Introdução: A leishmaniose é uma doença infecciosa causada por protozoários do gênero Leishmania, transmitida por flebotomíneos. Apresenta as formas clínicas cutânea, mucosa e visceral, constituindo um importante problema de saúde pública. O diagnóstico e o tratamento impõem desafios relacionados à variabilidade clínica, à toxicidade dos medicamentos e à resistência parasitária. Método: Foi realizada uma revisão bibliográfica narrativa na base de dados PubMed, utilizando os descritores “Leishmaniasis” e “Therapeutics”. Selecionaram-se artigos publicados nos últimos cinco anos, nos idiomas português e inglês, que abordassem os aspectos diagnósticos e terapêuticos da doença. Resultados: O diagnóstico pode ser estabelecido por métodos parasitológicos, histopatológicos, sorológicos e moleculares, com destaque para a microscopia, os testes sorológicos e a reação em cadeia da polimerase (PCR). O tratamento inclui antimoniais pentavalentes, anfotericina B e miltefosina, sendo definido conforme a forma clínica e a gravidade. Na leishmaniose cutânea, terapias locais, como crioterapia e termoterapia, podem ser empregadas em casos selecionados. Novas abordagens, incluindo nanotecnologia, fitoterapia e imunoterapia, apresentam resultados promissores. Conclusão: O diagnóstico precoce e a escolha adequada da terapia são fundamentais para o manejo da leishmaniose. Apesar dos avanços, permanece a necessidade de novos métodos diagnósticos e terapêuticos que proporcionem maior eficácia, segurança e menor risco de resistência.
Stress-related mucosal damage (SRMD) is a common complication in critical care medicine. The necessity of stress ulcer prophylaxis (SUP) is debated due to advancements in supportive care and challenges in accurately stratifying bleeding risk. This narrative review evaluates contemporary evidence on stress ulcer prophylaxis in critically ill patients, with emphasis on bleeding risk stratification and implications for clinical practice. To construct this narrative review, we searched Embase and PubMed for randomized controlled trials (RCTs) evaluating SUP in adult intensive care unit (ICU) populations. The Population, Intervention, Comparison, and Outcome (PICO) framework was used to guide the search through March 2026. Major trials, relevant subgroup analyses, meta-analyses, and contemporary international clinical practice guidelines were reviewed to contextualize trial-level findings. Large randomized controlled trials demonstrated that acid-suppressive therapy reduces the incidence of clinically significant gastrointestinal bleeding (GIB), without demonstrating a mortality benefit. Inconsistent definitions of risk factors for GIB limit reproducibility and reduce the precision of pooled risk estimates. Meta-analyses confirmed a reduction in GIB and highlighted clinical heterogeneity. Current guidelines recommend stress ulcer prophylaxis for patients with high-risk presentations. However, these recommendations remain conditional due to the absence of a multivariable prediction model. Despite current uncertainty regarding the effects of stress ulcer prophylaxis on gastrointestinal bleeding prevention and mortality, it is recommended to consider SUP in selected critically ill patients based on individual risk assessment. In parallel, clinicians should optimize supportive measures, including early enteral nutrition, hemodynamic stabilization, and discontinuation of prophylaxis once the patient is clinically stable. Further prospective research is needed to guide individualized prophylaxis and avoid unnecessary treatment in low-risk populations.
Background: Artificial intelligence (AI) is transforming medicine by enabling real-time data analysis and improved decision-making. In anaesthesiology, AI tools are increasingly used for perioperative risk assessment and intraoperative monitoring, but evidence on their real-world performance and safety remains limited. Methods: We conducted a systematic review and meta-analysis following PRISMA guidelines, including studies from 2010 to May 2025 that evaluated AI applications—machine learning (ML), deep learning, neural networks, and fuzzy logic— in adult patients undergoing general or regional anaesthesia. Primary outcomes were perioperative complications (e.g., hypotension, hypoxia, bradycardia, delirium, vomiting, cardiac arrest, mortality, acute kidney injury [AKI]); secondary outcomes included haemodynamic stability, ICU admission, and length of stay. Risk of bias was assessed using RoB 2 and ROBINS-I, and random-effects models were applied. Results: Eighteen studies with diverse surgical settings and sample sizes (60 to >450,000 patients) were included. ML models consistently outperformed conventional statistical methods. Ensemble algorithms, such as XGBoost and random forests, achieved AUROC values of 0.942 and 0.96, respectively. Deep learning models, including Max-Pooling Convolutional Neural Networks, predicted mortality with AUROC 0.867. Hypotension Prediction Index (HPI) trials showed 88% sensitivity, 87% specificity, and a 77% reduction in hypotension burden. Hybrid models integrating waveform and electronic health record data reported AUROCs of 0.807 for mortality and 0.766 for AKI. Conclusions: AI-based monitoring, especially ML and biomarker-guided strategies, offers substantial improvements in perioperative risk stratification and haemodynamic management. Wider clinical adoption requires external validation, explainable AI frameworks, and rigorously designed randomized controlled trials demonstrating meaningful patient outcome benefits.
Objective: Considering that breast cancer has the fifth highest mortality rate in the world, this study aims to evaluate the repercussions of the COVID-19 pandemic on the treatment, both surgical and systemic, of patients with cancer in general and those with breast cancer at Hospital Guilherme Álvaro (Santos, Brazil), between March 1st, 2019 and February 28, 2021. Methods: For this purpose, data were collected from both the hospital’s surgery record book and electronic medical records of patients who were followed up in the Mastology and Oncology sectors at Hospital Guilherme Álvaro. This information was tabulated, estimating the total number of surgeries, whether: benign elective surgeries, diagnostic surgeries, surgeries of cancer in general, surgeries exclusive to mastology, of cancer in mastology, benign surgery in mastology, and plastic reconstructive surgery. The percentage ratio between these numbers was calculated. Results: A 49% reduction in total surgeries was observed, comparing the period prior to the pandemic (2019–2020) with the pandemic period (2020–2021), with a decrease of 24.6% in the number of general cancer surgeries except for mastology, and 19.6% of surgeries exclusive to mastology. In other words, there was a total reduction of 22.9% in all oncological surgeries. Moreover, there was a decrease of 11.5% in the total number of patients treated with chemotherapy. In 2020, of the 214 new cases, 116 (54.2%) were mastology patients, being 45.8% of other oncology clinics. Conclusion: Thus, it is concluded that the reduction in the number of aesthetic, benign, and reconstructive surgeries was expected, as observed in the decrease in the number of chemotherapies, which could be due to a limitation on medical appointments. The number of diagnostic surgeries remained stable, which could lead to positive outcomes for oncology patients. It is not possible to predict the next repercussions of the COVID-19 pandemic on breast cancer treatment while the pandemic endures, requiring more studies on this topic.
