Publications by authors named "J Peralta-Romero"
Electroacupuncture efficacy in diabetic polyneuropathy: Study protocol for a double-blinded randomized controlled multicenter clinical trial.
BMC Complement Med Ther
February 2024
Background: Diabetic peripheral neuropathy (DPN) is the most common complication of type 2 diabetes mellitus (T2DM); its diagnosis and treatment are based on symptomatic improvement. However, as pharmacological therapy causes multiple adverse effects, the implementation of acupunctural techniques, such as electroacupuncture (EA) has been suggested as an alternative treatment. Nonetheless, there is a lack of scientific evidence, and its mechanisms are still unclear.
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- Chronic kidney disease significantly impacts global health, particularly among individuals of African ancestry and those in the Americas, who are often excluded from genetic studies.
- A comprehensive meta-analysis involving over 145,000 individuals from these groups led to the discovery of 41 significant genetic loci associated with kidney function, two of which hadn't been previously identified across any ancestry group.
- The study emphasizes the importance of diverse populations in genetic research for better understanding kidney disease and suggests that multi-ancestry polygenic scores can improve predictive capabilities and clinical applications.
Purpose: A variable number of tandem repeats (VNTR) in the insulin gene (INS) control region may be involved in type 2 diabetes (T2D). The TH01 microsatellite is near INS and may regulate it. We investigated whether the TH01 microsatellite and INS VNTR, assessed via the surrogate marker single nucleotide polymorphism rs689, are associated with T2D and serum insulin levels in a Mexican population.
View Article and Find Full Text PDFDiabetes Detection Models in Mexican Patients by Combining Machine Learning Algorithms and Feature Selection Techniques for Clinical and Paraclinical Attributes: A Comparative Evaluation.
J Diabetes Res
July 2023
The development of medical diagnostic models to support healthcare professionals has witnessed remarkable growth in recent years. Among the prevalent health conditions affecting the global population, diabetes stands out as a significant concern. In the domain of diabetes diagnosis, machine learning algorithms have been widely explored for generating disease detection models, leveraging diverse datasets primarily derived from clinical studies.
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