Application Programming Interfaces (APIs) and scripting tools are becoming essential in modern treatment planning systems in radiotherapy, enabling the automation and simplification of complex tasks. These tools not only improve operational efficiency but also contribute to scientific advancements by facilitating the development of research and the implementation of innovative techniques. A notable example is the Eclipse Scripting API (ESAPI), which allows users to create custom scripts to directly interact with the treatment planning system, optimizing plan creation and quality checks. In the clinical context, fluence smoothing in intensity-modulated radiotherapy (IMRT) plays a critical role in balancing plan complexity with dosimetric quality. Fluence regulation reduces the need for monitor units (MUs) but requires careful analysis to ensure dose gradient precision in critical areas. Additionally, controlling variability in bladder filling during pelvic radiotherapy treatments is essential to minimize dosimetric deviations and optimize treatment effectiveness. The implementation of bladder deformation models, as proposed in this study, can assist in standardizing patient preparation, reducing uncertainties, and mitigating side effects. The main objective of this research is to optimize radiotherapy planning strategies by evaluating dose variations in automatically generated plans for breast IMRT and developing a model to predict bladder deformation in prostate and uterine treatments. This study aims to provide practical insights to improve the safety and effectiveness of radiotherapy treatments.

Introduction: Pulmonary Mechanical Power (PMP) has been interpreted as the force applied by the ventilator to displace a volume of air within the lungs, deforming the epithelium and cellular endothelium, per unit of time, expressed dimensionally in Joules per second (J/s) or Joules per minute (J/min). This applied force generates energy, and the degree of energy transfer can lead to direct damage of the alveolar capillary membrane and mechanotransduction, as well as being dependent on the ventilatory parameters adjusted at the bedside. PMP is an indicator of Ventilator-Induced Lung Injury (VILI) which has a strong correlation with the risk of mortality, hence the importance of developing more protective ventilation strategies for patients with acute respiratory failure (IRA). Therefore, the aim of this study is to propose a care protocol model based on PMP analysis as a strategy for reducing morbidity and mortality in critically ill mechanically ventilated adult patients treated in public emergency departments (DE) in the Federal District. Methodology: This is a Systematic Review (RS) carried out using the PRISMA-P protocol, registered in PROSPERO (CRD42024581331) and with the searches carried out on January 20, 2024 in the databases: (i) for scientific literature - VHL (BIREME and LILACS), CINAHL (EBSCO), Cochrane Library, Embase, IEEEXplore, PEDro, PubMed/MEDLINE, SciELO, ScienceDirect, Scopus and Web of Science and; (ii) for gray literature - BMC/ISRCTNregistry, ClinicalTrials, European Union Clinical Trials Register, ICTRP - International Clinical Trials Registry Platform, PACTR - Pan African Clinical Trail Registry and ReBEC. Results and Discussion: We found 10 Randomized Clinical Trials (ECR) involving a total of 568 mechanically ventilated (VM) patients, the majority (n=392, 69%) treated in Intensive Care Units (UTI). All of the ECR had significant limitations in their development, 5 (50%) of them received funding and 6 (60%) were of the cluster type. The ECR were analyzed independently by pairs using the Rayyan software, with an overall percentage of agreement between the reviewers of 95.65% - Free Marginal Kappa of 0.91 [0.85-0.97], 95%CI. With regard to morbidity and mortality outcomes, 4 (40%) showed direct mortality data at 28 days of VM that was worse for the group with high PMP, as well as longer duration of VM and longer postoperative hospital stay for the same group. One ECR showed important data on lung ultra-protection - higher 60-day mortality - concluding that a significant reduction in PMP is also harmful. When analyzing the PMP safety limit, it was observed that the majority (60%) only reported the upper limit, of which 4 (40%) had a PMP limit >17.0 J/min, 1 (10%) a PMP limit ≥12.0 J/min and 1 (10%) a PMP between 17.0 and 20.0 J/min. The other 4 (40%) did not indicate PMP limits. All the ECR used equations for this calculation, and there was agreement that in the volume-cycled mode (VCV) the equations that best translate PMP are those of Gattinoni et al (2016) and, in the pressure-cycled mode (PCV), those of Becher et al (2019). It was noted that studies on this subject began in 2016 with the definition of PMP and the equations proposed by Gattinoni et al. All the ECR were analyzed for risk of bias and methodological quality using the RoB2.0 tool, showing a moderate risk overall. This is the first RS to jointly analyze these ECR and translates this importance to the DE, where the implementation of a PMP protocol is important and will be associated with improvements in the provision of VM care to critically ill patients and, consequently, in their clinical outcomes. It is known that there is a temporal link between ventilator management in the initial period of IRA and the development of subsequent complications, so early adherence to optimized ventilation can improve the outcome. The RS has not yet been completed and is still in the meta-analysis phase using the RevMan 5.4 platform. It will then go on to draw up a model care protocol in the form of a flowchart for adult DE clinical practice. However, despite the progress of the RS, it still complies with AMSTAR 2 - a critical evaluation tool for RS of ECR - in 13 of the 16 questions proposed in this questionnaire so far, which makes it important for the scientific society researching this. Conclusion: PMP is an important ventilatory parameter for adult patients on VM because it is associated with the risk of death and morbidity in urgent and emergency care. This bedside calculation has been hampered by the extensive equations proposed to date. There are no proposed protocols and/or RS that analyze these ECR and translate them for application in the DE. Few randomized clinical trials can be identified that address the subject, and therefore more robust prospective ECR are needed aimed at the practical application of PMP in the patient's clinic.

Generative adversarial networks (GANs) are an artificial intelligence model widely used in image processing. This paper will address the use of this tool to aid in the reconstruction of magnetic resonance images made with undersampled single-coil data.

The GAN was used to improve the quality metrics of images reconstructed with different data acquisition trajectories, which presented distortions due to not respecting the Nyquist–Shannon sampling theorem.

Distorted images were used in the training of the algorithm and typical resonance images as a reference. Tests are performed by feeding the trained model with images made with undersampled data and then generating new improved images. Finally, metrics are extracted from the produced images such as structural similarity index and signal-to-noise ratio.

Increases of up to 100% in the average structural similarity index and up to 128.74% in the average signal-to-noise ratio were obtained (Test 78). The results obtained are similar to the results of other models that used the same data set and outperformed other GAN models in this same aspect.

Type 2 Diabetes Mellitus is a metabolic disease that commonly manifests in adults, often linked to an unhealthy diet and lack of physical activity. According to the World Health Organization, its incidence is continuously increasing worldwide. Diabetes Mellitus is highly associated with Autonomic Neuropathy (AN), a condition initially asymptomatic in the early years of diabetes diagnosis but significantly impacting the quality of life and increasing patient mortality. Early indicators of AN, even if the subject is still asymptomatic, can be used as predictors for the development of diabetes. The Autonomic Nervous System (ANS), part of the peripheral nervous system, regulates involuntary body actions such as cardiovascular function. This control influences heart rate, causing fluctuations between heartbeats, known as Heart Rate Variability (HRV). HRV is also influenced by the respiratory cycle and variations in blood pressure. It is possible to quantitatively assess the action of the ANS on cardiac regulation through physiological indices, measuring the impact of ANS dysfunction in diabetic individuals, with or without the presence of AN symptoms. Using this approach, the objective of this study is to determine quantitative ANS indices using univariate and multivariate techniques that can differentiate individuals with type 2 diabetes (T2DM) from control subjects. Additionally, the study aims to verify combinations of such indices that can predict diabetic individuals. These indices of autonomic function are determined from the processing and analysis of physiological signals such as electrocardiogram (ECG), continuous blood pressure, and respiratory signals. Initially, the study investigates the use of predictive models using indices of autonomic function determined from a single physiological signal, such as ECG or continuous blood pressure. Then predictive models using multivariate indices, that require data obtained from pairs of physiological signals, using both spectral techniques and system identification in the time domain as parameters for prediction, are developed. Data for this study was obtained from the public database “Cerebromicrovascular Disease in Elderly with Diabetes” (CDED). It is comprised of ECG, continuous blood pressure, and respiratory flow data from individuals aged 55 to 75 years, both with and without type 2 diabetes. The data were filtered and preprocessed using the CRSIDLab toolbox in MATLAB to extract autonomic indices. These indices were selected from the literature and categorized into three approaches: 1) statistical indices in the time domain, 2) spectral indices in the frequency domain, and 3) system identification indices in the time and frequency domains. Univariate time domain analyses provides statistical characteristics about a signal, such as mean, standard deviation, or event counts of interest. In the spectral domain, the estimation of power spectral density (PSD) of R-to-R time intervals (obtained from the ECG) and the time series of beat-to-beat systolic and/or diastolic blood pressure values (respectively the maximum peak and nadir in each heart beat interval) provide useful quantitative indices of autonomic function, based on the power density in different frequency regions of the PSD, and has been used in many studies to evaluate cardiac autonomic function in health and disease. However, since it is based on a single signal, other variables that may be related to the oscillations measured by the PSD in different frequency regions cannot be accounted for. The more specifically understand how the influence of different physiological signals, such as blood pressure and respiration, can cause changes in another signal, such as HRV, implies the need for an alternative approach that explicitly describes this tempora cause-and-effect behavior. To overcome the inherent limitations of univariate analyses, we also use system identification models in the time-domain to mathematically characterize the dynamic coupling between pairs of cardiovascular variables, using a causal impulse response (IR) model (in which the present output is restricted to depend on present and past, but not future, values of the input), from which additional indices of autonomic function can be obtained. All extracted indices of autonomic function were then analyzed using Pearson and Spearman correlations, and the One-way Anova statistical method. Correlation analyses were applied to verify the similarity between indices and the degree of influence of one index on another, while the One-way Anova technique was used to identify which indices are able to measure a statistical significant difference between diabetic and control groups. Subsequently, the indices were used as parameters in classification algorithms. The indices were first used individually and then in groupings. Different statistical and machine learning prediction models were used to quantify their performance in predicting T2DM vs. control subjects. Four machine learning algorithms were used in this study: 1) Support Vector Machines (SVM), 2) K-Nearest Neighbor (KNN), 3) Decision Tree (DT), and 4) Logistic Regression (LR). The different prediction models used indices that were grouped based on their signal origin and the extraction methodology used, as presented in Appendix B.1. The index groupings resulted in eight sets: A) HRV - statistical indices in the time domain and spectral indices in the frequency domain related exclusively to HRV; B) BPV - spectral indices in the frequency domain of Blood Pressure Variability (BPV); C) FRF - combination of frequency response function indices obtained from spectral analysis of HRV, BPV, and system identification by impulse response (IR); D) IR - system identification indices by impulse response in the time and frequency domains; E) HRV + BPV - the combination of indices in sets A and B; F) HRV + IR - the combination of indices in sets A and D; G) Special 1 - indices whose null hypothesis indicated a possible distinction between diabetic and control individuals with statistical significance; H) Special 2 - indices that achieved high performance in the prediction of diabetic and control individuals, present in all machine learning algorithms used. The classification of the test set based on individual indices showed excellent results when using indices calculated form multivariable models, such as the dynamic gain indices associated with cardiorespiratory coupling (CRC) or respiratory input of the coupling system with mutual influence of CRC and baroreflex coupling (BRC) were used. Following the respiration indices, the best indices for group prediction were the statistical indices of HRV combined with BPV, thus also a multivariate prediction model. The results with index groupings also showed that the use of HRV-related indices as model parameters can improve the accuracy, sensitivity, and precision performance of other marker sets in the classification and prediction of diabetic and control individuals. However, the grouping of indices related exclusively to HRV did not rank among the best performances in group prediction. The special groups 1 and 2 stood out as the best index groupings for predicting the studied groups of individuals, both containing statistical indices in the time domain and dynamic gain indices associated with the HRV-respiration pair of variables. Performance variation among the special groups occurred according to the machine learning algorithms used, where the groups alternated between the best results. The exception occurred for group G, which performed below the overall average when trained with the Decision Tree algorithm, which was led by grouping E. In all other algorithms, groupings G and H led the prediction performance. Finally, the special index groupings showed a slight improvement in group prediction compared to the prediction with individual indices using dynamic gain indices associated with respiration. This indicates a robust influence of respiration on HRV from a frequency perspective. It also highlights the importance of exploring index characteristics to avoid potential information conflicts when used as attributes for machine learning, as seen in the special groups.

This study explores the dynamics of visual perception through the use of the Müller-Lyer illusion and the techniques of Electroencephalography (EEG) and Event-Related Potentials (ERPs) to understand how different configurations of this illusion affect participants' perception. The methodology involved presenting several versions of the illusion to healthy individuals, measuring electrophysiological responses to identify specific patterns of visual processing. The results indicated possible significant differences in brain responses associated with variations of the illusion, highlighting the influence of geometric aspects on perceptual processing and the possible prevalence of bottom-up processing in the processing pathway of this illusion.

Artificial Neural Networks (ANNs) were developed in the 1940s with the aim of simulating the processing capacity of the human brain. They resurfaced and gained prominence in the 1980s, especially in solving complex problems, in addition to being able to deal with non-linear issues. When prior physical knowledge is incorporated into these networks, forming what we know as Physics-Informed Neural Networks (PINNs), it is possible to improve the understanding of the pharmacokinetic cycle in the human body. This includes modeling the behavior of iodinated contrast media, which improves diagnostic accuracy. In this work, we investigate the ability of Physics-Informed Neural Networks to model, from experimental data, the pharmacokinetics of iodinated contrast media. To this end, three distinct differential models were analyzed, and the parameters of each were estimated based on experimental data. The results obtained demonstrate the feasibility of using PINNs in modeling the pharmacokinetics of contrast media in the human body. These findings can contribute significantly to the optimization of contrast doses in medical examinations, ensuring improvements in image quality and diagnostic accuracy. It is essential to highlight that the research demonstrated the effectiveness of PINNs in modeling and predicting the pharmacokinetic patterns of iodinated contrast agents, offering a promising approach for dose adjustment and individualization of examinations. In addition, the importance of new studies on the use of Physics-Informed Neural Networks is highlighted, aiming to generate more robust evidence and results that can be applied in clinical practice.

Environmental health refers to the quality of the environment in which people live and interact, directly influencing their well-being and health. In hospital settings, it is particularly important because factors such as air quality can affect patient recovery, treatment effectiveness, and the safety of all occupants. Ensuring a healthy environment is essential to prevent complications, reduce hospital admissions, and promote efficient care. Studying air quality in these settings is fundamental to understanding how factors such as carbon dioxide (CO2), humidity, and temperature can impact the health of users and the efficient functioning of hospital facilities. Inadequate parameters can result in respiratory problems, fatigue, thermal discomfort, and even compromise the effectiveness of medical procedures, making continuous monitoring and strict control of these variables crucial to ensure safety and quality of care. This study evaluated the air quality in a public hospital in the Federal District, aiming to verify the compliance of CO2, humidity, and temperature levels with standards set by regulatory bodies and analyze the impact of these variables on users' health. A cross-sectional observational study was conducted, with data collected from ten strategic points in the hospital using the HOBO MX1102 Temperature, Humidity, and CO2 Datalogger, which allowed continuous measurement of environmental conditions. The results were compared with current standards. The data showed that, in several areas of the hospital, environmental conditions were partially compliant with established standards. In critical areas such as the Surgical Center and ICU, temperature and humidity control were adequate, providing a safe environment for infection prevention and patient recovery. However, elevated CO2 levels in these spaces suggest the need for improvements in ventilation systems. In high-traffic areas, such as waiting rooms and reception areas, high temperatures and low humidity caused thermal discomfort, affecting the well-being of patients and visitors. Outdoor areas, such as parking lots and loading/unloading zones, faced additional challenges due to prolonged heat exposure, exacerbated by Brasília's dry climate. Thus, inadequate ventilation, especially in critical areas, can compromise both patient recovery and the performance of healthcare professionals. Improvement suggestions made throughout this study include the installation of more efficient ventilation systems, better climate control in high-traffic areas, and the creation of shaded zones in outdoor areas to mitigate the adverse effects of heat. The study also highlighted the importance of continuous monitoring of environmental parameters to ensure quick and appropriate responses, promoting a safe and comfortable environment. In conclusion, strict control of environmental conditions is essential for the safety and well-being of all hospital users. Implementing improvements will reduce the risks of hospital complications, as well as provide an environment more conducive to patient recovery. The study also suggests the creation of public policies based on continuous monitoring and the adoption of hospital environmental management practices with the use of technology.

Hands-on observation of the challenges in public management, particularly within complex scenarios such as those in public health, reveals itself as the cornerstone for implementing robust public policy solutions using the problem-solving expertise of Engineering, as demonstrated in this case study conducted within the Health Secretariat of the Federal District (SES-DF). The translation of the real-world scenario of the Federal District (DF) into Public Governance shows promise as a bridge for connection and synergy between social responsibility and decision-making, with health infrastructure needs and the public interest. This qualitative case study presents the multifaceted aspects mined and polished during a single year of operation of the Special Engineering Clinic Advisory—integrated by the researcher within the Health Infrastructure Sub-secretariat (SES/SINFRA)—to understand the current phenomenon of Public Governance in health. Outlining each facet, the concept of holistic sustainability—a vision introduced by the researcher as a scope for optimized public policies—is based on principles of interconnectedness, interdependence, integration, and dynamism that lead to a true understanding of the health system. Progressively, a practical public policy proposal for obsolescence dedicated to health is consolidated through the researcher's directive—within their Clinical Engineering competencies—defining, in a procedural, fluid, and institutionally secure manner, a Standard Operating Procedure (POP). This measure catalyzed a reaction to mobilize the entire life cycle of medical-hospital equipment, maximizing the efficiency and effectiveness of district public management, and having a complete systemic impact, paving the way for integrated health care, not only from the perspective of the care area but also integrated with clinical engineering. Finally, the research is marked by the dissemination of qualitative results to validate the importance and effects of the directive and its subsequent internal institutional normative consolidation as a legal-administrative reference. Furthermore, it presents discussions on Governance regarding the critical shortcomings of the SES-DF for improved operational performance, as well as institutional cultural modernizations, enabling governance transformation, such as: the evolution of organizational culture; a multidisciplinary vision provided by the Biomedical Engineering scientific academy for the formulation of public health policies and decision-making; the consolidation of the Structure-Process-Results triad as a governance strategy through holistic sustainability; and other strategies.

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.

This work presents a proposal to reduce data and energy consumed in the signal conversion process, in the digital and analog domains. For this, twoconverter systems are used. An analog-digital (A/D) system with information compression and adaptive sampling using the Lipschitz coefficient andthe properties of the Wavelet Transform. The Lipschitz coefficient characterizes the waveform at a given point. Using the exponent compresses theinformation, reducing the number of samples necessary for the conversion process, yet, maintaining a high resolution. Another system developed inthis work is the dedicated digital-to-analog (D/A) converter, which performs the process of reconstruction of the analog signal by a polynomialarrangement. The signal is reconstructed with amplitude, time and exponent information. These are the A/D outputs, and consequently, the D/Ainputs. A reconstruction based on the morphology of the signal using a polynomial approximation method is used, significantly reducing the samplingrate.Circuits were developed that perform the calculation of the exponent using the Transform Wavelet. After building the necessary circuits in theVirtuoso tool, four waveforms were tested with known exponents, in order to verify that the system can perform detection. There was then a variationcircuit, temperature and power parameters, generating 300 samples for each signal. It was found statistically through the T test for the signs thatshowed a normal distribution, and using the test of Wilcoxon for the signal that did not present it. The results obtained were exponents that did notdeviate considerably from the value nominal, indicating that it is possible to obtain reliability in obtaining the exponent.In the construction of the D/A system, it was necessary to generate four base waves by combining and weighting them to generate any exponent n.In this work, 16 exponents were tested (4 bits in the exponent information) spaced in different ways, varying the value between 2 and 0.25. Thewaves obtained by the designed circuits were tested comparing them with the ideal waveforms. The system output shows an RMS error of less than636 uV for the steepest curve. The reconstruction of an ECG signal was also performed, where there is a reduction in the sampling rate of 95.08% inrelation to an ideal converter with a linear sampling rate of 2 KS/s. A high reduction in the information needed for the reconstruction, even whenperforming a comparison with a converter that has a low sampling rate.

Diabetic Foot Ulcer (DUP) is a complication of Type 1 and 2 Diabetes Mellitus (DM) characterized by wounds associated with ischemia, neuropathy, and deformities that can lead to a clinical picture of low probability of healing. The conventional method of treatment is based on dressings and drug intervention. Notably, assertive treatment depends on an accurate diagnosis, capable of directing care according to the individual need of the patient. The diagnosis, in turn, results from an assessment that is essentially based on the health professional's experience. In this sense, the present work proposes to present a diagnostic aid tool. Objective: To develop an algorithm that classifies UPD through photographic records, according to the parameters of the University of Texas Classification, using the UPD records obtained in the RAPHA® protocol as a sample, using latex biomembranes, with and without curcumin. Methodology: The writing of the algorithm used the Python language because it supports AI. Thus, the Machine Learning (AM) technique was selected, since it allows AI to work from learning based on examples (samples) in order to obtain a desirable result. Results: In the end, an initial version of the classifier was obtained, with preliminary tests of execution and validation of results carried out by health professionals. The results obtained proved to be satisfactory, according to the analysis of accuracy generated by the program itself and confirmed by health professionals, it had assertiveness between 98% and 100%, however, it needs refinement to optimize performance.

INTRODUCTION: DIABETES MELLITUS (DM) IS DEFINED AS A SET OF MULTI-ETHNIC METABOLIC DISORDERS CAUSED BY THE LACK OF INSULIN AND/OR THE INABILITY OF INSULIN TO EXERCISE FULL EFFECT, WHOSE MAIN CHARACTERISTICS IS HYPERGLYCEMIA, THAT IS, THE “EXCESS OF SUGAR IN THE BLOOD”. THIS CONDITION MAY RESULT IN MACRO AND MICROVASCULAR COMPLICATIONS, RESULTING IN A FRAMEWORK OF KIDNEY INSUFFICIENCY, RETINOPATHY (VISION COMPROMISE), INFARCTION, STROKE, LOSS OF SENSITIVITY IN THE LOWER LIMBS, among others. IT IS CURRENTLY CONSIDERED A PUBLIC HEALTH PROBLEM, DUE TO THE GROWING NUMBER OF AFFECTED AND HIGH PUBLIC INVESTMENTS IN THE TREATMENT OF THE DISEASE. IN THIS SCENARIO, COMPLICATIONS RELATED TO THE LOWER LIMBS, CALLED “DISEASES OF THE FOOT IN DIABETES” ARE HIGHLIGHTED BY THE DIFFICULTY OF HEALING, WHICH CAN EXTEND THE TREATMENT FOR YEARS, AND ARE THE BIGGEST CAUSING FACTOR OF AMPUTATION OF THE LIMBS. AT THIS juncture, RESEARCHERS AT THE UNIVERSITY OF BRASÍLIA (UNB) DEVELOPED A PORTABLE TECHNOLOGY THAT ASSOCIATES THE USE OF NATURAL LATEX BIOMEMBRANE (OF THE RUBBER TREES HEVEA BRASILIENSE) AND LED PHOTOTHERAPY TO TREAT WOUNDS IN THE LOWER LIMBS OF DIABETIC PATIENTS, CALLED RAPHA® MEANS TO HEAL IN HEBREW. HAVING PROMISING RESULTS IN CLINICAL STUDIES, THE TECHNOLOGY IS BASED ON AN IN-HOUSE TREATMENT, PERFORMED BY THE PATIENT AND THEIR FAMILY, DISTANCE THE PATIENT FROM HEALTH CENTERS AND STIMULATE SELF-CARE. OBJECTIVE: TO ANALYZE THE PROCESS OF SELF-CARE AS A THERAPEUTIC STRENGTH IN THE SET OF THE RAPHA® PROTOCOL. METHODOLOGY: THIS RESEARCH IS SUPPORTED IN THE THEORETICAL METHODOLOGICAL FRAMEWORK OF QUALITATIVE RESEARCH. SUCH METHOD IS A TOOL THAT ALLOWS ACCESS TO THE STUDY PHENOMENON IN A SUBJECTIVE CONTEXT. THUS, WITH THE ASSUMPTION THAT NURSING OPERATES DURINGLY IN THE SELF-CARE PROCESS, A QUALITATIVE LOOK LINKED TO EVENTS IS JUSTIFIED. THE QUALITATIVE ANALYSIS WAS BASED ON PARTICIPANT OBSERVATION AND DOCUMENTAL ANALYSIS OF DATA COLLECTED DURING THE CLINICAL PHASE OF THE RAPHA® PROJECT, IN THE YEARS 2018 AND 2019. RESULTS: THE INTEGRATION OF THE SELF-CARE PROCESS INTO THE CLINICAL TEST (Once RAPHANO WHILE TECHNIQUES DEPENDS ON THE ASSERTIVE CONDUCT OF THE PATIENT) PROVIDED PARTICIPANTS A GREATER UNDERSTANDING OF THE HEALTH/DISEASE PROCESS AND DYNAMICS OF CARE, SUCH AS WOUND CLEANING. IN THE SAME WAY, IT STIMULATES PARTICIPATION AND SUPPORT OF FAMILY IN THE PROCESS, WHICH IMPACTS THE PARTICIPANT'S QUALITY OF LIFE. IT IS IMPORTANT TO UNDERSTAND THAT AS IT IS A HOME TREATMENT, REMOVAL FROM HEALTH CENTERS OPERATES POSITIVELY ON THE RESULTS, SINCE IT REDUCES THE RISK OF CONTACT WITH OTHER DISEASES AND COSTS WITH TRAVELING TO THE HEALTH CENTER. FINALLY, SELF-CARE PROVIDE TOOLS IN ADDITION TO CLOSING THE WOUND TO AVOID THE EMERGENCE OF NEW TOOLS, THROUGH DAILY CARE, WHICH IMPLIES SAVINGS WITH TREATMENT AND MEDICATION. CONCLUSION: THE OUTCOMES ACHIEVED IN THE RAPHA® RESEARCH DEMONSTRATED IN THE STATISTICAL DATA INDICATE THAT SELF-CARE CAN BE CONSIDERED A THERAPEUTIC STRENGTH, HAVING ACHIEVED RESULTS BIGGER THAN SUS COVERAGE IN CARE.

Data from the National Registry of Accidents and Traffic Statistics (RENAEST) provide important alerts regarding automobile accident rates, and the resulting numbers of victims, injuries, and deaths in the last four years, in Brazil. Despite a 50\% reduction in the accident indicators per 100 thousand inhabitants, registered in the last 10 years, the numbers reported by the organizations are worrisome. Associated with the number of victims in traffic accidents, one can emphasize the percentage of victims with head lesions. According to the World Health Organization (WHO), head injuries are the leading cause of death and debilitating trauma in motor vehicle users.

Today, international government agencies and the New Car Assessment Program (NCAP) are widespread and conduct the important work of submitting new cars to a wide range of crash tests, to provide consumers with independent information regarding the safety levels of marketed vehicle models. These programs use injury criteria for different parts of the body, such as the head injury criterion (HIC). Despite the HIC's importance in quantifying one of the aspects of legion risks for a given vehicle, we observed some limitations in its central concept, as well as a shortage of specific studies about correlated aspects. For example, HIC includes a translational acceleration component in its computation but does not include angular acceleration components. Also, there is a HIC threshold below which the vehicle is considered safe against severe injuries (threshold of 700, according to NCAP), but we did not find studies about light and moderate lesions for HIC values below such threshold.

In this context, we propose the definition, implementation, and evaluation of a new head injury criterion that takes into account not only the translational acceleration but also rotational moment components, i.e., the centripetal and tangential components. The proposed aspect is that the new index, which we call the modified HIC, should evaluate aspects that are already present in the standard HIC, but also rotational HIC components that are not predicted by that standard.

To evaluate the proposed index, we considered acceleration and force signals obtained in dummies during frontal collisions. We compared the results from the new index to those existing criteria, and the modified HIC proved more sensitive than the standard HIC in predicting brain lesion risk. The study also predicts the possibility of use in the Male Dummy Hybrid III 50\%, instead of the MALE THOR 50\% model when computing angular velocities and accelerations. This can reduce the costs of collision tests for evaluating the proposed index.