Serum vitamin d levels and early diagnosis of peripheral arterial disease in patients with type 2 diabetes mellitus of UMF no. 53 leon gto.

Existe evidencia creciente que relaciona la deficiencia de vitamina D y la predisposición a desarrollar diabetes mellitus tipo 2, así como la evolución de ambas enfermedades. Por su parte, la hiperglicemia sostenida ocasiona fenómenos bioquímicos que se manifiestan como enfermedades micro y macrovasculares que se pueden identificar mediante parámetros no invasivos como la medición del índice tobillo-brazo.  El presente estudio pretende identificar la asociación de los niveles séricos de vitamina D y el diagnóstico precoz de la enfermedad arterial periférica en pacientes con Diabetes Mellitus tipo 2. Se realizó un estudio observacional, transversal-analítico de correlación. Los seleccionados fueron sometidos a mediciones antropométricas y bioquímicas, y así mismo se calculó el índice tobillo-brazo. Se estudiaron 61 pacientes encontrando 52.5% con deficiencia de vitamina D y 47.5% con rangos normales. Los valores del índice Tobillo-brazo tanto en hombres como en mujeres sin diferencias significativas. Los valores de vitamina D y el tiempo de evolución de la diabetes arrojaron una correlación negativa, no así con la variable colesterol, donde se observó una correlación indirecta significativa. El estudio no identifica asociación alguna entre los niveles séricos de vitamina D y el diagnóstico precoz de la enfermedad arterial periférica en pacientes con Diabetes Mellitus.    There is growing evidence linking vitamin D deficiency and the predisposition to develop type 2 diabetes mellitus, as well as the evolution of both diseases. For its part, sustained hyperglycemia causes biochemical phenomena that manifest as micro and macrovascular diseases that can be identified by non-invasive parameters such as the measurement of the ankle-brachial index. The present study aims to identify the association of serum vitamin D levels and early diagnosis of peripheral arterial disease in patients with type 2 Diabetes Mellitus. An observational, cross-analytical correlation study was carried out. Those selected underwent anthropometric and biochemical measurements, and the ankle-brachial index was also calculated. 61 patients were studied, finding 52.5% with vitamin D deficiency and 47.5% with normal ranges. Ankle-brachial index values ​​in both men and women without significant differences. The values ​​of vitamin D and the time of evolution of the diabetes showed a negative correlation, but not with the cholesterol variable, where a significant indirect correlation was observed. The study does not identify any association between serum vitamin D levels and early diagnosis of peripheral arterial disease in patients with Diabetes Mellitus

Dinapenia y niveles séricos de vitamina D en pacientes con diagnóstico de diabetes mellitus tipo 2 adscritos a la unidad de medicina familiar 53, León, Guanajuato

Objective: To determine the relationship between Dinapenia and the serum levels of vitamin D in patients diagnosed with Type 2 Diabetes mellitus assigned to the family medicine unit 53, León, Gto. Material and methods: A study with a cross-sectional, analytical, observational, prospective comparative design with a quantitative approach was carried out. Statistical analysis was descriptive statistics with measurement of means and standard deviation. SPSS was performed using descriptive and inferential statistics. The relationship of qualitative variables was by comparison of proportions tests (Chi-square), using a significance level of p<0.05; For proportions of quantitative variables, the Pearson test was performed. Results: 61 patients diagnosed with type 2 diabetes mellitus were included, of whom 15 were men (24.6) and 46 (75.4%) women. There was no significant relationship between dynapenia and serum levels of Vitamin D (p=0.87); 27.9% of the patients presented dynapenia and vitamin D deficiency of the total population studied. Conclusion: There was no correlation between Dynapenia and serum levels of vitamin D in patients with type 2 diabetes mellitus. However, a greater deficiency of Vitamin D and alteration of the lipid profile was demonstrated in patients with Dynapenia.Objetivo: Determinar la relación entre Dinapenia y los niveles séricos de vitamina D en pacientes con diagnóstico de Diabetes mellitus tipo 2 adscritos a la unidad de medicina familiar 53, León, Gto. Material y métodos: Se realizó un estudio con diseño transversal analítico, observacional, prospectivo comparativo con enfoque cuantitativo. Análisis estadístico fue estadística descriptiva con medición de medias y desviación estándar. Se realizó SPSS mediante estadística descriptiva e inferencial. La relación de variables cualitativas fue por pruebas de comparación de proporciones (Chi-cuadrada), usando un nivel de significancia de p<0.05; para proporciones de variables cuantitativas se realizó prueba de Pearson.Resultados: Se incluyeron a 61 pacientes con diagnóstico de diabetes mellitus tipo 2, de los cuales 15 fueron hombres (24.6) y 46 (75.4%) mujeres. No existió una relación significativa entre la dinapenia y niveles séricos de Vitamina D (p=0.87); el 27.9% de los pacientes presentaron dinapenia y deficiencia de vitamina D del total de la población estudiada. Conclusiones: No existió correlación entre  Dinapenia y  niveles séricos de vitamina D en pacientes con diabetes mellitus tipo 2. Sin embargo se demostró mayor deficiencia de Vitamina D y alteración del perfil de lípidos en los pacientes con Dinapenia. &nbsp

Applications of Artificial Intelligence in the Classification of Magnetic Resonance Images: Advances and Perspectives

This chapter examines the advances and perspectives of the applications of artificial intelligence (AI) in the classification of magnetic resonance (MR) images. It focuses on the development of AI-based automatic classification models that have achieved competitive results compared to the state-of-the-art. Accurate and efficient classification of MR images is essential for medical diagnosis but can be challenging due to the complexity and variability of the data. AI offers tools and techniques that can effectively address these challenges. The chapter first addresses the fundamentals of artificial intelligence applied to the classification of medical images, including machine learning techniques and convolutional neural networks. Here, recent advances in the use of AI to classify MRI images in various clinical applications, such as brain tumor detection, are explored. Additionally, advantages and challenges associated with implementing AI models in clinical settings are discussed, such as the interpretability of results and integration with existing radiology systems. Prospects for AI in MR image classification are also highlighted, including the combination of multiple imaging modalities and the use of more advanced AI approaches such as reinforcement learning and model generation