using Fourier transform, selecting different derivations (electrodes).

In the pilot study, algorithms were implemented to classify the EEG signal of nocturnal frontal lobe epilepsy. The results obtained with SVM and Random Forest suggest that these classifiers can identify relevant patterns from EEG signals: the highest accuracy (mean of 5 folds) was 60% in both approaches. In addition, comparisons of the work with these approaches showed that the spatial distribution of information is spatially homogeneous. On the other hand, it was not possible to distinguish the EEG signals of participants with epilepsy, even using different architectures, varying the number of layers, activation functions, number of neurons and dropout.

Then, new experiments were implemented using the results of preliminary studies as a guideline. For this, new classifiers were trained with data that went through other stages of preparation from signal windowing and signal filtering. As a result, there was an improvement in the performance metrics of the classifiers.

Introduction: The present work aims to analyze the impact of the Innovation Regulatory Framework on the Research, Development and Innovation (RD&I) process in Health within the scope of the University of Brasília (UnB), in the context of research projects managed by the Development Center Technological Innovation (NIT) at UnB in partnership with the Deanship of Research and Innovation (DPI), for the translation of technologies to the health area linked to the area of Biomedical Engineering. Objectives: The objectives of this work are: i. analyze the interface between the Legal Framework for Innovation and researchers linked to the public university; ii. Analyze the contributory mechanisms between the Legal Innovation Framework and the Technological Innovation Centers of public universities; iii. categorize research projects institutionalized at the University of Brasília through the Chamber of Projects, Agreements, Contracts and Related Instruments (Capro) of the Deanship of Research and Innovation (DPI) in the area of Biomedical Engineering; iv. correlate research projects with patent filings and/or technology transfers. Methodology: The descriptive, documentary and experimental method will be used, through the qualitative and quantitative analysis of processes, policies, databases and existing documents. In the background, the types of contracts and partnerships signed in the area of biomedical engineering and contemplated in the years 2011 to 2021 will be mapped with a view to identifying probable gaps within the scope of the Legal Framework and their consequences in the UnB RD&I process. Results: Numerous research projects, within the scope of UnB, managed to achieve their purpose, generating royalties and technology transfer, thus fulfilling the translation of research. However, very few projects are linked to the health area in Biomedical Engineering. Conclusion: Based on the data presented, it is possible to provisionally conclude that the University of Brasília has a large scientific and technological production, with emphasis on other areas of research besides health. It is possible to conclude that, although the University of Brasília operates through innovation and technological transfer management bodies, such as ACT, CITT, CDT and DPI, generating a notable number of research projects, technological transfers and deposits of patents, projects aimed at the health sector are still substantially reduced, particularly in the context of Biomedical Engineering.

Breast cancer constitutes the highest incidence rate of cancer among women worldwide. As with other types of cancer, the faster it is discovered, the less invasive the treatment, and the greater the chances of the patient surviving. Having a tool that helps the analysis of mammograms to discover breast lesions and their classification is a way to help prevent cancer. With the development of technology, Artificial Intelligence has also grown, as well as its ramifications. Machine Learning and Deep Learning have developed and achieved good performance in tasks such as image classification and object detection. The mammogram analysis is an image classification task, and having a tool that helps in this task, which can be more than a black box classification in which it is not known how the response is obtained if the Machine Learning model were capable of explaining how it has achieved at the final classification, it would be possible to better direct physicians to use this model in their day-to-day analysis of mammograms.

Currently, some solutions address the classification of breast lesions, however, they use traditional Machine Learning algorithms, which do not have a performance as high as Deep Learning algorithms. In addition, they are considered black box solutions, which means that an answer is given from an input image, and it is not possible to know which factors influenced the final result of the classification. An Artificial Intelligence model that has its results explainable, that is, it is possible to understand what factors led it to reach the final classification result, gives more reliability to the users who use it. With this prediction explanation, it is possible to comply with laws such as the General Personal Data Protection Act (LGPD) and even the General Data Protection Regulation, the acronym in English GDPR, which is the European data protection law. An explainable model can fit into laws and make the solution be applied in a real domain. The diagnosis of cancer is a sensitive moment, and because of this, knowing how a Deep Learning or Machine Learning model reached a certain result can better direct doctors to investigate cases in a more targeted way, thus giving more emphasis to some characteristics of the image, in addition to generating more reliability in model prediction results.

The use of Deep Learning for image classification tasks has obtained surprising results, which match and in some cases even exceed human capacity, which is why this approach will be discussed in this dissertation. Combining a powerful classification tool with techniques that allow leaving the created models with explainable predictions, thus making a breast lesion classification tool with high-reliability potential for its end users, the medical specialists. To achieve these goals, Deep Learning architectures such as VGG16 and explainability techniques of trained models such as LIME, which is a well-performing and simple-to-use explainability framework, are investigated.

This dissertation aims to develop a Deep Learning model that uses Explainable Artificial Intelligence (XAI) techniques, that is, it has explainable predictions that classify breast lesions and identify the important characteristics that led the model to achieve such a result. result.

After model training using VGG16 architecture, the analyzed metrics were accuracy, specificity, and sensitivity, the results obtained were respectively 68 %, 77 %, and 65 %. Higher results were found in the literature, but these are not reproducible results. In many cases, the databases are private to hospitals where the team surveyed mammograms over the last 20 years, created the dataset, and performed the tests. There was a master's thesis done in 2020 by Adam J. with a similar approach and which obtained results close to those presented in this dissertation. The author reported an accuracy result of 67 %.

Introduction: The use of customized orthopedic insoles has been studied and indicated by health professionals in order to avoid injuries due to postural derangement, anatomical changes, limb dysmetria, pain, among other factors. However, there is still no consensus on the benefits of this intervention for athletes. Objective: To evaluate whether the use of a personalized insole in adult men who run improves the distribution of plantar pressure when compared to the common insole. Method: Systematic review, with search conducted in the following databases: Medline (via Pubmed), Embase, Web of Science, Virtual Health Library, Scopus, SportDiscus and Proquest. Intervention studies that used baropodometry assessment in men who run. Studies with high-performance athletes and people who had some morbidity were excluded from the review. There was no restriction regarding the period of publication and language of origin of the studies. To assess the risk of bias, we used the Robins-I and Rob 2.0 tool from the Cochrane collaboration. Meta-analyses of random effects, using the hedging technique, were conducted to measure the standardized mean difference and respective 95% confidence interval. Results: 1171 studies were detected, of which 13 were part of the systematic review. The sample included 434 male runners, aged 19 to 53 years. The studies were conducted and published between 2004 and 2021. Most of the non-randomised trials were at high risk of bias and the only randomised controlled trial was at high risk of bias. In addition, the meta-analysis indicated that there was no statistically significant difference between the angle of peak dorsoflexion between male runners who used custom insoles when compared to those who did not. Conclusion: The use of insoles did not show significant benefit for athletes. However, few studies were detected, and most of them were at high risk of bias. In this sense, it is recommended that new studies be conducted, with methodological robustness, in order to ratify or reject the hypothesis.

The environment of the Surgical Center, although designed for anestheticsurgical procedures, often does not favor the medical team, leading to injuries and accidents that harm the well-being of healthcare professionals. Osteomuscular symptoms are common due to these unfavorable conditions. In this context, the need arises to create a safe environment for professionals, adhering to ergonomics standards. Technology, especially Artificial Intelligence, emerges as a crucial solution. This study focused on the implementation of an Expert System to identify ergonomic risks in the surgical sector. Data collection was carried out through the analysis of standards and questionnaires. Based on these data, the system was developed using Expert Sinta software and, after testing and validation, proved to be effective in identifying ergonomic risks before the workspace is used by professionals. This allows preventive interventions, improving safety and health in the Surgical Center.

High-cost drugs within the scope of the Unified Health System represent a complex challenge that involves ethical, political, economic and management issues. Ensuring equitable and efficient access to these medicines is essential for proper patient care. In this context, it is important to analyze the different related aspects, including clinical evaluation, economic impact and public policies, as this analysis can contribute to improving the management and planning of resources, aiming to guarantee the best therapeutic result for patients and financial sustainability of the health system. Objectives: outline different actions for different types of users in order to improve the management of medicines in CEAF DF, based on demographic and epidemiological data, in the years 2012, 2018 and 2021, so that such contributions can serve as a basis for implementing improvements locally and/or or in similar services at the state and municipal level in other locations. Methodology: The present work is characterized as an exploratory study, of a descriptive, observational nature, carried out through bibliographical research with the use of TEMAC, to obtain scientific information of greater relevance, following the bibliometric laws in the main databases, resulting in an integrative approach and analysis of data from a national bank with records of medication use from CEAF.

Schizophrenia is a mental disease with many clinical manifestations, making the diagnosis a significant challenge. Until a correct diagnosis is attained, the patient experiments with mental suffering that can lead to social conflicts, involuntary accidents, and suicides. Despite the clinical complexity, early diagnosis is of utmost importance, and several recent studies focus on analyzing structural brain modifications that have been correlated to schizophrenia and can be detected in anatomical magnetic resonance images. Previous research applying machine learning to such images presented promising results. However, the scope was limited to analyzing only one or few slices of the brain while not using recent algorithms at the core of the classifiers. Furthermore, using fewer slices and simple algorithms can lead to information loss due to sub-optimal feature extraction. In this study, we created machine learning models based on Convolutional Neural Networks (CNN) and evaluated the best training parameters based on a Magnetic Resonance Images (MRI) dataset from schizophrenia-diagnosed patients and a subjects control group. In addition, we analyzed the performance of the classifiers, first trained with individual slices of the brain and later with different combinations of multiple magnetic resonance slices. We obtained the slices by extracting them one by one from the scanned volume output of the MRI scanning process. First, the slices were numbered based on the axial index number of the scanned volume. Then, we experimented with selecting the slices using metrics like covariance and entropy. The best results were obtained when we used the entropy concept to evaluate the slices images. The slices were sorted by the greatest entropy, and using this criterion, we evaluated each slice individually, using a machine learning model and the collections of slices. Our strategy was to create datasets with incremented number of slices ordered by the entropy. First, we started training with a dataset containing only one individual slice from the scanned volumes. Then, in a second step, we used two slices to build the dataset, and so on, until we created a dataset with all the images extracted from the volume. Each dataset, created from combinations of slices, was used to train the ML model and was evaluated to obtain the performance indicators. Our results suggest that it is possible to obtain an accuracy near 80% when trained with a previously selected combination of slices. Also, we explore the use of Explainable Artificial Intelligence (XAI) to comprehend the model output classification.

Pacemakers are formidable equipment, and an expressive number of people depend on them, with different levels of vital commitment, but they are exposed to electromagnetic interference from the most varied sources. Among them, an increasingly present type is the one produced by anti-theft systems in stores, making it a type of interference difficult to avoid, since they use antennas positioned at the access doors of commercial establishments. As they make use of the emission and reception of electromagnetic waves, these systems can affect the functioning of the pacemakers, causing discomfort or even putting the lives of users at risk. Studies have shown that the Magnetic Acoustic type is more likely to alter the functioning of pacemakers. Pacemaker manufacturers reserve the right to only communicate that their equipment meets the international standard ISO 14117 (Active Implantable Medical Devices – Electromagnetic Compatibility – Electromagnetic Compatibility Test Protocols for Implantable Cardiac Pacemakers, Implantable Defibrillators and Cardiac Resynchronization Devices), Updated in 2019, which defines in item 4.8 Protection Against Exposure to AC Magnetic Fields in the Range from 1 Hz to 140 kHz, an intensity of 150 A/m to which implantable cardiac devices must be exposed without presenting changes in their functioning. Manufacturers of goods security systems do not report the power of their equipment, but the SPCICED TEAS study carried out in 1998, with the participation of the main American manufacturers of these systems, reported that they measured a magnetic field strength of 310 A/m close to the broadcasting antennas. Therefore, this study explored the possibility of attenuating magnetic fields generated at the same frequency used by these magnetic acoustic anti-theft systems through silicon steel and aluminum plates, which have shown to be promising in the manufacture of personal shields. Silicon steel showed considerable shielding power even at this frequency, but was surpassed by aluminum in all positions where at least one face was transverse to the magnetic flux lines. The results provided great support for the development of shields to attenuate high frequency magnetic fields.

Diabetes Mellitus (DM) is a metabolic, chronic, non-communicable disease, with heterogeneous etiopathogenesis, triggered by defects in insulin secretion and/or action, causing disorders in carbohydrate, fat and protein metabolism. It is considered a public health problem due to its prevalence, impact on morbidity and mortality, and its complications. The IDF registered 537 million individuals with DM in 2021, with 80% of this population living in low- and middle-income countries. Brazil is the fourth country in the world in the number of DM cases, and 50% of diabetics are unaware of their diagnosis. Early diagnosis and initiation of treatment prevents and delays acute or chronic complications, micro and macrovascular, renal, and neurological; decreasing mortality and morbidity; and reducing the cost for families and the health system. The time of DM diagnosis is associated with increased risk of developing diabetic foot disease, one of the most frequent complications in these individuals. The health care expenditure for DM patients is $727 billion dollars annually, measured by the IDF in 2021. The objective of this study is to analyze the economic efficiency of the technologies made available by SUS for the treatment of wounds and foot ulcers in patients with type I and II diabetes mellitus. This research was conducted through a systematic review in which different sources were analyzed and discussed. As a result, 10 eligible articles were screened, 5 of which were RCTs, for this study and in this analysis it was observed that telemedicine in the treatment of the diabetic foot was effective and generated significant savings to the health system; the human dehydrated amnion and chorion allograft showed better cost-utility when compared to the standard isolated treatment of the diabetic foot, generating savings. A significant economic and health burden for the health system was observed in the analyzed studies, which highlights the urgency of public policies aimed at prevention. It was also observed the need to identify the best health technologies and their cost-effectiveness for the public health system. Conclusion: Telemedicine proved to be efficient in rapidly screening diabetic foot ulcers, was effective in the course of treatment, and proved to be economically feasible and less costly when compared to outpatient care. With respect to treatment, the dehydrated human amnion and chorion allograft showed cost-effectiveness compared to treatment alone. Further economic studies and intervention projects are needed to insert these changes in the health system permanently improving the treatment and prognosis of patients with diabetic foot ulcer, and reducing the costs related to these individuals in the SUS.

Introduction: Cystic Fibrosis is an autosomal-recessive and multisystemic disease, resulting from mutations in the seventh human chromosome at position 7q13, pathogenic variants in both alleles of the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) gene, which encodes a protein of the same name, responsible by encoding the transmembrane conductance regulatory protein of chlorine, causing dysregulation of electrolyte levels in the body's cells, often considered a severe disease that greatly impacts the respiratory system.
Objective: To study the current picture of the treatment of this disease and the conditions for improving the daily quality of life of patients treated with CFTR dysfunction modulators.
Methodology: Initially, an exploratory literature review was performed followed by a systematic review guided by the PICO strategy, acronym for Patient, Intervention, Comparison and “Outcomes”, translated as Outcome.
Conclusion: CFTR modulating drugs (ivacaftor, lumacaftor and tezacaftor), administered as monotherapy or in combination, are revolutionizing the treatment of CF. Although they are not curative, these therapies already represent prospects for prolonging and increasing the quality of life of the CF population, mainly reducing the number of pulmonary exacerbations.