Artificial intelligence in the diagnosis of cardiovascular disease.

Artificial intelligence (AI) is a field of computer science that aims to mimic human thought processes. AI techniques have been applied in cardiovascular medicine to explore novel genotypes and phenotypes in existing diseases, improve the quality of patient care, enabling cost-effectiveness, and reducing readmission and mortality rates. The potential of AI in cardiovascular medicine is tremendous; however, ignorance of the challenges may overshadow its potential clinical impact. This paper gives a glimpse of AI's application in cardiovascular clinical care and discusses its potential role in facilitating precision cardiovascular medicine.

Evaluation of KI-67 expression in uterine leiomyoma and in healthy myometrium: a pilot study.

OBJECTIVE: Evaluate the expression of KI-67 in uterine leiomyomas and adjacent myometrial tissue and verify the existence of a correlation between clinical parameters and KI-67 expression in tumors. METHODS: This is a cross-sectional, controlled, analytical study. Samples of leiomyomas and myometrium were obtained from patients who underwent hysterectomy. The samples were processed by immunohistochemistry using KI-67 antibody, and the expression was evaluated by two blinded observers. Student>s T-test was used for comparison of means, and Pearson>s P test for correlation with clinical parameters. RESULTS: A total of 9 patients were included in the study. The mean age was 40.7 years, ranging from 35 to 44 years. The mean expression of KI-67 in myometrium was 1.63%, and, in leiomyomas, 5.96% (p <0.001). The highest expression of KI-67 was moderately related to the severity of anemia, bleeding, and pain level. CONCLUSION: The expression of KI-67 in normal myometrium was significantly lower than in leiomyomas. The highest expression of KI-67 was moderately related to the severity of anemia, bleeding, and pain level in the patients of this study.

Artificial intelligence in the diagnosis of cardiovascular disease

SUMMARY Artificial intelligence (AI) is a field of computer science that aims to mimic human thought processes. AI techniques have been applied in cardiovascular medicine to explore novel genotypes and phenotypes in existing diseases, improve the quality of patient care, enabling cost-effectiveness, and reducing readmission and mortality rates. The potential of AI in cardiovascular medicine is tremendous; however, ignorance of the challenges may overshadow its potential clinical impact. This paper gives a glimpse of AI's application in cardiovascular clinical care and discusses its potential role in facilitating precision cardiovascular medicine.

RESUMO A inteligência artificial (IA) é um campo da ciência da computação que tem como objetivo imitar os processos de pensamento humano. Técnicas de IA têm sido aplicadas na medicina cardiovascular para explorar novos genótipos e fenótipos em doenças existentes, melhorar a qualidade do atendimento ao paciente, possibilitar custo-efetividade e reduzir taxas de readmissão e mortalidade. Existe um grande potencial da IA na medicina cardiovascular; no entanto, a ignorância dos desafios pode ofuscar seu impacto clínico. Esse artigo fornece a aplicação da IA no atendimento clínico cardiovascular e discute seu papel potencial na facilitação da medicina cardiovascular de precisão.

Evaluation of KI-67 expression in uterine leiomyoma and in healthy myometrium: a pilot study

SUMMARY OBJECTIVE Evaluate the expression of KI-67 in uterine leiomyomas and adjacent myometrial tissue and verify the existence of a correlation between clinical parameters and KI-67 expression in tumors. METHODS This is a cross-sectional, controlled, analytical study. Samples of leiomyomas and myometrium were obtained from patients who underwent hysterectomy. The samples were processed by immunohistochemistry using KI-67 antibody, and the expression was evaluated by two blinded observers. Student›s T-test was used for comparison of means, and Pearson›s P test for correlation with clinical parameters. RESULTS A total of 9 patients were included in the study. The mean age was 40.7 years, ranging from 35 to 44 years. The mean expression of KI-67 in myometrium was 1.63%, and, in leiomyomas, 5.96% (p <0.001). The highest expression of KI-67 was moderately related to the severity of anemia, bleeding, and pain level. CONCLUSION The expression of KI-67 in normal myometrium was significantly lower than in leiomyomas. The highest expression of KI-67 was moderately related to the severity of anemia, bleeding, and pain level in the patients of this study.

RESUMO OBJETIVO Avaliar a expressão do KI-67 em leiomiomas uterinos e tecido miometrial adjacente e verificar a existência de correlação entre parâmetros clínicos e expressão do KI-67 em tumores. MÉTODOS Estudo transversal, controlado e analítico. Amostras de leiomiomas e miométrio foram obtidas de pacientes que realizaram histerectomia. As amostras foram processadas por imuno-histoquímica utilizando anticorpo para KI-67 e a expressão avaliada por dois observadores cegos. O teste t de Student foi utilizado para comparação de médias e o teste P de Pearson para correlação com parâmetros clínicos. RESULTADOS Um total de 9 pacientes foi incluído no estudo. A idade média foi de 40,7 anos, variando de 35 a 44 anos. A expressão média do KI-67 no miométrio foi de 1,63% e nos leiomiomas de 5,96% (p <0,001). A maior expressão do KI-67 foi moderadamente relacionada com a gravidade da anemia, sangramento e nível de dor. CONCLUSÃO A expressão do KI-67 no miométrio normal foi significativamente menor que nos leiomiomas. A maior expressão do KI-67 foi moderadamente relacionada à gravidade da anemia, sangramento e nível de dor nos pacientes deste estudo.