prevention

The National Health Surveillance Agency (ANVISA) is a public institution created by Law 9782/1999, which integrates the field of Collective Health and acts in the prevention, control, and inspection of sanitary conditions in various sectors, such as food, health, pharmacies, hospitals, and commercial establishments. Its main objective is to guarantee the protection of public health, ensuring that products, services, and environments comply with established standards, to promote health and preventing diseases. To exercise its inspection and control function, Sanitary Surveillance has the power of sanitary police, which gives it the authority to apply necessary measures and curb practices that pose risks to the health of the population.The performance of the Sanitary Surveillance is fundamental to prevent health risks in any part of society and to guarantee national programs for patient safety. It is also essential to prevent diseases, control risks, and monitor compliance with health standards and specific regulations. The police power of Sanitary Surveillance consists of establishing norms and technical regulations, carrying out inspections and inspections, issuing temporary or permanent interdiction orders in establishments that do not comply with the norms, as well as forwarding complaints to the Public Ministry in cases of crimes against public health. Arrests carried out by Sanitary Surveillance agents are extreme measures and applied in serious situations of risk to public health or when there is repeated disrespect for sanitary regulations. The prohibited establishments have the right of defense and may seek to regularize the situation to obtain the release of activities.The performance of the Sanitary Surveillance has significant impacts on the prevention of disease outbreaks and epidemics. Identifying and correcting inappropriate practices helps to prevent the spread of pathogenic agents and reduce the risk of contagion in collective environments. The institution’s preventive and supervisory activities contribute to reducing risks to the population’s health, preventing disease outbreaks, food poisoning, and the proliferation of pests, among other problems. Finally, the importance of exercising police power with transparency, impartiality, and respect for individual rights is emphasized, always aiming at the collective interest and the promotion of the common good. In summary, Health Surveillance plays an essential role in protecting public health, seeking to ensure that the population has access to safe products and services, contributing to the improvement of quality of life and disease prevention.

Stroke is a clinically defined syndrome of acute focal neurological deficit attributed to vascular injury (infarction, hemorrhage) of the central nervous system. Stroke is the second leading cause of death and disability worldwide. Stroke is not a single disease but can be caused by a wide range of risk factors, disease processes and mechanisms. Approximately 15% of strokes worldwide are the result of intracerebral hemorrhage, which can be deep (basal ganglia, brainstem), cerebellar or lobar. A minority (about 20%) of intracerebral hemorrhages are caused by macrovascular lesions (vascular malformations, aneurysms, cavernomas), venous sinus thrombosis or rarer causes.

Chronic Kidney Disease (CKD) is a significant public health issue with a rising prevalence globally. Diabetic kidney disease (DKD), a leading cause of CKD, necessitates improved biomarkers for early detection and effective management. Traditional markers such as serum creatinine, estimated glomerular filtration rate (eGFR), and albuminuria have notable limitations in sensitivity and specificity, especially for early detection. Fetuin-A, specifically its post-translationally modified form (PTM-Fetuin-A), has emerged as a potential novel biomarker for DKD. This study evaluates PTM-Fetuin-A in a cohort of Bulgarian patients with type 1 and type 2 diabetes, assessing its correlation with traditional markers such as albuminuria and eGFR. Significant correlations were observed between PTM-Fetuin-A and these indicators (e.g., Pearson’s r = 0.447, p = 0.025 for albuminuria), highlighting its ability to detect early kidney function decline. Furthermore, PTM-Fetuin-A demonstrated potential as a non-invasive tool for identifying normoalbuminuric DKD, addressing gaps left by conventional biomarkers. By offering additional prognostic value, PTM-Fetuin-A could improve the early diagnosis and clinical management of diabetic patients, reducing the burden of CKD.

Background: The health professional appears to be concerned given the number of questions surrounding the patient’s post-transplant survival, patient selection method, and the continuous search for scores that reflect their reality and reconcile the results they desire with the patient’s expectations. It is from these considerations that the present work was born with the purpose of understanding the reality of patients hospitalized for hepatocellular carcinoma and what adherence to the Model For End-Stage Liver Disease (MELD) criteria meant in Brazil. Methods: This study is a discrete, univariate time series on emergency hospitalizations that occurred between 2000 and 2018, in the city of São Paulo, Brazil. Python version 3.11 was the software used for statistical treatment and analysis of the time series. Results: There were 6887 hospitalizations for malignant neoplasm of liver and intrahepatic bile ducts, in the period 2000-2006, of which 2898 were elective and 3915 (56.85%) were urgent in the period. There was an increase of 63.97% in the number of emergency hospitalizations in relation to the period 2000-2006 and 2007-2013. To highlight, in 2013 there were 1270 emergency admissions, which represented an increase of 109.22% in relation to the number of emergency admissions in 2006. The contingency correlation analysis demonstrated a significant association between the variables in which the chi-square value was 82.18, the p-value was 1.24 x 10-19, and the critical value was 3.84 with one degree of freedom. There was a 123.65% increase in the number of hospitalizations for chronic hepatitis, not elsewhere classified, between the periods 2000-2006 and 2007-2013. The chi-square value of the contingency association was 221.22, with a p-value of 4.90 x 10-50, a critical value of 9.488, with a significance level of 5% and one degree of freedom. The study showed an increase in hospitalizations of 21.88% for alcoholic liver disease between the periods 2000-2006 and 2007-2013. In the period 2000-2006, there were 21330 hospitalizations, 19224 of which were urgent (90.13%). In the period 2007-2013, there were 25997 hospitalizations, of which 22,802 (87.71%) occurred urgently. The chi-square value was 68.95, the p-value was 1 x 10-16, and the critical value was 3.84, with one degree of freedom. Conclusion: The MELD score, as it is sensitive to the patient’s severity, selects those who are at an advanced stage of the disease for liver transplantation. In this scenario, patients do not necessarily have access to outpatient services and begin to attend them regularly when MELD identifies the advanced stage of the disease, opening a gap between prevention, treatment, and rehabilitation.

The integration of deep learning and genetic analysis has transformed the assessment of elite sports performance, particularly in competitive swimming. This study examines the fusion of deep learning techniques with DNA markers, physiological biometrics, and performance analytics to enhance the prediction and optimization of swimmer performance. A structured dataset comprising genetic sequences, physiological parameters, and biomechanical attributes was utilized to train a neural network model capable of categorizing swimmers based on genetic predisposition and athletic potential. The model achieved high classification accuracy, demonstrating a strong link between genetic markers, physiological traits, and competitive swimming outcomes. The findings emphasize the potential of AI-driven analytics in talent identification, customized training adaptations, and injury prevention. Furthermore, the study highlights the effectiveness of deep learning in analyzing complex genomic and physiological data to generate meaningful insights for performance enhancement. While the results validate the feasibility of using genetic and AI-based models for performance prediction, further studies are needed to broaden dataset diversity, integrate epigenetic influences, and test the model across varied athlete populations. This research contributes to the expanding field of AI-driven sports science and provides a solid foundation for incorporating genomics with deep learning to enhance elite athletic performance.